{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1b2c3d4",
   "metadata": {},
   "source": [
    "# BitBullet:Lessons: Binary Classification\n",
    "\n",
    "Most machine learning workflows look like this: load data, throw it at a model, get a number, ship it. **BitBullet is built for the teams who know that's not enough.**\n",
    "\n",
    "In this tutorial we build a *complete*, production-grade binary classification pipeline — from raw data to a serialised, fully-evaluated, SHAP-explained model — using nothing but `bitbullet` and standard Python. Every design decision mirrors what you'd do in a real production system.\n",
    "\n",
    "**Dataset**: [E-Commerce Shipping Data](https://www.kaggle.com/datasets/prachi13/customer-analytics) — 11,000 shipments, 10 features.  \n",
    "**Task**: Predict whether a shipment will arrive on schedule (`Reached.on.Time_Y.N = 1`).\n",
    "\n",
    "---\n",
    "\n",
    "### What You Will Build\n",
    "\n",
    "| Step | Component | What BitBullet Handles For You |\n",
    "|------|-----------|--------------------------------|\n",
    "| 1 | Feature profiling | `generate_feature_stats` — full audit in one call |\n",
    "| 2 | Hold-out test split | Stratified 80/20 split before any preprocessing |\n",
    "| 3 | Transform pipeline | Fit on train only, serialise, reload, apply to test |\n",
    "| 4 | Hyperparameter search | Optuna TPE + Stratified K-Fold + early stopping |\n",
    "| 5 | Cross-model benchmark | One config change, four architectures |\n",
    "| 6 | Test-set evaluation | `ModelEvaluator` + `evaluate_classification` — ROC AUC, PR AUC, Kappa, confusion matrix, JSON-ready report numbers |\n",
    "| 7 | Model serialisation | `ModelSerializer` + `ModelMetadata` — full provenance |\n",
    "| 8 | Explainability | SHAP computed inside the trainer — zero extra code |\n",
    "| 9 | Formal report | `TrainingReportGenerator` — one call |\n",
    "| 10 | Inference | Load pipeline + model from disk, predict on new records |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27560253",
   "metadata": {},
   "source": [
    "> **Configure the complete classification lifecycle without writing the orchestration.**\n",
    "> [BitBullet Platform](https://bitbullet.co.uk/platform/classification) centralises your data, managed compute, storage, configurations, and results in one guided environment. Use the AI assistant to explore authorised data and prepare a draft, compare candidate models and diagnostic evidence, then export the fitted model, preprocessing, metadata, and generated inference code. This lesson shows the same workflow directly with the BitBullet SDK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1c2d3e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bitbullet  : vdev\n",
      "All imports successful.\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# Section 1: Environment & Imports\n",
    "# =============================================================================\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# ── BitBullet core ────────────────────────────────────────────────────────────\n",
    "# When running this notebook from bitbullet/lessons in a local clone, prefer the local SDK source.\n",
    "sdk_root = os.path.abspath(\"..\")\n",
    "if sdk_root not in sys.path:\n",
    "    sys.path.insert(0, sdk_root)\n",
    "\n",
    "import bitbullet\n",
    "print(f\"bitbullet  : v{getattr(bitbullet, '__version__', 'dev')}\")\n",
    "\n",
    "from bitbullet.transform import TransformPipeline\n",
    "from bitbullet.transform.eda import generate_feature_stats, plot_pipeline_transformations\n",
    "from bitbullet.train import TrainConfig, OptunaTrainer, TrainingReportGenerator\n",
    "from bitbullet.model import ModelMetadata, ModelSerializer\n",
    "\n",
    "# ── BitBullet Evaluate — comprehensive model evaluation ──────────────────────\n",
    "from bitbullet.evaluate import EvaluationConfig, ModelEvaluator, evaluate_classification\n",
    "\n",
    "print(\"All imports successful.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d2e3f4",
   "metadata": {},
   "source": [
    "## 2. Load the Dataset\n",
    "\n",
    "Each row represents a single shipment. Features describe the warehouse origin, shipping mode, customer behaviour, and product characteristics. The binary target `Reached.on.Time_Y.N` records whether the parcel arrived on schedule.\n",
    "\n",
    "**Dataset:** E-Commerce Shipping Dataset  \n",
    "**Source:** [Kaggle — prachi13/customer-analytics](https://www.kaggle.com/datasets/prachi13/customer-analytics)  \n",
    "**File:** `e-commerce-shipping-data.csv`\n",
    "\n",
    "> **Before running this cell:** download `e-commerce-shipping-data.csv` from the link above and\n",
    "> place it in the **same directory as this notebook**. If you store it elsewhere,\n",
    "> update `data_path` in the code cell below to match your chosen location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1e2f3g4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded — Shape: (10999, 12)\n",
      "Features : 10\n",
      "Target   : {1: 6563, 0: 4436}  (1 = on time, 0 = delayed)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Warehouse_block</th>\n",
       "      <th>Mode_of_Shipment</th>\n",
       "      <th>Customer_care_calls</th>\n",
       "      <th>Customer_rating</th>\n",
       "      <th>Cost_of_the_Product</th>\n",
       "      <th>Prior_purchases</th>\n",
       "      <th>Product_importance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Discount_offered</th>\n",
       "      <th>Weight_in_gms</th>\n",
       "      <th>Reached.on.Time_Y.N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>1233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>216</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>59</td>\n",
       "      <td>3088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>4</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>48</td>\n",
       "      <td>3374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>Flight</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>176</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>10</td>\n",
       "      <td>1177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>46</td>\n",
       "      <td>2484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Warehouse_block Mode_of_Shipment  Customer_care_calls  Customer_rating  \\\n",
       "0   1               D           Flight                    4                2   \n",
       "1   2               F           Flight                    4                5   \n",
       "2   3               A           Flight                    2                2   \n",
       "3   4               B           Flight                    3                3   \n",
       "4   5               C           Flight                    2                2   \n",
       "\n",
       "   Cost_of_the_Product  Prior_purchases Product_importance Gender  \\\n",
       "0                  177                3                low      F   \n",
       "1                  216                2                low      M   \n",
       "2                  183                4                low      M   \n",
       "3                  176                4             medium      M   \n",
       "4                  184                3             medium      F   \n",
       "\n",
       "   Discount_offered  Weight_in_gms  Reached.on.Time_Y.N  \n",
       "0                44           1233                    1  \n",
       "1                59           3088                    1  \n",
       "2                48           3374                    1  \n",
       "3                10           1177                    1  \n",
       "4                46           2484                    1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Update this path if you stored the file in a different location.\n",
    "data_path = \"e-commerce-shipping-data.csv\"\n",
    "\n",
    "df = pd.read_csv(data_path)\n",
    "print(f\"Dataset loaded — Shape: {df.shape}\")\n",
    "\n",
    "target_col = 'Reached.on.Time_Y.N'\n",
    "X = df.drop(columns=[target_col, 'ID'])\n",
    "y = df[target_col]\n",
    "\n",
    "print(f\"Features : {X.shape[1]}\")\n",
    "print(f\"Target   : {y.value_counts().to_dict()}  (1 = on time, 0 = delayed)\")\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f2g3h4",
   "metadata": {},
   "source": [
    "## 3. Exploratory Feature Analysis\n",
    "\n",
    "Before touching a model we need to understand our data. `generate_feature_stats` produces a comprehensive feature-level audit in a single call — detecting missing values, skewness, kurtosis, and cardinality across all columns simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1g2h3i4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical   (6): ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms']\n",
      "Categorical (4): ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th>dtype</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>missing_percent</th>\n",
       "      <th>unique_values</th>\n",
       "      <th>zero_count</th>\n",
       "      <th>negative_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>skewness</th>\n",
       "      <th>kurtosis</th>\n",
       "      <th>mean_percentile</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "    <tr>\n",
       "      <th>Warehouse_block</th>\n",
       "      <td>object</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_of_Shipment</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Ship</td>\n",
       "      <td>7462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_care_calls</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_rating</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.990545</td>\n",
       "      <td>1.413603</td>\n",
       "      <td>0.00436</td>\n",
       "      <td>-1.295654</td>\n",
       "      <td>40.003637</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost_of_the_Product</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210.196836</td>\n",
       "      <td>48.063272</td>\n",
       "      <td>-0.157117</td>\n",
       "      <td>-0.97216</td>\n",
       "      <td>48.049823</td>\n",
       "      <td>96.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prior_purchases</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.567597</td>\n",
       "      <td>1.52286</td>\n",
       "      <td>1.681897</td>\n",
       "      <td>4.006342</td>\n",
       "      <td>59.587235</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_importance</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>low</td>\n",
       "      <td>5297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>F</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount_offered</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.373216</td>\n",
       "      <td>16.205527</td>\n",
       "      <td>1.798929</td>\n",
       "      <td>2.000586</td>\n",
       "      <td>77.407037</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_in_gms</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4034</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3634.016729</td>\n",
       "      <td>1635.377251</td>\n",
       "      <td>-0.249747</td>\n",
       "      <td>-1.447671</td>\n",
       "      <td>42.722066</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>1839.5</td>\n",
       "      <td>4149.0</td>\n",
       "      <td>5050.0</td>\n",
       "      <td>7846.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dtype  missing_count  missing_percent  unique_values  \\\n",
       "Warehouse_block      object              0              0.0              5   \n",
       "Mode_of_Shipment     object              0              0.0              3   \n",
       "Customer_care_calls   int64              0              0.0              6   \n",
       "Customer_rating       int64              0              0.0              5   \n",
       "Cost_of_the_Product   int64              0              0.0            215   \n",
       "Prior_purchases       int64              0              0.0              8   \n",
       "Product_importance   object              0              0.0              3   \n",
       "Gender               object              0              0.0              2   \n",
       "Discount_offered      int64              0              0.0             65   \n",
       "Weight_in_gms         int64              0              0.0           4034   \n",
       "\n",
       "                     zero_count  negative_count         mean          std  \\\n",
       "Warehouse_block               0               0            -            -   \n",
       "Mode_of_Shipment              0               0            -            -   \n",
       "Customer_care_calls           0               0     4.054459      1.14149   \n",
       "Customer_rating               0               0     2.990545     1.413603   \n",
       "Cost_of_the_Product           0               0   210.196836    48.063272   \n",
       "Prior_purchases               0               0     3.567597      1.52286   \n",
       "Product_importance            0               0            -            -   \n",
       "Gender                        0               0            -            -   \n",
       "Discount_offered              0               0    13.373216    16.205527   \n",
       "Weight_in_gms                 0               0  3634.016729  1635.377251   \n",
       "\n",
       "                     skewness  kurtosis mean_percentile     min     25%  \\\n",
       "Warehouse_block             -         -               -       -       -   \n",
       "Mode_of_Shipment            -         -               -       -       -   \n",
       "Customer_care_calls  0.391926 -0.308995       67.387944     2.0     3.0   \n",
       "Customer_rating       0.00436 -1.295654       40.003637     1.0     2.0   \n",
       "Cost_of_the_Product -0.157117  -0.97216       48.049823    96.0   169.0   \n",
       "Prior_purchases      1.681897  4.006342       59.587235     2.0     3.0   \n",
       "Product_importance          -         -               -       -       -   \n",
       "Gender                      -         -               -       -       -   \n",
       "Discount_offered     1.798929  2.000586       77.407037     1.0     4.0   \n",
       "Weight_in_gms       -0.249747 -1.447671       42.722066  1001.0  1839.5   \n",
       "\n",
       "                        50%     75%     max   top  freq  \n",
       "Warehouse_block           -       -       -     F  3666  \n",
       "Mode_of_Shipment          -       -       -  Ship  7462  \n",
       "Customer_care_calls     4.0     5.0     7.0     -     -  \n",
       "Customer_rating         3.0     4.0     5.0     -     -  \n",
       "Cost_of_the_Product   214.0   251.0   310.0     -     -  \n",
       "Prior_purchases         3.0     4.0    10.0     -     -  \n",
       "Product_importance        -       -       -   low  5297  \n",
       "Gender                    -       -       -     F  5545  \n",
       "Discount_offered        7.0    10.0    65.0     -     -  \n",
       "Weight_in_gms        4149.0  5050.0  7846.0     -     -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target balance — On Time: 59.7%  |  Delayed: 40.3%\n"
     ]
    }
   ],
   "source": [
    "stats_df, numerical_cols, categorical_cols = generate_feature_stats(X)\n",
    "\n",
    "print(f\"Numerical   ({len(numerical_cols)}): {numerical_cols}\")\n",
    "print(f\"Categorical ({len(categorical_cols)}): {categorical_cols}\")\n",
    "print(\"-\" * 60)\n",
    "display(stats_df)\n",
    "\n",
    "balance = y.value_counts(normalize=True)\n",
    "print(f\"\\nTarget balance — On Time: {balance[1]:.1%}  |  Delayed: {balance[0]:.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "g1h2i3j4",
   "metadata": {},
   "source": [
    "**Observations from the audit:**\n",
    "\n",
    "- `Discount_offered`: skewness = 1.80, kurtosis = 2.00 — heavily right-skewed. A `log1p` transform will compress the long tail.\n",
    "- `Prior_purchases`: skewness = 1.68 — `boxcox` normalisation followed by `standard_scale` is the correct treatment.\n",
    "- `Weight_in_gms`: kurtosis = −1.45 indicating a bimodal distribution — `standard_scale` preserves shape while keeping values manageable.\n",
    "- All four categoricals are low-cardinality — ideal for one-hot encoding.\n",
    "- **No missing values** anywhere — we can proceed without imputation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "h1i2j3k4",
   "metadata": {},
   "source": [
    "## 4. Establishing the Hold-Out Test Set\n",
    "\n",
    "> **The cardinal rule of model evaluation: split your data before any preprocessing.**\n",
    "\n",
    "Fitting a transform on the full dataset and *then* splitting leaks test-set information into your training process — e.g., a `StandardScaler` whose mean was computed from test rows, or a `BoxCox` lambda tuned on the full distribution. The result is an optimistically biased evaluation score that overstates real-world performance.\n",
    "\n",
    "We use **stratified splitting** (`stratify=y`) to preserve the exact class ratio in both partitions. This matters most for imbalanced targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "i1j2k3l4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training : 8,799 samples  (on-time rate: 59.7%)\n",
      "Test     : 2,200  samples  (on-time rate: 59.7%)\n",
      "\n",
      "The test set is now sealed. It will not be seen by any fitting step.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    test_size=0.20,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(f\"Training : {X_train.shape[0]:,} samples  (on-time rate: {y_train.mean():.1%})\")\n",
    "print(f\"Test     : {X_test.shape[0]:,}  samples  (on-time rate: {y_test.mean():.1%})\")\n",
    "print(\"\\nThe test set is now sealed. It will not be seen by any fitting step.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "j1k2l3m4",
   "metadata": {},
   "source": [
    "## 5. Feature Engineering with `TransformPipeline`\n",
    "\n",
    "### 5.1 Designing the Pipeline\n",
    "\n",
    "With `bitbullet.transform` you specify *what* to transform and *how* — nothing more. The pipeline manages fitting, chaining, state, and serialisation automatically.\n",
    "\n",
    "**Key capability on display here:** `bitbullet` supports **sequential chaining** of multiple transforms on the same column in a single fluent API call. The pipeline executes them in order — `boxcox` normalises the distribution, then `standard_scale` brings it to unit variance. No separate pipeline stages or custom wrappers needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "k1l2m3n4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TransformPipeline(name='shipping_delay_pipeline', steps=5, status=not fitted)\n",
      "\n",
      "  [numerical   ] log1p                → ['Discount_offered']\n",
      "  [numerical   ] boxcox               → ['Prior_purchases']\n",
      "  [numerical   ] standard_scale       → ['Prior_purchases']\n",
      "  [numerical   ] standard_scale       → ['Weight_in_gms', 'Cost_of_the_Product']\n",
      "  [categorical ] onehot_encode        → ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']\n"
     ]
    }
   ],
   "source": [
    "pipeline = TransformPipeline(name=\"shipping_delay_pipeline\")\n",
    "\n",
    "# Compress the heavy right tail in discount offers\n",
    "pipeline.add(\n",
    "    transformer_type=\"numerical\",\n",
    "    method=\"log1p\",\n",
    "    columns=['Discount_offered']\n",
    ")\n",
    "\n",
    "# Normalise the skewed purchase-count distribution, then scale to unit variance\n",
    "# Note: bitbullet supports chaining multiple transforms on the same column\n",
    "pipeline.add(\n",
    "    transformer_type=\"numerical\",\n",
    "    method=\"boxcox\",\n",
    "    columns=['Prior_purchases']\n",
    ").add(\n",
    "    transformer_type=\"numerical\",\n",
    "    method=\"standard_scale\",\n",
    "    columns=['Prior_purchases']\n",
    ")\n",
    "\n",
    "# Standardise remaining numerics\n",
    "pipeline.add(\n",
    "    transformer_type=\"numerical\",\n",
    "    method=\"standard_scale\",\n",
    "    columns=['Weight_in_gms', 'Cost_of_the_Product']\n",
    ")\n",
    "\n",
    "# Encode all categoricals\n",
    "pipeline.add(\n",
    "    transformer_type=\"categorical\",\n",
    "    method=\"onehot_encode\",\n",
    "    columns=['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']\n",
    ")\n",
    "\n",
    "print(pipeline)\n",
    "print()\n",
    "for step in pipeline.list_steps():\n",
    "    print(f\"  [{step['type']:12s}] {step['method']:20s} → {step['columns']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "l1m2n3o4",
   "metadata": {},
   "source": [
    "### 5.2 Fit on Training Data Only\n",
    "\n",
    "We call `fit_transform()` **exclusively on `X_train`**. Every learned parameter — the Box-Cox lambda, the scaler mean and variance, the one-hot encoder category vocabulary — is derived from the training partition only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "m1n2o3p4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training features : 10 raw → 19 post-transform\n",
      "Training samples  : 8,799\n",
      "Pipeline fitted   : True\n"
     ]
    }
   ],
   "source": [
    "X_train_transformed = pipeline.fit_transform(X_train)\n",
    "\n",
    "print(f\"Training features : {X_train.shape[1]} raw → {X_train_transformed.shape[1]} post-transform\")\n",
    "print(f\"Training samples  : {X_train_transformed.shape[0]:,}\")\n",
    "print(f\"Pipeline fitted   : {pipeline.is_fitted}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n1o2p3q4",
   "metadata": {},
   "source": [
    "### 5.3 Serialise the Fitted Pipeline — Reproducibility Guaranteed\n",
    "\n",
    "A trained model is only as good as the preprocessing that feeds it. By serialising the *fitted* pipeline — with all its learned parameters intact — we guarantee that every future inference applies identical transforms.\n",
    "\n",
    "We save the pipeline, reload it from disk, and apply it to the test set. In production, this reload step would live in your inference service, completely decoupled from the training environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "o1p2q3r4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline serialised → pipeline_binary.pkl\n",
      "Pipeline reloaded  — fitted: True  |  steps: 5\n",
      "\n",
      "Test set  : (2200, 10) → (2200, 19)\n",
      "Column alignment: True\n"
     ]
    }
   ],
   "source": [
    "pipeline_path = \"pipeline_binary.pkl\"\n",
    "pipeline.save(pipeline_path)\n",
    "print(f\"Pipeline serialised → {pipeline_path}\")\n",
    "\n",
    "# Reload from disk — simulating a production inference environment\n",
    "loaded_pipeline = TransformPipeline.load(pipeline_path)\n",
    "print(f\"Pipeline reloaded  — fitted: {loaded_pipeline.is_fitted}  |  steps: {len(loaded_pipeline)}\")\n",
    "\n",
    "# Apply learned parameters to the test set\n",
    "X_test_transformed = loaded_pipeline.transform(X_test)\n",
    "\n",
    "print(f\"\\nTest set  : {X_test.shape} → {X_test_transformed.shape}\")\n",
    "print(f\"Column alignment: {list(X_train_transformed.columns) == list(X_test_transformed.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p1q2r3s4",
   "metadata": {},
   "source": [
    "### 5.4 Visualise the Transformation Results\n",
    "\n",
    "Because the `TransformPipeline` tracks full state of what it transformed, `plot_pipeline_transformations` can automatically render before-and-after distributions for every modified feature — including decoding one-hot columns back to their original categorical form. Use the dropdown to explore each transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "q1r2s3t4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "978cbb8422c44a8dbf64e92e46ec9453",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='feature', options=('Cost_of_the_Product', 'Discount_offered', 'Gen…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pipeline_transformations(pipeline, X_train, X_train_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "r1s2t3u4",
   "metadata": {},
   "source": [
    "## 6. Model Training\n",
    "\n",
    "### 6.1 Train the Champion Model\n",
    "\n",
    "Training a production-grade gradient-boosted model normally means crafting a custom Optuna study, wiring up stratified cross-validation, managing early stopping inside each fold, and separately computing feature importance and SHAP values. **`bitbullet.train` compresses all of that into a single config declaration.**\n",
    "\n",
    "| Parameter | Value | Why |\n",
    "|-----------|-------|-----|\n",
    "| `n_trials` | 50 | Balanced speed vs. search quality (use 200+ in production) |\n",
    "| `cv_folds` | 5 | Robust CV estimate with acceptable variance |\n",
    "| `optimize_threshold` | `True` | Youden's J finds the optimal decision boundary — not naïvely 0.5 |\n",
    "| `generate_shap` | `True` | SHAP computed inside the trainer — zero extra code |\n",
    "| `save_feature_importance` | `True` | Native tree importance stored automatically in training state |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "s1t2u3v4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:11:58,028] A new study created in memory with name: shipping_delay_lgbm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "BitBullet Train - shipping_delay_lgbm\n",
      "======================================================================\n",
      "\n",
      "Training samples: 8799\n",
      "Features: 19\n",
      "Class distribution: {1: 5250, 0: 3549}\n",
      "\n",
      "Starting hyperparameter optimization (optuna)...\n",
      "Trials: 5, CV Folds: 5\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:12:03,465] Trial 1 finished with value: 0.741780080955881 and parameters: {'num_leaves': 101, 'max_depth': 5, 'min_child_samples': 12, 'lambda_l1': 5.676984647356448, 'lambda_l2': 1.6838303342028776e-05, 'min_gain_to_split': 0.20599930872563244, 'feature_fraction': 0.5236829320328156, 'bagging_fraction': 0.8068332236316801, 'bagging_freq': 6, 'learning_rate': 0.06669632833052522, 'max_bin': 159}. Best is trial 1 with value: 0.741780080955881.\n",
      "[I 2026-05-05 06:12:05,334] Trial 4 finished with value: 0.7425801371463383 and parameters: {'num_leaves': 125, 'max_depth': 7, 'min_child_samples': 53, 'lambda_l1': 6.590011235194716e-07, 'lambda_l2': 7.259104745433034e-07, 'min_gain_to_split': 0.16842722147088796, 'feature_fraction': 0.9460292540594877, 'bagging_fraction': 0.5649193881943484, 'bagging_freq': 2, 'learning_rate': 0.09153075700296597, 'max_bin': 63}. Best is trial 4 with value: 0.7425801371463383.\n",
      "[I 2026-05-05 06:12:28,781] Trial 0 finished with value: 0.7406091170435873 and parameters: {'num_leaves': 175, 'max_depth': 6, 'min_child_samples': 24, 'lambda_l1': 0.7746983115991543, 'lambda_l2': 0.00020178628857019844, 'min_gain_to_split': 0.20025153454243405, 'feature_fraction': 0.7139297149143418, 'bagging_fraction': 0.6041193157217839, 'bagging_freq': 1, 'learning_rate': 0.009249970223670419, 'max_bin': 159}. Best is trial 4 with value: 0.7425801371463383.\n",
      "[I 2026-05-05 06:12:48,642] Trial 2 finished with value: 0.739534439547487 and parameters: {'num_leaves': 291, 'max_depth': 9, 'min_child_samples': 20, 'lambda_l1': 6.439181537353084e-08, 'lambda_l2': 1.7249089840159149e-06, 'min_gain_to_split': 0.9675323487351434, 'feature_fraction': 0.6963879821641755, 'bagging_fraction': 0.5692611727350684, 'bagging_freq': 1, 'learning_rate': 0.0066258025932934535, 'max_bin': 127}. Best is trial 4 with value: 0.7425801371463383.\n",
      "[I 2026-05-05 06:13:46,839] Trial 3 finished with value: 0.7346561475103559 and parameters: {'num_leaves': 60, 'max_depth': 12, 'min_child_samples': 5, 'lambda_l1': 0.05655632782389785, 'lambda_l2': 1.533417822319273e-06, 'min_gain_to_split': 0.10072405223000014, 'feature_fraction': 0.7061100679113412, 'bagging_fraction': 0.8481301643691066, 'bagging_freq': 5, 'learning_rate': 0.008252909857164148, 'max_bin': 223}. Best is trial 4 with value: 0.7425801371463383.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "Optuna Optimization Summary\n",
      "======================================================================\n",
      "Number of finished trials: 5\n",
      "Best trial: 4\n",
      "Best value: 0.742580\n",
      "\n",
      "Best hyperparameters:\n",
      "  num_leaves: 125\n",
      "  max_depth: 7\n",
      "  min_child_samples: 53\n",
      "  lambda_l1: 0.000001\n",
      "  lambda_l2: 0.000001\n",
      "  min_gain_to_split: 0.168427\n",
      "  feature_fraction: 0.946029\n",
      "  bagging_fraction: 0.564919\n",
      "  bagging_freq: 2\n",
      "  learning_rate: 0.091531\n",
      "  max_bin: 63\n",
      "\n",
      "Additional attributes:\n",
      "  cv_scores: [0.7602481556002683, 0.735627095908786, 0.7370811535881958, 0.7252193158953723, 0.754724964739069]\n",
      "  cv_std: 0.01295868381030559\n",
      "  mean_training_loss: 0.43233581979803226\n",
      "  mean_validation_loss: 0.5164681044108509\n",
      "  best_iteration: 48\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Optimization complete! Best score: 0.7426\n",
      "Time: 108.8s\n",
      "\n",
      "Training final model with best parameters...\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877\n",
      "[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07\n",
      "[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796\n",
      "[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484\n",
      "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877\n",
      "[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07\n",
      "[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796\n",
      "[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484\n",
      "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
      "[LightGBM] [Info] Number of positive: 5250, number of negative: 3549\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001649 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 228\n",
      "[LightGBM] [Info] Number of data points in the train set: 8799, number of used features: 19\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.596659 -> initscore=0.391562\n",
      "[LightGBM] [Info] Start training from score 0.391562\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "Optimizing classification threshold...\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877\n",
      "[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07\n",
      "[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796\n",
      "[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484\n",
      "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
      "Optimal threshold: 0.498\n",
      "\n",
      "Generating SHAP explanations...\n",
      "SHAP values computed for 500 samples\n",
      "Top 5 SHAP features:\n",
      "  Discount_offered: 1.4766\n",
      "  Weight_in_gms: 0.6442\n",
      "  Prior_purchases: 0.1425\n",
      "  Cost_of_the_Product: 0.1280\n",
      "  Customer_rating: 0.0455\n",
      "\n",
      "======================================================================\n",
      "Training Complete!\n",
      "======================================================================\n",
      "=== Training Summary ===\n",
      "Model: LGBMClassifier\n",
      "Best Score: 0.7426\n",
      "CV Score: 0.0000 ± 0.0000\n",
      "Features: 19/19\n",
      "Optimal Threshold: 0.498\n",
      "Training Time: 111.3s\n",
      "Optimization Time: 108.8s\n",
      "\n",
      "Best Hyperparameters:\n",
      "  num_leaves: 125\n",
      "  max_depth: 7\n",
      "  min_child_samples: 53\n",
      "  lambda_l1: 0.0000\n",
      "  lambda_l2: 0.0000\n",
      "  min_gain_to_split: 0.1684\n",
      "  feature_fraction: 0.9460\n",
      "  bagging_fraction: 0.5649\n",
      "  bagging_freq: 2\n",
      "  learning_rate: 0.0915\n",
      "  max_bin: 63\n",
      "  n_estimators: 48\n",
      "======================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lgbm_config = TrainConfig(\n",
    "    name=\"shipping_delay_lgbm\",\n",
    "    model_type=\"lgbm\",\n",
    "    task=\"binary_classification\",\n",
    "    optimization_metric=\"roc_auc\",\n",
    "    optuna_sampler=\"tpe\",\n",
    "    n_trials=5,\n",
    "    cv_folds=5,\n",
    "    optimize_threshold=True,\n",
    "    threshold_optimization_method=\"youden\",\n",
    "    save_feature_importance=True,\n",
    "    generate_shap=True,\n",
    "    shap_n_samples=500,\n",
    "    shap_n_background=100,\n",
    "    verbose=True,\n",
    "    optuna_show_progress=False,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "lgbm_trainer = OptunaTrainer(config=lgbm_config)\n",
    "lgbm_trainer.fit(X_train_transformed, y_train)\n",
    "lgbm_state = lgbm_trainer.state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t1u2v3w4",
   "metadata": {},
   "source": [
    "### 6.2 Inspecting the Rich Training State\n",
    "\n",
    "After `fit()` completes, `trainer.state` is a `TrainState` object that captures *everything* produced by the training run: the trained model, hyperparameters, threshold optimisation, feature importance, SHAP values, and timing — all in one place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "u1v2w3x4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Training Summary ===\n",
      "Model: LGBMClassifier\n",
      "Best Score: 0.7426\n",
      "CV Score: 0.0000 ± 0.0000\n",
      "Features: 19/19\n",
      "Optimal Threshold: 0.498\n",
      "Training Time: 111.3s\n",
      "Optimization Time: 108.8s\n",
      "\n",
      "Best Hyperparameters:\n",
      "  num_leaves: 125\n",
      "  max_depth: 7\n",
      "  min_child_samples: 53\n",
      "  lambda_l1: 0.0000\n",
      "  lambda_l2: 0.0000\n",
      "  min_gain_to_split: 0.1684\n",
      "  feature_fraction: 0.9460\n",
      "  bagging_fraction: 0.5649\n",
      "  bagging_freq: 2\n",
      "  learning_rate: 0.0915\n",
      "  max_bin: 63\n",
      "  n_estimators: 48\n",
      "\n",
      "Best trial — per-fold AUC : [0.7602, 0.7356, 0.7371, 0.7252, 0.7547]\n",
      "Mean ± Std                : 0.7426 ± 0.0130\n",
      "\n",
      "Training state snapshot:\n",
      "  Model               : LGBMClassifierWrapper\n",
      "  Optimal threshold   : 0.4984\n",
      "  Feature importance  : Available\n",
      "  SHAP values         : Available\n",
      "  Training time       : 111.3s\n"
     ]
    }
   ],
   "source": [
    "print(lgbm_state.summary())\n",
    "\n",
    "# Per-fold CV scores are stored in the best Optuna trial's user attributes\n",
    "if lgbm_state.study:\n",
    "    best_trial  = lgbm_state.study.best_trial\n",
    "    fold_scores = best_trial.user_attrs.get('cv_scores', [])\n",
    "    fold_std    = best_trial.user_attrs.get('cv_std', 0.0)\n",
    "    if fold_scores:\n",
    "        print(f\"\\nBest trial — per-fold AUC : {[round(s, 4) for s in fold_scores]}\")\n",
    "        print(f\"Mean ± Std                : {np.mean(fold_scores):.4f} ± {fold_std:.4f}\")\n",
    "\n",
    "print(f\"\\nTraining state snapshot:\")\n",
    "print(f\"  Model               : {type(lgbm_state.model).__name__}\")\n",
    "print(f\"  Optimal threshold   : {lgbm_state.optimal_threshold:.4f}\")\n",
    "print(f\"  Feature importance  : {'Available' if lgbm_state.feature_importance is not None else 'Not computed'}\")\n",
    "print(f\"  SHAP values         : {'Available' if lgbm_state.shap_values is not None else 'Not computed'}\")\n",
    "print(f\"  Training time       : {lgbm_state.training_time_seconds:.1f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "v1w2x3y4",
   "metadata": {},
   "source": [
    "## 7. Cross-Model Benchmarking\n",
    "\n",
    "One of `bitbullet.train`'s most powerful capabilities is **architecture-agnostic training**. Because every model shares the same `TrainConfig` interface, switching from LightGBM to XGBoost, Random Forest, or Logistic Regression is a single string change — no bespoke API knowledge required.\n",
    "\n",
    "> We use `n_trials=20` here for benchmark speed. For production decisions, run at least 100 trials per architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "w1x2y3z4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:14:12,299] A new study created in memory with name: shipping_delay_xgb\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "──────────────────────────────────────────────────  XGB\n",
      "[0]\tvalidation_0-logloss:0.66719\tvalidation_1-logloss:0.66690\n",
      "[0]\tvalidation_0-logloss:0.67410\tvalidation_1-logloss:0.67420\n",
      "[0]\tvalidation_0-logloss:0.63842\tvalidation_1-logloss:0.64121\n",
      "[1]\tvalidation_0-logloss:0.66056\tvalidation_1-logloss:0.66029\n",
      "[1]\tvalidation_0-logloss:0.66883\tvalidation_1-logloss:0.66901\n",
      "[2]\tvalidation_0-logloss:0.65406\tvalidation_1-logloss:0.65358\n",
      "[1]\tvalidation_0-logloss:0.60192\tvalidation_1-logloss:0.60931\n",
      "[2]\tvalidation_0-logloss:0.66445\tvalidation_1-logloss:0.66456\n",
      "[3]\tvalidation_0-logloss:0.64858\tvalidation_1-logloss:0.64802\n",
      "[2]\tvalidation_0-logloss:0.59515\tvalidation_1-logloss:0.60936[3]\tvalidation_0-logloss:0.66434\tvalidation_1-logloss:0.66445\n",
      "\n",
      "[4]\tvalidation_0-logloss:0.64229\tvalidation_1-logloss:0.64187\n",
      "[5]\tvalidation_0-logloss:0.63683\tvalidation_1-logloss:0.63626\n",
      "[4]\tvalidation_0-logloss:0.66004\tvalidation_1-logloss:0.66010\n",
      "[3]\tvalidation_0-logloss:0.57419\tvalidation_1-logloss:0.59117\n",
      "[6]\tvalidation_0-logloss:0.63154\tvalidation_1-logloss:0.63101\n",
      "[5]\tvalidation_0-logloss:0.65978\tvalidation_1-logloss:0.65997\n",
      "[4]\tvalidation_0-logloss:0.55410\tvalidation_1-logloss:0.57368\n",
      "[7]\tvalidation_0-logloss:0.62629\tvalidation_1-logloss:0.62567\n",
      "[6]\tvalidation_0-logloss:0.65501\tvalidation_1-logloss:0.65507\n",
      "[5]\tvalidation_0-logloss:0.53692\tvalidation_1-logloss:0.55929\n",
      "[8]\tvalidation_0-logloss:0.62143\tvalidation_1-logloss:0.62086\n",
      "[7]\tvalidation_0-logloss:0.65120\tvalidation_1-logloss:0.65129\n",
      "[9]\tvalidation_0-logloss:0.61658\tvalidation_1-logloss:0.61595\n",
      "[8]\tvalidation_0-logloss:0.64666\tvalidation_1-logloss:0.64672\n",
      "[6]\tvalidation_0-logloss:0.52251\tvalidation_1-logloss:0.54921\n",
      "[10]\tvalidation_0-logloss:0.61231\tvalidation_1-logloss:0.61153\n",
      "[9]\tvalidation_0-logloss:0.64257\tvalidation_1-logloss:0.64255\n",
      "[7]\tvalidation_0-logloss:0.51166\tvalidation_1-logloss:0.54116[11]\tvalidation_0-logloss:0.60818\tvalidation_1-logloss:0.60733\n",
      "\n",
      "[10]\tvalidation_0-logloss:0.63861\tvalidation_1-logloss:0.63851\n",
      "[12]\tvalidation_0-logloss:0.60466\tvalidation_1-logloss:0.60379\n",
      "[11]\tvalidation_0-logloss:0.63462\tvalidation_1-logloss:0.63439[8]\tvalidation_0-logloss:0.50305\tvalidation_1-logloss:0.53420\n",
      "\n",
      "[13]\tvalidation_0-logloss:0.60069\tvalidation_1-logloss:0.59968\n",
      "[12]\tvalidation_0-logloss:0.63134\tvalidation_1-logloss:0.63101\n",
      "[14]\tvalidation_0-logloss:0.59718\tvalidation_1-logloss:0.59609\n",
      "[9]\tvalidation_0-logloss:0.49579\tvalidation_1-logloss:0.52955\n",
      "[13]\tvalidation_0-logloss:0.62748\tvalidation_1-logloss:0.62721\n",
      "[15]\tvalidation_0-logloss:0.59371\tvalidation_1-logloss:0.59249\n",
      "[16]\tvalidation_0-logloss:0.59034\tvalidation_1-logloss:0.58916[14]\tvalidation_0-logloss:0.62432\tvalidation_1-logloss:0.62408\n",
      "\n",
      "[10]\tvalidation_0-logloss:0.48951\tvalidation_1-logloss:0.52608\n",
      "[17]\tvalidation_0-logloss:0.58744\tvalidation_1-logloss:0.58629\n",
      "[15]\tvalidation_0-logloss:0.62097\tvalidation_1-logloss:0.62075\n",
      "[11]\tvalidation_0-logloss:0.48479\tvalidation_1-logloss:0.52306\n",
      "[18]\tvalidation_0-logloss:0.58711\tvalidation_1-logloss:0.58610\n",
      "[16]\tvalidation_0-logloss:0.61802\tvalidation_1-logloss:0.61781\n",
      "[19]\tvalidation_0-logloss:0.58410\tvalidation_1-logloss:0.58303\n",
      "[12]\tvalidation_0-logloss:0.47839\tvalidation_1-logloss:0.51987\n",
      "[17]\tvalidation_0-logloss:0.61515\tvalidation_1-logloss:0.61491\n",
      "[20]\tvalidation_0-logloss:0.58379\tvalidation_1-logloss:0.58286\n",
      "[21]\tvalidation_0-logloss:0.58061\tvalidation_1-logloss:0.57959\n",
      "[18]\tvalidation_0-logloss:0.61495\tvalidation_1-logloss:0.61481\n",
      "[13]\tvalidation_0-logloss:0.47376\tvalidation_1-logloss:0.51841\n",
      "[19]\tvalidation_0-logloss:0.61159\tvalidation_1-logloss:0.61143[22]\tvalidation_0-logloss:0.57785\tvalidation_1-logloss:0.57679\n",
      "\n",
      "[14]\tvalidation_0-logloss:0.46803\tvalidation_1-logloss:0.51653\n",
      "[23]\tvalidation_0-logloss:0.57492\tvalidation_1-logloss:0.57387\n",
      "[20]\tvalidation_0-logloss:0.61141\tvalidation_1-logloss:0.61139\n",
      "[15]\tvalidation_0-logloss:0.46531\tvalidation_1-logloss:0.51492\n",
      "[24]\tvalidation_0-logloss:0.57237\tvalidation_1-logloss:0.57132\n",
      "[21]\tvalidation_0-logloss:0.60806\tvalidation_1-logloss:0.60809\n",
      "[25]\tvalidation_0-logloss:0.56970\tvalidation_1-logloss:0.56858\n",
      "[22]\tvalidation_0-logloss:0.60552\tvalidation_1-logloss:0.60546\n",
      "[26]\tvalidation_0-logloss:0.56728\tvalidation_1-logloss:0.56620[16]\tvalidation_0-logloss:0.46188\tvalidation_1-logloss:0.51358\n",
      "\n",
      "[23]\tvalidation_0-logloss:0.60541\tvalidation_1-logloss:0.60544\n",
      "[27]\tvalidation_0-logloss:0.56520\tvalidation_1-logloss:0.56411\n",
      "[17]\tvalidation_0-logloss:0.45851\tvalidation_1-logloss:0.51291\n",
      "[24]\tvalidation_0-logloss:0.60278\tvalidation_1-logloss:0.60280\n",
      "[28]\tvalidation_0-logloss:0.56323\tvalidation_1-logloss:0.56212\n",
      "[18]\tvalidation_0-logloss:0.45532\tvalidation_1-logloss:0.51364\n",
      "[25]\tvalidation_0-logloss:0.60034\tvalidation_1-logloss:0.60035\n",
      "[29]\tvalidation_0-logloss:0.56135\tvalidation_1-logloss:0.56025\n",
      "[26]\tvalidation_0-logloss:0.59753\tvalidation_1-logloss:0.59749[30]\tvalidation_0-logloss:0.55914\tvalidation_1-logloss:0.55795\n",
      "\n",
      "[19]\tvalidation_0-logloss:0.45218\tvalidation_1-logloss:0.51368\n",
      "[27]\tvalidation_0-logloss:0.59526\tvalidation_1-logloss:0.59520\n",
      "[31]\tvalidation_0-logloss:0.55718\tvalidation_1-logloss:0.55613\n",
      "[20]\tvalidation_0-logloss:0.44888\tvalidation_1-logloss:0.51301\n",
      "[28]\tvalidation_0-logloss:0.59264\tvalidation_1-logloss:0.59249\n",
      "[32]\tvalidation_0-logloss:0.55510\tvalidation_1-logloss:0.55401\n",
      "[33]\tvalidation_0-logloss:0.55486\tvalidation_1-logloss:0.55391\n",
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      "[21]\tvalidation_0-logloss:0.44391\tvalidation_1-logloss:0.51295\n",
      "[34]\tvalidation_0-logloss:0.55316\tvalidation_1-logloss:0.55222\n",
      "[30]\tvalidation_0-logloss:0.58811\tvalidation_1-logloss:0.58779\n",
      "[35]\tvalidation_0-logloss:0.55154\tvalidation_1-logloss:0.55057\n",
      "[22]\tvalidation_0-logloss:0.43997\tvalidation_1-logloss:0.51273\n",
      "[31]\tvalidation_0-logloss:0.58591\tvalidation_1-logloss:0.58559\n",
      "[36]\tvalidation_0-logloss:0.55001\tvalidation_1-logloss:0.54906\n",
      "[32]\tvalidation_0-logloss:0.58342\tvalidation_1-logloss:0.58315[37]\tvalidation_0-logloss:0.54820\tvalidation_1-logloss:0.54716\n",
      "\n",
      "[23]\tvalidation_0-logloss:0.43853\tvalidation_1-logloss:0.51206\n",
      "[38]\tvalidation_0-logloss:0.54658\tvalidation_1-logloss:0.54562\n",
      "[33]\tvalidation_0-logloss:0.58116\tvalidation_1-logloss:0.58093\n",
      "[39]\tvalidation_0-logloss:0.54520\tvalidation_1-logloss:0.54425\n",
      "[24]\tvalidation_0-logloss:0.43680\tvalidation_1-logloss:0.51297\n",
      "[34]\tvalidation_0-logloss:0.57876\tvalidation_1-logloss:0.57867\n",
      "[40]\tvalidation_0-logloss:0.54498\tvalidation_1-logloss:0.54421\n",
      "[25]\tvalidation_0-logloss:0.43268\tvalidation_1-logloss:0.51341\n",
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      "[41]\tvalidation_0-logloss:0.54348\tvalidation_1-logloss:0.54275\n",
      "[42]\tvalidation_0-logloss:0.54189\tvalidation_1-logloss:0.54112[36]\tvalidation_0-logloss:0.57524\tvalidation_1-logloss:0.57518\n",
      "\n",
      "[26]\tvalidation_0-logloss:0.43094\tvalidation_1-logloss:0.51371\n",
      "[43]\tvalidation_0-logloss:0.54039\tvalidation_1-logloss:0.53956\n",
      "[37]\tvalidation_0-logloss:0.57514\tvalidation_1-logloss:0.57510\n",
      "[27]\tvalidation_0-logloss:0.42755\tvalidation_1-logloss:0.51316\n",
      "[44]\tvalidation_0-logloss:0.53894\tvalidation_1-logloss:0.53820\n",
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      "[45]\tvalidation_0-logloss:0.53780\tvalidation_1-logloss:0.53709\n",
      "[39]\tvalidation_0-logloss:0.57163\tvalidation_1-logloss:0.57152\n",
      "[28]\tvalidation_0-logloss:0.42610\tvalidation_1-logloss:0.51290\n",
      "[46]\tvalidation_0-logloss:0.53647\tvalidation_1-logloss:0.53578\n",
      "[40]\tvalidation_0-logloss:0.56954\tvalidation_1-logloss:0.56952\n",
      "[47]\tvalidation_0-logloss:0.53513\tvalidation_1-logloss:0.53442\n",
      "[29]\tvalidation_0-logloss:0.42269\tvalidation_1-logloss:0.51455\n",
      "[41]\tvalidation_0-logloss:0.56793\tvalidation_1-logloss:0.56798\n",
      "[48]\tvalidation_0-logloss:0.53389\tvalidation_1-logloss:0.53327\n",
      "[30]\tvalidation_0-logloss:0.42069\tvalidation_1-logloss:0.51545\n",
      "[49]\tvalidation_0-logloss:0.53274\tvalidation_1-logloss:0.53203[42]\tvalidation_0-logloss:0.56638\tvalidation_1-logloss:0.56645\n",
      "\n",
      "[43]\tvalidation_0-logloss:0.56498\tvalidation_1-logloss:0.56505\n",
      "[50]\tvalidation_0-logloss:0.53154\tvalidation_1-logloss:0.53081\n",
      "[31]\tvalidation_0-logloss:0.41783\tvalidation_1-logloss:0.51645\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1472]\tvalidation_0-logloss:0.41503\tvalidation_1-logloss:0.51958\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1675]\tvalidation_0-logloss:0.41810\tvalidation_1-logloss:0.51265\n",
      "[1940]\tvalidation_0-logloss:0.39723\tvalidation_1-logloss:0.52596\n",
      "[1209]\tvalidation_0-logloss:0.04051\tvalidation_1-logloss:0.88157\n",
      "[1676]\tvalidation_0-logloss:0.41808\tvalidation_1-logloss:0.51266\n",
      "[1941]\tvalidation_0-logloss:0.39719\tvalidation_1-logloss:0.52598\n",
      "[1210]\tvalidation_0-logloss:0.04046\tvalidation_1-logloss:0.88156\n",
      "[1942]\tvalidation_0-logloss:0.39711\tvalidation_1-logloss:0.52604\n",
      "[1677]\tvalidation_0-logloss:0.41803\tvalidation_1-logloss:0.51269\n",
      "[1943]\tvalidation_0-logloss:0.39708\tvalidation_1-logloss:0.52602\n",
      "[1678]\tvalidation_0-logloss:0.41800\tvalidation_1-logloss:0.51270\n",
      "[1211]\tvalidation_0-logloss:0.04041\tvalidation_1-logloss:0.88198[1944]\tvalidation_0-logloss:0.39704\tvalidation_1-logloss:0.52609\n",
      "\n",
      "[1945]\tvalidation_0-logloss:0.39701\tvalidation_1-logloss:0.52612\n",
      "[1679]\tvalidation_0-logloss:0.41795\tvalidation_1-logloss:0.51268\n",
      "[1212]\tvalidation_0-logloss:0.04036\tvalidation_1-logloss:0.88239\n",
      "[1946]\tvalidation_0-logloss:0.39697\tvalidation_1-logloss:0.52619\n",
      "[1680]\tvalidation_0-logloss:0.41791\tvalidation_1-logloss:0.51270\n",
      "[1947]\tvalidation_0-logloss:0.39694\tvalidation_1-logloss:0.52612\n",
      "[1213]\tvalidation_0-logloss:0.04031\tvalidation_1-logloss:0.88283\n",
      "[1681]\tvalidation_0-logloss:0.41786\tvalidation_1-logloss:0.51271\n",
      "[1948]\tvalidation_0-logloss:0.39693\tvalidation_1-logloss:0.52611\n",
      "[1214]\tvalidation_0-logloss:0.04028\tvalidation_1-logloss:0.88293\n",
      "[1949]\tvalidation_0-logloss:0.39689\tvalidation_1-logloss:0.52610\n",
      "[1682]\tvalidation_0-logloss:0.41781\tvalidation_1-logloss:0.51279\n",
      "[1950]\tvalidation_0-logloss:0.39685\tvalidation_1-logloss:0.52611\n",
      "[1215]\tvalidation_0-logloss:0.04025\tvalidation_1-logloss:0.88295\n",
      "[1951]\tvalidation_0-logloss:0.39681\tvalidation_1-logloss:0.52616\n",
      "[1683]\tvalidation_0-logloss:0.41778\tvalidation_1-logloss:0.51282\n",
      "[1216]\tvalidation_0-logloss:0.04022\tvalidation_1-logloss:0.88318\n",
      "[1952]\tvalidation_0-logloss:0.39679\tvalidation_1-logloss:0.52619\n",
      "[1684]\tvalidation_0-logloss:0.41774\tvalidation_1-logloss:0.51286\n",
      "[1217]\tvalidation_0-logloss:0.04020\tvalidation_1-logloss:0.88317\n",
      "[1953]\tvalidation_0-logloss:0.39674\tvalidation_1-logloss:0.52613\n",
      "[1685]\tvalidation_0-logloss:0.41772\tvalidation_1-logloss:0.51284\n",
      "[1954]\tvalidation_0-logloss:0.39672\tvalidation_1-logloss:0.52617\n",
      "[1218]\tvalidation_0-logloss:0.04017\tvalidation_1-logloss:0.88332\n",
      "[1955]\tvalidation_0-logloss:0.39666\tvalidation_1-logloss:0.52620\n",
      "[1219]\tvalidation_0-logloss:0.04015\tvalidation_1-logloss:0.88336\n",
      "[1956]\tvalidation_0-logloss:0.39664\tvalidation_1-logloss:0.52624\n",
      "[1686]\tvalidation_0-logloss:0.41771\tvalidation_1-logloss:0.51284\n",
      "[1957]\tvalidation_0-logloss:0.39661\tvalidation_1-logloss:0.52625\n",
      "[1687]\tvalidation_0-logloss:0.41763\tvalidation_1-logloss:0.51284\n",
      "[1958]\tvalidation_0-logloss:0.39657\tvalidation_1-logloss:0.52628\n",
      "[1220]\tvalidation_0-logloss:0.04012\tvalidation_1-logloss:0.88354\n",
      "[1688]\tvalidation_0-logloss:0.41761\tvalidation_1-logloss:0.51284\n",
      "[1959]\tvalidation_0-logloss:0.39654\tvalidation_1-logloss:0.52633\n",
      "[1689]\tvalidation_0-logloss:0.41755\tvalidation_1-logloss:0.51279\n",
      "[1221]\tvalidation_0-logloss:0.04009\tvalidation_1-logloss:0.88355\n",
      "[1960]\tvalidation_0-logloss:0.39651\tvalidation_1-logloss:0.52632\n",
      "[1690]\tvalidation_0-logloss:0.41750\tvalidation_1-logloss:0.51288\n",
      "[1961]\tvalidation_0-logloss:0.39646\tvalidation_1-logloss:0.52639\n",
      "[1222]\tvalidation_0-logloss:0.04006\tvalidation_1-logloss:0.88367\n",
      "[1691]\tvalidation_0-logloss:0.41746\tvalidation_1-logloss:0.51287\n",
      "[1962]\tvalidation_0-logloss:0.39643\tvalidation_1-logloss:0.52640\n",
      "[1963]\tvalidation_0-logloss:0.39638\tvalidation_1-logloss:0.52634\n",
      "[1692]\tvalidation_0-logloss:0.41745\tvalidation_1-logloss:0.51286\n",
      "[1223]\tvalidation_0-logloss:0.04000\tvalidation_1-logloss:0.88422\n",
      "[1964]\tvalidation_0-logloss:0.39635\tvalidation_1-logloss:0.52636\n",
      "[1693]\tvalidation_0-logloss:0.41740\tvalidation_1-logloss:0.51286\n",
      "[1965]\tvalidation_0-logloss:0.39632\tvalidation_1-logloss:0.52638\n",
      "[1224]\tvalidation_0-logloss:0.03999\tvalidation_1-logloss:0.88436\n",
      "[1694]\tvalidation_0-logloss:0.41739\tvalidation_1-logloss:0.51284\n",
      "[1966]\tvalidation_0-logloss:0.39629\tvalidation_1-logloss:0.52639\n",
      "[1695]\tvalidation_0-logloss:0.41736\tvalidation_1-logloss:0.51281\n",
      "[1967]\tvalidation_0-logloss:0.39626\tvalidation_1-logloss:0.52642\n",
      "[1225]\tvalidation_0-logloss:0.03996\tvalidation_1-logloss:0.88450\n",
      "[1968]\tvalidation_0-logloss:0.39623\tvalidation_1-logloss:0.52644\n",
      "[1696]\tvalidation_0-logloss:0.41733\tvalidation_1-logloss:0.51280\n",
      "[1969]\tvalidation_0-logloss:0.39618\tvalidation_1-logloss:0.52647\n",
      "[1226]\tvalidation_0-logloss:0.03992\tvalidation_1-logloss:0.88472\n",
      "[1697]\tvalidation_0-logloss:0.41729\tvalidation_1-logloss:0.51283\n",
      "[1970]\tvalidation_0-logloss:0.39615\tvalidation_1-logloss:0.52648\n",
      "[1698]\tvalidation_0-logloss:0.41726\tvalidation_1-logloss:0.51284\n",
      "[1227]\tvalidation_0-logloss:0.03989\tvalidation_1-logloss:0.88488\n",
      "[1971]\tvalidation_0-logloss:0.39612\tvalidation_1-logloss:0.52643\n",
      "[1699]\tvalidation_0-logloss:0.41723\tvalidation_1-logloss:0.51286\n",
      "[1228]\tvalidation_0-logloss:0.03985\tvalidation_1-logloss:0.88506\n",
      "[1972]\tvalidation_0-logloss:0.39609\tvalidation_1-logloss:0.52655\n",
      "[1700]\tvalidation_0-logloss:0.41720\tvalidation_1-logloss:0.51289\n",
      "[1973]\tvalidation_0-logloss:0.39606\tvalidation_1-logloss:0.52663\n",
      "[1229]\tvalidation_0-logloss:0.03980\tvalidation_1-logloss:0.88494\n",
      "[1974]\tvalidation_0-logloss:0.39604\tvalidation_1-logloss:0.52668\n",
      "[1701]\tvalidation_0-logloss:0.41715\tvalidation_1-logloss:0.51290\n",
      "[1975]\tvalidation_0-logloss:0.39600\tvalidation_1-logloss:0.52667\n",
      "[1230]\tvalidation_0-logloss:0.03978\tvalidation_1-logloss:0.88501\n",
      "[1702]\tvalidation_0-logloss:0.41708\tvalidation_1-logloss:0.51296\n",
      "[1976]\tvalidation_0-logloss:0.39595\tvalidation_1-logloss:0.52675\n",
      "[1703]\tvalidation_0-logloss:0.41704\tvalidation_1-logloss:0.51299\n",
      "[1977]\tvalidation_0-logloss:0.39591\tvalidation_1-logloss:0.52678\n",
      "[1978]\tvalidation_0-logloss:0.39588\tvalidation_1-logloss:0.52677[1231]\tvalidation_0-logloss:0.03976\tvalidation_1-logloss:0.88517\n",
      "\n",
      "[1704]\tvalidation_0-logloss:0.41699\tvalidation_1-logloss:0.51300\n",
      "[1232]\tvalidation_0-logloss:0.03974\tvalidation_1-logloss:0.88533[1705]\tvalidation_0-logloss:0.41696\tvalidation_1-logloss:0.51301\n",
      "\n",
      "[1979]\tvalidation_0-logloss:0.39584\tvalidation_1-logloss:0.52676\n",
      "[1980]\tvalidation_0-logloss:0.39580\tvalidation_1-logloss:0.52674\n",
      "[1706]\tvalidation_0-logloss:0.41694\tvalidation_1-logloss:0.51301\n",
      "[1233]\tvalidation_0-logloss:0.03971\tvalidation_1-logloss:0.88528\n",
      "[1981]\tvalidation_0-logloss:0.39577\tvalidation_1-logloss:0.52675\n",
      "[1707]\tvalidation_0-logloss:0.41691\tvalidation_1-logloss:0.51303\n",
      "[1234]\tvalidation_0-logloss:0.03968\tvalidation_1-logloss:0.88540\n",
      "[1982]\tvalidation_0-logloss:0.39572\tvalidation_1-logloss:0.52673[1708]\tvalidation_0-logloss:0.41685\tvalidation_1-logloss:0.51304\n",
      "\n",
      "[1983]\tvalidation_0-logloss:0.39571\tvalidation_1-logloss:0.52676\n",
      "[1709]\tvalidation_0-logloss:0.41683\tvalidation_1-logloss:0.51303\n",
      "[1984]\tvalidation_0-logloss:0.39570\tvalidation_1-logloss:0.52676\n",
      "[1710]\tvalidation_0-logloss:0.41679\tvalidation_1-logloss:0.51304\n",
      "[1235]\tvalidation_0-logloss:0.03963\tvalidation_1-logloss:0.88549\n",
      "[1985]\tvalidation_0-logloss:0.39567\tvalidation_1-logloss:0.52680\n",
      "[1711]\tvalidation_0-logloss:0.41677\tvalidation_1-logloss:0.51305\n",
      "[1986]\tvalidation_0-logloss:0.39564\tvalidation_1-logloss:0.52683\n",
      "[1236]\tvalidation_0-logloss:0.03958\tvalidation_1-logloss:0.88576\n",
      "[1712]\tvalidation_0-logloss:0.41673\tvalidation_1-logloss:0.51307\n",
      "[1987]\tvalidation_0-logloss:0.39561\tvalidation_1-logloss:0.52681\n",
      "[1237]\tvalidation_0-logloss:0.03955\tvalidation_1-logloss:0.88594[1713]\tvalidation_0-logloss:0.41672\tvalidation_1-logloss:0.51309\n",
      "[1988]\tvalidation_0-logloss:0.39557\tvalidation_1-logloss:0.52681\n",
      "\n",
      "[1989]\tvalidation_0-logloss:0.39554\tvalidation_1-logloss:0.52688\n",
      "[1714]\tvalidation_0-logloss:0.41667\tvalidation_1-logloss:0.51311\n",
      "[1238]\tvalidation_0-logloss:0.03951\tvalidation_1-logloss:0.88600\n",
      "[1990]\tvalidation_0-logloss:0.39549\tvalidation_1-logloss:0.52685\n",
      "[1991]\tvalidation_0-logloss:0.39544\tvalidation_1-logloss:0.52685\n",
      "[1715]\tvalidation_0-logloss:0.41667\tvalidation_1-logloss:0.51310\n",
      "[1239]\tvalidation_0-logloss:0.03948\tvalidation_1-logloss:0.88629\n",
      "[1992]\tvalidation_0-logloss:0.39541\tvalidation_1-logloss:0.52679\n",
      "[1716]\tvalidation_0-logloss:0.41661\tvalidation_1-logloss:0.51312\n",
      "[1993]\tvalidation_0-logloss:0.39538\tvalidation_1-logloss:0.52684\n",
      "[1994]\tvalidation_0-logloss:0.39531\tvalidation_1-logloss:0.52687\n",
      "[1717]\tvalidation_0-logloss:0.41659\tvalidation_1-logloss:0.51312[1240]\tvalidation_0-logloss:0.03945\tvalidation_1-logloss:0.88640\n",
      "\n",
      "[1995]\tvalidation_0-logloss:0.39528\tvalidation_1-logloss:0.52692\n",
      "[1241]\tvalidation_0-logloss:0.03942\tvalidation_1-logloss:0.88647\n",
      "[1996]\tvalidation_0-logloss:0.39524\tvalidation_1-logloss:0.52686\n",
      "[1718]\tvalidation_0-logloss:0.41656\tvalidation_1-logloss:0.51313\n",
      "[1997]\tvalidation_0-logloss:0.39519\tvalidation_1-logloss:0.52687\n",
      "[1242]\tvalidation_0-logloss:0.03940\tvalidation_1-logloss:0.88657\n",
      "[1998]\tvalidation_0-logloss:0.39517\tvalidation_1-logloss:0.52690\n",
      "[1719]\tvalidation_0-logloss:0.41654\tvalidation_1-logloss:0.51314\n",
      "[1999]\tvalidation_0-logloss:0.39513\tvalidation_1-logloss:0.52686\n",
      "[1720]\tvalidation_0-logloss:0.41651\tvalidation_1-logloss:0.51314\n",
      "[1243]\tvalidation_0-logloss:0.03936\tvalidation_1-logloss:0.88702\n",
      "[2000]\tvalidation_0-logloss:0.39510\tvalidation_1-logloss:0.52693\n",
      "[1721]\tvalidation_0-logloss:0.41644\tvalidation_1-logloss:0.51311\n",
      "[2001]\tvalidation_0-logloss:0.39508\tvalidation_1-logloss:0.52689\n",
      "[1244]\tvalidation_0-logloss:0.03934\tvalidation_1-logloss:0.88730\n",
      "[2002]\tvalidation_0-logloss:0.39503\tvalidation_1-logloss:0.52692\n",
      "[1722]\tvalidation_0-logloss:0.41641\tvalidation_1-logloss:0.51312\n",
      "[2003]\tvalidation_0-logloss:0.39499\tvalidation_1-logloss:0.52706\n",
      "[1723]\tvalidation_0-logloss:0.41633\tvalidation_1-logloss:0.51317\n",
      "[2004]\tvalidation_0-logloss:0.39496\tvalidation_1-logloss:0.52705\n",
      "[1245]\tvalidation_0-logloss:0.03929\tvalidation_1-logloss:0.88730\n",
      "[2005]\tvalidation_0-logloss:0.39492\tvalidation_1-logloss:0.52708\n",
      "[1724]\tvalidation_0-logloss:0.41629\tvalidation_1-logloss:0.51318\n",
      "[2006]\tvalidation_0-logloss:0.39488\tvalidation_1-logloss:0.52708[1246]\tvalidation_0-logloss:0.03928\tvalidation_1-logloss:0.88750\n",
      "\n",
      "[1725]\tvalidation_0-logloss:0.41624\tvalidation_1-logloss:0.51322\n",
      "[2007]\tvalidation_0-logloss:0.39485\tvalidation_1-logloss:0.52713\n",
      "[1247]\tvalidation_0-logloss:0.03926\tvalidation_1-logloss:0.88766\n",
      "[1726]\tvalidation_0-logloss:0.41618\tvalidation_1-logloss:0.51318\n",
      "[2008]\tvalidation_0-logloss:0.39481\tvalidation_1-logloss:0.52716\n",
      "[1727]\tvalidation_0-logloss:0.41613\tvalidation_1-logloss:0.51321\n",
      "[1248]\tvalidation_0-logloss:0.03923\tvalidation_1-logloss:0.88781\n",
      "[2009]\tvalidation_0-logloss:0.39477\tvalidation_1-logloss:0.52716\n",
      "[1728]\tvalidation_0-logloss:0.41612\tvalidation_1-logloss:0.51322\n",
      "[2010]\tvalidation_0-logloss:0.39473\tvalidation_1-logloss:0.52725\n",
      "[1729]\tvalidation_0-logloss:0.41607\tvalidation_1-logloss:0.51324\n",
      "[1249]\tvalidation_0-logloss:0.03920\tvalidation_1-logloss:0.88808\n",
      "[2011]\tvalidation_0-logloss:0.39471\tvalidation_1-logloss:0.52733\n",
      "[2012]\tvalidation_0-logloss:0.39469\tvalidation_1-logloss:0.52737\n",
      "[1730]\tvalidation_0-logloss:0.41602\tvalidation_1-logloss:0.51324[1250]\tvalidation_0-logloss:0.03919\tvalidation_1-logloss:0.88814\n",
      "\n",
      "[2013]\tvalidation_0-logloss:0.39465\tvalidation_1-logloss:0.52732\n",
      "[1731]\tvalidation_0-logloss:0.41599\tvalidation_1-logloss:0.51325\n",
      "[1251]\tvalidation_0-logloss:0.03918\tvalidation_1-logloss:0.88815\n",
      "[2014]\tvalidation_0-logloss:0.39462\tvalidation_1-logloss:0.52739\n",
      "[1732]\tvalidation_0-logloss:0.41593\tvalidation_1-logloss:0.51328\n",
      "[2015]\tvalidation_0-logloss:0.39459\tvalidation_1-logloss:0.52745\n",
      "[1252]\tvalidation_0-logloss:0.03914\tvalidation_1-logloss:0.88850\n",
      "[1733]\tvalidation_0-logloss:0.41591\tvalidation_1-logloss:0.51330\n",
      "[2016]\tvalidation_0-logloss:0.39457\tvalidation_1-logloss:0.52747\n",
      "[1253]\tvalidation_0-logloss:0.03910\tvalidation_1-logloss:0.88867\n",
      "[2017]\tvalidation_0-logloss:0.39452\tvalidation_1-logloss:0.52743\n",
      "[1734]\tvalidation_0-logloss:0.41588\tvalidation_1-logloss:0.51332\n",
      "[2018]\tvalidation_0-logloss:0.39450\tvalidation_1-logloss:0.52742[1735]\tvalidation_0-logloss:0.41584\tvalidation_1-logloss:0.51333\n",
      "\n",
      "[1254]\tvalidation_0-logloss:0.03908\tvalidation_1-logloss:0.88875\n",
      "[1736]\tvalidation_0-logloss:0.41579\tvalidation_1-logloss:0.51333\n",
      "[2019]\tvalidation_0-logloss:0.39448\tvalidation_1-logloss:0.52746\n",
      "[1737]\tvalidation_0-logloss:0.41578\tvalidation_1-logloss:0.51332\n",
      "[2020]\tvalidation_0-logloss:0.39445\tvalidation_1-logloss:0.52743\n",
      "[1738]\tvalidation_0-logloss:0.41576\tvalidation_1-logloss:0.51332\n",
      "[1255]\tvalidation_0-logloss:0.03904\tvalidation_1-logloss:0.88886\n",
      "[2021]\tvalidation_0-logloss:0.39441\tvalidation_1-logloss:0.52743\n",
      "[2022]\tvalidation_0-logloss:0.39436\tvalidation_1-logloss:0.52737\n",
      "[1739]\tvalidation_0-logloss:0.41573\tvalidation_1-logloss:0.51331[1256]\tvalidation_0-logloss:0.03901\tvalidation_1-logloss:0.88906\n",
      "\n",
      "[2023]\tvalidation_0-logloss:0.39434\tvalidation_1-logloss:0.52733\n",
      "[2024]\tvalidation_0-logloss:0.39429\tvalidation_1-logloss:0.52728\n",
      "[1740]\tvalidation_0-logloss:0.41570\tvalidation_1-logloss:0.51333\n",
      "[2025]\tvalidation_0-logloss:0.39424\tvalidation_1-logloss:0.52731\n",
      "[1741]\tvalidation_0-logloss:0.41569\tvalidation_1-logloss:0.51331\n",
      "[1257]\tvalidation_0-logloss:0.03898\tvalidation_1-logloss:0.88914\n",
      "[2026]\tvalidation_0-logloss:0.39422\tvalidation_1-logloss:0.52728\n",
      "[1742]\tvalidation_0-logloss:0.41566\tvalidation_1-logloss:0.51332\n",
      "[2027]\tvalidation_0-logloss:0.39418\tvalidation_1-logloss:0.52728\n",
      "[1258]\tvalidation_0-logloss:0.03897\tvalidation_1-logloss:0.88924\n",
      "[1743]\tvalidation_0-logloss:0.41564\tvalidation_1-logloss:0.51334\n",
      "[2028]\tvalidation_0-logloss:0.39415\tvalidation_1-logloss:0.52729\n",
      "[2029]\tvalidation_0-logloss:0.39412\tvalidation_1-logloss:0.52729\n",
      "[1259]\tvalidation_0-logloss:0.03893\tvalidation_1-logloss:0.88966\n",
      "[1744]\tvalidation_0-logloss:0.41559\tvalidation_1-logloss:0.51338\n",
      "[2030]\tvalidation_0-logloss:0.39410\tvalidation_1-logloss:0.52735\n",
      "[1745]\tvalidation_0-logloss:0.41557\tvalidation_1-logloss:0.51336\n",
      "[1260]\tvalidation_0-logloss:0.03889\tvalidation_1-logloss:0.88976\n",
      "[1746]\tvalidation_0-logloss:0.41551\tvalidation_1-logloss:0.51337[2031]\tvalidation_0-logloss:0.39406\tvalidation_1-logloss:0.52733\n",
      "\n",
      "[1261]\tvalidation_0-logloss:0.03887\tvalidation_1-logloss:0.88987\n",
      "[2032]\tvalidation_0-logloss:0.39403\tvalidation_1-logloss:0.52735\n",
      "[1747]\tvalidation_0-logloss:0.41550\tvalidation_1-logloss:0.51341\n",
      "[2033]\tvalidation_0-logloss:0.39402\tvalidation_1-logloss:0.52741\n",
      "[1262]\tvalidation_0-logloss:0.03884\tvalidation_1-logloss:0.89000[1748]\tvalidation_0-logloss:0.41546\tvalidation_1-logloss:0.51342\n",
      "\n",
      "[2034]\tvalidation_0-logloss:0.39400\tvalidation_1-logloss:0.52737\n",
      "[1749]\tvalidation_0-logloss:0.41543\tvalidation_1-logloss:0.51344\n",
      "[2035]\tvalidation_0-logloss:0.39396\tvalidation_1-logloss:0.52737\n",
      "[1263]\tvalidation_0-logloss:0.03882\tvalidation_1-logloss:0.89003\n",
      "[1750]\tvalidation_0-logloss:0.41542\tvalidation_1-logloss:0.51344\n",
      "[2036]\tvalidation_0-logloss:0.39392\tvalidation_1-logloss:0.52740\n",
      "[1264]\tvalidation_0-logloss:0.03878\tvalidation_1-logloss:0.89001\n",
      "[1751]\tvalidation_0-logloss:0.41536\tvalidation_1-logloss:0.51342\n",
      "[2037]\tvalidation_0-logloss:0.39387\tvalidation_1-logloss:0.52744\n",
      "[1265]\tvalidation_0-logloss:0.03876\tvalidation_1-logloss:0.89011\n",
      "[1752]\tvalidation_0-logloss:0.41530\tvalidation_1-logloss:0.51346\n",
      "[2038]\tvalidation_0-logloss:0.39385\tvalidation_1-logloss:0.52748\n",
      "[1266]\tvalidation_0-logloss:0.03871\tvalidation_1-logloss:0.89011\n",
      "[2039]\tvalidation_0-logloss:0.39381\tvalidation_1-logloss:0.52751\n",
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      "\n",
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      "\n",
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      "\n",
      "[1807]\tvalidation_0-logloss:0.41331\tvalidation_1-logloss:0.51405\n",
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      "\n",
      "[2118]\tvalidation_0-logloss:0.39099\tvalidation_1-logloss:0.52922\n",
      "[2119]\tvalidation_0-logloss:0.39095\tvalidation_1-logloss:0.52923\n",
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      "\n",
      "[2131]\tvalidation_0-logloss:0.39057\tvalidation_1-logloss:0.52933\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2257]\tvalidation_0-logloss:0.38627\tvalidation_1-logloss:0.53143\n",
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      "\n",
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      "\n",
      "[2276]\tvalidation_0-logloss:0.38566\tvalidation_1-logloss:0.53178\n",
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      "\n",
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      "\n",
      "[1967]\tvalidation_0-logloss:0.40842\tvalidation_1-logloss:0.51586\n",
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      "\n",
      "[1970]\tvalidation_0-logloss:0.40836\tvalidation_1-logloss:0.51586\n",
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      "\n",
      "[1987]\tvalidation_0-logloss:0.40782\tvalidation_1-logloss:0.51611\n",
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      "[1991]\tvalidation_0-logloss:0.40771\tvalidation_1-logloss:0.51612\n",
      "[2317]\tvalidation_0-logloss:0.38439\tvalidation_1-logloss:0.53210\n",
      "[2318]\tvalidation_0-logloss:0.38435\tvalidation_1-logloss:0.53210\n",
      "[1444]\tvalidation_0-logloss:0.03396\tvalidation_1-logloss:0.91886\n",
      "[1992]\tvalidation_0-logloss:0.40769\tvalidation_1-logloss:0.51612\n",
      "[2319]\tvalidation_0-logloss:0.38433\tvalidation_1-logloss:0.53212\n",
      "[1993]\tvalidation_0-logloss:0.40767\tvalidation_1-logloss:0.51615\n",
      "[1445]\tvalidation_0-logloss:0.03395\tvalidation_1-logloss:0.91890\n",
      "[2320]\tvalidation_0-logloss:0.38429\tvalidation_1-logloss:0.53208\n",
      "[1994]\tvalidation_0-logloss:0.40762\tvalidation_1-logloss:0.51616\n",
      "[1446]\tvalidation_0-logloss:0.03393\tvalidation_1-logloss:0.91909\n",
      "[2321]\tvalidation_0-logloss:0.38425\tvalidation_1-logloss:0.53206\n",
      "[1995]\tvalidation_0-logloss:0.40760\tvalidation_1-logloss:0.51617\n",
      "[1996]\tvalidation_0-logloss:0.40759\tvalidation_1-logloss:0.51616\n",
      "[2322]\tvalidation_0-logloss:0.38423\tvalidation_1-logloss:0.53208\n",
      "[1447]\tvalidation_0-logloss:0.03390\tvalidation_1-logloss:0.91918[1997]\tvalidation_0-logloss:0.40757\tvalidation_1-logloss:0.51617\n",
      "\n",
      "[1998]\tvalidation_0-logloss:0.40753\tvalidation_1-logloss:0.51619\n",
      "[2323]\tvalidation_0-logloss:0.38420\tvalidation_1-logloss:0.53215\n",
      "[1448]\tvalidation_0-logloss:0.03388\tvalidation_1-logloss:0.91928\n",
      "[1999]\tvalidation_0-logloss:0.40750\tvalidation_1-logloss:0.51624\n",
      "[2324]\tvalidation_0-logloss:0.38417\tvalidation_1-logloss:0.53213\n",
      "[2000]\tvalidation_0-logloss:0.40747\tvalidation_1-logloss:0.51622\n",
      "[1449]\tvalidation_0-logloss:0.03386\tvalidation_1-logloss:0.91947\n",
      "[2325]\tvalidation_0-logloss:0.38413\tvalidation_1-logloss:0.53214\n",
      "[2001]\tvalidation_0-logloss:0.40744\tvalidation_1-logloss:0.51625\n",
      "[2326]\tvalidation_0-logloss:0.38410\tvalidation_1-logloss:0.53224\n",
      "[2002]\tvalidation_0-logloss:0.40739\tvalidation_1-logloss:0.51624\n",
      "[1450]\tvalidation_0-logloss:0.03384\tvalidation_1-logloss:0.91948\n",
      "[2327]\tvalidation_0-logloss:0.38408\tvalidation_1-logloss:0.53232\n",
      "[2328]\tvalidation_0-logloss:0.38404\tvalidation_1-logloss:0.53229\n",
      "[1451]\tvalidation_0-logloss:0.03380\tvalidation_1-logloss:0.91979\n",
      "[2003]\tvalidation_0-logloss:0.40735\tvalidation_1-logloss:0.51625\n",
      "[2329]\tvalidation_0-logloss:0.38401\tvalidation_1-logloss:0.53224\n",
      "[2004]\tvalidation_0-logloss:0.40730\tvalidation_1-logloss:0.51625\n",
      "[1452]\tvalidation_0-logloss:0.03378\tvalidation_1-logloss:0.92016\n",
      "[2330]\tvalidation_0-logloss:0.38398\tvalidation_1-logloss:0.53221\n",
      "[2331]\tvalidation_0-logloss:0.38396\tvalidation_1-logloss:0.53225\n",
      "[1453]\tvalidation_0-logloss:0.03377\tvalidation_1-logloss:0.92028\n",
      "[2005]\tvalidation_0-logloss:0.40725\tvalidation_1-logloss:0.51624\n",
      "[2332]\tvalidation_0-logloss:0.38393\tvalidation_1-logloss:0.53230\n",
      "[2006]\tvalidation_0-logloss:0.40720\tvalidation_1-logloss:0.51626\n",
      "[1454]\tvalidation_0-logloss:0.03375\tvalidation_1-logloss:0.92046\n",
      "[2333]\tvalidation_0-logloss:0.38391\tvalidation_1-logloss:0.53233\n",
      "[2007]\tvalidation_0-logloss:0.40719\tvalidation_1-logloss:0.51626\n",
      "[2334]\tvalidation_0-logloss:0.38388\tvalidation_1-logloss:0.53236\n",
      "[1455]\tvalidation_0-logloss:0.03373\tvalidation_1-logloss:0.92074[2008]\tvalidation_0-logloss:0.40718\tvalidation_1-logloss:0.51627\n",
      "\n",
      "[2335]\tvalidation_0-logloss:0.38385\tvalidation_1-logloss:0.53238\n",
      "[2009]\tvalidation_0-logloss:0.40715\tvalidation_1-logloss:0.51631\n",
      "[2336]\tvalidation_0-logloss:0.38381\tvalidation_1-logloss:0.53243\n",
      "[1456]\tvalidation_0-logloss:0.03372\tvalidation_1-logloss:0.92090\n",
      "[2337]\tvalidation_0-logloss:0.38379\tvalidation_1-logloss:0.53246\n",
      "[1457]\tvalidation_0-logloss:0.03369\tvalidation_1-logloss:0.92095\n",
      "[2338]\tvalidation_0-logloss:0.38376\tvalidation_1-logloss:0.53249\n",
      "[2010]\tvalidation_0-logloss:0.40712\tvalidation_1-logloss:0.51634\n",
      "[2339]\tvalidation_0-logloss:0.38373\tvalidation_1-logloss:0.53250\n",
      "[2011]\tvalidation_0-logloss:0.40706\tvalidation_1-logloss:0.51635\n",
      "[1458]\tvalidation_0-logloss:0.03367\tvalidation_1-logloss:0.92112\n",
      "[2340]\tvalidation_0-logloss:0.38371\tvalidation_1-logloss:0.53246\n",
      "[2012]\tvalidation_0-logloss:0.40704\tvalidation_1-logloss:0.51633\n",
      "[1459]\tvalidation_0-logloss:0.03366\tvalidation_1-logloss:0.92115[2341]\tvalidation_0-logloss:0.38367\tvalidation_1-logloss:0.53250\n",
      "\n",
      "[2013]\tvalidation_0-logloss:0.40699\tvalidation_1-logloss:0.51638\n",
      "[2342]\tvalidation_0-logloss:0.38364\tvalidation_1-logloss:0.53251\n",
      "[2014]\tvalidation_0-logloss:0.40697\tvalidation_1-logloss:0.51638\n",
      "[1460]\tvalidation_0-logloss:0.03364\tvalidation_1-logloss:0.92124\n",
      "[2015]\tvalidation_0-logloss:0.40695\tvalidation_1-logloss:0.51639\n",
      "[2343]\tvalidation_0-logloss:0.38361\tvalidation_1-logloss:0.53251\n",
      "[2016]\tvalidation_0-logloss:0.40689\tvalidation_1-logloss:0.51641[1461]\tvalidation_0-logloss:0.03362\tvalidation_1-logloss:0.92139\n",
      "\n",
      "[2344]\tvalidation_0-logloss:0.38356\tvalidation_1-logloss:0.53250\n",
      "[2345]\tvalidation_0-logloss:0.38356\tvalidation_1-logloss:0.53250\n",
      "[2017]\tvalidation_0-logloss:0.40687\tvalidation_1-logloss:0.51642\n",
      "[1462]\tvalidation_0-logloss:0.03361\tvalidation_1-logloss:0.92149\n",
      "[2346]\tvalidation_0-logloss:0.38354\tvalidation_1-logloss:0.53257\n",
      "[2347]\tvalidation_0-logloss:0.38348\tvalidation_1-logloss:0.53262\n",
      "[2018]\tvalidation_0-logloss:0.40683\tvalidation_1-logloss:0.51639\n",
      "[1463]\tvalidation_0-logloss:0.03360\tvalidation_1-logloss:0.92147\n",
      "[2348]\tvalidation_0-logloss:0.38344\tvalidation_1-logloss:0.53259\n",
      "[2019]\tvalidation_0-logloss:0.40678\tvalidation_1-logloss:0.51641\n",
      "[2349]\tvalidation_0-logloss:0.38340\tvalidation_1-logloss:0.53255\n",
      "[2350]\tvalidation_0-logloss:0.38337\tvalidation_1-logloss:0.53254\n",
      "[1464]\tvalidation_0-logloss:0.03357\tvalidation_1-logloss:0.92155\n",
      "[2020]\tvalidation_0-logloss:0.40674\tvalidation_1-logloss:0.51640\n",
      "[2351]\tvalidation_0-logloss:0.38333\tvalidation_1-logloss:0.53259\n",
      "[2021]\tvalidation_0-logloss:0.40668\tvalidation_1-logloss:0.51641\n",
      "[1465]\tvalidation_0-logloss:0.03356\tvalidation_1-logloss:0.92157\n",
      "[2352]\tvalidation_0-logloss:0.38329\tvalidation_1-logloss:0.53264\n",
      "[2022]\tvalidation_0-logloss:0.40666\tvalidation_1-logloss:0.51643\n",
      "[2353]\tvalidation_0-logloss:0.38327\tvalidation_1-logloss:0.53263\n",
      "[1466]\tvalidation_0-logloss:0.03354\tvalidation_1-logloss:0.92165\n",
      "[2354]\tvalidation_0-logloss:0.38324\tvalidation_1-logloss:0.53262\n",
      "[2023]\tvalidation_0-logloss:0.40665\tvalidation_1-logloss:0.51643\n",
      "[1467]\tvalidation_0-logloss:0.03353\tvalidation_1-logloss:0.92184\n",
      "[2355]\tvalidation_0-logloss:0.38322\tvalidation_1-logloss:0.53262\n",
      "[2024]\tvalidation_0-logloss:0.40662\tvalidation_1-logloss:0.51641\n",
      "[2356]\tvalidation_0-logloss:0.38320\tvalidation_1-logloss:0.53254\n",
      "[2025]\tvalidation_0-logloss:0.40657\tvalidation_1-logloss:0.51647\n",
      "[1468]\tvalidation_0-logloss:0.03350\tvalidation_1-logloss:0.92176\n",
      "[2357]\tvalidation_0-logloss:0.38318\tvalidation_1-logloss:0.53258\n",
      "[2026]\tvalidation_0-logloss:0.40655\tvalidation_1-logloss:0.51647\n",
      "[2358]\tvalidation_0-logloss:0.38314\tvalidation_1-logloss:0.53260\n",
      "[1469]\tvalidation_0-logloss:0.03348\tvalidation_1-logloss:0.92192\n",
      "[2027]\tvalidation_0-logloss:0.40651\tvalidation_1-logloss:0.51645\n",
      "[2359]\tvalidation_0-logloss:0.38310\tvalidation_1-logloss:0.53261\n",
      "[2028]\tvalidation_0-logloss:0.40650\tvalidation_1-logloss:0.51646\n",
      "[1470]\tvalidation_0-logloss:0.03346\tvalidation_1-logloss:0.92199\n",
      "[2360]\tvalidation_0-logloss:0.38306\tvalidation_1-logloss:0.53265\n",
      "[2029]\tvalidation_0-logloss:0.40648\tvalidation_1-logloss:0.51647\n",
      "[2030]\tvalidation_0-logloss:0.40641\tvalidation_1-logloss:0.51649[2361]\tvalidation_0-logloss:0.38303\tvalidation_1-logloss:0.53270\n",
      "\n",
      "[1471]\tvalidation_0-logloss:0.03344\tvalidation_1-logloss:0.92213\n",
      "[2362]\tvalidation_0-logloss:0.38300\tvalidation_1-logloss:0.53266\n",
      "[1472]\tvalidation_0-logloss:0.03341\tvalidation_1-logloss:0.92255\n",
      "[2031]\tvalidation_0-logloss:0.40637\tvalidation_1-logloss:0.51648\n",
      "[2363]\tvalidation_0-logloss:0.38298\tvalidation_1-logloss:0.53268\n",
      "[2032]\tvalidation_0-logloss:0.40635\tvalidation_1-logloss:0.51647\n",
      "[1473]\tvalidation_0-logloss:0.03338\tvalidation_1-logloss:0.92290\n",
      "[2364]\tvalidation_0-logloss:0.38295\tvalidation_1-logloss:0.53270\n",
      "[1474]\tvalidation_0-logloss:0.03336\tvalidation_1-logloss:0.92308\n",
      "[2033]\tvalidation_0-logloss:0.40630\tvalidation_1-logloss:0.51650\n",
      "[2365]\tvalidation_0-logloss:0.38294\tvalidation_1-logloss:0.53274[2034]\tvalidation_0-logloss:0.40626\tvalidation_1-logloss:0.51650\n",
      "\n",
      "[2035]\tvalidation_0-logloss:0.40621\tvalidation_1-logloss:0.51652[1475]\tvalidation_0-logloss:0.03332\tvalidation_1-logloss:0.92320\n",
      "\n",
      "[2366]\tvalidation_0-logloss:0.38290\tvalidation_1-logloss:0.53279\n",
      "[2036]\tvalidation_0-logloss:0.40616\tvalidation_1-logloss:0.51648\n",
      "[2367]\tvalidation_0-logloss:0.38287\tvalidation_1-logloss:0.53280\n",
      "[1476]\tvalidation_0-logloss:0.03331\tvalidation_1-logloss:0.92333\n",
      "[2037]\tvalidation_0-logloss:0.40611\tvalidation_1-logloss:0.51648[2368]\tvalidation_0-logloss:0.38284\tvalidation_1-logloss:0.53283\n",
      "\n",
      "[2038]\tvalidation_0-logloss:0.40607\tvalidation_1-logloss:0.51651\n",
      "[1477]\tvalidation_0-logloss:0.03329\tvalidation_1-logloss:0.92335\n",
      "[2369]\tvalidation_0-logloss:0.38281\tvalidation_1-logloss:0.53288\n",
      "[2370]\tvalidation_0-logloss:0.38277\tvalidation_1-logloss:0.53292\n",
      "[2039]\tvalidation_0-logloss:0.40602\tvalidation_1-logloss:0.51655\n",
      "[1478]\tvalidation_0-logloss:0.03327\tvalidation_1-logloss:0.92347\n",
      "[2040]\tvalidation_0-logloss:0.40601\tvalidation_1-logloss:0.51657\n",
      "[2371]\tvalidation_0-logloss:0.38275\tvalidation_1-logloss:0.53288\n",
      "[1479]\tvalidation_0-logloss:0.03323\tvalidation_1-logloss:0.92384\n",
      "[2041]\tvalidation_0-logloss:0.40599\tvalidation_1-logloss:0.51661\n",
      "[2372]\tvalidation_0-logloss:0.38271\tvalidation_1-logloss:0.53290\n",
      "[1480]\tvalidation_0-logloss:0.03320\tvalidation_1-logloss:0.92393\n",
      "[2042]\tvalidation_0-logloss:0.40596\tvalidation_1-logloss:0.51668\n",
      "[2373]\tvalidation_0-logloss:0.38268\tvalidation_1-logloss:0.53291\n",
      "[2374]\tvalidation_0-logloss:0.38267\tvalidation_1-logloss:0.53293\n",
      "[2043]\tvalidation_0-logloss:0.40591\tvalidation_1-logloss:0.51664\n",
      "[1481]\tvalidation_0-logloss:0.03317\tvalidation_1-logloss:0.92420\n",
      "[2044]\tvalidation_0-logloss:0.40587\tvalidation_1-logloss:0.51664\n",
      "[2375]\tvalidation_0-logloss:0.38265\tvalidation_1-logloss:0.53293\n",
      "[1482]\tvalidation_0-logloss:0.03316\tvalidation_1-logloss:0.92421\n",
      "[2376]\tvalidation_0-logloss:0.38263\tvalidation_1-logloss:0.53295\n",
      "[2045]\tvalidation_0-logloss:0.40583\tvalidation_1-logloss:0.51663\n",
      "[2046]\tvalidation_0-logloss:0.40578\tvalidation_1-logloss:0.51664\n",
      "[2377]\tvalidation_0-logloss:0.38263\tvalidation_1-logloss:0.53295\n",
      "[1483]\tvalidation_0-logloss:0.03314\tvalidation_1-logloss:0.92435\n",
      "[2047]\tvalidation_0-logloss:0.40574\tvalidation_1-logloss:0.51667\n",
      "[2378]\tvalidation_0-logloss:0.38261\tvalidation_1-logloss:0.53296\n",
      "[1484]\tvalidation_0-logloss:0.03312\tvalidation_1-logloss:0.92441\n",
      "[2048]\tvalidation_0-logloss:0.40573\tvalidation_1-logloss:0.51669\n",
      "[2379]\tvalidation_0-logloss:0.38258\tvalidation_1-logloss:0.53299\n",
      "[1485]\tvalidation_0-logloss:0.03309\tvalidation_1-logloss:0.92482\n",
      "[2049]\tvalidation_0-logloss:0.40571\tvalidation_1-logloss:0.51670[2380]\tvalidation_0-logloss:0.38252\tvalidation_1-logloss:0.53301\n",
      "\n",
      "[1486]\tvalidation_0-logloss:0.03306\tvalidation_1-logloss:0.92503\n",
      "[2381]\tvalidation_0-logloss:0.38249\tvalidation_1-logloss:0.53306\n",
      "[2050]\tvalidation_0-logloss:0.40568\tvalidation_1-logloss:0.51672\n",
      "[2382]\tvalidation_0-logloss:0.38246\tvalidation_1-logloss:0.53308\n",
      "[1487]\tvalidation_0-logloss:0.03305\tvalidation_1-logloss:0.92507\n",
      "[2383]\tvalidation_0-logloss:0.38242\tvalidation_1-logloss:0.53307[2051]\tvalidation_0-logloss:0.40564\tvalidation_1-logloss:0.51673\n",
      "\n",
      "[1488]\tvalidation_0-logloss:0.03302\tvalidation_1-logloss:0.92521\n",
      "[2384]\tvalidation_0-logloss:0.38237\tvalidation_1-logloss:0.53308\n",
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      "[2385]\tvalidation_0-logloss:0.38235\tvalidation_1-logloss:0.53309\n",
      "[2053]\tvalidation_0-logloss:0.40557\tvalidation_1-logloss:0.51672\n",
      "[1489]\tvalidation_0-logloss:0.03300\tvalidation_1-logloss:0.92550\n",
      "[2386]\tvalidation_0-logloss:0.38232\tvalidation_1-logloss:0.53317\n",
      "[2054]\tvalidation_0-logloss:0.40556\tvalidation_1-logloss:0.51673\n",
      "[2387]\tvalidation_0-logloss:0.38228\tvalidation_1-logloss:0.53313\n",
      "[2388]\tvalidation_0-logloss:0.38227\tvalidation_1-logloss:0.53310[2055]\tvalidation_0-logloss:0.40554\tvalidation_1-logloss:0.51675\n",
      "[1490]\tvalidation_0-logloss:0.03297\tvalidation_1-logloss:0.92559\n",
      "\n",
      "[2056]\tvalidation_0-logloss:0.40554\tvalidation_1-logloss:0.51676\n",
      "[2389]\tvalidation_0-logloss:0.38224\tvalidation_1-logloss:0.53313\n",
      "[1491]\tvalidation_0-logloss:0.03295\tvalidation_1-logloss:0.92574\n",
      "[2390]\tvalidation_0-logloss:0.38222\tvalidation_1-logloss:0.53316[2057]\tvalidation_0-logloss:0.40551\tvalidation_1-logloss:0.51676\n",
      "\n",
      "[1492]\tvalidation_0-logloss:0.03294\tvalidation_1-logloss:0.92587\n",
      "[2058]\tvalidation_0-logloss:0.40550\tvalidation_1-logloss:0.51678\n",
      "[2391]\tvalidation_0-logloss:0.38221\tvalidation_1-logloss:0.53320\n",
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      "[2060]\tvalidation_0-logloss:0.40544\tvalidation_1-logloss:0.51676\n",
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      "[2068]\tvalidation_0-logloss:0.40519\tvalidation_1-logloss:0.51687\n",
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      "[1500]\tvalidation_0-logloss:0.03279\tvalidation_1-logloss:0.92686\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2128]\tvalidation_0-logloss:0.40346\tvalidation_1-logloss:0.51741\n",
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      "\n",
      "[2141]\tvalidation_0-logloss:0.40307\tvalidation_1-logloss:0.51765\n",
      "[2488]\tvalidation_0-logloss:0.37918\tvalidation_1-logloss:0.53461[2142]\tvalidation_0-logloss:0.40303\tvalidation_1-logloss:0.51765\n",
      "\n",
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      "\n",
      "[2147]\tvalidation_0-logloss:0.40284\tvalidation_1-logloss:0.51771\n",
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      "\n",
      "[2153]\tvalidation_0-logloss:0.40267\tvalidation_1-logloss:0.51779\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2612]\tvalidation_0-logloss:0.37525\tvalidation_1-logloss:0.53705\n",
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      "\n",
      "[2616]\tvalidation_0-logloss:0.37511\tvalidation_1-logloss:0.53703\n",
      "[1635]\tvalidation_0-logloss:0.03028\tvalidation_1-logloss:0.94401\n",
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      "\n",
      "[2618]\tvalidation_0-logloss:0.37503\tvalidation_1-logloss:0.53709\n",
      "[2255]\tvalidation_0-logloss:0.39973\tvalidation_1-logloss:0.51894\n",
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      "[2259]\tvalidation_0-logloss:0.39959\tvalidation_1-logloss:0.51897\n",
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      "[2261]\tvalidation_0-logloss:0.39954\tvalidation_1-logloss:0.51905\n",
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      "\n",
      "[2262]\tvalidation_0-logloss:0.39952\tvalidation_1-logloss:0.51906\n",
      "[2627]\tvalidation_0-logloss:0.37478\tvalidation_1-logloss:0.53722\n",
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      "[1641]\tvalidation_0-logloss:0.03018\tvalidation_1-logloss:0.94499\n",
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      "\n",
      "[1642]\tvalidation_0-logloss:0.03017\tvalidation_1-logloss:0.94515\n",
      "[2629]\tvalidation_0-logloss:0.37472\tvalidation_1-logloss:0.53726\n",
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      "[2267]\tvalidation_0-logloss:0.39932\tvalidation_1-logloss:0.51914\n",
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      "[1645]\tvalidation_0-logloss:0.03010\tvalidation_1-logloss:0.94527[2633]\tvalidation_0-logloss:0.37460\tvalidation_1-logloss:0.53739\n",
      "[2268]\tvalidation_0-logloss:0.39927\tvalidation_1-logloss:0.51911\n",
      "\n",
      "[2634]\tvalidation_0-logloss:0.37456\tvalidation_1-logloss:0.53736\n",
      "[2269]\tvalidation_0-logloss:0.39925\tvalidation_1-logloss:0.51916\n",
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      "[2635]\tvalidation_0-logloss:0.37453\tvalidation_1-logloss:0.53737\n",
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      "[2637]\tvalidation_0-logloss:0.37445\tvalidation_1-logloss:0.53748\n",
      "[1648]\tvalidation_0-logloss:0.03006\tvalidation_1-logloss:0.94565\n",
      "[2272]\tvalidation_0-logloss:0.39921\tvalidation_1-logloss:0.51911\n",
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      "[2273]\tvalidation_0-logloss:0.39913\tvalidation_1-logloss:0.51908\n",
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      "[1651]\tvalidation_0-logloss:0.03001\tvalidation_1-logloss:0.94612[2643]\tvalidation_0-logloss:0.37426\tvalidation_1-logloss:0.53747\n",
      "\n",
      "[2277]\tvalidation_0-logloss:0.39899\tvalidation_1-logloss:0.51914\n",
      "[2644]\tvalidation_0-logloss:0.37422\tvalidation_1-logloss:0.53748\n",
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      "[2646]\tvalidation_0-logloss:0.37412\tvalidation_1-logloss:0.53754\n",
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      "[2279]\tvalidation_0-logloss:0.39895\tvalidation_1-logloss:0.51910\n",
      "[2647]\tvalidation_0-logloss:0.37410\tvalidation_1-logloss:0.53756\n",
      "[2280]\tvalidation_0-logloss:0.39892\tvalidation_1-logloss:0.51911\n",
      "[2648]\tvalidation_0-logloss:0.37404\tvalidation_1-logloss:0.53753\n",
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      "[2281]\tvalidation_0-logloss:0.39890\tvalidation_1-logloss:0.51913\n",
      "[2649]\tvalidation_0-logloss:0.37402\tvalidation_1-logloss:0.53753\n",
      "[2282]\tvalidation_0-logloss:0.39888\tvalidation_1-logloss:0.51913\n",
      "[2650]\tvalidation_0-logloss:0.37401\tvalidation_1-logloss:0.53757[1655]\tvalidation_0-logloss:0.02994\tvalidation_1-logloss:0.94656\n",
      "\n",
      "[2283]\tvalidation_0-logloss:0.39885\tvalidation_1-logloss:0.51914\n",
      "[2651]\tvalidation_0-logloss:0.37396\tvalidation_1-logloss:0.53759\n",
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      "[1657]\tvalidation_0-logloss:0.02990\tvalidation_1-logloss:0.94662[2654]\tvalidation_0-logloss:0.37386\tvalidation_1-logloss:0.53771\n",
      "\n",
      "[2655]\tvalidation_0-logloss:0.37384\tvalidation_1-logloss:0.53773\n",
      "[2287]\tvalidation_0-logloss:0.39875\tvalidation_1-logloss:0.51922\n",
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      "[2660]\tvalidation_0-logloss:0.37370\tvalidation_1-logloss:0.53776\n",
      "[2291]\tvalidation_0-logloss:0.39867\tvalidation_1-logloss:0.51924\n",
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      "[1662]\tvalidation_0-logloss:0.02983\tvalidation_1-logloss:0.94731\n",
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      "[2665]\tvalidation_0-logloss:0.37356\tvalidation_1-logloss:0.53785\n",
      "[2295]\tvalidation_0-logloss:0.39854\tvalidation_1-logloss:0.51929\n",
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      "[2666]\tvalidation_0-logloss:0.37353\tvalidation_1-logloss:0.53784\n",
      "[2297]\tvalidation_0-logloss:0.39851\tvalidation_1-logloss:0.51928\n",
      "[2667]\tvalidation_0-logloss:0.37350\tvalidation_1-logloss:0.53781\n",
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      "[1665]\tvalidation_0-logloss:0.02981\tvalidation_1-logloss:0.94766\n",
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      "[1666]\tvalidation_0-logloss:0.02979\tvalidation_1-logloss:0.94774\n",
      "[2670]\tvalidation_0-logloss:0.37337\tvalidation_1-logloss:0.53798\n",
      "[2300]\tvalidation_0-logloss:0.39842\tvalidation_1-logloss:0.51929\n",
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      "[2672]\tvalidation_0-logloss:0.37331\tvalidation_1-logloss:0.53798\n",
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      "\n",
      "[2673]\tvalidation_0-logloss:0.37328\tvalidation_1-logloss:0.53796\n",
      "[2674]\tvalidation_0-logloss:0.37326\tvalidation_1-logloss:0.53799\n",
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      "[2675]\tvalidation_0-logloss:0.37321\tvalidation_1-logloss:0.53793\n",
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      "[2677]\tvalidation_0-logloss:0.37315\tvalidation_1-logloss:0.53799\n",
      "[2678]\tvalidation_0-logloss:0.37313\tvalidation_1-logloss:0.53802\n",
      "[2304]\tvalidation_0-logloss:0.39828\tvalidation_1-logloss:0.51932\n",
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      "[2679]\tvalidation_0-logloss:0.37311\tvalidation_1-logloss:0.53798\n",
      "[2305]\tvalidation_0-logloss:0.39827\tvalidation_1-logloss:0.51933\n",
      "[2680]\tvalidation_0-logloss:0.37307\tvalidation_1-logloss:0.53802\n",
      "[1671]\tvalidation_0-logloss:0.02969\tvalidation_1-logloss:0.94853\n",
      "[2681]\tvalidation_0-logloss:0.37305\tvalidation_1-logloss:0.53808\n",
      "[2306]\tvalidation_0-logloss:0.39826\tvalidation_1-logloss:0.51931\n",
      "[2307]\tvalidation_0-logloss:0.39822\tvalidation_1-logloss:0.51935\n",
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      "[2682]\tvalidation_0-logloss:0.37303\tvalidation_1-logloss:0.53806\n",
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      "[1673]\tvalidation_0-logloss:0.02966\tvalidation_1-logloss:0.94872\n",
      "[2683]\tvalidation_0-logloss:0.37300\tvalidation_1-logloss:0.53811\n",
      "[2309]\tvalidation_0-logloss:0.39816\tvalidation_1-logloss:0.51940\n",
      "[2684]\tvalidation_0-logloss:0.37297\tvalidation_1-logloss:0.53816\n",
      "[2310]\tvalidation_0-logloss:0.39814\tvalidation_1-logloss:0.51942\n",
      "[2685]\tvalidation_0-logloss:0.37293\tvalidation_1-logloss:0.53814\n",
      "[2311]\tvalidation_0-logloss:0.39810\tvalidation_1-logloss:0.51942\n",
      "[1674]\tvalidation_0-logloss:0.02963\tvalidation_1-logloss:0.94873\n",
      "[2686]\tvalidation_0-logloss:0.37292\tvalidation_1-logloss:0.53813\n",
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      "[2312]\tvalidation_0-logloss:0.39808\tvalidation_1-logloss:0.51944\n",
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      "[1734]\tvalidation_0-logloss:0.02867\tvalidation_1-logloss:0.95729\n",
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      "[2397]\tvalidation_0-logloss:0.39577\tvalidation_1-logloss:0.52043\n",
      "[1735]\tvalidation_0-logloss:0.02866\tvalidation_1-logloss:0.95741\n",
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      "[2398]\tvalidation_0-logloss:0.39573\tvalidation_1-logloss:0.52044\n",
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      "[1736]\tvalidation_0-logloss:0.02864\tvalidation_1-logloss:0.95748\n",
      "[2399]\tvalidation_0-logloss:0.39570\tvalidation_1-logloss:0.52047\n",
      "[2788]\tvalidation_0-logloss:0.36998\tvalidation_1-logloss:0.53967[2400]\tvalidation_0-logloss:0.39568\tvalidation_1-logloss:0.52047\n",
      "\n",
      "[1737]\tvalidation_0-logloss:0.02862\tvalidation_1-logloss:0.95755\n",
      "[2789]\tvalidation_0-logloss:0.36994\tvalidation_1-logloss:0.53971\n",
      "[2401]\tvalidation_0-logloss:0.39567\tvalidation_1-logloss:0.52048\n",
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      "[2790]\tvalidation_0-logloss:0.36990\tvalidation_1-logloss:0.53973\n",
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      "[1739]\tvalidation_0-logloss:0.02860\tvalidation_1-logloss:0.95775[2791]\tvalidation_0-logloss:0.36987\tvalidation_1-logloss:0.53974\n",
      "\n",
      "[2792]\tvalidation_0-logloss:0.36982\tvalidation_1-logloss:0.53980\n",
      "[2403]\tvalidation_0-logloss:0.39561\tvalidation_1-logloss:0.52046\n",
      "[1740]\tvalidation_0-logloss:0.02858\tvalidation_1-logloss:0.95781\n",
      "[2793]\tvalidation_0-logloss:0.36978\tvalidation_1-logloss:0.53980\n",
      "[2404]\tvalidation_0-logloss:0.39558\tvalidation_1-logloss:0.52047\n",
      "[1741]\tvalidation_0-logloss:0.02857\tvalidation_1-logloss:0.95807\n",
      "[2794]\tvalidation_0-logloss:0.36974\tvalidation_1-logloss:0.53978\n",
      "[2405]\tvalidation_0-logloss:0.39556\tvalidation_1-logloss:0.52051\n",
      "[2795]\tvalidation_0-logloss:0.36971\tvalidation_1-logloss:0.53981\n",
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      "[2406]\tvalidation_0-logloss:0.39551\tvalidation_1-logloss:0.52053[2796]\tvalidation_0-logloss:0.36966\tvalidation_1-logloss:0.53987\n",
      "\n",
      "[2797]\tvalidation_0-logloss:0.36963\tvalidation_1-logloss:0.53988\n",
      "[1743]\tvalidation_0-logloss:0.02854\tvalidation_1-logloss:0.95860[2407]\tvalidation_0-logloss:0.39550\tvalidation_1-logloss:0.52053\n",
      "\n",
      "[2798]\tvalidation_0-logloss:0.36960\tvalidation_1-logloss:0.53990\n",
      "[2408]\tvalidation_0-logloss:0.39547\tvalidation_1-logloss:0.52060\n",
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      "[1744]\tvalidation_0-logloss:0.02853\tvalidation_1-logloss:0.95846\n",
      "[2800]\tvalidation_0-logloss:0.36955\tvalidation_1-logloss:0.53988\n",
      "[2409]\tvalidation_0-logloss:0.39543\tvalidation_1-logloss:0.52055\n",
      "[1745]\tvalidation_0-logloss:0.02852\tvalidation_1-logloss:0.95858\n",
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      "[2410]\tvalidation_0-logloss:0.39540\tvalidation_1-logloss:0.52057\n",
      "[2802]\tvalidation_0-logloss:0.36948\tvalidation_1-logloss:0.53984\n",
      "[2803]\tvalidation_0-logloss:0.36944\tvalidation_1-logloss:0.53985\n",
      "[2411]\tvalidation_0-logloss:0.39536\tvalidation_1-logloss:0.52060\n",
      "[1746]\tvalidation_0-logloss:0.02851\tvalidation_1-logloss:0.95890\n",
      "[2804]\tvalidation_0-logloss:0.36943\tvalidation_1-logloss:0.53984\n",
      "[2412]\tvalidation_0-logloss:0.39535\tvalidation_1-logloss:0.52061\n",
      "[2805]\tvalidation_0-logloss:0.36940\tvalidation_1-logloss:0.53986\n",
      "[1747]\tvalidation_0-logloss:0.02848\tvalidation_1-logloss:0.95921\n",
      "[2413]\tvalidation_0-logloss:0.39530\tvalidation_1-logloss:0.52064\n",
      "[2806]\tvalidation_0-logloss:0.36937\tvalidation_1-logloss:0.53994\n",
      "[2414]\tvalidation_0-logloss:0.39528\tvalidation_1-logloss:0.52065\n",
      "[2807]\tvalidation_0-logloss:0.36935\tvalidation_1-logloss:0.53993\n",
      "[1748]\tvalidation_0-logloss:0.02847\tvalidation_1-logloss:0.95935\n",
      "[2415]\tvalidation_0-logloss:0.39525\tvalidation_1-logloss:0.52070\n",
      "[2808]\tvalidation_0-logloss:0.36934\tvalidation_1-logloss:0.53990\n",
      "[1749]\tvalidation_0-logloss:0.02845\tvalidation_1-logloss:0.95953\n",
      "[2809]\tvalidation_0-logloss:0.36932\tvalidation_1-logloss:0.53988\n",
      "[2416]\tvalidation_0-logloss:0.39520\tvalidation_1-logloss:0.52073\n",
      "[2810]\tvalidation_0-logloss:0.36929\tvalidation_1-logloss:0.53988\n",
      "[1750]\tvalidation_0-logloss:0.02844\tvalidation_1-logloss:0.95968\n",
      "[2417]\tvalidation_0-logloss:0.39518\tvalidation_1-logloss:0.52076\n",
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      "[1751]\tvalidation_0-logloss:0.02843\tvalidation_1-logloss:0.95994\n",
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      "[2812]\tvalidation_0-logloss:0.36924\tvalidation_1-logloss:0.53984\n",
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      "[2419]\tvalidation_0-logloss:0.39515\tvalidation_1-logloss:0.52079\n",
      "[1752]\tvalidation_0-logloss:0.02842\tvalidation_1-logloss:0.96015\n",
      "[2420]\tvalidation_0-logloss:0.39513\tvalidation_1-logloss:0.52080\n",
      "[2814]\tvalidation_0-logloss:0.36918\tvalidation_1-logloss:0.53984\n",
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      "[1753]\tvalidation_0-logloss:0.02840\tvalidation_1-logloss:0.96015\n",
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      "[2427]\tvalidation_0-logloss:0.39492\tvalidation_1-logloss:0.52084\n",
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      "[1757]\tvalidation_0-logloss:0.02835\tvalidation_1-logloss:0.96037\n",
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      "[1760]\tvalidation_0-logloss:0.02832\tvalidation_1-logloss:0.96048[2828]\tvalidation_0-logloss:0.36886\tvalidation_1-logloss:0.54006\n",
      "\n",
      "[2431]\tvalidation_0-logloss:0.39484\tvalidation_1-logloss:0.52099\n",
      "[2829]\tvalidation_0-logloss:0.36882\tvalidation_1-logloss:0.54010\n",
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      "\n",
      "[2830]\tvalidation_0-logloss:0.36880\tvalidation_1-logloss:0.54017\n",
      "[2831]\tvalidation_0-logloss:0.36876\tvalidation_1-logloss:0.54019\n",
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      "[1765]\tvalidation_0-logloss:0.02827\tvalidation_1-logloss:0.96086\n",
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      "[1770]\tvalidation_0-logloss:0.02821\tvalidation_1-logloss:0.96156\n",
      "[2443]\tvalidation_0-logloss:0.39458\tvalidation_1-logloss:0.52101\n",
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      "[2445]\tvalidation_0-logloss:0.39455\tvalidation_1-logloss:0.52102[2845]\tvalidation_0-logloss:0.36836\tvalidation_1-logloss:0.54044\n",
      "\n",
      "[1771]\tvalidation_0-logloss:0.02819\tvalidation_1-logloss:0.96193\n",
      "[2846]\tvalidation_0-logloss:0.36833\tvalidation_1-logloss:0.54042\n",
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      "[1772]\tvalidation_0-logloss:0.02817\tvalidation_1-logloss:0.96209\n",
      "[2847]\tvalidation_0-logloss:0.36829\tvalidation_1-logloss:0.54041\n",
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      "[2848]\tvalidation_0-logloss:0.36825\tvalidation_1-logloss:0.54039\n",
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      "[1774]\tvalidation_0-logloss:0.02815\tvalidation_1-logloss:0.96210\n",
      "[2449]\tvalidation_0-logloss:0.39442\tvalidation_1-logloss:0.52101\n",
      "[2850]\tvalidation_0-logloss:0.36818\tvalidation_1-logloss:0.54041\n",
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      "[2450]\tvalidation_0-logloss:0.39436\tvalidation_1-logloss:0.52098\n",
      "[2852]\tvalidation_0-logloss:0.36813\tvalidation_1-logloss:0.54046\n",
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      "[1776]\tvalidation_0-logloss:0.02813\tvalidation_1-logloss:0.96226\n",
      "[2451]\tvalidation_0-logloss:0.39433\tvalidation_1-logloss:0.52098\n",
      "[2854]\tvalidation_0-logloss:0.36808\tvalidation_1-logloss:0.54051[2452]\tvalidation_0-logloss:0.39431\tvalidation_1-logloss:0.52099\n",
      "\n",
      "[1777]\tvalidation_0-logloss:0.02812\tvalidation_1-logloss:0.96238\n",
      "[2855]\tvalidation_0-logloss:0.36807\tvalidation_1-logloss:0.54053\n",
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      "[2857]\tvalidation_0-logloss:0.36801\tvalidation_1-logloss:0.54045\n",
      "[2455]\tvalidation_0-logloss:0.39425\tvalidation_1-logloss:0.52103\n",
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      "[2457]\tvalidation_0-logloss:0.39420\tvalidation_1-logloss:0.52105\n",
      "[2862]\tvalidation_0-logloss:0.36790\tvalidation_1-logloss:0.54061\n",
      "[2458]\tvalidation_0-logloss:0.39419\tvalidation_1-logloss:0.52106\n",
      "[2863]\tvalidation_0-logloss:0.36787\tvalidation_1-logloss:0.54064\n",
      "[1781]\tvalidation_0-logloss:0.02806\tvalidation_1-logloss:0.96301\n",
      "[2864]\tvalidation_0-logloss:0.36784\tvalidation_1-logloss:0.54063\n",
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      "[2460]\tvalidation_0-logloss:0.39413\tvalidation_1-logloss:0.52112\n",
      "[2867]\tvalidation_0-logloss:0.36778\tvalidation_1-logloss:0.54064\n",
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      "[2869]\tvalidation_0-logloss:0.36773\tvalidation_1-logloss:0.54064\n",
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      "[2870]\tvalidation_0-logloss:0.36771\tvalidation_1-logloss:0.54063\n",
      "[1784]\tvalidation_0-logloss:0.02801\tvalidation_1-logloss:0.96352\n",
      "[2463]\tvalidation_0-logloss:0.39408\tvalidation_1-logloss:0.52112\n",
      "[2871]\tvalidation_0-logloss:0.36766\tvalidation_1-logloss:0.54063\n",
      "[2872]\tvalidation_0-logloss:0.36765\tvalidation_1-logloss:0.54059[2464]\tvalidation_0-logloss:0.39406\tvalidation_1-logloss:0.52112\n",
      "\n",
      "[1785]\tvalidation_0-logloss:0.02800\tvalidation_1-logloss:0.96363\n",
      "[2873]\tvalidation_0-logloss:0.36762\tvalidation_1-logloss:0.54061\n",
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      "[2879]\tvalidation_0-logloss:0.36748\tvalidation_1-logloss:0.54077\n",
      "[2880]\tvalidation_0-logloss:0.36746\tvalidation_1-logloss:0.54080\n",
      "[2470]\tvalidation_0-logloss:0.39385\tvalidation_1-logloss:0.52119\n",
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      "[2471]\tvalidation_0-logloss:0.39382\tvalidation_1-logloss:0.52127\n",
      "[2881]\tvalidation_0-logloss:0.36742\tvalidation_1-logloss:0.54076\n",
      "[1791]\tvalidation_0-logloss:0.02792\tvalidation_1-logloss:0.96427\n",
      "[2882]\tvalidation_0-logloss:0.36738\tvalidation_1-logloss:0.54081[2472]\tvalidation_0-logloss:0.39379\tvalidation_1-logloss:0.52130\n",
      "\n",
      "[2883]\tvalidation_0-logloss:0.36735\tvalidation_1-logloss:0.54085\n",
      "[2473]\tvalidation_0-logloss:0.39378\tvalidation_1-logloss:0.52131\n",
      "[2884]\tvalidation_0-logloss:0.36733\tvalidation_1-logloss:0.54085\n",
      "[1792]\tvalidation_0-logloss:0.02791\tvalidation_1-logloss:0.96453\n",
      "[2474]\tvalidation_0-logloss:0.39375\tvalidation_1-logloss:0.52131\n",
      "[2885]\tvalidation_0-logloss:0.36728\tvalidation_1-logloss:0.54085\n",
      "[1793]\tvalidation_0-logloss:0.02790\tvalidation_1-logloss:0.96475\n",
      "[2886]\tvalidation_0-logloss:0.36723\tvalidation_1-logloss:0.54083\n",
      "[2475]\tvalidation_0-logloss:0.39375\tvalidation_1-logloss:0.52129\n",
      "[2887]\tvalidation_0-logloss:0.36718\tvalidation_1-logloss:0.54086\n",
      "[2476]\tvalidation_0-logloss:0.39371\tvalidation_1-logloss:0.52131\n",
      "[1794]\tvalidation_0-logloss:0.02788\tvalidation_1-logloss:0.96510\n",
      "[2888]\tvalidation_0-logloss:0.36715\tvalidation_1-logloss:0.54087\n",
      "[2477]\tvalidation_0-logloss:0.39370\tvalidation_1-logloss:0.52135\n",
      "[1795]\tvalidation_0-logloss:0.02787\tvalidation_1-logloss:0.96510[2889]\tvalidation_0-logloss:0.36712\tvalidation_1-logloss:0.54091\n",
      "\n",
      "[2478]\tvalidation_0-logloss:0.39367\tvalidation_1-logloss:0.52134\n",
      "[2890]\tvalidation_0-logloss:0.36710\tvalidation_1-logloss:0.54091\n",
      "[2479]\tvalidation_0-logloss:0.39364\tvalidation_1-logloss:0.52137\n",
      "[2891]\tvalidation_0-logloss:0.36708\tvalidation_1-logloss:0.54088\n",
      "[1796]\tvalidation_0-logloss:0.02786\tvalidation_1-logloss:0.96525\n",
      "[2480]\tvalidation_0-logloss:0.39363\tvalidation_1-logloss:0.52135\n",
      "[2892]\tvalidation_0-logloss:0.36703\tvalidation_1-logloss:0.54093\n",
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      "[2481]\tvalidation_0-logloss:0.39359\tvalidation_1-logloss:0.52136\n",
      "[2893]\tvalidation_0-logloss:0.36700\tvalidation_1-logloss:0.54094\n",
      "[2894]\tvalidation_0-logloss:0.36699\tvalidation_1-logloss:0.54094\n",
      "[2482]\tvalidation_0-logloss:0.39356\tvalidation_1-logloss:0.52134\n",
      "[1798]\tvalidation_0-logloss:0.02783\tvalidation_1-logloss:0.96569\n",
      "[2895]\tvalidation_0-logloss:0.36695\tvalidation_1-logloss:0.54090\n",
      "[2483]\tvalidation_0-logloss:0.39354\tvalidation_1-logloss:0.52135\n",
      "[1799]\tvalidation_0-logloss:0.02781\tvalidation_1-logloss:0.96579\n",
      "[2896]\tvalidation_0-logloss:0.36693\tvalidation_1-logloss:0.54096\n",
      "[2484]\tvalidation_0-logloss:0.39351\tvalidation_1-logloss:0.52135\n",
      "[2897]\tvalidation_0-logloss:0.36689\tvalidation_1-logloss:0.54098\n",
      "[1800]\tvalidation_0-logloss:0.02780\tvalidation_1-logloss:0.96585[2485]\tvalidation_0-logloss:0.39349\tvalidation_1-logloss:0.52136\n",
      "\n",
      "[2898]\tvalidation_0-logloss:0.36686\tvalidation_1-logloss:0.54099\n",
      "[2486]\tvalidation_0-logloss:0.39347\tvalidation_1-logloss:0.52140\n",
      "[2899]\tvalidation_0-logloss:0.36684\tvalidation_1-logloss:0.54100\n",
      "[1801]\tvalidation_0-logloss:0.02778\tvalidation_1-logloss:0.96587\n",
      "[2900]\tvalidation_0-logloss:0.36682\tvalidation_1-logloss:0.54103\n",
      "[2487]\tvalidation_0-logloss:0.39345\tvalidation_1-logloss:0.52140\n",
      "[2901]\tvalidation_0-logloss:0.36679\tvalidation_1-logloss:0.54099\n",
      "[2488]\tvalidation_0-logloss:0.39343\tvalidation_1-logloss:0.52142\n",
      "[1802]\tvalidation_0-logloss:0.02777\tvalidation_1-logloss:0.96592\n",
      "[2902]\tvalidation_0-logloss:0.36677\tvalidation_1-logloss:0.54100\n",
      "[2489]\tvalidation_0-logloss:0.39340\tvalidation_1-logloss:0.52141\n",
      "[1803]\tvalidation_0-logloss:0.02775\tvalidation_1-logloss:0.96584\n",
      "[2903]\tvalidation_0-logloss:0.36675\tvalidation_1-logloss:0.54100\n",
      "[2490]\tvalidation_0-logloss:0.39338\tvalidation_1-logloss:0.52143\n",
      "[2904]\tvalidation_0-logloss:0.36672\tvalidation_1-logloss:0.54099\n",
      "[1804]\tvalidation_0-logloss:0.02774\tvalidation_1-logloss:0.96591\n",
      "[2905]\tvalidation_0-logloss:0.36670\tvalidation_1-logloss:0.54101\n",
      "[2491]\tvalidation_0-logloss:0.39336\tvalidation_1-logloss:0.52144\n",
      "[2906]\tvalidation_0-logloss:0.36666\tvalidation_1-logloss:0.54103\n",
      "[2907]\tvalidation_0-logloss:0.36663\tvalidation_1-logloss:0.54102[2492]\tvalidation_0-logloss:0.39331\tvalidation_1-logloss:0.52142\n",
      "\n",
      "[1805]\tvalidation_0-logloss:0.02774\tvalidation_1-logloss:0.96596\n",
      "[2493]\tvalidation_0-logloss:0.39326\tvalidation_1-logloss:0.52147\n",
      "[2908]\tvalidation_0-logloss:0.36657\tvalidation_1-logloss:0.54102\n",
      "[2494]\tvalidation_0-logloss:0.39322\tvalidation_1-logloss:0.52150\n",
      "[2909]\tvalidation_0-logloss:0.36654\tvalidation_1-logloss:0.54102\n",
      "[1806]\tvalidation_0-logloss:0.02773\tvalidation_1-logloss:0.96597\n",
      "[2910]\tvalidation_0-logloss:0.36650\tvalidation_1-logloss:0.54106\n",
      "[1807]\tvalidation_0-logloss:0.02772\tvalidation_1-logloss:0.96608\n",
      "[2495]\tvalidation_0-logloss:0.39319\tvalidation_1-logloss:0.52153\n",
      "[2911]\tvalidation_0-logloss:0.36647\tvalidation_1-logloss:0.54106\n",
      "[2496]\tvalidation_0-logloss:0.39316\tvalidation_1-logloss:0.52153\n",
      "[1808]\tvalidation_0-logloss:0.02770\tvalidation_1-logloss:0.96621\n",
      "[2912]\tvalidation_0-logloss:0.36643\tvalidation_1-logloss:0.54107\n",
      "[2913]\tvalidation_0-logloss:0.36639\tvalidation_1-logloss:0.54110\n",
      "[2914]\tvalidation_0-logloss:0.36635\tvalidation_1-logloss:0.54116\n",
      "[2497]\tvalidation_0-logloss:0.39312\tvalidation_1-logloss:0.52150\n",
      "[1809]\tvalidation_0-logloss:0.02769\tvalidation_1-logloss:0.96632\n",
      "[2498]\tvalidation_0-logloss:0.39307\tvalidation_1-logloss:0.52156\n",
      "[2915]\tvalidation_0-logloss:0.36633\tvalidation_1-logloss:0.54118\n",
      "[2499]\tvalidation_0-logloss:0.39302\tvalidation_1-logloss:0.52155\n",
      "[2916]\tvalidation_0-logloss:0.36632\tvalidation_1-logloss:0.54120\n",
      "[1810]\tvalidation_0-logloss:0.02767\tvalidation_1-logloss:0.96659\n",
      "[2500]\tvalidation_0-logloss:0.39300\tvalidation_1-logloss:0.52157\n",
      "[1811]\tvalidation_0-logloss:0.02766\tvalidation_1-logloss:0.96671\n",
      "[2917]\tvalidation_0-logloss:0.36626\tvalidation_1-logloss:0.54131\n",
      "[2501]\tvalidation_0-logloss:0.39296\tvalidation_1-logloss:0.52159\n",
      "[2918]\tvalidation_0-logloss:0.36623\tvalidation_1-logloss:0.54133\n",
      "[2502]\tvalidation_0-logloss:0.39290\tvalidation_1-logloss:0.52158\n",
      "[1812]\tvalidation_0-logloss:0.02764\tvalidation_1-logloss:0.96675\n",
      "[2919]\tvalidation_0-logloss:0.36622\tvalidation_1-logloss:0.54130\n",
      "[2503]\tvalidation_0-logloss:0.39289\tvalidation_1-logloss:0.52158\n",
      "[2920]\tvalidation_0-logloss:0.36619\tvalidation_1-logloss:0.54128\n",
      "[2504]\tvalidation_0-logloss:0.39286\tvalidation_1-logloss:0.52154\n",
      "[1813]\tvalidation_0-logloss:0.02763\tvalidation_1-logloss:0.96689\n",
      "[2921]\tvalidation_0-logloss:0.36617\tvalidation_1-logloss:0.54126\n",
      "[2505]\tvalidation_0-logloss:0.39283\tvalidation_1-logloss:0.52159\n",
      "[2922]\tvalidation_0-logloss:0.36613\tvalidation_1-logloss:0.54126\n",
      "[1814]\tvalidation_0-logloss:0.02762\tvalidation_1-logloss:0.96693\n",
      "[2923]\tvalidation_0-logloss:0.36610\tvalidation_1-logloss:0.54121\n",
      "[2506]\tvalidation_0-logloss:0.39281\tvalidation_1-logloss:0.52158\n",
      "[1815]\tvalidation_0-logloss:0.02761\tvalidation_1-logloss:0.96707\n",
      "[2924]\tvalidation_0-logloss:0.36608\tvalidation_1-logloss:0.54123\n",
      "[2507]\tvalidation_0-logloss:0.39279\tvalidation_1-logloss:0.52158\n",
      "[2925]\tvalidation_0-logloss:0.36604\tvalidation_1-logloss:0.54126\n",
      "[1816]\tvalidation_0-logloss:0.02760\tvalidation_1-logloss:0.96716\n",
      "[2508]\tvalidation_0-logloss:0.39276\tvalidation_1-logloss:0.52163\n",
      "[2926]\tvalidation_0-logloss:0.36601\tvalidation_1-logloss:0.54135\n",
      "[1817]\tvalidation_0-logloss:0.02759\tvalidation_1-logloss:0.96734\n",
      "[2927]\tvalidation_0-logloss:0.36599\tvalidation_1-logloss:0.54139\n",
      "[2509]\tvalidation_0-logloss:0.39271\tvalidation_1-logloss:0.52161\n",
      "[2928]\tvalidation_0-logloss:0.36597\tvalidation_1-logloss:0.54143\n",
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      "[2510]\tvalidation_0-logloss:0.39268\tvalidation_1-logloss:0.52158\n",
      "[2929]\tvalidation_0-logloss:0.36594\tvalidation_1-logloss:0.54146\n",
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      "[2930]\tvalidation_0-logloss:0.36593\tvalidation_1-logloss:0.54148[2512]\tvalidation_0-logloss:0.39264\tvalidation_1-logloss:0.52161\n",
      "\n",
      "[2513]\tvalidation_0-logloss:0.39261\tvalidation_1-logloss:0.52163\n",
      "[1820]\tvalidation_0-logloss:0.02755\tvalidation_1-logloss:0.96776\n",
      "[2931]\tvalidation_0-logloss:0.36590\tvalidation_1-logloss:0.54148\n",
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      "[2932]\tvalidation_0-logloss:0.36587\tvalidation_1-logloss:0.54156\n",
      "[2515]\tvalidation_0-logloss:0.39257\tvalidation_1-logloss:0.52165\n",
      "[2933]\tvalidation_0-logloss:0.36584\tvalidation_1-logloss:0.54156\n",
      "[1821]\tvalidation_0-logloss:0.02753\tvalidation_1-logloss:0.96772\n",
      "[2516]\tvalidation_0-logloss:0.39256\tvalidation_1-logloss:0.52165\n",
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      "[2934]\tvalidation_0-logloss:0.36581\tvalidation_1-logloss:0.54153\n",
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      "[2935]\tvalidation_0-logloss:0.36576\tvalidation_1-logloss:0.54159\n",
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      "[2936]\tvalidation_0-logloss:0.36573\tvalidation_1-logloss:0.54159\n",
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      "[1823]\tvalidation_0-logloss:0.02751\tvalidation_1-logloss:0.96807\n",
      "[2937]\tvalidation_0-logloss:0.36569\tvalidation_1-logloss:0.54156\n",
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      "[2938]\tvalidation_0-logloss:0.36567\tvalidation_1-logloss:0.54160\n",
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      "[2521]\tvalidation_0-logloss:0.39246\tvalidation_1-logloss:0.52166\n",
      "[2939]\tvalidation_0-logloss:0.36564\tvalidation_1-logloss:0.54160\n",
      "[1825]\tvalidation_0-logloss:0.02749\tvalidation_1-logloss:0.96828[2522]\tvalidation_0-logloss:0.39246\tvalidation_1-logloss:0.52166\n",
      "\n",
      "[2940]\tvalidation_0-logloss:0.36560\tvalidation_1-logloss:0.54165\n",
      "[2523]\tvalidation_0-logloss:0.39243\tvalidation_1-logloss:0.52170[1826]\tvalidation_0-logloss:0.02748\tvalidation_1-logloss:0.96846\n",
      "\n",
      "[2941]\tvalidation_0-logloss:0.36555\tvalidation_1-logloss:0.54172\n",
      "[2524]\tvalidation_0-logloss:0.39240\tvalidation_1-logloss:0.52169\n",
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      "[2944]\tvalidation_0-logloss:0.36545\tvalidation_1-logloss:0.54176\n",
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      "[2945]\tvalidation_0-logloss:0.36541\tvalidation_1-logloss:0.54174\n",
      "[1828]\tvalidation_0-logloss:0.02745\tvalidation_1-logloss:0.96876\n",
      "[2946]\tvalidation_0-logloss:0.36536\tvalidation_1-logloss:0.54175\n",
      "[2527]\tvalidation_0-logloss:0.39234\tvalidation_1-logloss:0.52171\n",
      "[2947]\tvalidation_0-logloss:0.36532\tvalidation_1-logloss:0.54176\n",
      "[1829]\tvalidation_0-logloss:0.02744\tvalidation_1-logloss:0.96884\n",
      "[2528]\tvalidation_0-logloss:0.39230\tvalidation_1-logloss:0.52172\n",
      "[2948]\tvalidation_0-logloss:0.36529\tvalidation_1-logloss:0.54182\n",
      "[2529]\tvalidation_0-logloss:0.39227\tvalidation_1-logloss:0.52173\n",
      "[1830]\tvalidation_0-logloss:0.02742\tvalidation_1-logloss:0.96903\n",
      "[2949]\tvalidation_0-logloss:0.36525\tvalidation_1-logloss:0.54183\n",
      "[2950]\tvalidation_0-logloss:0.36522\tvalidation_1-logloss:0.54190\n",
      "[2530]\tvalidation_0-logloss:0.39225\tvalidation_1-logloss:0.52176\n",
      "[1831]\tvalidation_0-logloss:0.02740\tvalidation_1-logloss:0.96920\n",
      "[2951]\tvalidation_0-logloss:0.36520\tvalidation_1-logloss:0.54194\n",
      "[2531]\tvalidation_0-logloss:0.39222\tvalidation_1-logloss:0.52175\n",
      "[2952]\tvalidation_0-logloss:0.36516\tvalidation_1-logloss:0.54195\n",
      "[2532]\tvalidation_0-logloss:0.39220\tvalidation_1-logloss:0.52176\n",
      "[1832]\tvalidation_0-logloss:0.02738\tvalidation_1-logloss:0.96934\n",
      "[2953]\tvalidation_0-logloss:0.36514\tvalidation_1-logloss:0.54203\n",
      "[2533]\tvalidation_0-logloss:0.39217\tvalidation_1-logloss:0.52176\n",
      "[2954]\tvalidation_0-logloss:0.36512\tvalidation_1-logloss:0.54206\n",
      "[2534]\tvalidation_0-logloss:0.39216\tvalidation_1-logloss:0.52178[1833]\tvalidation_0-logloss:0.02736\tvalidation_1-logloss:0.96958\n",
      "\n",
      "[2955]\tvalidation_0-logloss:0.36509\tvalidation_1-logloss:0.54210\n",
      "[2535]\tvalidation_0-logloss:0.39213\tvalidation_1-logloss:0.52178\n",
      "[1834]\tvalidation_0-logloss:0.02734\tvalidation_1-logloss:0.96987\n",
      "[2956]\tvalidation_0-logloss:0.36506\tvalidation_1-logloss:0.54209\n",
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      "[2957]\tvalidation_0-logloss:0.36503\tvalidation_1-logloss:0.54208\n",
      "[1835]\tvalidation_0-logloss:0.02733\tvalidation_1-logloss:0.96989\n",
      "[2537]\tvalidation_0-logloss:0.39208\tvalidation_1-logloss:0.52182\n",
      "[2958]\tvalidation_0-logloss:0.36501\tvalidation_1-logloss:0.54207\n",
      "[2959]\tvalidation_0-logloss:0.36498\tvalidation_1-logloss:0.54212\n",
      "[2538]\tvalidation_0-logloss:0.39207\tvalidation_1-logloss:0.52183\n",
      "[1836]\tvalidation_0-logloss:0.02732\tvalidation_1-logloss:0.97008\n",
      "[2539]\tvalidation_0-logloss:0.39205\tvalidation_1-logloss:0.52183[2960]\tvalidation_0-logloss:0.36494\tvalidation_1-logloss:0.54214\n",
      "\n",
      "[1837]\tvalidation_0-logloss:0.02731\tvalidation_1-logloss:0.97016\n",
      "[2961]\tvalidation_0-logloss:0.36492\tvalidation_1-logloss:0.54216\n",
      "[2540]\tvalidation_0-logloss:0.39202\tvalidation_1-logloss:0.52183\n",
      "[2962]\tvalidation_0-logloss:0.36489\tvalidation_1-logloss:0.54218\n",
      "[2541]\tvalidation_0-logloss:0.39200\tvalidation_1-logloss:0.52183\n",
      "[1838]\tvalidation_0-logloss:0.02729\tvalidation_1-logloss:0.97011\n",
      "[2963]\tvalidation_0-logloss:0.36487\tvalidation_1-logloss:0.54217\n",
      "[2542]\tvalidation_0-logloss:0.39196\tvalidation_1-logloss:0.52189\n",
      "[2964]\tvalidation_0-logloss:0.36484\tvalidation_1-logloss:0.54217\n",
      "[1839]\tvalidation_0-logloss:0.02728\tvalidation_1-logloss:0.97009\n",
      "[2543]\tvalidation_0-logloss:0.39193\tvalidation_1-logloss:0.52183\n",
      "[2965]\tvalidation_0-logloss:0.36481\tvalidation_1-logloss:0.54218\n",
      "[2966]\tvalidation_0-logloss:0.36479\tvalidation_1-logloss:0.54219\n",
      "[2544]\tvalidation_0-logloss:0.39189\tvalidation_1-logloss:0.52182\n",
      "[1840]\tvalidation_0-logloss:0.02727\tvalidation_1-logloss:0.97028\n",
      "[2967]\tvalidation_0-logloss:0.36475\tvalidation_1-logloss:0.54227\n",
      "[2545]\tvalidation_0-logloss:0.39186\tvalidation_1-logloss:0.52183\n",
      "[1841]\tvalidation_0-logloss:0.02725\tvalidation_1-logloss:0.97039\n",
      "[2968]\tvalidation_0-logloss:0.36472\tvalidation_1-logloss:0.54228\n",
      "[2546]\tvalidation_0-logloss:0.39182\tvalidation_1-logloss:0.52184\n",
      "[2969]\tvalidation_0-logloss:0.36468\tvalidation_1-logloss:0.54230\n",
      "[2547]\tvalidation_0-logloss:0.39180\tvalidation_1-logloss:0.52189[1842]\tvalidation_0-logloss:0.02725\tvalidation_1-logloss:0.97049\n",
      "\n",
      "[2970]\tvalidation_0-logloss:0.36464\tvalidation_1-logloss:0.54232\n",
      "[2971]\tvalidation_0-logloss:0.36462\tvalidation_1-logloss:0.54232\n",
      "[2548]\tvalidation_0-logloss:0.39177\tvalidation_1-logloss:0.52189\n",
      "[1843]\tvalidation_0-logloss:0.02722\tvalidation_1-logloss:0.97056\n",
      "[2972]\tvalidation_0-logloss:0.36459\tvalidation_1-logloss:0.54227\n",
      "[2973]\tvalidation_0-logloss:0.36456\tvalidation_1-logloss:0.54224\n",
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      "[2549]\tvalidation_0-logloss:0.39174\tvalidation_1-logloss:0.52190\n",
      "[2974]\tvalidation_0-logloss:0.36452\tvalidation_1-logloss:0.54223\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3062]\tvalidation_0-logloss:0.36201\tvalidation_1-logloss:0.54359\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[1963]\tvalidation_0-logloss:0.02576\tvalidation_1-logloss:0.98189[2714]\tvalidation_0-logloss:0.38759\tvalidation_1-logloss:0.52395\n",
      "\n",
      "[3156]\tvalidation_0-logloss:0.35954\tvalidation_1-logloss:0.54525\n",
      "[1964]\tvalidation_0-logloss:0.02575\tvalidation_1-logloss:0.98200\n",
      "[3157]\tvalidation_0-logloss:0.35950\tvalidation_1-logloss:0.54526\n",
      "[2715]\tvalidation_0-logloss:0.38757\tvalidation_1-logloss:0.52396\n",
      "[3158]\tvalidation_0-logloss:0.35946\tvalidation_1-logloss:0.54533\n",
      "[1965]\tvalidation_0-logloss:0.02573\tvalidation_1-logloss:0.98218[2716]\tvalidation_0-logloss:0.38755\tvalidation_1-logloss:0.52398\n",
      "\n",
      "[3159]\tvalidation_0-logloss:0.35944\tvalidation_1-logloss:0.54535\n",
      "[2717]\tvalidation_0-logloss:0.38752\tvalidation_1-logloss:0.52395\n",
      "[1966]\tvalidation_0-logloss:0.02572\tvalidation_1-logloss:0.98229\n",
      "[2718]\tvalidation_0-logloss:0.38750\tvalidation_1-logloss:0.52396\n",
      "[3160]\tvalidation_0-logloss:0.35941\tvalidation_1-logloss:0.54536\n",
      "[3161]\tvalidation_0-logloss:0.35940\tvalidation_1-logloss:0.54539[2719]\tvalidation_0-logloss:0.38748\tvalidation_1-logloss:0.52395\n",
      "\n",
      "[1967]\tvalidation_0-logloss:0.02570\tvalidation_1-logloss:0.98260\n",
      "[2720]\tvalidation_0-logloss:0.38743\tvalidation_1-logloss:0.52396\n",
      "[3162]\tvalidation_0-logloss:0.35938\tvalidation_1-logloss:0.54537\n",
      "[1968]\tvalidation_0-logloss:0.02568\tvalidation_1-logloss:0.98274\n",
      "[2721]\tvalidation_0-logloss:0.38742\tvalidation_1-logloss:0.52398\n",
      "[3163]\tvalidation_0-logloss:0.35936\tvalidation_1-logloss:0.54538\n",
      "[1969]\tvalidation_0-logloss:0.02567\tvalidation_1-logloss:0.98288\n",
      "[2722]\tvalidation_0-logloss:0.38741\tvalidation_1-logloss:0.52399\n",
      "[3164]\tvalidation_0-logloss:0.35933\tvalidation_1-logloss:0.54541\n",
      "[1970]\tvalidation_0-logloss:0.02566\tvalidation_1-logloss:0.98314[2723]\tvalidation_0-logloss:0.38738\tvalidation_1-logloss:0.52399\n",
      "\n",
      "[3165]\tvalidation_0-logloss:0.35929\tvalidation_1-logloss:0.54547\n",
      "[2724]\tvalidation_0-logloss:0.38735\tvalidation_1-logloss:0.52403\n",
      "[3166]\tvalidation_0-logloss:0.35928\tvalidation_1-logloss:0.54549\n",
      "[1971]\tvalidation_0-logloss:0.02565\tvalidation_1-logloss:0.98338\n",
      "[2725]\tvalidation_0-logloss:0.38732\tvalidation_1-logloss:0.52402\n",
      "[3167]\tvalidation_0-logloss:0.35924\tvalidation_1-logloss:0.54552\n",
      "[1972]\tvalidation_0-logloss:0.02563\tvalidation_1-logloss:0.98353\n",
      "[2726]\tvalidation_0-logloss:0.38729\tvalidation_1-logloss:0.52402\n",
      "[3168]\tvalidation_0-logloss:0.35923\tvalidation_1-logloss:0.54552\n",
      "[2727]\tvalidation_0-logloss:0.38727\tvalidation_1-logloss:0.52402\n",
      "[1973]\tvalidation_0-logloss:0.02562\tvalidation_1-logloss:0.98359\n",
      "[3169]\tvalidation_0-logloss:0.35920\tvalidation_1-logloss:0.54560\n",
      "[2728]\tvalidation_0-logloss:0.38725\tvalidation_1-logloss:0.52403\n",
      "[1974]\tvalidation_0-logloss:0.02561\tvalidation_1-logloss:0.98353\n",
      "[3170]\tvalidation_0-logloss:0.35918\tvalidation_1-logloss:0.54562\n",
      "[2729]\tvalidation_0-logloss:0.38723\tvalidation_1-logloss:0.52406\n",
      "[3171]\tvalidation_0-logloss:0.35914\tvalidation_1-logloss:0.54561\n",
      "[1975]\tvalidation_0-logloss:0.02561\tvalidation_1-logloss:0.98363\n",
      "[2730]\tvalidation_0-logloss:0.38722\tvalidation_1-logloss:0.52407\n",
      "[3172]\tvalidation_0-logloss:0.35912\tvalidation_1-logloss:0.54560\n",
      "[3173]\tvalidation_0-logloss:0.35907\tvalidation_1-logloss:0.54555\n",
      "[2731]\tvalidation_0-logloss:0.38720\tvalidation_1-logloss:0.52407[1976]\tvalidation_0-logloss:0.02559\tvalidation_1-logloss:0.98377\n",
      "\n",
      "[3174]\tvalidation_0-logloss:0.35905\tvalidation_1-logloss:0.54553\n",
      "[2732]\tvalidation_0-logloss:0.38717\tvalidation_1-logloss:0.52405\n",
      "[1977]\tvalidation_0-logloss:0.02558\tvalidation_1-logloss:0.98384\n",
      "[3175]\tvalidation_0-logloss:0.35901\tvalidation_1-logloss:0.54552\n",
      "[2733]\tvalidation_0-logloss:0.38716\tvalidation_1-logloss:0.52407\n",
      "[3176]\tvalidation_0-logloss:0.35898\tvalidation_1-logloss:0.54551\n",
      "[1978]\tvalidation_0-logloss:0.02557\tvalidation_1-logloss:0.98399\n",
      "[3177]\tvalidation_0-logloss:0.35896\tvalidation_1-logloss:0.54552\n",
      "[2734]\tvalidation_0-logloss:0.38714\tvalidation_1-logloss:0.52406\n",
      "[3178]\tvalidation_0-logloss:0.35894\tvalidation_1-logloss:0.54550\n",
      "[1979]\tvalidation_0-logloss:0.02556\tvalidation_1-logloss:0.98413\n",
      "[3179]\tvalidation_0-logloss:0.35890\tvalidation_1-logloss:0.54549\n",
      "[2735]\tvalidation_0-logloss:0.38713\tvalidation_1-logloss:0.52408\n",
      "[1980]\tvalidation_0-logloss:0.02555\tvalidation_1-logloss:0.98419\n",
      "[3180]\tvalidation_0-logloss:0.35886\tvalidation_1-logloss:0.54550\n",
      "[3181]\tvalidation_0-logloss:0.35883\tvalidation_1-logloss:0.54547\n",
      "[2736]\tvalidation_0-logloss:0.38711\tvalidation_1-logloss:0.52407\n",
      "[1981]\tvalidation_0-logloss:0.02554\tvalidation_1-logloss:0.98419\n",
      "[3182]\tvalidation_0-logloss:0.35881\tvalidation_1-logloss:0.54555\n",
      "[2737]\tvalidation_0-logloss:0.38707\tvalidation_1-logloss:0.52411\n",
      "[1982]\tvalidation_0-logloss:0.02553\tvalidation_1-logloss:0.98429[2738]\tvalidation_0-logloss:0.38705\tvalidation_1-logloss:0.52411\n",
      "\n",
      "[3183]\tvalidation_0-logloss:0.35878\tvalidation_1-logloss:0.54554\n",
      "[3184]\tvalidation_0-logloss:0.35876\tvalidation_1-logloss:0.54553\n",
      "[1983]\tvalidation_0-logloss:0.02552\tvalidation_1-logloss:0.98446\n",
      "[2739]\tvalidation_0-logloss:0.38704\tvalidation_1-logloss:0.52410\n",
      "[3185]\tvalidation_0-logloss:0.35875\tvalidation_1-logloss:0.54557\n",
      "[2740]\tvalidation_0-logloss:0.38702\tvalidation_1-logloss:0.52409[1984]\tvalidation_0-logloss:0.02552\tvalidation_1-logloss:0.98454\n",
      "\n",
      "[3186]\tvalidation_0-logloss:0.35871\tvalidation_1-logloss:0.54561\n",
      "[2741]\tvalidation_0-logloss:0.38700\tvalidation_1-logloss:0.52412\n",
      "[3187]\tvalidation_0-logloss:0.35870\tvalidation_1-logloss:0.54563\n",
      "[1985]\tvalidation_0-logloss:0.02551\tvalidation_1-logloss:0.98457\n",
      "[3188]\tvalidation_0-logloss:0.35869\tvalidation_1-logloss:0.54566[2742]\tvalidation_0-logloss:0.38696\tvalidation_1-logloss:0.52422\n",
      "\n",
      "[1986]\tvalidation_0-logloss:0.02549\tvalidation_1-logloss:0.98465\n",
      "[3189]\tvalidation_0-logloss:0.35867\tvalidation_1-logloss:0.54571\n",
      "[2743]\tvalidation_0-logloss:0.38694\tvalidation_1-logloss:0.52423\n",
      "[3190]\tvalidation_0-logloss:0.35865\tvalidation_1-logloss:0.54568\n",
      "[2744]\tvalidation_0-logloss:0.38692\tvalidation_1-logloss:0.52424\n",
      "[3191]\tvalidation_0-logloss:0.35862\tvalidation_1-logloss:0.54569\n",
      "[1987]\tvalidation_0-logloss:0.02548\tvalidation_1-logloss:0.98484\n",
      "[2745]\tvalidation_0-logloss:0.38690\tvalidation_1-logloss:0.52427\n",
      "[3192]\tvalidation_0-logloss:0.35858\tvalidation_1-logloss:0.54578\n",
      "[1988]\tvalidation_0-logloss:0.02547\tvalidation_1-logloss:0.98509\n",
      "[2746]\tvalidation_0-logloss:0.38689\tvalidation_1-logloss:0.52427\n",
      "[3193]\tvalidation_0-logloss:0.35855\tvalidation_1-logloss:0.54580\n",
      "[2747]\tvalidation_0-logloss:0.38684\tvalidation_1-logloss:0.52429\n",
      "[3194]\tvalidation_0-logloss:0.35853\tvalidation_1-logloss:0.54585\n",
      "[1989]\tvalidation_0-logloss:0.02547\tvalidation_1-logloss:0.98504[2748]\tvalidation_0-logloss:0.38682\tvalidation_1-logloss:0.52428\n",
      "\n",
      "[3195]\tvalidation_0-logloss:0.35849\tvalidation_1-logloss:0.54589\n",
      "[2749]\tvalidation_0-logloss:0.38680\tvalidation_1-logloss:0.52429\n",
      "[3196]\tvalidation_0-logloss:0.35845\tvalidation_1-logloss:0.54588\n",
      "[1990]\tvalidation_0-logloss:0.02545\tvalidation_1-logloss:0.98512\n",
      "[2750]\tvalidation_0-logloss:0.38672\tvalidation_1-logloss:0.52432\n",
      "[3197]\tvalidation_0-logloss:0.35844\tvalidation_1-logloss:0.54589\n",
      "[1991]\tvalidation_0-logloss:0.02545\tvalidation_1-logloss:0.98526\n",
      "[2751]\tvalidation_0-logloss:0.38671\tvalidation_1-logloss:0.52433\n",
      "[3198]\tvalidation_0-logloss:0.35841\tvalidation_1-logloss:0.54594\n",
      "[2752]\tvalidation_0-logloss:0.38669\tvalidation_1-logloss:0.52434\n",
      "[3199]\tvalidation_0-logloss:0.35838\tvalidation_1-logloss:0.54587\n",
      "[1992]\tvalidation_0-logloss:0.02543\tvalidation_1-logloss:0.98529\n",
      "[2753]\tvalidation_0-logloss:0.38666\tvalidation_1-logloss:0.52439\n",
      "[3200]\tvalidation_0-logloss:0.35835\tvalidation_1-logloss:0.54590\n",
      "[3201]\tvalidation_0-logloss:0.35832\tvalidation_1-logloss:0.54592[1993]\tvalidation_0-logloss:0.02542\tvalidation_1-logloss:0.98524\n",
      "\n",
      "[2754]\tvalidation_0-logloss:0.38661\tvalidation_1-logloss:0.52446\n",
      "[3202]\tvalidation_0-logloss:0.35829\tvalidation_1-logloss:0.54588\n",
      "[2755]\tvalidation_0-logloss:0.38656\tvalidation_1-logloss:0.52451\n",
      "[3203]\tvalidation_0-logloss:0.35828\tvalidation_1-logloss:0.54589\n",
      "[2756]\tvalidation_0-logloss:0.38653\tvalidation_1-logloss:0.52456\n",
      "[1994]\tvalidation_0-logloss:0.02540\tvalidation_1-logloss:0.98525\n",
      "[3204]\tvalidation_0-logloss:0.35824\tvalidation_1-logloss:0.54591\n",
      "[2757]\tvalidation_0-logloss:0.38648\tvalidation_1-logloss:0.52458\n",
      "[3205]\tvalidation_0-logloss:0.35821\tvalidation_1-logloss:0.54592\n",
      "[1995]\tvalidation_0-logloss:0.02539\tvalidation_1-logloss:0.98536\n",
      "[2758]\tvalidation_0-logloss:0.38645\tvalidation_1-logloss:0.52458\n",
      "[3206]\tvalidation_0-logloss:0.35819\tvalidation_1-logloss:0.54593\n",
      "[1996]\tvalidation_0-logloss:0.02537\tvalidation_1-logloss:0.98547\n",
      "[2759]\tvalidation_0-logloss:0.38641\tvalidation_1-logloss:0.52457\n",
      "[3207]\tvalidation_0-logloss:0.35816\tvalidation_1-logloss:0.54597\n",
      "[2760]\tvalidation_0-logloss:0.38640\tvalidation_1-logloss:0.52461\n",
      "[3208]\tvalidation_0-logloss:0.35815\tvalidation_1-logloss:0.54598\n",
      "[1997]\tvalidation_0-logloss:0.02537\tvalidation_1-logloss:0.98546\n",
      "[2761]\tvalidation_0-logloss:0.38639\tvalidation_1-logloss:0.52460\n",
      "[3209]\tvalidation_0-logloss:0.35813\tvalidation_1-logloss:0.54597\n",
      "[2762]\tvalidation_0-logloss:0.38637\tvalidation_1-logloss:0.52460[3210]\tvalidation_0-logloss:0.35811\tvalidation_1-logloss:0.54600\n",
      "\n",
      "[1998]\tvalidation_0-logloss:0.02535\tvalidation_1-logloss:0.98550\n",
      "[3211]\tvalidation_0-logloss:0.35809\tvalidation_1-logloss:0.54604\n",
      "[2763]\tvalidation_0-logloss:0.38633\tvalidation_1-logloss:0.52465\n",
      "[3212]\tvalidation_0-logloss:0.35806\tvalidation_1-logloss:0.54601\n",
      "[3213]\tvalidation_0-logloss:0.35804\tvalidation_1-logloss:0.54604[2764]\tvalidation_0-logloss:0.38632\tvalidation_1-logloss:0.52468\n",
      "\n",
      "[1999]\tvalidation_0-logloss:0.02533\tvalidation_1-logloss:0.98565\n",
      "[3214]\tvalidation_0-logloss:0.35801\tvalidation_1-logloss:0.54610\n",
      "[2765]\tvalidation_0-logloss:0.38630\tvalidation_1-logloss:0.52469\n",
      "[2000]\tvalidation_0-logloss:0.02532\tvalidation_1-logloss:0.98572\n",
      "[2766]\tvalidation_0-logloss:0.38628\tvalidation_1-logloss:0.52467\n",
      "[3215]\tvalidation_0-logloss:0.35797\tvalidation_1-logloss:0.54610\n",
      "[2001]\tvalidation_0-logloss:0.02531\tvalidation_1-logloss:0.98583\n",
      "[2767]\tvalidation_0-logloss:0.38624\tvalidation_1-logloss:0.52469\n",
      "[3216]\tvalidation_0-logloss:0.35792\tvalidation_1-logloss:0.54612\n",
      "[3217]\tvalidation_0-logloss:0.35790\tvalidation_1-logloss:0.54616\n",
      "[2002]\tvalidation_0-logloss:0.02530\tvalidation_1-logloss:0.98579\n",
      "[2768]\tvalidation_0-logloss:0.38622\tvalidation_1-logloss:0.52469\n",
      "[3218]\tvalidation_0-logloss:0.35787\tvalidation_1-logloss:0.54623\n",
      "[2003]\tvalidation_0-logloss:0.02529\tvalidation_1-logloss:0.98587\n",
      "[3219]\tvalidation_0-logloss:0.35785\tvalidation_1-logloss:0.54622\n",
      "[2769]\tvalidation_0-logloss:0.38619\tvalidation_1-logloss:0.52471\n",
      "[2770]\tvalidation_0-logloss:0.38615\tvalidation_1-logloss:0.52468\n",
      "[3220]\tvalidation_0-logloss:0.35781\tvalidation_1-logloss:0.54620\n",
      "[2004]\tvalidation_0-logloss:0.02528\tvalidation_1-logloss:0.98596\n",
      "[2771]\tvalidation_0-logloss:0.38613\tvalidation_1-logloss:0.52470\n",
      "[3221]\tvalidation_0-logloss:0.35779\tvalidation_1-logloss:0.54620\n",
      "[2005]\tvalidation_0-logloss:0.02527\tvalidation_1-logloss:0.98602[2772]\tvalidation_0-logloss:0.38610\tvalidation_1-logloss:0.52470\n",
      "\n",
      "[3222]\tvalidation_0-logloss:0.35776\tvalidation_1-logloss:0.54626\n",
      "[2773]\tvalidation_0-logloss:0.38609\tvalidation_1-logloss:0.52476\n",
      "[3223]\tvalidation_0-logloss:0.35774\tvalidation_1-logloss:0.54631\n",
      "[2006]\tvalidation_0-logloss:0.02526\tvalidation_1-logloss:0.98599\n",
      "[2774]\tvalidation_0-logloss:0.38608\tvalidation_1-logloss:0.52474\n",
      "[3224]\tvalidation_0-logloss:0.35772\tvalidation_1-logloss:0.54635\n",
      "[2775]\tvalidation_0-logloss:0.38607\tvalidation_1-logloss:0.52475\n",
      "[2007]\tvalidation_0-logloss:0.02526\tvalidation_1-logloss:0.98610\n",
      "[3225]\tvalidation_0-logloss:0.35768\tvalidation_1-logloss:0.54635\n",
      "[2008]\tvalidation_0-logloss:0.02524\tvalidation_1-logloss:0.98641\n",
      "[3226]\tvalidation_0-logloss:0.35766\tvalidation_1-logloss:0.54628\n",
      "[2776]\tvalidation_0-logloss:0.38602\tvalidation_1-logloss:0.52478\n",
      "[2009]\tvalidation_0-logloss:0.02523\tvalidation_1-logloss:0.98637\n",
      "[3227]\tvalidation_0-logloss:0.35763\tvalidation_1-logloss:0.54633\n",
      "[2777]\tvalidation_0-logloss:0.38599\tvalidation_1-logloss:0.52480\n",
      "[3228]\tvalidation_0-logloss:0.35759\tvalidation_1-logloss:0.54636\n",
      "[2778]\tvalidation_0-logloss:0.38598\tvalidation_1-logloss:0.52482[2010]\tvalidation_0-logloss:0.02521\tvalidation_1-logloss:0.98653\n",
      "\n",
      "[3229]\tvalidation_0-logloss:0.35756\tvalidation_1-logloss:0.54640\n",
      "[2779]\tvalidation_0-logloss:0.38597\tvalidation_1-logloss:0.52483\n",
      "[2011]\tvalidation_0-logloss:0.02520\tvalidation_1-logloss:0.98667\n",
      "[3230]\tvalidation_0-logloss:0.35755\tvalidation_1-logloss:0.54647\n",
      "[3231]\tvalidation_0-logloss:0.35752\tvalidation_1-logloss:0.54649\n",
      "[2780]\tvalidation_0-logloss:0.38595\tvalidation_1-logloss:0.52486\n",
      "[2012]\tvalidation_0-logloss:0.02519\tvalidation_1-logloss:0.98681[3232]\tvalidation_0-logloss:0.35747\tvalidation_1-logloss:0.54646\n",
      "\n",
      "[3233]\tvalidation_0-logloss:0.35745\tvalidation_1-logloss:0.54650\n",
      "[2781]\tvalidation_0-logloss:0.38592\tvalidation_1-logloss:0.52486\n",
      "[2013]\tvalidation_0-logloss:0.02518\tvalidation_1-logloss:0.98682\n",
      "[3234]\tvalidation_0-logloss:0.35741\tvalidation_1-logloss:0.54652\n",
      "[2782]\tvalidation_0-logloss:0.38589\tvalidation_1-logloss:0.52488\n",
      "[2783]\tvalidation_0-logloss:0.38587\tvalidation_1-logloss:0.52487\n",
      "[3235]\tvalidation_0-logloss:0.35737\tvalidation_1-logloss:0.54653\n",
      "[2014]\tvalidation_0-logloss:0.02517\tvalidation_1-logloss:0.98692\n",
      "[3236]\tvalidation_0-logloss:0.35734\tvalidation_1-logloss:0.54658\n",
      "[2015]\tvalidation_0-logloss:0.02516\tvalidation_1-logloss:0.98696\n",
      "[2784]\tvalidation_0-logloss:0.38586\tvalidation_1-logloss:0.52488[3237]\tvalidation_0-logloss:0.35731\tvalidation_1-logloss:0.54661\n",
      "\n",
      "[2785]\tvalidation_0-logloss:0.38582\tvalidation_1-logloss:0.52489\n",
      "[3238]\tvalidation_0-logloss:0.35730\tvalidation_1-logloss:0.54670\n",
      "[2016]\tvalidation_0-logloss:0.02515\tvalidation_1-logloss:0.98721\n",
      "[2786]\tvalidation_0-logloss:0.38581\tvalidation_1-logloss:0.52490\n",
      "[3239]\tvalidation_0-logloss:0.35728\tvalidation_1-logloss:0.54671\n",
      "[2787]\tvalidation_0-logloss:0.38579\tvalidation_1-logloss:0.52488[3240]\tvalidation_0-logloss:0.35724\tvalidation_1-logloss:0.54672\n",
      "\n",
      "[2017]\tvalidation_0-logloss:0.02513\tvalidation_1-logloss:0.98739\n",
      "[3241]\tvalidation_0-logloss:0.35722\tvalidation_1-logloss:0.54670\n",
      "[2788]\tvalidation_0-logloss:0.38577\tvalidation_1-logloss:0.52490\n",
      "[3242]\tvalidation_0-logloss:0.35718\tvalidation_1-logloss:0.54670\n",
      "[2018]\tvalidation_0-logloss:0.02512\tvalidation_1-logloss:0.98749\n",
      "[2789]\tvalidation_0-logloss:0.38575\tvalidation_1-logloss:0.52488\n",
      "[3243]\tvalidation_0-logloss:0.35715\tvalidation_1-logloss:0.54671\n",
      "[3244]\tvalidation_0-logloss:0.35711\tvalidation_1-logloss:0.54679[2790]\tvalidation_0-logloss:0.38573\tvalidation_1-logloss:0.52490\n",
      "\n",
      "[2019]\tvalidation_0-logloss:0.02511\tvalidation_1-logloss:0.98751\n",
      "[2791]\tvalidation_0-logloss:0.38571\tvalidation_1-logloss:0.52489\n",
      "[3245]\tvalidation_0-logloss:0.35708\tvalidation_1-logloss:0.54683\n",
      "[2020]\tvalidation_0-logloss:0.02511\tvalidation_1-logloss:0.98746\n",
      "[2792]\tvalidation_0-logloss:0.38566\tvalidation_1-logloss:0.52489\n",
      "[3246]\tvalidation_0-logloss:0.35707\tvalidation_1-logloss:0.54686\n",
      "[3247]\tvalidation_0-logloss:0.35703\tvalidation_1-logloss:0.54690\n",
      "[2793]\tvalidation_0-logloss:0.38562\tvalidation_1-logloss:0.52488[2021]\tvalidation_0-logloss:0.02510\tvalidation_1-logloss:0.98775\n",
      "\n",
      "[3248]\tvalidation_0-logloss:0.35700\tvalidation_1-logloss:0.54688\n",
      "[3249]\tvalidation_0-logloss:0.35698\tvalidation_1-logloss:0.54683\n",
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      "[2026]\tvalidation_0-logloss:0.02506\tvalidation_1-logloss:0.98821\n",
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      "\n",
      "[3259]\tvalidation_0-logloss:0.35666\tvalidation_1-logloss:0.54699\n",
      "[2804]\tvalidation_0-logloss:0.38529\tvalidation_1-logloss:0.52514\n",
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      "[2029]\tvalidation_0-logloss:0.02504\tvalidation_1-logloss:0.98863\n",
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      "[3263]\tvalidation_0-logloss:0.35654\tvalidation_1-logloss:0.54703\n",
      "[2030]\tvalidation_0-logloss:0.02504\tvalidation_1-logloss:0.98881\n",
      "[3264]\tvalidation_0-logloss:0.35651\tvalidation_1-logloss:0.54699\n",
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      "[3265]\tvalidation_0-logloss:0.35647\tvalidation_1-logloss:0.54700\n",
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      "[3266]\tvalidation_0-logloss:0.35641\tvalidation_1-logloss:0.54707\n",
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      "\n",
      "[3268]\tvalidation_0-logloss:0.35635\tvalidation_1-logloss:0.54712\n",
      "[2811]\tvalidation_0-logloss:0.38516\tvalidation_1-logloss:0.52513\n",
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      "[2039]\tvalidation_0-logloss:0.02494\tvalidation_1-logloss:0.98988[2820]\tvalidation_0-logloss:0.38494\tvalidation_1-logloss:0.52525\n",
      "\n",
      "[3280]\tvalidation_0-logloss:0.35601\tvalidation_1-logloss:0.54735\n",
      "[2821]\tvalidation_0-logloss:0.38492\tvalidation_1-logloss:0.52525\n",
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      "[2041]\tvalidation_0-logloss:0.02492\tvalidation_1-logloss:0.99014\n",
      "[3283]\tvalidation_0-logloss:0.35592\tvalidation_1-logloss:0.54739\n",
      "[2824]\tvalidation_0-logloss:0.38486\tvalidation_1-logloss:0.52537[3284]\tvalidation_0-logloss:0.35590\tvalidation_1-logloss:0.54741\n",
      "\n",
      "[2042]\tvalidation_0-logloss:0.02491\tvalidation_1-logloss:0.99026\n",
      "[2825]\tvalidation_0-logloss:0.38485\tvalidation_1-logloss:0.52535\n",
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      "\n",
      "[2836]\tvalidation_0-logloss:0.38461\tvalidation_1-logloss:0.52542\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3310]\tvalidation_0-logloss:0.35516\tvalidation_1-logloss:0.54778\n",
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      "\n",
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      "\n",
      "[2065]\tvalidation_0-logloss:0.02468\tvalidation_1-logloss:0.99289\n",
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      "\n",
      "[2068]\tvalidation_0-logloss:0.02466\tvalidation_1-logloss:0.99307\n",
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      "\n",
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      "\n",
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      "\n",
      "[2090]\tvalidation_0-logloss:0.02444\tvalidation_1-logloss:0.99549\n",
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      "\n",
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      "\n",
      "[3373]\tvalidation_0-logloss:0.35355\tvalidation_1-logloss:0.54862\n",
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      "\n",
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      "\n",
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      "\n",
      "[3404]\tvalidation_0-logloss:0.35269\tvalidation_1-logloss:0.54938[2934]\tvalidation_0-logloss:0.38240\tvalidation_1-logloss:0.52639\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2976]\tvalidation_0-logloss:0.38136\tvalidation_1-logloss:0.52691\n",
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      "\n",
      "[2986]\tvalidation_0-logloss:0.38112\tvalidation_1-logloss:0.52709\n",
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      "\n",
      "[2158]\tvalidation_0-logloss:0.02380\tvalidation_1-logloss:1.00152\n",
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      "\n",
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      "\n",
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      "\n",
      "[2186]\tvalidation_0-logloss:0.02350\tvalidation_1-logloss:1.00450\n",
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      "\n",
      "[2190]\tvalidation_0-logloss:0.02347\tvalidation_1-logloss:1.00457\n",
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      "\n",
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      "\n",
      "[3051]\tvalidation_0-logloss:0.37964\tvalidation_1-logloss:0.52746\n",
      "[3535]\tvalidation_0-logloss:0.34936\tvalidation_1-logloss:0.55100\n",
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      "\n",
      "[2210]\tvalidation_0-logloss:0.02327\tvalidation_1-logloss:1.00633\n",
      "[3546]\tvalidation_0-logloss:0.34911\tvalidation_1-logloss:0.55100\n",
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      "\n",
      "[2211]\tvalidation_0-logloss:0.02326\tvalidation_1-logloss:1.00637\n",
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      "\n",
      "[3557]\tvalidation_0-logloss:0.34882\tvalidation_1-logloss:0.55130\n",
      "[2217]\tvalidation_0-logloss:0.02321\tvalidation_1-logloss:1.00669\n",
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      "\n",
      "[3072]\tvalidation_0-logloss:0.37913\tvalidation_1-logloss:0.52780\n",
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      "\n",
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      "\n",
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      "\n",
      "[3594]\tvalidation_0-logloss:0.34790\tvalidation_1-logloss:0.55208\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3893]\tvalidation_0-logloss:0.34061\tvalidation_1-logloss:0.55659\n",
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      "\n",
      "[2432]\tvalidation_0-logloss:0.02157\tvalidation_1-logloss:1.02359\n",
      "[3896]\tvalidation_0-logloss:0.34056\tvalidation_1-logloss:0.55659\n",
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      "\n",
      "[3397]\tvalidation_0-logloss:0.37186\tvalidation_1-logloss:0.53147\n",
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      "\n",
      "[3402]\tvalidation_0-logloss:0.37175\tvalidation_1-logloss:0.53147\n",
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      "\n",
      "[3405]\tvalidation_0-logloss:0.37172\tvalidation_1-logloss:0.53150\n",
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      "\n",
      "[2486]\tvalidation_0-logloss:0.02118\tvalidation_1-logloss:1.02793\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3480]\tvalidation_0-logloss:0.37018\tvalidation_1-logloss:0.53229\n",
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      "\n",
      "[2523]\tvalidation_0-logloss:0.02095\tvalidation_1-logloss:1.03045\n",
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      "\n",
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      "\n",
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      "\n",
      "[3501]\tvalidation_0-logloss:0.36976\tvalidation_1-logloss:0.53251\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3515]\tvalidation_0-logloss:0.36944\tvalidation_1-logloss:0.53274\n",
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      "\n",
      "[4078]\tvalidation_0-logloss:0.33637\tvalidation_1-logloss:0.55927\n",
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      "[3523]\tvalidation_0-logloss:0.36931\tvalidation_1-logloss:0.53286\n",
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      "[3524]\tvalidation_0-logloss:0.36931\tvalidation_1-logloss:0.53286\n",
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      "\n",
      "[3525]\tvalidation_0-logloss:0.36930\tvalidation_1-logloss:0.53288\n",
      "[4086]\tvalidation_0-logloss:0.33616\tvalidation_1-logloss:0.55941\n",
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      "[3529]\tvalidation_0-logloss:0.36921\tvalidation_1-logloss:0.53285\n",
      "\n",
      "[4092]\tvalidation_0-logloss:0.33601\tvalidation_1-logloss:0.55966\n",
      "[3530]\tvalidation_0-logloss:0.36920\tvalidation_1-logloss:0.53284\n",
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      "\n",
      "[2562]\tvalidation_0-logloss:0.02071\tvalidation_1-logloss:1.03286\n",
      "[3535]\tvalidation_0-logloss:0.36910\tvalidation_1-logloss:0.53296\n",
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      "\n",
      "[4101]\tvalidation_0-logloss:0.33582\tvalidation_1-logloss:0.55957\n",
      "[3537]\tvalidation_0-logloss:0.36907\tvalidation_1-logloss:0.53298\n",
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      "[3546]\tvalidation_0-logloss:0.36887\tvalidation_1-logloss:0.53304[4111]\tvalidation_0-logloss:0.33558\tvalidation_1-logloss:0.55967\n",
      "[2572]\tvalidation_0-logloss:0.02066\tvalidation_1-logloss:1.03336\n",
      "\n",
      "[4112]\tvalidation_0-logloss:0.33556\tvalidation_1-logloss:0.55967\n",
      "[2573]\tvalidation_0-logloss:0.02066\tvalidation_1-logloss:1.03337\n",
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      "[3553]\tvalidation_0-logloss:0.36873\tvalidation_1-logloss:0.53306[4121]\tvalidation_0-logloss:0.33532\tvalidation_1-logloss:0.55988\n",
      "\n",
      "[2578]\tvalidation_0-logloss:0.02063\tvalidation_1-logloss:1.03348\n",
      "[4122]\tvalidation_0-logloss:0.33529\tvalidation_1-logloss:0.55988\n",
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      "\n",
      "[4125]\tvalidation_0-logloss:0.33521\tvalidation_1-logloss:0.55986\n",
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      "[4135]\tvalidation_0-logloss:0.33499\tvalidation_1-logloss:0.55985[3567]\tvalidation_0-logloss:0.36839\tvalidation_1-logloss:0.53318\n",
      "\n",
      "[2588]\tvalidation_0-logloss:0.02058\tvalidation_1-logloss:1.03424\n",
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      "\n",
      "[4149]\tvalidation_0-logloss:0.33469\tvalidation_1-logloss:0.56007\n",
      "[3580]\tvalidation_0-logloss:0.36811\tvalidation_1-logloss:0.53332\n",
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      "[3594]\tvalidation_0-logloss:0.36786\tvalidation_1-logloss:0.53326\n",
      "[3595]\tvalidation_0-logloss:0.36785\tvalidation_1-logloss:0.53326\n",
      "[4163]\tvalidation_0-logloss:0.33432\tvalidation_1-logloss:0.56020\n",
      "[2607]\tvalidation_0-logloss:0.02045\tvalidation_1-logloss:1.03584\n",
      "[3596]\tvalidation_0-logloss:0.36784\tvalidation_1-logloss:0.53324\n",
      "[4164]\tvalidation_0-logloss:0.33429\tvalidation_1-logloss:0.56019\n",
      "[2608]\tvalidation_0-logloss:0.02044\tvalidation_1-logloss:1.03578\n",
      "[4165]\tvalidation_0-logloss:0.33427\tvalidation_1-logloss:0.56021\n",
      "[3597]\tvalidation_0-logloss:0.36782\tvalidation_1-logloss:0.53324\n",
      "[4166]\tvalidation_0-logloss:0.33425\tvalidation_1-logloss:0.56016\n",
      "[3598]\tvalidation_0-logloss:0.36781\tvalidation_1-logloss:0.53323\n",
      "[2609]\tvalidation_0-logloss:0.02044\tvalidation_1-logloss:1.03589\n",
      "[4167]\tvalidation_0-logloss:0.33423\tvalidation_1-logloss:0.56017\n",
      "[3599]\tvalidation_0-logloss:0.36778\tvalidation_1-logloss:0.53325\n",
      "[2610]\tvalidation_0-logloss:0.02043\tvalidation_1-logloss:1.03607\n",
      "[4168]\tvalidation_0-logloss:0.33420\tvalidation_1-logloss:0.56016\n",
      "[2611]\tvalidation_0-logloss:0.02043\tvalidation_1-logloss:1.03607\n",
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      "\n",
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      "\n",
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      "\n",
      "[4199]\tvalidation_0-logloss:0.33353\tvalidation_1-logloss:0.56064\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2731]\tvalidation_0-logloss:0.01977\tvalidation_1-logloss:1.04444\n",
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      "\n",
      "[2745]\tvalidation_0-logloss:0.01971\tvalidation_1-logloss:1.04566\n",
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      "\n",
      "[4397]\tvalidation_0-logloss:0.32890\tvalidation_1-logloss:0.56329\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2845]\tvalidation_0-logloss:0.01927\tvalidation_1-logloss:1.05151\n",
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      "[3930]\tvalidation_0-logloss:0.36089\tvalidation_1-logloss:0.53663\n",
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      "[2856]\tvalidation_0-logloss:0.01922\tvalidation_1-logloss:1.05209\n",
      "[3932]\tvalidation_0-logloss:0.36080\tvalidation_1-logloss:0.53667\n",
      "[4556]\tvalidation_0-logloss:0.32547\tvalidation_1-logloss:0.56525\n",
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      "[2857]\tvalidation_0-logloss:0.01922\tvalidation_1-logloss:1.05216[3933]\tvalidation_0-logloss:0.36078\tvalidation_1-logloss:0.53671\n",
      "\n",
      "[4558]\tvalidation_0-logloss:0.32544\tvalidation_1-logloss:0.56526\n",
      "[3934]\tvalidation_0-logloss:0.36076\tvalidation_1-logloss:0.53671\n",
      "[2858]\tvalidation_0-logloss:0.01921\tvalidation_1-logloss:1.05220[4559]\tvalidation_0-logloss:0.32542\tvalidation_1-logloss:0.56528\n",
      "\n",
      "[3935]\tvalidation_0-logloss:0.36073\tvalidation_1-logloss:0.53671\n",
      "[4560]\tvalidation_0-logloss:0.32540\tvalidation_1-logloss:0.56530\n",
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      "[3936]\tvalidation_0-logloss:0.36072\tvalidation_1-logloss:0.53668\n",
      "[4561]\tvalidation_0-logloss:0.32538\tvalidation_1-logloss:0.56532\n",
      "[3937]\tvalidation_0-logloss:0.36071\tvalidation_1-logloss:0.53668\n",
      "[2860]\tvalidation_0-logloss:0.01920\tvalidation_1-logloss:1.05245\n",
      "[4562]\tvalidation_0-logloss:0.32536\tvalidation_1-logloss:0.56529\n",
      "[3938]\tvalidation_0-logloss:0.36068\tvalidation_1-logloss:0.53669\n",
      "[4563]\tvalidation_0-logloss:0.32533\tvalidation_1-logloss:0.56532\n",
      "[3939]\tvalidation_0-logloss:0.36065\tvalidation_1-logloss:0.53669\n",
      "[2861]\tvalidation_0-logloss:0.01920\tvalidation_1-logloss:1.05253[4564]\tvalidation_0-logloss:0.32529\tvalidation_1-logloss:0.56537\n",
      "\n",
      "[3940]\tvalidation_0-logloss:0.36062\tvalidation_1-logloss:0.53671\n",
      "[4565]\tvalidation_0-logloss:0.32526\tvalidation_1-logloss:0.56545\n",
      "[2862]\tvalidation_0-logloss:0.01919\tvalidation_1-logloss:1.05258\n",
      "[4566]\tvalidation_0-logloss:0.32524\tvalidation_1-logloss:0.56549\n",
      "[3941]\tvalidation_0-logloss:0.36060\tvalidation_1-logloss:0.53672\n",
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      "[4567]\tvalidation_0-logloss:0.32522\tvalidation_1-logloss:0.56554\n",
      "[3942]\tvalidation_0-logloss:0.36058\tvalidation_1-logloss:0.53673\n",
      "[2864]\tvalidation_0-logloss:0.01918\tvalidation_1-logloss:1.05268\n",
      "[4568]\tvalidation_0-logloss:0.32518\tvalidation_1-logloss:0.56560\n",
      "[3943]\tvalidation_0-logloss:0.36057\tvalidation_1-logloss:0.53674\n",
      "[4569]\tvalidation_0-logloss:0.32515\tvalidation_1-logloss:0.56566\n",
      "[3944]\tvalidation_0-logloss:0.36054\tvalidation_1-logloss:0.53678\n",
      "[2865]\tvalidation_0-logloss:0.01918\tvalidation_1-logloss:1.05267\n",
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      "[2866]\tvalidation_0-logloss:0.01918\tvalidation_1-logloss:1.05271\n",
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      "[3947]\tvalidation_0-logloss:0.36047\tvalidation_1-logloss:0.53675\n",
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      "[2869]\tvalidation_0-logloss:0.01917\tvalidation_1-logloss:1.05271\n",
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      "[2876]\tvalidation_0-logloss:0.01914\tvalidation_1-logloss:1.05320\n",
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      "[3959]\tvalidation_0-logloss:0.36027\tvalidation_1-logloss:0.53679[2877]\tvalidation_0-logloss:0.01914\tvalidation_1-logloss:1.05323\n",
      "\n",
      "[4589]\tvalidation_0-logloss:0.32473\tvalidation_1-logloss:0.56587\n",
      "[3960]\tvalidation_0-logloss:0.36025\tvalidation_1-logloss:0.53677\n",
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      "[4604]\tvalidation_0-logloss:0.32441\tvalidation_1-logloss:0.56623[3971]\tvalidation_0-logloss:0.35997\tvalidation_1-logloss:0.53707\n",
      "\n",
      "[4605]\tvalidation_0-logloss:0.32439\tvalidation_1-logloss:0.56627\n",
      "[2887]\tvalidation_0-logloss:0.01910\tvalidation_1-logloss:1.05361\n",
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      "[2889]\tvalidation_0-logloss:0.01909\tvalidation_1-logloss:1.05368\n",
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      "[3975]\tvalidation_0-logloss:0.35989\tvalidation_1-logloss:0.53711\n",
      "[2890]\tvalidation_0-logloss:0.01908\tvalidation_1-logloss:1.05391\n",
      "[3976]\tvalidation_0-logloss:0.35987\tvalidation_1-logloss:0.53710\n",
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      "[3977]\tvalidation_0-logloss:0.35984\tvalidation_1-logloss:0.53709\n",
      "[2891]\tvalidation_0-logloss:0.01908\tvalidation_1-logloss:1.05395\n",
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      "[3978]\tvalidation_0-logloss:0.35981\tvalidation_1-logloss:0.53708\n",
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      "[3979]\tvalidation_0-logloss:0.35981\tvalidation_1-logloss:0.53708\n",
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      "[2893]\tvalidation_0-logloss:0.01907\tvalidation_1-logloss:1.05423\n",
      "[3980]\tvalidation_0-logloss:0.35979\tvalidation_1-logloss:0.53707\n",
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      "[2896]\tvalidation_0-logloss:0.01905\tvalidation_1-logloss:1.05449\n",
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      "[3988]\tvalidation_0-logloss:0.35962\tvalidation_1-logloss:0.53712\n",
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      "[3989]\tvalidation_0-logloss:0.35961\tvalidation_1-logloss:0.53715\n",
      "[2899]\tvalidation_0-logloss:0.01904\tvalidation_1-logloss:1.05473\n",
      "[4625]\tvalidation_0-logloss:0.32393\tvalidation_1-logloss:0.56645\n",
      "[3990]\tvalidation_0-logloss:0.35958\tvalidation_1-logloss:0.53719\n",
      "[2900]\tvalidation_0-logloss:0.01903\tvalidation_1-logloss:1.05488\n",
      "[4626]\tvalidation_0-logloss:0.32391\tvalidation_1-logloss:0.56644\n",
      "[3991]\tvalidation_0-logloss:0.35955\tvalidation_1-logloss:0.53721\n",
      "[4627]\tvalidation_0-logloss:0.32390\tvalidation_1-logloss:0.56644\n",
      "[2901]\tvalidation_0-logloss:0.01903\tvalidation_1-logloss:1.05488\n",
      "[4628]\tvalidation_0-logloss:0.32387\tvalidation_1-logloss:0.56651\n",
      "[3992]\tvalidation_0-logloss:0.35954\tvalidation_1-logloss:0.53725\n",
      "[2902]\tvalidation_0-logloss:0.01903\tvalidation_1-logloss:1.05489\n",
      "[4629]\tvalidation_0-logloss:0.32386\tvalidation_1-logloss:0.56655\n",
      "[3993]\tvalidation_0-logloss:0.35953\tvalidation_1-logloss:0.53726\n",
      "[2903]\tvalidation_0-logloss:0.01903\tvalidation_1-logloss:1.05489[4630]\tvalidation_0-logloss:0.32384\tvalidation_1-logloss:0.56659\n",
      "\n",
      "[3994]\tvalidation_0-logloss:0.35952\tvalidation_1-logloss:0.53728\n",
      "[4631]\tvalidation_0-logloss:0.32383\tvalidation_1-logloss:0.56662\n",
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      "[2904]\tvalidation_0-logloss:0.01902\tvalidation_1-logloss:1.05497\n",
      "[3996]\tvalidation_0-logloss:0.35943\tvalidation_1-logloss:0.53732\n",
      "[4633]\tvalidation_0-logloss:0.32380\tvalidation_1-logloss:0.56670\n",
      "[2905]\tvalidation_0-logloss:0.01902\tvalidation_1-logloss:1.05496\n",
      "[3997]\tvalidation_0-logloss:0.35940\tvalidation_1-logloss:0.53734\n",
      "[4634]\tvalidation_0-logloss:0.32377\tvalidation_1-logloss:0.56676\n",
      "[3998]\tvalidation_0-logloss:0.35936\tvalidation_1-logloss:0.53734\n",
      "[2906]\tvalidation_0-logloss:0.01902\tvalidation_1-logloss:1.05495\n",
      "[4635]\tvalidation_0-logloss:0.32375\tvalidation_1-logloss:0.56679\n",
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      "\n",
      "[4637]\tvalidation_0-logloss:0.32370\tvalidation_1-logloss:0.56683\n",
      "[4000]\tvalidation_0-logloss:0.35933\tvalidation_1-logloss:0.53737\n",
      "[4638]\tvalidation_0-logloss:0.32368\tvalidation_1-logloss:0.56686\n",
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      "\n",
      "[4640]\tvalidation_0-logloss:0.32363\tvalidation_1-logloss:0.56686\n",
      "[4003]\tvalidation_0-logloss:0.35927\tvalidation_1-logloss:0.53735\n",
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      "\n",
      "[4005]\tvalidation_0-logloss:0.35921\tvalidation_1-logloss:0.53732\n",
      "[4643]\tvalidation_0-logloss:0.32359\tvalidation_1-logloss:0.56681\n",
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      "[4654]\tvalidation_0-logloss:0.32337\tvalidation_1-logloss:0.56701\n",
      "[4655]\tvalidation_0-logloss:0.32335\tvalidation_1-logloss:0.56701\n",
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      "\n",
      "[4656]\tvalidation_0-logloss:0.32332\tvalidation_1-logloss:0.56707\n",
      "[4020]\tvalidation_0-logloss:0.35892\tvalidation_1-logloss:0.53747\n",
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      "[4657]\tvalidation_0-logloss:0.32331\tvalidation_1-logloss:0.56707\n",
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      "[4658]\tvalidation_0-logloss:0.32329\tvalidation_1-logloss:0.56711\n",
      "[4022]\tvalidation_0-logloss:0.35888\tvalidation_1-logloss:0.53748\n",
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      "[4659]\tvalidation_0-logloss:0.32326\tvalidation_1-logloss:0.56718\n",
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      "[4660]\tvalidation_0-logloss:0.32323\tvalidation_1-logloss:0.56719\n",
      "[2923]\tvalidation_0-logloss:0.01893\tvalidation_1-logloss:1.05604\n",
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      "[4661]\tvalidation_0-logloss:0.32320\tvalidation_1-logloss:0.56724\n",
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      "\n",
      "[4662]\tvalidation_0-logloss:0.32317\tvalidation_1-logloss:0.56731\n",
      "[4026]\tvalidation_0-logloss:0.35877\tvalidation_1-logloss:0.53743\n",
      "[4663]\tvalidation_0-logloss:0.32315\tvalidation_1-logloss:0.56734\n",
      "[2925]\tvalidation_0-logloss:0.01892\tvalidation_1-logloss:1.05617\n",
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      "[4664]\tvalidation_0-logloss:0.32313\tvalidation_1-logloss:0.56738\n",
      "[4028]\tvalidation_0-logloss:0.35874\tvalidation_1-logloss:0.53741\n",
      "[4665]\tvalidation_0-logloss:0.32313\tvalidation_1-logloss:0.56740\n",
      "[2926]\tvalidation_0-logloss:0.01891\tvalidation_1-logloss:1.05620\n",
      "[4666]\tvalidation_0-logloss:0.32309\tvalidation_1-logloss:0.56743\n",
      "[2927]\tvalidation_0-logloss:0.01891\tvalidation_1-logloss:1.05629\n",
      "[4029]\tvalidation_0-logloss:0.35872\tvalidation_1-logloss:0.53741\n",
      "[4667]\tvalidation_0-logloss:0.32306\tvalidation_1-logloss:0.56738\n",
      "[2928]\tvalidation_0-logloss:0.01891\tvalidation_1-logloss:1.05645\n",
      "[4668]\tvalidation_0-logloss:0.32303\tvalidation_1-logloss:0.56744\n",
      "[4030]\tvalidation_0-logloss:0.35870\tvalidation_1-logloss:0.53743\n",
      "[4669]\tvalidation_0-logloss:0.32300\tvalidation_1-logloss:0.56750\n",
      "[4031]\tvalidation_0-logloss:0.35869\tvalidation_1-logloss:0.53744\n",
      "[4670]\tvalidation_0-logloss:0.32297\tvalidation_1-logloss:0.56747\n",
      "[2929]\tvalidation_0-logloss:0.01890\tvalidation_1-logloss:1.05640\n",
      "[4032]\tvalidation_0-logloss:0.35866\tvalidation_1-logloss:0.53744\n",
      "[4671]\tvalidation_0-logloss:0.32295\tvalidation_1-logloss:0.56745\n",
      "[4033]\tvalidation_0-logloss:0.35864\tvalidation_1-logloss:0.53745\n",
      "[2930]\tvalidation_0-logloss:0.01890\tvalidation_1-logloss:1.05641\n",
      "[4672]\tvalidation_0-logloss:0.32292\tvalidation_1-logloss:0.56746[4034]\tvalidation_0-logloss:0.35863\tvalidation_1-logloss:0.53743\n",
      "\n",
      "[2931]\tvalidation_0-logloss:0.01890\tvalidation_1-logloss:1.05643\n",
      "[4673]\tvalidation_0-logloss:0.32290\tvalidation_1-logloss:0.56748\n",
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      "[4036]\tvalidation_0-logloss:0.35859\tvalidation_1-logloss:0.53741[4675]\tvalidation_0-logloss:0.32285\tvalidation_1-logloss:0.56748\n",
      "\n",
      "[2933]\tvalidation_0-logloss:0.01888\tvalidation_1-logloss:1.05654\n",
      "[4037]\tvalidation_0-logloss:0.35855\tvalidation_1-logloss:0.53743\n",
      "[4676]\tvalidation_0-logloss:0.32284\tvalidation_1-logloss:0.56747\n",
      "[2934]\tvalidation_0-logloss:0.01887\tvalidation_1-logloss:1.05670\n",
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      "[4677]\tvalidation_0-logloss:0.32283\tvalidation_1-logloss:0.56754\n",
      "[2935]\tvalidation_0-logloss:0.01887\tvalidation_1-logloss:1.05669[4039]\tvalidation_0-logloss:0.35853\tvalidation_1-logloss:0.53745\n",
      "\n",
      "[4678]\tvalidation_0-logloss:0.32281\tvalidation_1-logloss:0.56757\n",
      "[4679]\tvalidation_0-logloss:0.32279\tvalidation_1-logloss:0.56756\n",
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      "\n",
      "[4042]\tvalidation_0-logloss:0.35847\tvalidation_1-logloss:0.53755\n",
      "[4683]\tvalidation_0-logloss:0.32269\tvalidation_1-logloss:0.56760\n",
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      "[4049]\tvalidation_0-logloss:0.35833\tvalidation_1-logloss:0.53766[4690]\tvalidation_0-logloss:0.32252\tvalidation_1-logloss:0.56758\n",
      "\n",
      "[2943]\tvalidation_0-logloss:0.01883\tvalidation_1-logloss:1.05723\n",
      "[4691]\tvalidation_0-logloss:0.32249\tvalidation_1-logloss:0.56760\n",
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      "[2952]\tvalidation_0-logloss:0.01879\tvalidation_1-logloss:1.05777[4702]\tvalidation_0-logloss:0.32226\tvalidation_1-logloss:0.56782\n",
      "\n",
      "[4060]\tvalidation_0-logloss:0.35808\tvalidation_1-logloss:0.53786\n",
      "[4703]\tvalidation_0-logloss:0.32223\tvalidation_1-logloss:0.56785\n",
      "[2953]\tvalidation_0-logloss:0.01878\tvalidation_1-logloss:1.05785\n",
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      "[4704]\tvalidation_0-logloss:0.32221\tvalidation_1-logloss:0.56786\n",
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      "[4706]\tvalidation_0-logloss:0.32218\tvalidation_1-logloss:0.56791[4063]\tvalidation_0-logloss:0.35805\tvalidation_1-logloss:0.53792\n",
      "\n",
      "[2955]\tvalidation_0-logloss:0.01877\tvalidation_1-logloss:1.05781\n",
      "[4707]\tvalidation_0-logloss:0.32216\tvalidation_1-logloss:0.56790\n",
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      "[4066]\tvalidation_0-logloss:0.35801\tvalidation_1-logloss:0.53794[4710]\tvalidation_0-logloss:0.32212\tvalidation_1-logloss:0.56798\n",
      "\n",
      "[2957]\tvalidation_0-logloss:0.01877\tvalidation_1-logloss:1.05803\n",
      "[4711]\tvalidation_0-logloss:0.32211\tvalidation_1-logloss:0.56799[4067]\tvalidation_0-logloss:0.35798\tvalidation_1-logloss:0.53795\n",
      "\n",
      "[2958]\tvalidation_0-logloss:0.01876\tvalidation_1-logloss:1.05812\n",
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      "[2966]\tvalidation_0-logloss:0.01874\tvalidation_1-logloss:1.05846\n",
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      "[4078]\tvalidation_0-logloss:0.35781\tvalidation_1-logloss:0.53809[4723]\tvalidation_0-logloss:0.32185\tvalidation_1-logloss:0.56823\n",
      "\n",
      "[2967]\tvalidation_0-logloss:0.01874\tvalidation_1-logloss:1.05851\n",
      "[4079]\tvalidation_0-logloss:0.35780\tvalidation_1-logloss:0.53812\n",
      "[4724]\tvalidation_0-logloss:0.32182\tvalidation_1-logloss:0.56831\n",
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      "[2969]\tvalidation_0-logloss:0.01873\tvalidation_1-logloss:1.05869[4081]\tvalidation_0-logloss:0.35776\tvalidation_1-logloss:0.53814\n",
      "\n",
      "[4725]\tvalidation_0-logloss:0.32180\tvalidation_1-logloss:0.56841\n",
      "[4082]\tvalidation_0-logloss:0.35771\tvalidation_1-logloss:0.53816\n",
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      "[2970]\tvalidation_0-logloss:0.01872\tvalidation_1-logloss:1.05868\n",
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      "[2971]\tvalidation_0-logloss:0.01872\tvalidation_1-logloss:1.05873[4084]\tvalidation_0-logloss:0.35766\tvalidation_1-logloss:0.53816\n",
      "\n",
      "[4728]\tvalidation_0-logloss:0.32176\tvalidation_1-logloss:0.56844\n",
      "[4729]\tvalidation_0-logloss:0.32174\tvalidation_1-logloss:0.56842\n",
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      "\n",
      "[4736]\tvalidation_0-logloss:0.32162\tvalidation_1-logloss:0.56839\n",
      "[2975]\tvalidation_0-logloss:0.01871\tvalidation_1-logloss:1.05877\n",
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      "[2978]\tvalidation_0-logloss:0.01870\tvalidation_1-logloss:1.05882[4095]\tvalidation_0-logloss:0.35745\tvalidation_1-logloss:0.53831\n",
      "\n",
      "[4741]\tvalidation_0-logloss:0.32151\tvalidation_1-logloss:0.56847\n",
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      "\n",
      "[4743]\tvalidation_0-logloss:0.32147\tvalidation_1-logloss:0.56850\n",
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      "[2984]\tvalidation_0-logloss:0.01868\tvalidation_1-logloss:1.05906\n",
      "[4749]\tvalidation_0-logloss:0.32134\tvalidation_1-logloss:0.56847\n",
      "[4102]\tvalidation_0-logloss:0.35732\tvalidation_1-logloss:0.53831\n",
      "[4750]\tvalidation_0-logloss:0.32132\tvalidation_1-logloss:0.56850[4103]\tvalidation_0-logloss:0.35729\tvalidation_1-logloss:0.53831\n",
      "\n",
      "[2985]\tvalidation_0-logloss:0.01867\tvalidation_1-logloss:1.05913\n",
      "[4751]\tvalidation_0-logloss:0.32130\tvalidation_1-logloss:0.56856[4104]\tvalidation_0-logloss:0.35726\tvalidation_1-logloss:0.53830\n",
      "\n",
      "[2986]\tvalidation_0-logloss:0.01867\tvalidation_1-logloss:1.05921\n",
      "[4105]\tvalidation_0-logloss:0.35725\tvalidation_1-logloss:0.53831[4752]\tvalidation_0-logloss:0.32129\tvalidation_1-logloss:0.56852\n",
      "\n",
      "[4753]\tvalidation_0-logloss:0.32128\tvalidation_1-logloss:0.56853\n",
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      "[2989]\tvalidation_0-logloss:0.01865\tvalidation_1-logloss:1.05965\n",
      "[4757]\tvalidation_0-logloss:0.32119\tvalidation_1-logloss:0.56855\n",
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      "[2990]\tvalidation_0-logloss:0.01865\tvalidation_1-logloss:1.05967\n",
      "[4758]\tvalidation_0-logloss:0.32117\tvalidation_1-logloss:0.56863\n",
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      "[2991]\tvalidation_0-logloss:0.01865\tvalidation_1-logloss:1.05971\n",
      "[4759]\tvalidation_0-logloss:0.32115\tvalidation_1-logloss:0.56866\n",
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      "[2992]\tvalidation_0-logloss:0.01864\tvalidation_1-logloss:1.05970[4112]\tvalidation_0-logloss:0.35711\tvalidation_1-logloss:0.53841\n",
      "\n",
      "[4761]\tvalidation_0-logloss:0.32111\tvalidation_1-logloss:0.56871\n",
      "[4113]\tvalidation_0-logloss:0.35710\tvalidation_1-logloss:0.53842\n",
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      "\n",
      "[4764]\tvalidation_0-logloss:0.32103\tvalidation_1-logloss:0.56881\n",
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      "\n",
      "[3007]\tvalidation_0-logloss:0.01858\tvalidation_1-logloss:1.06041\n",
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      "[4141]\tvalidation_0-logloss:0.35643\tvalidation_1-logloss:0.53867[4794]\tvalidation_0-logloss:0.32040\tvalidation_1-logloss:0.56901\n",
      "\n",
      "[4795]\tvalidation_0-logloss:0.32037\tvalidation_1-logloss:0.56901\n",
      "[3012]\tvalidation_0-logloss:0.01856\tvalidation_1-logloss:1.06065\n",
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      "\n",
      "[3013]\tvalidation_0-logloss:0.01855\tvalidation_1-logloss:1.06091\n",
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      "\n",
      "[4158]\tvalidation_0-logloss:0.35608\tvalidation_1-logloss:0.53887\n",
      "[4814]\tvalidation_0-logloss:0.32003\tvalidation_1-logloss:0.56918\n",
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      "\n",
      "[4824]\tvalidation_0-logloss:0.31986\tvalidation_1-logloss:0.56927\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[4219]\tvalidation_0-logloss:0.35509\tvalidation_1-logloss:0.53933[3066]\tvalidation_0-logloss:0.01831\tvalidation_1-logloss:1.06394\n",
      "\n",
      "[4884]\tvalidation_0-logloss:0.31868\tvalidation_1-logloss:0.56983\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4937]\tvalidation_0-logloss:0.31747\tvalidation_1-logloss:0.57023\n",
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      "\n",
      "[4269]\tvalidation_0-logloss:0.35435\tvalidation_1-logloss:0.53951\n",
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      "\n",
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      "\n",
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      "\n",
      "[4301]\tvalidation_0-logloss:0.35374\tvalidation_1-logloss:0.53993[3129]\tvalidation_0-logloss:0.01808\tvalidation_1-logloss:1.06720\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4488]\tvalidation_0-logloss:0.35034\tvalidation_1-logloss:0.54215\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4650]\tvalidation_0-logloss:0.34743\tvalidation_1-logloss:0.54430\n",
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      "\n",
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      "\n",
      "[3416]\tvalidation_0-logloss:0.01716\tvalidation_1-logloss:1.07999\n",
      "[395]\tvalidation_0-logloss:0.47149\tvalidation_1-logloss:0.51092[4666]\tvalidation_0-logloss:0.34712\tvalidation_1-logloss:0.54449\n",
      "\n",
      "[3417]\tvalidation_0-logloss:0.01715\tvalidation_1-logloss:1.08005\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4811]\tvalidation_0-logloss:0.34453\tvalidation_1-logloss:0.54565\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4260]\tvalidation_0-logloss:0.01515\tvalidation_1-logloss:1.11231\n",
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      "\n",
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      "\n",
      "[767]\tvalidation_0-logloss:0.44963\tvalidation_1-logloss:0.51584\n",
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      "\n",
      "[780]\tvalidation_0-logloss:0.44897\tvalidation_1-logloss:0.51596\n",
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      "\n",
      "[789]\tvalidation_0-logloss:0.44857\tvalidation_1-logloss:0.51610\n",
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      "[4293]\tvalidation_0-logloss:0.01509\tvalidation_1-logloss:1.11306\n",
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      "[796]\tvalidation_0-logloss:0.44825\tvalidation_1-logloss:0.51621\n",
      "\n",
      "[797]\tvalidation_0-logloss:0.44819\tvalidation_1-logloss:0.51619\n",
      "[4295]\tvalidation_0-logloss:0.01509\tvalidation_1-logloss:1.11309\n",
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      "\n",
      "[814]\tvalidation_0-logloss:0.44725\tvalidation_1-logloss:0.51649\n",
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      "[823]\tvalidation_0-logloss:0.44671\tvalidation_1-logloss:0.51663[1758]\tvalidation_0-logloss:0.39697\tvalidation_1-logloss:0.53372\n",
      "\n",
      "[1759]\tvalidation_0-logloss:0.39693\tvalidation_1-logloss:0.53378\n",
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      "\n",
      "[1763]\tvalidation_0-logloss:0.39675\tvalidation_1-logloss:0.53387\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[1949]\tvalidation_0-logloss:0.38951\tvalidation_1-logloss:0.53734\n",
      "[4442]\tvalidation_0-logloss:0.01480\tvalidation_1-logloss:1.11802[989]\tvalidation_0-logloss:0.43866\tvalidation_1-logloss:0.51950\n",
      "\n",
      "[1950]\tvalidation_0-logloss:0.38948\tvalidation_1-logloss:0.53722\n",
      "[990]\tvalidation_0-logloss:0.43860\tvalidation_1-logloss:0.51954\n",
      "[1951]\tvalidation_0-logloss:0.38944\tvalidation_1-logloss:0.53722\n",
      "[4443]\tvalidation_0-logloss:0.01480\tvalidation_1-logloss:1.11817[1952]\tvalidation_0-logloss:0.38942\tvalidation_1-logloss:0.53723\n",
      "\n",
      "[1953]\tvalidation_0-logloss:0.38937\tvalidation_1-logloss:0.53727\n",
      "[991]\tvalidation_0-logloss:0.43857\tvalidation_1-logloss:0.51963\n",
      "[1954]\tvalidation_0-logloss:0.38933\tvalidation_1-logloss:0.53728\n",
      "[992]\tvalidation_0-logloss:0.43854\tvalidation_1-logloss:0.51959\n",
      "[1955]\tvalidation_0-logloss:0.38927\tvalidation_1-logloss:0.53732[4444]\tvalidation_0-logloss:0.01480\tvalidation_1-logloss:1.11805\n",
      "\n",
      "[993]\tvalidation_0-logloss:0.43847\tvalidation_1-logloss:0.51962\n",
      "[1956]\tvalidation_0-logloss:0.38925\tvalidation_1-logloss:0.53734\n",
      "[994]\tvalidation_0-logloss:0.43845\tvalidation_1-logloss:0.51960[1957]\tvalidation_0-logloss:0.38916\tvalidation_1-logloss:0.53742\n",
      "\n",
      "[4445]\tvalidation_0-logloss:0.01479\tvalidation_1-logloss:1.11813\n",
      "[4446]\tvalidation_0-logloss:0.01479\tvalidation_1-logloss:1.11824\n",
      "[995]\tvalidation_0-logloss:0.43837\tvalidation_1-logloss:0.51963\n",
      "[1958]\tvalidation_0-logloss:0.38913\tvalidation_1-logloss:0.53745\n",
      "[4447]\tvalidation_0-logloss:0.01479\tvalidation_1-logloss:1.11829\n",
      "[1959]\tvalidation_0-logloss:0.38911\tvalidation_1-logloss:0.53752\n",
      "[996]\tvalidation_0-logloss:0.43830\tvalidation_1-logloss:0.51961\n",
      "[4448]\tvalidation_0-logloss:0.01479\tvalidation_1-logloss:1.11829\n",
      "[997]\tvalidation_0-logloss:0.43826\tvalidation_1-logloss:0.51963\n",
      "[1960]\tvalidation_0-logloss:0.38907\tvalidation_1-logloss:0.53754\n",
      "[4449]\tvalidation_0-logloss:0.01479\tvalidation_1-logloss:1.11830\n",
      "[998]\tvalidation_0-logloss:0.43823\tvalidation_1-logloss:0.51957\n",
      "[999]\tvalidation_0-logloss:0.43822\tvalidation_1-logloss:0.51957\n",
      "[1961]\tvalidation_0-logloss:0.38903\tvalidation_1-logloss:0.53748\n",
      "[1000]\tvalidation_0-logloss:0.43818\tvalidation_1-logloss:0.51959\n",
      "[4450]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11830\n",
      "[1962]\tvalidation_0-logloss:0.38898\tvalidation_1-logloss:0.53752\n",
      "[1001]\tvalidation_0-logloss:0.43812\tvalidation_1-logloss:0.51962\n",
      "[4451]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11829\n",
      "[1963]\tvalidation_0-logloss:0.38893\tvalidation_1-logloss:0.53757\n",
      "[1002]\tvalidation_0-logloss:0.43807\tvalidation_1-logloss:0.51969\n",
      "[4452]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11833\n",
      "[1003]\tvalidation_0-logloss:0.43800\tvalidation_1-logloss:0.51964\n",
      "[1964]\tvalidation_0-logloss:0.38890\tvalidation_1-logloss:0.53760\n",
      "[4453]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11833\n",
      "[1965]\tvalidation_0-logloss:0.38885\tvalidation_1-logloss:0.53764\n",
      "[1004]\tvalidation_0-logloss:0.43794\tvalidation_1-logloss:0.51965\n",
      "[4454]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11832\n",
      "[1005]\tvalidation_0-logloss:0.43788\tvalidation_1-logloss:0.51969\n",
      "[4455]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11832\n",
      "[1966]\tvalidation_0-logloss:0.38882\tvalidation_1-logloss:0.53753\n",
      "[1006]\tvalidation_0-logloss:0.43785\tvalidation_1-logloss:0.51965\n",
      "[1967]\tvalidation_0-logloss:0.38879\tvalidation_1-logloss:0.53756\n",
      "[1968]\tvalidation_0-logloss:0.38875\tvalidation_1-logloss:0.53757[4456]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11833\n",
      "\n",
      "[1007]\tvalidation_0-logloss:0.43782\tvalidation_1-logloss:0.51967\n",
      "[1969]\tvalidation_0-logloss:0.38873\tvalidation_1-logloss:0.53759\n",
      "[1008]\tvalidation_0-logloss:0.43777\tvalidation_1-logloss:0.51968\n",
      "[4457]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11833\n",
      "[1970]\tvalidation_0-logloss:0.38870\tvalidation_1-logloss:0.53763\n",
      "[1009]\tvalidation_0-logloss:0.43775\tvalidation_1-logloss:0.51970\n",
      "[1971]\tvalidation_0-logloss:0.38867\tvalidation_1-logloss:0.53775\n",
      "[4458]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11832\n",
      "[1010]\tvalidation_0-logloss:0.43771\tvalidation_1-logloss:0.51969\n",
      "[1972]\tvalidation_0-logloss:0.38863\tvalidation_1-logloss:0.53777\n",
      "[1011]\tvalidation_0-logloss:0.43768\tvalidation_1-logloss:0.51973\n",
      "[1973]\tvalidation_0-logloss:0.38857\tvalidation_1-logloss:0.53777\n",
      "[1012]\tvalidation_0-logloss:0.43763\tvalidation_1-logloss:0.51974\n",
      "[4459]\tvalidation_0-logloss:0.01478\tvalidation_1-logloss:1.11834\n",
      "[1974]\tvalidation_0-logloss:0.38853\tvalidation_1-logloss:0.53779\n",
      "[1013]\tvalidation_0-logloss:0.43756\tvalidation_1-logloss:0.51976\n",
      "[1975]\tvalidation_0-logloss:0.38846\tvalidation_1-logloss:0.53774\n",
      "[1014]\tvalidation_0-logloss:0.43754\tvalidation_1-logloss:0.51978\n",
      "[4460]\tvalidation_0-logloss:0.01477\tvalidation_1-logloss:1.11846\n",
      "[1976]\tvalidation_0-logloss:0.38842\tvalidation_1-logloss:0.53777\n",
      "[1015]\tvalidation_0-logloss:0.43750\tvalidation_1-logloss:0.51974\n",
      "[4461]\tvalidation_0-logloss:0.01477\tvalidation_1-logloss:1.11860\n",
      "[1977]\tvalidation_0-logloss:0.38840\tvalidation_1-logloss:0.53784\n",
      "[1016]\tvalidation_0-logloss:0.43744\tvalidation_1-logloss:0.51976\n",
      "[4462]\tvalidation_0-logloss:0.01477\tvalidation_1-logloss:1.11864\n",
      "[1978]\tvalidation_0-logloss:0.38836\tvalidation_1-logloss:0.53784\n",
      "[1017]\tvalidation_0-logloss:0.43736\tvalidation_1-logloss:0.51973\n",
      "[4463]\tvalidation_0-logloss:0.01477\tvalidation_1-logloss:1.11864\n",
      "[1979]\tvalidation_0-logloss:0.38833\tvalidation_1-logloss:0.53789\n",
      "[1018]\tvalidation_0-logloss:0.43725\tvalidation_1-logloss:0.51975\n",
      "[1980]\tvalidation_0-logloss:0.38829\tvalidation_1-logloss:0.53783\n",
      "[1019]\tvalidation_0-logloss:0.43719\tvalidation_1-logloss:0.51978[4464]\tvalidation_0-logloss:0.01476\tvalidation_1-logloss:1.11869\n",
      "\n",
      "[1981]\tvalidation_0-logloss:0.38827\tvalidation_1-logloss:0.53789\n",
      "[4465]\tvalidation_0-logloss:0.01476\tvalidation_1-logloss:1.11869\n",
      "[1020]\tvalidation_0-logloss:0.43714\tvalidation_1-logloss:0.51979\n",
      "[1982]\tvalidation_0-logloss:0.38824\tvalidation_1-logloss:0.53792\n",
      "[1021]\tvalidation_0-logloss:0.43706\tvalidation_1-logloss:0.51986\n",
      "[4466]\tvalidation_0-logloss:0.01476\tvalidation_1-logloss:1.11877\n",
      "[1983]\tvalidation_0-logloss:0.38819\tvalidation_1-logloss:0.53797\n",
      "[1022]\tvalidation_0-logloss:0.43702\tvalidation_1-logloss:0.51990\n",
      "[1984]\tvalidation_0-logloss:0.38815\tvalidation_1-logloss:0.53795\n",
      "[4467]\tvalidation_0-logloss:0.01476\tvalidation_1-logloss:1.11881\n",
      "[1023]\tvalidation_0-logloss:0.43697\tvalidation_1-logloss:0.51995\n",
      "[1985]\tvalidation_0-logloss:0.38811\tvalidation_1-logloss:0.53798\n",
      "[4468]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11889\n",
      "[1024]\tvalidation_0-logloss:0.43690\tvalidation_1-logloss:0.51997\n",
      "[1986]\tvalidation_0-logloss:0.38808\tvalidation_1-logloss:0.53797\n",
      "[1025]\tvalidation_0-logloss:0.43684\tvalidation_1-logloss:0.51998\n",
      "[1987]\tvalidation_0-logloss:0.38804\tvalidation_1-logloss:0.53796\n",
      "[4469]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11896\n",
      "[1026]\tvalidation_0-logloss:0.43683\tvalidation_1-logloss:0.51996\n",
      "[1988]\tvalidation_0-logloss:0.38799\tvalidation_1-logloss:0.53798\n",
      "[1027]\tvalidation_0-logloss:0.43678\tvalidation_1-logloss:0.52000\n",
      "[4470]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11900\n",
      "[1989]\tvalidation_0-logloss:0.38795\tvalidation_1-logloss:0.53802\n",
      "[1028]\tvalidation_0-logloss:0.43673\tvalidation_1-logloss:0.52008\n",
      "[1990]\tvalidation_0-logloss:0.38792\tvalidation_1-logloss:0.53801\n",
      "[4471]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11899\n",
      "[4472]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11899\n",
      "[1029]\tvalidation_0-logloss:0.43670\tvalidation_1-logloss:0.52008\n",
      "[1991]\tvalidation_0-logloss:0.38784\tvalidation_1-logloss:0.53804\n",
      "[1030]\tvalidation_0-logloss:0.43665\tvalidation_1-logloss:0.52007\n",
      "[4473]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11900\n",
      "[1992]\tvalidation_0-logloss:0.38782\tvalidation_1-logloss:0.53808\n",
      "[1031]\tvalidation_0-logloss:0.43662\tvalidation_1-logloss:0.52004\n",
      "[4474]\tvalidation_0-logloss:0.01475\tvalidation_1-logloss:1.11900\n",
      "[1993]\tvalidation_0-logloss:0.38779\tvalidation_1-logloss:0.53806[1032]\tvalidation_0-logloss:0.43657\tvalidation_1-logloss:0.52009\n",
      "\n",
      "[4475]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11896\n",
      "[1994]\tvalidation_0-logloss:0.38777\tvalidation_1-logloss:0.53809\n",
      "[1033]\tvalidation_0-logloss:0.43653\tvalidation_1-logloss:0.52011\n",
      "[1034]\tvalidation_0-logloss:0.43652\tvalidation_1-logloss:0.52013[4476]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11898\n",
      "\n",
      "[1995]\tvalidation_0-logloss:0.38772\tvalidation_1-logloss:0.53822\n",
      "[1035]\tvalidation_0-logloss:0.43647\tvalidation_1-logloss:0.52021\n",
      "[1996]\tvalidation_0-logloss:0.38770\tvalidation_1-logloss:0.53825\n",
      "[4477]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11902\n",
      "[1036]\tvalidation_0-logloss:0.43642\tvalidation_1-logloss:0.52017\n",
      "[1037]\tvalidation_0-logloss:0.43637\tvalidation_1-logloss:0.52018\n",
      "[4478]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11907\n",
      "[1997]\tvalidation_0-logloss:0.38765\tvalidation_1-logloss:0.53823\n",
      "[1038]\tvalidation_0-logloss:0.43631\tvalidation_1-logloss:0.52021\n",
      "[1998]\tvalidation_0-logloss:0.38762\tvalidation_1-logloss:0.53821\n",
      "[1039]\tvalidation_0-logloss:0.43625\tvalidation_1-logloss:0.52028\n",
      "[4479]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11904\n",
      "[1040]\tvalidation_0-logloss:0.43620\tvalidation_1-logloss:0.52025\n",
      "[1999]\tvalidation_0-logloss:0.38760\tvalidation_1-logloss:0.53824\n",
      "[1041]\tvalidation_0-logloss:0.43615\tvalidation_1-logloss:0.52028\n",
      "[4480]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11910\n",
      "[2000]\tvalidation_0-logloss:0.38755\tvalidation_1-logloss:0.53830\n",
      "[4481]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11912\n",
      "[1042]\tvalidation_0-logloss:0.43614\tvalidation_1-logloss:0.52027\n",
      "[2001]\tvalidation_0-logloss:0.38752\tvalidation_1-logloss:0.53834\n",
      "[4482]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11910[1043]\tvalidation_0-logloss:0.43607\tvalidation_1-logloss:0.52029\n",
      "\n",
      "[2002]\tvalidation_0-logloss:0.38748\tvalidation_1-logloss:0.53845\n",
      "[1044]\tvalidation_0-logloss:0.43600\tvalidation_1-logloss:0.52027\n",
      "[2003]\tvalidation_0-logloss:0.38744\tvalidation_1-logloss:0.53852\n",
      "[1045]\tvalidation_0-logloss:0.43596\tvalidation_1-logloss:0.52027\n",
      "[4483]\tvalidation_0-logloss:0.01474\tvalidation_1-logloss:1.11923\n",
      "[2004]\tvalidation_0-logloss:0.38741\tvalidation_1-logloss:0.53856\n",
      "[1046]\tvalidation_0-logloss:0.43591\tvalidation_1-logloss:0.52031\n",
      "[2005]\tvalidation_0-logloss:0.38737\tvalidation_1-logloss:0.53861\n",
      "[1047]\tvalidation_0-logloss:0.43587\tvalidation_1-logloss:0.52030\n",
      "[4484]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11923\n",
      "[2006]\tvalidation_0-logloss:0.38733\tvalidation_1-logloss:0.53856\n",
      "[1048]\tvalidation_0-logloss:0.43582\tvalidation_1-logloss:0.52033\n",
      "[2007]\tvalidation_0-logloss:0.38730\tvalidation_1-logloss:0.53858\n",
      "[4485]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11920\n",
      "[1049]\tvalidation_0-logloss:0.43576\tvalidation_1-logloss:0.52028\n",
      "[4486]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11920[2008]\tvalidation_0-logloss:0.38726\tvalidation_1-logloss:0.53855\n",
      "\n",
      "[1050]\tvalidation_0-logloss:0.43574\tvalidation_1-logloss:0.52032\n",
      "[2009]\tvalidation_0-logloss:0.38723\tvalidation_1-logloss:0.53860\n",
      "[4487]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11919\n",
      "[2010]\tvalidation_0-logloss:0.38720\tvalidation_1-logloss:0.53860\n",
      "[1051]\tvalidation_0-logloss:0.43569\tvalidation_1-logloss:0.52034\n",
      "[2011]\tvalidation_0-logloss:0.38715\tvalidation_1-logloss:0.53859\n",
      "[1052]\tvalidation_0-logloss:0.43562\tvalidation_1-logloss:0.52034\n",
      "[4488]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11924\n",
      "[2012]\tvalidation_0-logloss:0.38712\tvalidation_1-logloss:0.53860\n",
      "[1053]\tvalidation_0-logloss:0.43559\tvalidation_1-logloss:0.52037\n",
      "[2013]\tvalidation_0-logloss:0.38708\tvalidation_1-logloss:0.53863\n",
      "[4489]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11926\n",
      "[2014]\tvalidation_0-logloss:0.38704\tvalidation_1-logloss:0.53863\n",
      "[1054]\tvalidation_0-logloss:0.43554\tvalidation_1-logloss:0.52036\n",
      "[2015]\tvalidation_0-logloss:0.38702\tvalidation_1-logloss:0.53860\n",
      "[1055]\tvalidation_0-logloss:0.43549\tvalidation_1-logloss:0.52037\n",
      "[4490]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11939\n",
      "[2016]\tvalidation_0-logloss:0.38698\tvalidation_1-logloss:0.53859\n",
      "[1056]\tvalidation_0-logloss:0.43543\tvalidation_1-logloss:0.52040\n",
      "[2017]\tvalidation_0-logloss:0.38695\tvalidation_1-logloss:0.53860\n",
      "[4491]\tvalidation_0-logloss:0.01473\tvalidation_1-logloss:1.11941\n",
      "[1057]\tvalidation_0-logloss:0.43536\tvalidation_1-logloss:0.52041[2018]\tvalidation_0-logloss:0.38690\tvalidation_1-logloss:0.53865\n",
      "\n",
      "[4492]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11937\n",
      "[2019]\tvalidation_0-logloss:0.38686\tvalidation_1-logloss:0.53868\n",
      "[1058]\tvalidation_0-logloss:0.43534\tvalidation_1-logloss:0.52042\n",
      "[2020]\tvalidation_0-logloss:0.38682\tvalidation_1-logloss:0.53869\n",
      "[1059]\tvalidation_0-logloss:0.43529\tvalidation_1-logloss:0.52043[4493]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11941\n",
      "\n",
      "[2021]\tvalidation_0-logloss:0.38679\tvalidation_1-logloss:0.53868\n",
      "[4494]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11933[1060]\tvalidation_0-logloss:0.43526\tvalidation_1-logloss:0.52040\n",
      "\n",
      "[2022]\tvalidation_0-logloss:0.38678\tvalidation_1-logloss:0.53867\n",
      "[2023]\tvalidation_0-logloss:0.38675\tvalidation_1-logloss:0.53867\n",
      "[1061]\tvalidation_0-logloss:0.43521\tvalidation_1-logloss:0.52046\n",
      "[4495]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11932\n",
      "[1062]\tvalidation_0-logloss:0.43519\tvalidation_1-logloss:0.52045\n",
      "[2024]\tvalidation_0-logloss:0.38671\tvalidation_1-logloss:0.53860\n",
      "[4496]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11937\n",
      "[1063]\tvalidation_0-logloss:0.43514\tvalidation_1-logloss:0.52050[2025]\tvalidation_0-logloss:0.38669\tvalidation_1-logloss:0.53862\n",
      "\n",
      "[4497]\tvalidation_0-logloss:0.01472\tvalidation_1-logloss:1.11937\n",
      "[2026]\tvalidation_0-logloss:0.38666\tvalidation_1-logloss:0.53861\n",
      "[1064]\tvalidation_0-logloss:0.43511\tvalidation_1-logloss:0.52050\n",
      "[4498]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11936\n",
      "[2027]\tvalidation_0-logloss:0.38664\tvalidation_1-logloss:0.53862\n",
      "[1065]\tvalidation_0-logloss:0.43504\tvalidation_1-logloss:0.52050\n",
      "[2028]\tvalidation_0-logloss:0.38660\tvalidation_1-logloss:0.53871\n",
      "[4499]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11943\n",
      "[1066]\tvalidation_0-logloss:0.43498\tvalidation_1-logloss:0.52053\n",
      "[2029]\tvalidation_0-logloss:0.38657\tvalidation_1-logloss:0.53874\n",
      "[4500]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11953\n",
      "[1067]\tvalidation_0-logloss:0.43493\tvalidation_1-logloss:0.52059\n",
      "[2030]\tvalidation_0-logloss:0.38653\tvalidation_1-logloss:0.53877\n",
      "[2031]\tvalidation_0-logloss:0.38648\tvalidation_1-logloss:0.53880\n",
      "[4501]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11956[1068]\tvalidation_0-logloss:0.43487\tvalidation_1-logloss:0.52057\n",
      "\n",
      "[2032]\tvalidation_0-logloss:0.38645\tvalidation_1-logloss:0.53876\n",
      "[2033]\tvalidation_0-logloss:0.38639\tvalidation_1-logloss:0.53873[1069]\tvalidation_0-logloss:0.43481\tvalidation_1-logloss:0.52060\n",
      "\n",
      "[4502]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11971\n",
      "[2034]\tvalidation_0-logloss:0.38636\tvalidation_1-logloss:0.53874\n",
      "[1070]\tvalidation_0-logloss:0.43480\tvalidation_1-logloss:0.52060\n",
      "[2035]\tvalidation_0-logloss:0.38633\tvalidation_1-logloss:0.53875\n",
      "[4503]\tvalidation_0-logloss:0.01471\tvalidation_1-logloss:1.11976\n",
      "[1071]\tvalidation_0-logloss:0.43476\tvalidation_1-logloss:0.52059\n",
      "[2036]\tvalidation_0-logloss:0.38628\tvalidation_1-logloss:0.53874\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1471]\tvalidation_0-logloss:0.41876\tvalidation_1-logloss:0.52568\n",
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      "\n",
      "[2517]\tvalidation_0-logloss:0.37038\tvalidation_1-logloss:0.54736\n",
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      "\n",
      "[1484]\tvalidation_0-logloss:0.41829\tvalidation_1-logloss:0.52580\n",
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      "\n",
      "[4843]\tvalidation_0-logloss:0.01420\tvalidation_1-logloss:1.12943\n",
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      "\n",
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      "\n",
      "[2547]\tvalidation_0-logloss:0.36944\tvalidation_1-logloss:0.54783\n",
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      "\n",
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      "\n",
      "[4859]\tvalidation_0-logloss:0.01417\tvalidation_1-logloss:1.12977\n",
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      "\n",
      "[4860]\tvalidation_0-logloss:0.01417\tvalidation_1-logloss:1.12976\n",
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      "\n",
      "[4861]\tvalidation_0-logloss:0.01417\tvalidation_1-logloss:1.12977\n",
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      "\n",
      "[4874]\tvalidation_0-logloss:0.01415\tvalidation_1-logloss:1.13010\n",
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      "\n",
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      "\n",
      "[1553]\tvalidation_0-logloss:0.41596\tvalidation_1-logloss:0.52677\n",
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      "\n",
      "[4892]\tvalidation_0-logloss:0.01413\tvalidation_1-logloss:1.13064[2610]\tvalidation_0-logloss:0.36725\tvalidation_1-logloss:0.54887\n",
      "\n",
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      "\n",
      "[2629]\tvalidation_0-logloss:0.36669\tvalidation_1-logloss:0.54925[4904]\tvalidation_0-logloss:0.01411\tvalidation_1-logloss:1.13096\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2683]\tvalidation_0-logloss:0.36499\tvalidation_1-logloss:0.54978\n",
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      "\n",
      "[2688]\tvalidation_0-logloss:0.36484\tvalidation_1-logloss:0.54984\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1692]\tvalidation_0-logloss:0.41118\tvalidation_1-logloss:0.52868[2774]\tvalidation_0-logloss:0.36230\tvalidation_1-logloss:0.55110\n",
      "\n",
      "[2775]\tvalidation_0-logloss:0.36226\tvalidation_1-logloss:0.55115\n",
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      "\n",
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      "\n",
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      "\n",
      "[1734]\tvalidation_0-logloss:0.40983\tvalidation_1-logloss:0.52909\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1786]\tvalidation_0-logloss:0.40819\tvalidation_1-logloss:0.52987\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[3107]\tvalidation_0-logloss:0.35257\tvalidation_1-logloss:0.55702[1980]\tvalidation_0-logloss:0.40201\tvalidation_1-logloss:0.53237\n",
      "\n",
      "[3108]\tvalidation_0-logloss:0.35255\tvalidation_1-logloss:0.55703\n",
      "[1981]\tvalidation_0-logloss:0.40197\tvalidation_1-logloss:0.53242\n",
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      "[193]\tvalidation_0-logloss:0.21597\tvalidation_1-logloss:0.60207[1991]\tvalidation_0-logloss:0.40168\tvalidation_1-logloss:0.53261\n",
      "\n",
      "[3120]\tvalidation_0-logloss:0.35225\tvalidation_1-logloss:0.55709\n",
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      "[1994]\tvalidation_0-logloss:0.40163\tvalidation_1-logloss:0.53263\n",
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      "[1995]\tvalidation_0-logloss:0.40161\tvalidation_1-logloss:0.53265\n",
      "[3127]\tvalidation_0-logloss:0.35210\tvalidation_1-logloss:0.55727\n",
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      "[1996]\tvalidation_0-logloss:0.40159\tvalidation_1-logloss:0.53265\n",
      "[3128]\tvalidation_0-logloss:0.35206\tvalidation_1-logloss:0.55730\n",
      "[198]\tvalidation_0-logloss:0.21201\tvalidation_1-logloss:0.60326\n",
      "[3129]\tvalidation_0-logloss:0.35205\tvalidation_1-logloss:0.55728[1997]\tvalidation_0-logloss:0.40154\tvalidation_1-logloss:0.53266\n",
      "\n",
      "[199]\tvalidation_0-logloss:0.21015\tvalidation_1-logloss:0.60373\n",
      "[3130]\tvalidation_0-logloss:0.35202\tvalidation_1-logloss:0.55727\n",
      "[1998]\tvalidation_0-logloss:0.40150\tvalidation_1-logloss:0.53263\n",
      "[200]\tvalidation_0-logloss:0.20926\tvalidation_1-logloss:0.60359[3131]\tvalidation_0-logloss:0.35199\tvalidation_1-logloss:0.55734\n",
      "[1999]\tvalidation_0-logloss:0.40147\tvalidation_1-logloss:0.53259\n",
      "\n",
      "[3132]\tvalidation_0-logloss:0.35197\tvalidation_1-logloss:0.55739\n",
      "[2000]\tvalidation_0-logloss:0.40144\tvalidation_1-logloss:0.53260\n",
      "[201]\tvalidation_0-logloss:0.20770\tvalidation_1-logloss:0.60448\n",
      "[3133]\tvalidation_0-logloss:0.35194\tvalidation_1-logloss:0.55740\n",
      "[2001]\tvalidation_0-logloss:0.40140\tvalidation_1-logloss:0.53263\n",
      "[3134]\tvalidation_0-logloss:0.35191\tvalidation_1-logloss:0.55739\n",
      "[3135]\tvalidation_0-logloss:0.35188\tvalidation_1-logloss:0.55742\n",
      "[2002]\tvalidation_0-logloss:0.40139\tvalidation_1-logloss:0.53264\n",
      "[3136]\tvalidation_0-logloss:0.35187\tvalidation_1-logloss:0.55746\n",
      "[202]\tvalidation_0-logloss:0.20744\tvalidation_1-logloss:0.60446\n",
      "[3137]\tvalidation_0-logloss:0.35183\tvalidation_1-logloss:0.55747\n",
      "[2003]\tvalidation_0-logloss:0.40136\tvalidation_1-logloss:0.53264\n",
      "[203]\tvalidation_0-logloss:0.20698\tvalidation_1-logloss:0.60536\n",
      "[3138]\tvalidation_0-logloss:0.35180\tvalidation_1-logloss:0.55751\n",
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      "[204]\tvalidation_0-logloss:0.20653\tvalidation_1-logloss:0.60605[2004]\tvalidation_0-logloss:0.40133\tvalidation_1-logloss:0.53266\n",
      "\n",
      "[3140]\tvalidation_0-logloss:0.35172\tvalidation_1-logloss:0.55749\n",
      "[205]\tvalidation_0-logloss:0.20582\tvalidation_1-logloss:0.60590\n",
      "[2005]\tvalidation_0-logloss:0.40131\tvalidation_1-logloss:0.53264\n",
      "[3141]\tvalidation_0-logloss:0.35168\tvalidation_1-logloss:0.55748\n",
      "[2006]\tvalidation_0-logloss:0.40129\tvalidation_1-logloss:0.53265\n",
      "[3142]\tvalidation_0-logloss:0.35165\tvalidation_1-logloss:0.55752\n",
      "[206]\tvalidation_0-logloss:0.20526\tvalidation_1-logloss:0.60633\n",
      "[2007]\tvalidation_0-logloss:0.40128\tvalidation_1-logloss:0.53267\n",
      "[3143]\tvalidation_0-logloss:0.35161\tvalidation_1-logloss:0.55749\n",
      "[2008]\tvalidation_0-logloss:0.40127\tvalidation_1-logloss:0.53268\n",
      "[3144]\tvalidation_0-logloss:0.35159\tvalidation_1-logloss:0.55747[207]\tvalidation_0-logloss:0.20436\tvalidation_1-logloss:0.60695\n",
      "\n",
      "[3145]\tvalidation_0-logloss:0.35156\tvalidation_1-logloss:0.55751\n",
      "[2009]\tvalidation_0-logloss:0.40126\tvalidation_1-logloss:0.53267\n",
      "[3146]\tvalidation_0-logloss:0.35151\tvalidation_1-logloss:0.55744\n",
      "[208]\tvalidation_0-logloss:0.20347\tvalidation_1-logloss:0.60758\n",
      "[3147]\tvalidation_0-logloss:0.35149\tvalidation_1-logloss:0.55753\n",
      "[2010]\tvalidation_0-logloss:0.40123\tvalidation_1-logloss:0.53268\n",
      "[209]\tvalidation_0-logloss:0.20332\tvalidation_1-logloss:0.60784\n",
      "[3148]\tvalidation_0-logloss:0.35148\tvalidation_1-logloss:0.55757\n",
      "[2011]\tvalidation_0-logloss:0.40121\tvalidation_1-logloss:0.53269\n",
      "[3149]\tvalidation_0-logloss:0.35146\tvalidation_1-logloss:0.55759\n",
      "[2012]\tvalidation_0-logloss:0.40119\tvalidation_1-logloss:0.53271\n",
      "[210]\tvalidation_0-logloss:0.20293\tvalidation_1-logloss:0.60816\n",
      "[3150]\tvalidation_0-logloss:0.35144\tvalidation_1-logloss:0.55760\n",
      "[2013]\tvalidation_0-logloss:0.40116\tvalidation_1-logloss:0.53278\n",
      "[3151]\tvalidation_0-logloss:0.35142\tvalidation_1-logloss:0.55767\n",
      "[211]\tvalidation_0-logloss:0.20237\tvalidation_1-logloss:0.60884\n",
      "[3152]\tvalidation_0-logloss:0.35141\tvalidation_1-logloss:0.55764\n",
      "[2014]\tvalidation_0-logloss:0.40112\tvalidation_1-logloss:0.53279\n",
      "[3153]\tvalidation_0-logloss:0.35138\tvalidation_1-logloss:0.55764\n",
      "[2015]\tvalidation_0-logloss:0.40110\tvalidation_1-logloss:0.53277\n",
      "[3154]\tvalidation_0-logloss:0.35135\tvalidation_1-logloss:0.55763\n",
      "[212]\tvalidation_0-logloss:0.20200\tvalidation_1-logloss:0.60951\n",
      "[2016]\tvalidation_0-logloss:0.40106\tvalidation_1-logloss:0.53277\n",
      "[3155]\tvalidation_0-logloss:0.35132\tvalidation_1-logloss:0.55763\n",
      "[2017]\tvalidation_0-logloss:0.40103\tvalidation_1-logloss:0.53278\n",
      "[213]\tvalidation_0-logloss:0.20073\tvalidation_1-logloss:0.61043\n",
      "[3156]\tvalidation_0-logloss:0.35131\tvalidation_1-logloss:0.55759\n",
      "[2018]\tvalidation_0-logloss:0.40101\tvalidation_1-logloss:0.53282\n",
      "[3157]\tvalidation_0-logloss:0.35128\tvalidation_1-logloss:0.55758\n",
      "[3158]\tvalidation_0-logloss:0.35125\tvalidation_1-logloss:0.55761\n",
      "[214]\tvalidation_0-logloss:0.20024\tvalidation_1-logloss:0.61100\n",
      "[2019]\tvalidation_0-logloss:0.40099\tvalidation_1-logloss:0.53283\n",
      "[3159]\tvalidation_0-logloss:0.35122\tvalidation_1-logloss:0.55766\n",
      "[2020]\tvalidation_0-logloss:0.40098\tvalidation_1-logloss:0.53284\n",
      "[3160]\tvalidation_0-logloss:0.35119\tvalidation_1-logloss:0.55766\n",
      "[2021]\tvalidation_0-logloss:0.40096\tvalidation_1-logloss:0.53285\n",
      "[215]\tvalidation_0-logloss:0.19984\tvalidation_1-logloss:0.61115\n",
      "[3161]\tvalidation_0-logloss:0.35118\tvalidation_1-logloss:0.55768\n",
      "[2022]\tvalidation_0-logloss:0.40095\tvalidation_1-logloss:0.53286\n",
      "[216]\tvalidation_0-logloss:0.19933\tvalidation_1-logloss:0.61153\n",
      "[2023]\tvalidation_0-logloss:0.40094\tvalidation_1-logloss:0.53286\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3748]\tvalidation_0-logloss:0.33618\tvalidation_1-logloss:0.56728\n",
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      "\n",
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      "\n",
      "[3767]\tvalidation_0-logloss:0.33574\tvalidation_1-logloss:0.56748\n",
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      "\n",
      "[3779]\tvalidation_0-logloss:0.33547\tvalidation_1-logloss:0.56763\n",
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      "\n",
      "[603]\tvalidation_0-logloss:0.07983\tvalidation_1-logloss:0.76297\n",
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      "\n",
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      "\n",
      "[619]\tvalidation_0-logloss:0.07761\tvalidation_1-logloss:0.76841\n",
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      "\n",
      "[623]\tvalidation_0-logloss:0.07704\tvalidation_1-logloss:0.76949\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[2770]\tvalidation_0-logloss:0.38042\tvalidation_1-logloss:0.54114\n",
      "\n",
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      "[4033]\tvalidation_0-logloss:0.32950\tvalidation_1-logloss:0.57142\n",
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      "\n",
      "[768]\tvalidation_0-logloss:0.06151\tvalidation_1-logloss:0.80811\n",
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      "\n",
      "[4063]\tvalidation_0-logloss:0.32879\tvalidation_1-logloss:0.57200\n",
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      "\n",
      "[4067]\tvalidation_0-logloss:0.32868\tvalidation_1-logloss:0.57201\n",
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      "\n",
      "[4076]\tvalidation_0-logloss:0.32849\tvalidation_1-logloss:0.57196\n",
      "[4077]\tvalidation_0-logloss:0.32847\tvalidation_1-logloss:0.57200\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3179]\tvalidation_0-logloss:0.37082\tvalidation_1-logloss:0.54610\n",
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      "\n",
      "[1068]\tvalidation_0-logloss:0.04379\tvalidation_1-logloss:0.87308\n",
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      "\n",
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      "\n",
      "[4537]\tvalidation_0-logloss:0.31815\tvalidation_1-logloss:0.57996\n",
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      "\n",
      "[3238]\tvalidation_0-logloss:0.36951\tvalidation_1-logloss:0.54672\n",
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      "\n",
      "[3246]\tvalidation_0-logloss:0.36937\tvalidation_1-logloss:0.54681\n",
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      "\n",
      "[4581]\tvalidation_0-logloss:0.31722\tvalidation_1-logloss:0.58098\n",
      "[3248]\tvalidation_0-logloss:0.36933\tvalidation_1-logloss:0.54682\n",
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      "\n",
      "[4588]\tvalidation_0-logloss:0.31707\tvalidation_1-logloss:0.58127\n",
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      "\n",
      "[3265]\tvalidation_0-logloss:0.36887\tvalidation_1-logloss:0.54706\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4649]\tvalidation_0-logloss:0.31591\tvalidation_1-logloss:0.58200\n",
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      "\n",
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      "\n",
      "[3316]\tvalidation_0-logloss:0.36770\tvalidation_1-logloss:0.54744\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3331]\tvalidation_0-logloss:0.36734\tvalidation_1-logloss:0.54759\n",
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      "\n",
      "[3346]\tvalidation_0-logloss:0.36694\tvalidation_1-logloss:0.54762\n",
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      "\n",
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      "\n",
      "[1197]\tvalidation_0-logloss:0.03898\tvalidation_1-logloss:0.89589\n",
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      "\n",
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      "\n",
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      "\n",
      "[4760]\tvalidation_0-logloss:0.31363\tvalidation_1-logloss:0.58375[3404]\tvalidation_0-logloss:0.36568\tvalidation_1-logloss:0.54818\n",
      "\n",
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      "\n",
      "[4761]\tvalidation_0-logloss:0.31359\tvalidation_1-logloss:0.58375\n",
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      "\n",
      "[3407]\tvalidation_0-logloss:0.36562\tvalidation_1-logloss:0.54821\n",
      "[4764]\tvalidation_0-logloss:0.31349\tvalidation_1-logloss:0.58383\n",
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      "[3420]\tvalidation_0-logloss:0.36532\tvalidation_1-logloss:0.54824[4780]\tvalidation_0-logloss:0.31317\tvalidation_1-logloss:0.58411\n",
      "\n",
      "[4781]\tvalidation_0-logloss:0.31315\tvalidation_1-logloss:0.58412\n",
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      "\n",
      "[3423]\tvalidation_0-logloss:0.36525\tvalidation_1-logloss:0.54826\n",
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      "\n",
      "[3435]\tvalidation_0-logloss:0.36499\tvalidation_1-logloss:0.54848\n",
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      "\n",
      "[4799]\tvalidation_0-logloss:0.31280\tvalidation_1-logloss:0.58441\n",
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      "\n",
      "[4805]\tvalidation_0-logloss:0.31268\tvalidation_1-logloss:0.58447\n",
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      "\n",
      "[4811]\tvalidation_0-logloss:0.31257\tvalidation_1-logloss:0.58450\n",
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      "\n",
      "[3449]\tvalidation_0-logloss:0.36470\tvalidation_1-logloss:0.54855\n",
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      "\n",
      "[4814]\tvalidation_0-logloss:0.31250\tvalidation_1-logloss:0.58450\n",
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      "\n",
      "[4829]\tvalidation_0-logloss:0.31219\tvalidation_1-logloss:0.58458\n",
      "[3463]\tvalidation_0-logloss:0.36439\tvalidation_1-logloss:0.54876[1262]\tvalidation_0-logloss:0.03693\tvalidation_1-logloss:0.90656\n",
      "\n",
      "[4830]\tvalidation_0-logloss:0.31217\tvalidation_1-logloss:0.58458\n",
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      "\n",
      "[1267]\tvalidation_0-logloss:0.03679\tvalidation_1-logloss:0.90754\n",
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      "\n",
      "[4839]\tvalidation_0-logloss:0.31195\tvalidation_1-logloss:0.58491[3471]\tvalidation_0-logloss:0.36421\tvalidation_1-logloss:0.54890\n",
      "\n",
      "[1268]\tvalidation_0-logloss:0.03675\tvalidation_1-logloss:0.90794\n",
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      "\n",
      "[4859]\tvalidation_0-logloss:0.31155\tvalidation_1-logloss:0.58519\n",
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      "\n",
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      "\n",
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      "\n",
      "[1289]\tvalidation_0-logloss:0.03625\tvalidation_1-logloss:0.91037\n",
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      "\n",
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      "\n",
      "[1296]\tvalidation_0-logloss:0.03604\tvalidation_1-logloss:0.91191\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3548]\tvalidation_0-logloss:0.36255\tvalidation_1-logloss:0.54987\n",
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      "\n",
      "[4952]\tvalidation_0-logloss:0.30964\tvalidation_1-logloss:0.58628\n",
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      "\n",
      "[3573]\tvalidation_0-logloss:0.36204\tvalidation_1-logloss:0.54998\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3606]\tvalidation_0-logloss:0.36134\tvalidation_1-logloss:0.55009\n",
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      "\n",
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      "\n",
      "[3627]\tvalidation_0-logloss:0.36087\tvalidation_1-logloss:0.55020\n",
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      "[3630]\tvalidation_0-logloss:0.36083\tvalidation_1-logloss:0.55021\n",
      "[5]\tvalidation_0-logloss:0.63601\tvalidation_1-logloss:0.63660[1389]\tvalidation_0-logloss:0.03358\tvalidation_1-logloss:0.92407\n",
      "\n",
      "[3631]\tvalidation_0-logloss:0.36080\tvalidation_1-logloss:0.55025\n",
      "[6]\tvalidation_0-logloss:0.63058\tvalidation_1-logloss:0.63124\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3831]\tvalidation_0-logloss:0.35688\tvalidation_1-logloss:0.55256\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1986]\tvalidation_0-logloss:0.02444\tvalidation_1-logloss:0.99247\n",
      "[4450]\tvalidation_0-logloss:0.34513\tvalidation_1-logloss:0.55785\n",
      "[943]\tvalidation_0-logloss:0.43733\tvalidation_1-logloss:0.51309\n",
      "[944]\tvalidation_0-logloss:0.43728\tvalidation_1-logloss:0.51303\n",
      "[1987]\tvalidation_0-logloss:0.02442\tvalidation_1-logloss:0.99251[4451]\tvalidation_0-logloss:0.34511\tvalidation_1-logloss:0.55786\n",
      "\n",
      "[945]\tvalidation_0-logloss:0.43725\tvalidation_1-logloss:0.51301\n",
      "[4452]\tvalidation_0-logloss:0.34509\tvalidation_1-logloss:0.55783\n",
      "[4453]\tvalidation_0-logloss:0.34504\tvalidation_1-logloss:0.55784\n",
      "[946]\tvalidation_0-logloss:0.43722\tvalidation_1-logloss:0.51302\n",
      "[4454]\tvalidation_0-logloss:0.34502\tvalidation_1-logloss:0.55787\n",
      "[1988]\tvalidation_0-logloss:0.02441\tvalidation_1-logloss:0.99274\n",
      "[947]\tvalidation_0-logloss:0.43718\tvalidation_1-logloss:0.51308\n",
      "[4455]\tvalidation_0-logloss:0.34501\tvalidation_1-logloss:0.55789\n",
      "[1989]\tvalidation_0-logloss:0.02439\tvalidation_1-logloss:0.99271\n",
      "[948]\tvalidation_0-logloss:0.43714\tvalidation_1-logloss:0.51306\n",
      "[4456]\tvalidation_0-logloss:0.34500\tvalidation_1-logloss:0.55789\n",
      "[949]\tvalidation_0-logloss:0.43710\tvalidation_1-logloss:0.51310\n",
      "[1990]\tvalidation_0-logloss:0.02439\tvalidation_1-logloss:0.99272\n",
      "[4457]\tvalidation_0-logloss:0.34497\tvalidation_1-logloss:0.55787\n",
      "[950]\tvalidation_0-logloss:0.43704\tvalidation_1-logloss:0.51308\n",
      "[1991]\tvalidation_0-logloss:0.02438\tvalidation_1-logloss:0.99273\n",
      "[951]\tvalidation_0-logloss:0.43699\tvalidation_1-logloss:0.51312\n",
      "[4458]\tvalidation_0-logloss:0.34496\tvalidation_1-logloss:0.55791\n",
      "[952]\tvalidation_0-logloss:0.43695\tvalidation_1-logloss:0.51311[4459]\tvalidation_0-logloss:0.34492\tvalidation_1-logloss:0.55788\n",
      "\n",
      "[1992]\tvalidation_0-logloss:0.02437\tvalidation_1-logloss:0.99288\n",
      "[953]\tvalidation_0-logloss:0.43690\tvalidation_1-logloss:0.51318\n",
      "[4460]\tvalidation_0-logloss:0.34491\tvalidation_1-logloss:0.55787\n",
      "[954]\tvalidation_0-logloss:0.43686\tvalidation_1-logloss:0.51322\n",
      "[1993]\tvalidation_0-logloss:0.02436\tvalidation_1-logloss:0.99310\n",
      "[4461]\tvalidation_0-logloss:0.34488\tvalidation_1-logloss:0.55785\n",
      "[955]\tvalidation_0-logloss:0.43682\tvalidation_1-logloss:0.51323\n",
      "[1994]\tvalidation_0-logloss:0.02435\tvalidation_1-logloss:0.99301\n",
      "[4462]\tvalidation_0-logloss:0.34487\tvalidation_1-logloss:0.55788\n",
      "[956]\tvalidation_0-logloss:0.43674\tvalidation_1-logloss:0.51323\n",
      "[957]\tvalidation_0-logloss:0.43669\tvalidation_1-logloss:0.51316\n",
      "[4463]\tvalidation_0-logloss:0.34486\tvalidation_1-logloss:0.55791[1995]\tvalidation_0-logloss:0.02433\tvalidation_1-logloss:0.99328\n",
      "\n",
      "[958]\tvalidation_0-logloss:0.43663\tvalidation_1-logloss:0.51313\n",
      "[4464]\tvalidation_0-logloss:0.34485\tvalidation_1-logloss:0.55792\n",
      "[959]\tvalidation_0-logloss:0.43658\tvalidation_1-logloss:0.51318[1996]\tvalidation_0-logloss:0.02432\tvalidation_1-logloss:0.99330\n",
      "\n",
      "[4465]\tvalidation_0-logloss:0.34484\tvalidation_1-logloss:0.55794\n",
      "[960]\tvalidation_0-logloss:0.43651\tvalidation_1-logloss:0.51314\n",
      "[1997]\tvalidation_0-logloss:0.02431\tvalidation_1-logloss:0.99338\n",
      "[4466]\tvalidation_0-logloss:0.34482\tvalidation_1-logloss:0.55800\n",
      "[961]\tvalidation_0-logloss:0.43648\tvalidation_1-logloss:0.51313\n",
      "[1998]\tvalidation_0-logloss:0.02430\tvalidation_1-logloss:0.99341[4467]\tvalidation_0-logloss:0.34479\tvalidation_1-logloss:0.55797\n",
      "\n",
      "[962]\tvalidation_0-logloss:0.43643\tvalidation_1-logloss:0.51317\n",
      "[4468]\tvalidation_0-logloss:0.34476\tvalidation_1-logloss:0.55800\n",
      "[963]\tvalidation_0-logloss:0.43641\tvalidation_1-logloss:0.51324\n",
      "[1999]\tvalidation_0-logloss:0.02429\tvalidation_1-logloss:0.99349\n",
      "[4469]\tvalidation_0-logloss:0.34473\tvalidation_1-logloss:0.55797\n",
      "[964]\tvalidation_0-logloss:0.43635\tvalidation_1-logloss:0.51322\n",
      "[4470]\tvalidation_0-logloss:0.34472\tvalidation_1-logloss:0.55798\n",
      "[965]\tvalidation_0-logloss:0.43630\tvalidation_1-logloss:0.51323\n",
      "[2000]\tvalidation_0-logloss:0.02429\tvalidation_1-logloss:0.99354\n",
      "[4471]\tvalidation_0-logloss:0.34470\tvalidation_1-logloss:0.55801\n",
      "[966]\tvalidation_0-logloss:0.43627\tvalidation_1-logloss:0.51323\n",
      "[2001]\tvalidation_0-logloss:0.02428\tvalidation_1-logloss:0.99362\n",
      "[4472]\tvalidation_0-logloss:0.34467\tvalidation_1-logloss:0.55804\n",
      "[967]\tvalidation_0-logloss:0.43621\tvalidation_1-logloss:0.51322\n",
      "[2002]\tvalidation_0-logloss:0.02427\tvalidation_1-logloss:0.99374\n",
      "[4473]\tvalidation_0-logloss:0.34466\tvalidation_1-logloss:0.55805\n",
      "[968]\tvalidation_0-logloss:0.43616\tvalidation_1-logloss:0.51321\n",
      "[969]\tvalidation_0-logloss:0.43611\tvalidation_1-logloss:0.51327\n",
      "[2003]\tvalidation_0-logloss:0.02425\tvalidation_1-logloss:0.99381\n",
      "[4474]\tvalidation_0-logloss:0.34465\tvalidation_1-logloss:0.55807\n",
      "[970]\tvalidation_0-logloss:0.43607\tvalidation_1-logloss:0.51320\n",
      "[2004]\tvalidation_0-logloss:0.02425\tvalidation_1-logloss:0.99379\n",
      "[971]\tvalidation_0-logloss:0.43601\tvalidation_1-logloss:0.51320\n",
      "[4475]\tvalidation_0-logloss:0.34465\tvalidation_1-logloss:0.55808\n",
      "[972]\tvalidation_0-logloss:0.43593\tvalidation_1-logloss:0.51326\n",
      "[2005]\tvalidation_0-logloss:0.02424\tvalidation_1-logloss:0.99396[4476]\tvalidation_0-logloss:0.34464\tvalidation_1-logloss:0.55810\n",
      "\n",
      "[973]\tvalidation_0-logloss:0.43585\tvalidation_1-logloss:0.51326\n",
      "[4477]\tvalidation_0-logloss:0.34462\tvalidation_1-logloss:0.55810\n",
      "[2006]\tvalidation_0-logloss:0.02422\tvalidation_1-logloss:0.99419\n",
      "[974]\tvalidation_0-logloss:0.43582\tvalidation_1-logloss:0.51330\n",
      "[4478]\tvalidation_0-logloss:0.34460\tvalidation_1-logloss:0.55810\n",
      "[975]\tvalidation_0-logloss:0.43578\tvalidation_1-logloss:0.51330\n",
      "[2007]\tvalidation_0-logloss:0.02422\tvalidation_1-logloss:0.99422\n",
      "[4479]\tvalidation_0-logloss:0.34459\tvalidation_1-logloss:0.55812\n",
      "[976]\tvalidation_0-logloss:0.43572\tvalidation_1-logloss:0.51332\n",
      "[2008]\tvalidation_0-logloss:0.02422\tvalidation_1-logloss:0.99423\n",
      "[4480]\tvalidation_0-logloss:0.34457\tvalidation_1-logloss:0.55814\n",
      "[977]\tvalidation_0-logloss:0.43568\tvalidation_1-logloss:0.51328\n",
      "[978]\tvalidation_0-logloss:0.43564\tvalidation_1-logloss:0.51331\n",
      "[4481]\tvalidation_0-logloss:0.34455\tvalidation_1-logloss:0.55818\n",
      "[2009]\tvalidation_0-logloss:0.02420\tvalidation_1-logloss:0.99438\n",
      "[2010]\tvalidation_0-logloss:0.02419\tvalidation_1-logloss:0.99454\n",
      "[4482]\tvalidation_0-logloss:0.34454\tvalidation_1-logloss:0.55818\n",
      "[979]\tvalidation_0-logloss:0.43560\tvalidation_1-logloss:0.51328\n",
      "[4483]\tvalidation_0-logloss:0.34453\tvalidation_1-logloss:0.55817\n",
      "[2011]\tvalidation_0-logloss:0.02418\tvalidation_1-logloss:0.99464\n",
      "[980]\tvalidation_0-logloss:0.43554\tvalidation_1-logloss:0.51330\n",
      "[981]\tvalidation_0-logloss:0.43550\tvalidation_1-logloss:0.51334\n",
      "[4484]\tvalidation_0-logloss:0.34450\tvalidation_1-logloss:0.55819\n",
      "[2012]\tvalidation_0-logloss:0.02417\tvalidation_1-logloss:0.99469\n",
      "[4485]\tvalidation_0-logloss:0.34449\tvalidation_1-logloss:0.55821\n",
      "[982]\tvalidation_0-logloss:0.43545\tvalidation_1-logloss:0.51340\n",
      "[2013]\tvalidation_0-logloss:0.02416\tvalidation_1-logloss:0.99479\n",
      "[983]\tvalidation_0-logloss:0.43542\tvalidation_1-logloss:0.51337\n",
      "[4486]\tvalidation_0-logloss:0.34447\tvalidation_1-logloss:0.55820\n",
      "[2014]\tvalidation_0-logloss:0.02415\tvalidation_1-logloss:0.99485\n",
      "[984]\tvalidation_0-logloss:0.43538\tvalidation_1-logloss:0.51343\n",
      "[4487]\tvalidation_0-logloss:0.34447\tvalidation_1-logloss:0.55821\n",
      "[985]\tvalidation_0-logloss:0.43535\tvalidation_1-logloss:0.51345\n",
      "[2015]\tvalidation_0-logloss:0.02414\tvalidation_1-logloss:0.99482[4488]\tvalidation_0-logloss:0.34446\tvalidation_1-logloss:0.55822\n",
      "\n",
      "[986]\tvalidation_0-logloss:0.43530\tvalidation_1-logloss:0.51349\n",
      "[987]\tvalidation_0-logloss:0.43526\tvalidation_1-logloss:0.51352\n",
      "[4489]\tvalidation_0-logloss:0.34445\tvalidation_1-logloss:0.55820\n",
      "[2016]\tvalidation_0-logloss:0.02413\tvalidation_1-logloss:0.99501[988]\tvalidation_0-logloss:0.43523\tvalidation_1-logloss:0.51352\n",
      "\n",
      "[4490]\tvalidation_0-logloss:0.34444\tvalidation_1-logloss:0.55821\n",
      "[989]\tvalidation_0-logloss:0.43518\tvalidation_1-logloss:0.51348\n",
      "[4491]\tvalidation_0-logloss:0.34443\tvalidation_1-logloss:0.55819\n",
      "[2017]\tvalidation_0-logloss:0.02412\tvalidation_1-logloss:0.99514\n",
      "[990]\tvalidation_0-logloss:0.43509\tvalidation_1-logloss:0.51345\n",
      "[4492]\tvalidation_0-logloss:0.34441\tvalidation_1-logloss:0.55822\n",
      "[2018]\tvalidation_0-logloss:0.02411\tvalidation_1-logloss:0.99509\n",
      "[991]\tvalidation_0-logloss:0.43501\tvalidation_1-logloss:0.51343\n",
      "[4493]\tvalidation_0-logloss:0.34439\tvalidation_1-logloss:0.55825\n",
      "[992]\tvalidation_0-logloss:0.43494\tvalidation_1-logloss:0.51342\n",
      "[2019]\tvalidation_0-logloss:0.02409\tvalidation_1-logloss:0.99521\n",
      "[993]\tvalidation_0-logloss:0.43489\tvalidation_1-logloss:0.51342\n",
      "[4494]\tvalidation_0-logloss:0.34437\tvalidation_1-logloss:0.55827\n",
      "[994]\tvalidation_0-logloss:0.43484\tvalidation_1-logloss:0.51339\n",
      "[2020]\tvalidation_0-logloss:0.02408\tvalidation_1-logloss:0.99529\n",
      "[4495]\tvalidation_0-logloss:0.34436\tvalidation_1-logloss:0.55827\n",
      "[4496]\tvalidation_0-logloss:0.34435\tvalidation_1-logloss:0.55826\n",
      "[995]\tvalidation_0-logloss:0.43478\tvalidation_1-logloss:0.51338\n",
      "[2021]\tvalidation_0-logloss:0.02407\tvalidation_1-logloss:0.99522\n",
      "[4497]\tvalidation_0-logloss:0.34432\tvalidation_1-logloss:0.55827\n",
      "[996]\tvalidation_0-logloss:0.43471\tvalidation_1-logloss:0.51338\n",
      "[997]\tvalidation_0-logloss:0.43467\tvalidation_1-logloss:0.51335\n",
      "[4498]\tvalidation_0-logloss:0.34430\tvalidation_1-logloss:0.55826\n",
      "[2022]\tvalidation_0-logloss:0.02405\tvalidation_1-logloss:0.99539\n",
      "[998]\tvalidation_0-logloss:0.43461\tvalidation_1-logloss:0.51335\n",
      "[4499]\tvalidation_0-logloss:0.34430\tvalidation_1-logloss:0.55827\n",
      "[2023]\tvalidation_0-logloss:0.02404\tvalidation_1-logloss:0.99543\n",
      "[999]\tvalidation_0-logloss:0.43456\tvalidation_1-logloss:0.51344\n",
      "[4500]\tvalidation_0-logloss:0.34427\tvalidation_1-logloss:0.55827\n",
      "[2024]\tvalidation_0-logloss:0.02404\tvalidation_1-logloss:0.99559\n",
      "[1000]\tvalidation_0-logloss:0.43451\tvalidation_1-logloss:0.51346\n",
      "[2025]\tvalidation_0-logloss:0.02403\tvalidation_1-logloss:0.99571\n",
      "[1001]\tvalidation_0-logloss:0.43444\tvalidation_1-logloss:0.51339\n",
      "[4501]\tvalidation_0-logloss:0.34424\tvalidation_1-logloss:0.55832\n",
      "[2026]\tvalidation_0-logloss:0.02401\tvalidation_1-logloss:0.99597\n",
      "[1002]\tvalidation_0-logloss:0.43439\tvalidation_1-logloss:0.51337\n",
      "[4502]\tvalidation_0-logloss:0.34424\tvalidation_1-logloss:0.55832\n",
      "[1003]\tvalidation_0-logloss:0.43434\tvalidation_1-logloss:0.51341\n",
      "[4503]\tvalidation_0-logloss:0.34420\tvalidation_1-logloss:0.55831\n",
      "[2027]\tvalidation_0-logloss:0.02400\tvalidation_1-logloss:0.99609\n",
      "[1004]\tvalidation_0-logloss:0.43429\tvalidation_1-logloss:0.51338\n",
      "[4504]\tvalidation_0-logloss:0.34418\tvalidation_1-logloss:0.55834\n",
      "[1005]\tvalidation_0-logloss:0.43423\tvalidation_1-logloss:0.51340\n",
      "[2028]\tvalidation_0-logloss:0.02399\tvalidation_1-logloss:0.99629\n",
      "[1006]\tvalidation_0-logloss:0.43418\tvalidation_1-logloss:0.51340\n",
      "[4505]\tvalidation_0-logloss:0.34415\tvalidation_1-logloss:0.55831\n",
      "[1007]\tvalidation_0-logloss:0.43413\tvalidation_1-logloss:0.51346\n",
      "[2029]\tvalidation_0-logloss:0.02398\tvalidation_1-logloss:0.99653\n",
      "[4506]\tvalidation_0-logloss:0.34413\tvalidation_1-logloss:0.55834\n",
      "[1008]\tvalidation_0-logloss:0.43410\tvalidation_1-logloss:0.51347\n",
      "[1009]\tvalidation_0-logloss:0.43406\tvalidation_1-logloss:0.51347[2030]\tvalidation_0-logloss:0.02397\tvalidation_1-logloss:0.99674\n",
      "[4507]\tvalidation_0-logloss:0.34413\tvalidation_1-logloss:0.55834\n",
      "\n",
      "[1010]\tvalidation_0-logloss:0.43403\tvalidation_1-logloss:0.51357\n",
      "[4508]\tvalidation_0-logloss:0.34411\tvalidation_1-logloss:0.55838\n",
      "[1011]\tvalidation_0-logloss:0.43396\tvalidation_1-logloss:0.51360\n",
      "[2031]\tvalidation_0-logloss:0.02396\tvalidation_1-logloss:0.99693\n",
      "[1012]\tvalidation_0-logloss:0.43390\tvalidation_1-logloss:0.51352\n",
      "[4509]\tvalidation_0-logloss:0.34407\tvalidation_1-logloss:0.55839\n",
      "[1013]\tvalidation_0-logloss:0.43384\tvalidation_1-logloss:0.51355\n",
      "[2032]\tvalidation_0-logloss:0.02395\tvalidation_1-logloss:0.99667\n",
      "[4510]\tvalidation_0-logloss:0.34405\tvalidation_1-logloss:0.55838\n",
      "[1014]\tvalidation_0-logloss:0.43378\tvalidation_1-logloss:0.51361\n",
      "[2033]\tvalidation_0-logloss:0.02394\tvalidation_1-logloss:0.99688\n",
      "[4511]\tvalidation_0-logloss:0.34404\tvalidation_1-logloss:0.55836\n",
      "[1015]\tvalidation_0-logloss:0.43373\tvalidation_1-logloss:0.51358\n",
      "[4512]\tvalidation_0-logloss:0.34403\tvalidation_1-logloss:0.55832\n",
      "[2034]\tvalidation_0-logloss:0.02393\tvalidation_1-logloss:0.99688\n",
      "[1016]\tvalidation_0-logloss:0.43367\tvalidation_1-logloss:0.51362\n",
      "[4513]\tvalidation_0-logloss:0.34403\tvalidation_1-logloss:0.55832\n",
      "[1017]\tvalidation_0-logloss:0.43360\tvalidation_1-logloss:0.51365\n",
      "[2035]\tvalidation_0-logloss:0.02392\tvalidation_1-logloss:0.99704\n",
      "[4514]\tvalidation_0-logloss:0.34401\tvalidation_1-logloss:0.55830\n",
      "[1018]\tvalidation_0-logloss:0.43355\tvalidation_1-logloss:0.51363\n",
      "[4515]\tvalidation_0-logloss:0.34400\tvalidation_1-logloss:0.55829\n",
      "[1019]\tvalidation_0-logloss:0.43349\tvalidation_1-logloss:0.51365\n",
      "[2036]\tvalidation_0-logloss:0.02391\tvalidation_1-logloss:0.99704\n",
      "[1020]\tvalidation_0-logloss:0.43346\tvalidation_1-logloss:0.51365\n",
      "[4516]\tvalidation_0-logloss:0.34399\tvalidation_1-logloss:0.55826\n",
      "[4517]\tvalidation_0-logloss:0.34398\tvalidation_1-logloss:0.55825\n",
      "[1021]\tvalidation_0-logloss:0.43339\tvalidation_1-logloss:0.51363\n",
      "[4518]\tvalidation_0-logloss:0.34397\tvalidation_1-logloss:0.55822\n",
      "[2037]\tvalidation_0-logloss:0.02391\tvalidation_1-logloss:0.99716\n",
      "[1022]\tvalidation_0-logloss:0.43332\tvalidation_1-logloss:0.51363\n",
      "[4519]\tvalidation_0-logloss:0.34395\tvalidation_1-logloss:0.55823\n",
      "[2038]\tvalidation_0-logloss:0.02390\tvalidation_1-logloss:0.99708\n",
      "[4520]\tvalidation_0-logloss:0.34391\tvalidation_1-logloss:0.55822\n",
      "[1023]\tvalidation_0-logloss:0.43327\tvalidation_1-logloss:0.51365\n",
      "[4521]\tvalidation_0-logloss:0.34388\tvalidation_1-logloss:0.55822\n",
      "[2039]\tvalidation_0-logloss:0.02389\tvalidation_1-logloss:0.99714[1024]\tvalidation_0-logloss:0.43322\tvalidation_1-logloss:0.51363\n",
      "\n",
      "[1025]\tvalidation_0-logloss:0.43316\tvalidation_1-logloss:0.51362\n",
      "[4522]\tvalidation_0-logloss:0.34387\tvalidation_1-logloss:0.55821\n",
      "[1026]\tvalidation_0-logloss:0.43309\tvalidation_1-logloss:0.51360\n",
      "[2040]\tvalidation_0-logloss:0.02389\tvalidation_1-logloss:0.99727\n",
      "[1027]\tvalidation_0-logloss:0.43303\tvalidation_1-logloss:0.51355\n",
      "[4523]\tvalidation_0-logloss:0.34386\tvalidation_1-logloss:0.55822\n",
      "[1028]\tvalidation_0-logloss:0.43300\tvalidation_1-logloss:0.51358\n",
      "[2041]\tvalidation_0-logloss:0.02388\tvalidation_1-logloss:0.99739\n",
      "[4524]\tvalidation_0-logloss:0.34383\tvalidation_1-logloss:0.55818\n",
      "[1029]\tvalidation_0-logloss:0.43293\tvalidation_1-logloss:0.51361\n",
      "[1030]\tvalidation_0-logloss:0.43288\tvalidation_1-logloss:0.51360\n",
      "[2042]\tvalidation_0-logloss:0.02387\tvalidation_1-logloss:0.99741\n",
      "[4525]\tvalidation_0-logloss:0.34381\tvalidation_1-logloss:0.55816\n",
      "[1031]\tvalidation_0-logloss:0.43282\tvalidation_1-logloss:0.51354\n",
      "[4526]\tvalidation_0-logloss:0.34379\tvalidation_1-logloss:0.55819\n",
      "[1032]\tvalidation_0-logloss:0.43278\tvalidation_1-logloss:0.51352\n",
      "[2043]\tvalidation_0-logloss:0.02387\tvalidation_1-logloss:0.99742\n",
      "[4527]\tvalidation_0-logloss:0.34376\tvalidation_1-logloss:0.55822\n",
      "[1033]\tvalidation_0-logloss:0.43275\tvalidation_1-logloss:0.51355\n",
      "[2044]\tvalidation_0-logloss:0.02385\tvalidation_1-logloss:0.99753\n",
      "[4528]\tvalidation_0-logloss:0.34375\tvalidation_1-logloss:0.55824\n",
      "[1034]\tvalidation_0-logloss:0.43275\tvalidation_1-logloss:0.51357\n",
      "[2045]\tvalidation_0-logloss:0.02385\tvalidation_1-logloss:0.99770[4529]\tvalidation_0-logloss:0.34375\tvalidation_1-logloss:0.55826\n",
      "\n",
      "[1035]\tvalidation_0-logloss:0.43271\tvalidation_1-logloss:0.51355\n",
      "[4530]\tvalidation_0-logloss:0.34375\tvalidation_1-logloss:0.55825\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4783]\tvalidation_0-logloss:0.33910\tvalidation_1-logloss:0.56009\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4826]\tvalidation_0-logloss:0.33835\tvalidation_1-logloss:0.56052\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2605]\tvalidation_0-logloss:0.01983\tvalidation_1-logloss:1.03795\n",
      "[1921]\tvalidation_0-logloss:0.39504\tvalidation_1-logloss:0.52839\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[316]\tvalidation_0-logloss:0.47975\tvalidation_1-logloss:0.50582\n",
      "[1979]\tvalidation_0-logloss:0.39293\tvalidation_1-logloss:0.52934\n",
      "[317]\tvalidation_0-logloss:0.47966\tvalidation_1-logloss:0.50578\n",
      "[1980]\tvalidation_0-logloss:0.39289\tvalidation_1-logloss:0.52931\n",
      "[2643]\tvalidation_0-logloss:0.01963\tvalidation_1-logloss:1.03999\n",
      "[1981]\tvalidation_0-logloss:0.39285\tvalidation_1-logloss:0.52931\n",
      "[318]\tvalidation_0-logloss:0.47956\tvalidation_1-logloss:0.50577\n",
      "[2644]\tvalidation_0-logloss:0.01962\tvalidation_1-logloss:1.04025\n",
      "[1982]\tvalidation_0-logloss:0.39282\tvalidation_1-logloss:0.52930\n",
      "[319]\tvalidation_0-logloss:0.47932\tvalidation_1-logloss:0.50581\n",
      "[1983]\tvalidation_0-logloss:0.39277\tvalidation_1-logloss:0.52931\n",
      "[320]\tvalidation_0-logloss:0.47923\tvalidation_1-logloss:0.50580[2645]\tvalidation_0-logloss:0.01962\tvalidation_1-logloss:1.04040\n",
      "\n",
      "[1984]\tvalidation_0-logloss:0.39274\tvalidation_1-logloss:0.52932\n",
      "[321]\tvalidation_0-logloss:0.47913\tvalidation_1-logloss:0.50580\n",
      "[1985]\tvalidation_0-logloss:0.39271\tvalidation_1-logloss:0.52931\n",
      "[2646]\tvalidation_0-logloss:0.01961\tvalidation_1-logloss:1.04043[322]\tvalidation_0-logloss:0.47907\tvalidation_1-logloss:0.50578\n",
      "\n",
      "[1986]\tvalidation_0-logloss:0.39265\tvalidation_1-logloss:0.52941\n",
      "[1987]\tvalidation_0-logloss:0.39262\tvalidation_1-logloss:0.52944\n",
      "[2647]\tvalidation_0-logloss:0.01961\tvalidation_1-logloss:1.04050\n",
      "[323]\tvalidation_0-logloss:0.47899\tvalidation_1-logloss:0.50576\n",
      "[1988]\tvalidation_0-logloss:0.39257\tvalidation_1-logloss:0.52949\n",
      "[324]\tvalidation_0-logloss:0.47891\tvalidation_1-logloss:0.50580\n",
      "[1989]\tvalidation_0-logloss:0.39253\tvalidation_1-logloss:0.52946\n",
      "[2648]\tvalidation_0-logloss:0.01960\tvalidation_1-logloss:1.04072\n",
      "[325]\tvalidation_0-logloss:0.47880\tvalidation_1-logloss:0.50582\n",
      "[1990]\tvalidation_0-logloss:0.39253\tvalidation_1-logloss:0.52948\n",
      "[326]\tvalidation_0-logloss:0.47876\tvalidation_1-logloss:0.50585\n",
      "[1991]\tvalidation_0-logloss:0.39249\tvalidation_1-logloss:0.52953\n",
      "[2649]\tvalidation_0-logloss:0.01960\tvalidation_1-logloss:1.04082\n",
      "[1992]\tvalidation_0-logloss:0.39246\tvalidation_1-logloss:0.52955\n",
      "[327]\tvalidation_0-logloss:0.47869\tvalidation_1-logloss:0.50580\n",
      "[2650]\tvalidation_0-logloss:0.01959\tvalidation_1-logloss:1.04088\n",
      "[1993]\tvalidation_0-logloss:0.39240\tvalidation_1-logloss:0.52964\n",
      "[328]\tvalidation_0-logloss:0.47861\tvalidation_1-logloss:0.50578\n",
      "[1994]\tvalidation_0-logloss:0.39237\tvalidation_1-logloss:0.52963\n",
      "[2651]\tvalidation_0-logloss:0.01959\tvalidation_1-logloss:1.04097\n",
      "[1995]\tvalidation_0-logloss:0.39233\tvalidation_1-logloss:0.52963\n",
      "[329]\tvalidation_0-logloss:0.47851\tvalidation_1-logloss:0.50574\n",
      "[2652]\tvalidation_0-logloss:0.01958\tvalidation_1-logloss:1.04107\n",
      "[1996]\tvalidation_0-logloss:0.39228\tvalidation_1-logloss:0.52964\n",
      "[330]\tvalidation_0-logloss:0.47842\tvalidation_1-logloss:0.50572\n",
      "[1997]\tvalidation_0-logloss:0.39226\tvalidation_1-logloss:0.52966\n",
      "[2653]\tvalidation_0-logloss:0.01958\tvalidation_1-logloss:1.04112\n",
      "[331]\tvalidation_0-logloss:0.47833\tvalidation_1-logloss:0.50577\n",
      "[1998]\tvalidation_0-logloss:0.39222\tvalidation_1-logloss:0.52967\n",
      "[2654]\tvalidation_0-logloss:0.01957\tvalidation_1-logloss:1.04128\n",
      "[332]\tvalidation_0-logloss:0.47831\tvalidation_1-logloss:0.50578\n",
      "[1999]\tvalidation_0-logloss:0.39217\tvalidation_1-logloss:0.52966\n",
      "[2655]\tvalidation_0-logloss:0.01957\tvalidation_1-logloss:1.04143\n",
      "[333]\tvalidation_0-logloss:0.47817\tvalidation_1-logloss:0.50578\n",
      "[2000]\tvalidation_0-logloss:0.39216\tvalidation_1-logloss:0.52965\n",
      "[334]\tvalidation_0-logloss:0.47813\tvalidation_1-logloss:0.50584\n",
      "[2001]\tvalidation_0-logloss:0.39212\tvalidation_1-logloss:0.52962\n",
      "[2656]\tvalidation_0-logloss:0.01956\tvalidation_1-logloss:1.04136\n",
      "[335]\tvalidation_0-logloss:0.47803\tvalidation_1-logloss:0.50589\n",
      "[2002]\tvalidation_0-logloss:0.39208\tvalidation_1-logloss:0.52966\n",
      "[336]\tvalidation_0-logloss:0.47797\tvalidation_1-logloss:0.50593\n",
      "[2003]\tvalidation_0-logloss:0.39205\tvalidation_1-logloss:0.52966\n",
      "[2657]\tvalidation_0-logloss:0.01956\tvalidation_1-logloss:1.04136\n",
      "[337]\tvalidation_0-logloss:0.47792\tvalidation_1-logloss:0.50594\n",
      "[2004]\tvalidation_0-logloss:0.39203\tvalidation_1-logloss:0.52964\n",
      "[2658]\tvalidation_0-logloss:0.01955\tvalidation_1-logloss:1.04144\n",
      "[2005]\tvalidation_0-logloss:0.39201\tvalidation_1-logloss:0.52965\n",
      "[338]\tvalidation_0-logloss:0.47783\tvalidation_1-logloss:0.50595\n",
      "[339]\tvalidation_0-logloss:0.47775\tvalidation_1-logloss:0.50602\n",
      "[2006]\tvalidation_0-logloss:0.39198\tvalidation_1-logloss:0.52969\n",
      "[2659]\tvalidation_0-logloss:0.01954\tvalidation_1-logloss:1.04171[340]\tvalidation_0-logloss:0.47769\tvalidation_1-logloss:0.50600\n",
      "\n",
      "[2007]\tvalidation_0-logloss:0.39195\tvalidation_1-logloss:0.52969\n",
      "[341]\tvalidation_0-logloss:0.47761\tvalidation_1-logloss:0.50599\n",
      "[2660]\tvalidation_0-logloss:0.01953\tvalidation_1-logloss:1.04172\n",
      "[2008]\tvalidation_0-logloss:0.39192\tvalidation_1-logloss:0.52966\n",
      "[342]\tvalidation_0-logloss:0.47753\tvalidation_1-logloss:0.50601\n",
      "[2661]\tvalidation_0-logloss:0.01953\tvalidation_1-logloss:1.04178\n",
      "[2009]\tvalidation_0-logloss:0.39189\tvalidation_1-logloss:0.52969\n",
      "[343]\tvalidation_0-logloss:0.47737\tvalidation_1-logloss:0.50600\n",
      "[2010]\tvalidation_0-logloss:0.39187\tvalidation_1-logloss:0.52972[2662]\tvalidation_0-logloss:0.01952\tvalidation_1-logloss:1.04172\n",
      "\n",
      "[344]\tvalidation_0-logloss:0.47726\tvalidation_1-logloss:0.50600\n",
      "[2011]\tvalidation_0-logloss:0.39182\tvalidation_1-logloss:0.52977\n",
      "[2663]\tvalidation_0-logloss:0.01952\tvalidation_1-logloss:1.04162\n",
      "[345]\tvalidation_0-logloss:0.47713\tvalidation_1-logloss:0.50591\n",
      "[2012]\tvalidation_0-logloss:0.39180\tvalidation_1-logloss:0.52971\n",
      "[346]\tvalidation_0-logloss:0.47706\tvalidation_1-logloss:0.50593\n",
      "[2013]\tvalidation_0-logloss:0.39178\tvalidation_1-logloss:0.52979[2664]\tvalidation_0-logloss:0.01951\tvalidation_1-logloss:1.04169\n",
      "\n",
      "[2014]\tvalidation_0-logloss:0.39173\tvalidation_1-logloss:0.52979\n",
      "[347]\tvalidation_0-logloss:0.47702\tvalidation_1-logloss:0.50595\n",
      "[2665]\tvalidation_0-logloss:0.01950\tvalidation_1-logloss:1.04169\n",
      "[2015]\tvalidation_0-logloss:0.39167\tvalidation_1-logloss:0.52980\n",
      "[348]\tvalidation_0-logloss:0.47691\tvalidation_1-logloss:0.50597\n",
      "[2016]\tvalidation_0-logloss:0.39162\tvalidation_1-logloss:0.52986\n",
      "[349]\tvalidation_0-logloss:0.47684\tvalidation_1-logloss:0.50596\n",
      "[2666]\tvalidation_0-logloss:0.01950\tvalidation_1-logloss:1.04169\n",
      "[2017]\tvalidation_0-logloss:0.39158\tvalidation_1-logloss:0.52988\n",
      "[2667]\tvalidation_0-logloss:0.01950\tvalidation_1-logloss:1.04169[350]\tvalidation_0-logloss:0.47680\tvalidation_1-logloss:0.50595\n",
      "\n",
      "[2018]\tvalidation_0-logloss:0.39152\tvalidation_1-logloss:0.52991\n",
      "[2019]\tvalidation_0-logloss:0.39149\tvalidation_1-logloss:0.52997[351]\tvalidation_0-logloss:0.47673\tvalidation_1-logloss:0.50595\n",
      "\n",
      "[2668]\tvalidation_0-logloss:0.01949\tvalidation_1-logloss:1.04169\n",
      "[2020]\tvalidation_0-logloss:0.39145\tvalidation_1-logloss:0.52998\n",
      "[352]\tvalidation_0-logloss:0.47664\tvalidation_1-logloss:0.50595\n",
      "[2021]\tvalidation_0-logloss:0.39138\tvalidation_1-logloss:0.53001\n",
      "[2669]\tvalidation_0-logloss:0.01949\tvalidation_1-logloss:1.04178\n",
      "[353]\tvalidation_0-logloss:0.47655\tvalidation_1-logloss:0.50593\n",
      "[2670]\tvalidation_0-logloss:0.01948\tvalidation_1-logloss:1.04185\n",
      "[354]\tvalidation_0-logloss:0.47646\tvalidation_1-logloss:0.50597\n",
      "[2022]\tvalidation_0-logloss:0.39137\tvalidation_1-logloss:0.53002\n",
      "[355]\tvalidation_0-logloss:0.47638\tvalidation_1-logloss:0.50598\n",
      "[2671]\tvalidation_0-logloss:0.01947\tvalidation_1-logloss:1.04189\n",
      "[2023]\tvalidation_0-logloss:0.39134\tvalidation_1-logloss:0.52999\n",
      "[356]\tvalidation_0-logloss:0.47633\tvalidation_1-logloss:0.50600\n",
      "[2024]\tvalidation_0-logloss:0.39129\tvalidation_1-logloss:0.53002\n",
      "[2672]\tvalidation_0-logloss:0.01947\tvalidation_1-logloss:1.04192\n",
      "[357]\tvalidation_0-logloss:0.47622\tvalidation_1-logloss:0.50605\n",
      "[2025]\tvalidation_0-logloss:0.39127\tvalidation_1-logloss:0.53005\n",
      "[2673]\tvalidation_0-logloss:0.01946\tvalidation_1-logloss:1.04199\n",
      "[358]\tvalidation_0-logloss:0.47615\tvalidation_1-logloss:0.50602\n",
      "[2026]\tvalidation_0-logloss:0.39125\tvalidation_1-logloss:0.53007\n",
      "[359]\tvalidation_0-logloss:0.47606\tvalidation_1-logloss:0.50604\n",
      "[2027]\tvalidation_0-logloss:0.39122\tvalidation_1-logloss:0.53014\n",
      "[2674]\tvalidation_0-logloss:0.01946\tvalidation_1-logloss:1.04205\n",
      "[2028]\tvalidation_0-logloss:0.39118\tvalidation_1-logloss:0.53015\n",
      "[360]\tvalidation_0-logloss:0.47600\tvalidation_1-logloss:0.50603\n",
      "[2675]\tvalidation_0-logloss:0.01946\tvalidation_1-logloss:1.04208\n",
      "[2029]\tvalidation_0-logloss:0.39115\tvalidation_1-logloss:0.53018\n",
      "[361]\tvalidation_0-logloss:0.47596\tvalidation_1-logloss:0.50605\n",
      "[2676]\tvalidation_0-logloss:0.01945\tvalidation_1-logloss:1.04224\n",
      "[2030]\tvalidation_0-logloss:0.39111\tvalidation_1-logloss:0.53017\n",
      "[362]\tvalidation_0-logloss:0.47587\tvalidation_1-logloss:0.50607\n",
      "[2031]\tvalidation_0-logloss:0.39108\tvalidation_1-logloss:0.53018\n",
      "[2677]\tvalidation_0-logloss:0.01945\tvalidation_1-logloss:1.04224\n",
      "[2032]\tvalidation_0-logloss:0.39102\tvalidation_1-logloss:0.53023\n",
      "[363]\tvalidation_0-logloss:0.47581\tvalidation_1-logloss:0.50611\n",
      "[2033]\tvalidation_0-logloss:0.39099\tvalidation_1-logloss:0.53021\n",
      "[2678]\tvalidation_0-logloss:0.01943\tvalidation_1-logloss:1.04213\n",
      "[364]\tvalidation_0-logloss:0.47568\tvalidation_1-logloss:0.50609\n",
      "[2034]\tvalidation_0-logloss:0.39097\tvalidation_1-logloss:0.53019\n",
      "[365]\tvalidation_0-logloss:0.47562\tvalidation_1-logloss:0.50611\n",
      "[2679]\tvalidation_0-logloss:0.01943\tvalidation_1-logloss:1.04220[2035]\tvalidation_0-logloss:0.39094\tvalidation_1-logloss:0.53017\n",
      "\n",
      "[366]\tvalidation_0-logloss:0.47555\tvalidation_1-logloss:0.50620\n",
      "[2036]\tvalidation_0-logloss:0.39092\tvalidation_1-logloss:0.53020\n",
      "[2680]\tvalidation_0-logloss:0.01942\tvalidation_1-logloss:1.04243\n",
      "[2037]\tvalidation_0-logloss:0.39089\tvalidation_1-logloss:0.53020\n",
      "[367]\tvalidation_0-logloss:0.47546\tvalidation_1-logloss:0.50620\n",
      "[2038]\tvalidation_0-logloss:0.39084\tvalidation_1-logloss:0.53020\n",
      "[2681]\tvalidation_0-logloss:0.01942\tvalidation_1-logloss:1.04257[368]\tvalidation_0-logloss:0.47540\tvalidation_1-logloss:0.50619\n",
      "\n",
      "[369]\tvalidation_0-logloss:0.47534\tvalidation_1-logloss:0.50623\n",
      "[2039]\tvalidation_0-logloss:0.39081\tvalidation_1-logloss:0.53027\n",
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      "[2040]\tvalidation_0-logloss:0.39080\tvalidation_1-logloss:0.53024\n",
      "[370]\tvalidation_0-logloss:0.47530\tvalidation_1-logloss:0.50623\n",
      "[2683]\tvalidation_0-logloss:0.01941\tvalidation_1-logloss:1.04265\n",
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      "[371]\tvalidation_0-logloss:0.47521\tvalidation_1-logloss:0.50621\n",
      "[2684]\tvalidation_0-logloss:0.01941\tvalidation_1-logloss:1.04273\n",
      "[2043]\tvalidation_0-logloss:0.39070\tvalidation_1-logloss:0.53018\n",
      "[372]\tvalidation_0-logloss:0.47511\tvalidation_1-logloss:0.50619\n",
      "[2685]\tvalidation_0-logloss:0.01941\tvalidation_1-logloss:1.04269\n",
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      "[2045]\tvalidation_0-logloss:0.39062\tvalidation_1-logloss:0.53027\n",
      "[2686]\tvalidation_0-logloss:0.01940\tvalidation_1-logloss:1.04279\n",
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      "[2046]\tvalidation_0-logloss:0.39059\tvalidation_1-logloss:0.53023\n",
      "[2687]\tvalidation_0-logloss:0.01940\tvalidation_1-logloss:1.04284\n",
      "[375]\tvalidation_0-logloss:0.47490\tvalidation_1-logloss:0.50627\n",
      "[2047]\tvalidation_0-logloss:0.39053\tvalidation_1-logloss:0.53020\n",
      "[2688]\tvalidation_0-logloss:0.01939\tvalidation_1-logloss:1.04297\n",
      "[376]\tvalidation_0-logloss:0.47483\tvalidation_1-logloss:0.50630\n",
      "[2689]\tvalidation_0-logloss:0.01939\tvalidation_1-logloss:1.04294\n",
      "[2048]\tvalidation_0-logloss:0.39052\tvalidation_1-logloss:0.53025\n",
      "[377]\tvalidation_0-logloss:0.47478\tvalidation_1-logloss:0.50631\n",
      "[2690]\tvalidation_0-logloss:0.01939\tvalidation_1-logloss:1.04300\n",
      "[378]\tvalidation_0-logloss:0.47468\tvalidation_1-logloss:0.50626\n",
      "[2049]\tvalidation_0-logloss:0.39049\tvalidation_1-logloss:0.53030\n",
      "[2691]\tvalidation_0-logloss:0.01938\tvalidation_1-logloss:1.04306\n",
      "[379]\tvalidation_0-logloss:0.47462\tvalidation_1-logloss:0.50632\n",
      "[2050]\tvalidation_0-logloss:0.39046\tvalidation_1-logloss:0.53034\n",
      "[2692]\tvalidation_0-logloss:0.01938\tvalidation_1-logloss:1.04322\n",
      "[380]\tvalidation_0-logloss:0.47461\tvalidation_1-logloss:0.50633[2051]\tvalidation_0-logloss:0.39043\tvalidation_1-logloss:0.53038\n",
      "\n",
      "[2052]\tvalidation_0-logloss:0.39038\tvalidation_1-logloss:0.53041\n",
      "[2693]\tvalidation_0-logloss:0.01937\tvalidation_1-logloss:1.04314\n",
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      "[2053]\tvalidation_0-logloss:0.39035\tvalidation_1-logloss:0.53040\n",
      "[382]\tvalidation_0-logloss:0.47447\tvalidation_1-logloss:0.50646\n",
      "[2694]\tvalidation_0-logloss:0.01937\tvalidation_1-logloss:1.04311\n",
      "[2054]\tvalidation_0-logloss:0.39033\tvalidation_1-logloss:0.53041\n",
      "[383]\tvalidation_0-logloss:0.47439\tvalidation_1-logloss:0.50646\n",
      "[2055]\tvalidation_0-logloss:0.39028\tvalidation_1-logloss:0.53042\n",
      "[2695]\tvalidation_0-logloss:0.01936\tvalidation_1-logloss:1.04332\n",
      "[384]\tvalidation_0-logloss:0.47434\tvalidation_1-logloss:0.50651\n",
      "[2056]\tvalidation_0-logloss:0.39026\tvalidation_1-logloss:0.53046\n",
      "[2696]\tvalidation_0-logloss:0.01936\tvalidation_1-logloss:1.04334\n",
      "[2057]\tvalidation_0-logloss:0.39022\tvalidation_1-logloss:0.53052\n",
      "[385]\tvalidation_0-logloss:0.47424\tvalidation_1-logloss:0.50663\n",
      "[2058]\tvalidation_0-logloss:0.39020\tvalidation_1-logloss:0.53050\n",
      "[386]\tvalidation_0-logloss:0.47416\tvalidation_1-logloss:0.50664\n",
      "[2059]\tvalidation_0-logloss:0.39016\tvalidation_1-logloss:0.53055\n",
      "[387]\tvalidation_0-logloss:0.47408\tvalidation_1-logloss:0.50660\n",
      "[2697]\tvalidation_0-logloss:0.01935\tvalidation_1-logloss:1.04336\n",
      "[2060]\tvalidation_0-logloss:0.39011\tvalidation_1-logloss:0.53058\n",
      "[388]\tvalidation_0-logloss:0.47402\tvalidation_1-logloss:0.50663\n",
      "[2698]\tvalidation_0-logloss:0.01935\tvalidation_1-logloss:1.04347\n",
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      "[389]\tvalidation_0-logloss:0.47393\tvalidation_1-logloss:0.50666\n",
      "[2062]\tvalidation_0-logloss:0.39003\tvalidation_1-logloss:0.53056\n",
      "[2699]\tvalidation_0-logloss:0.01935\tvalidation_1-logloss:1.04353\n",
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      "[390]\tvalidation_0-logloss:0.47383\tvalidation_1-logloss:0.50665\n",
      "[2700]\tvalidation_0-logloss:0.01934\tvalidation_1-logloss:1.04363[2064]\tvalidation_0-logloss:0.38996\tvalidation_1-logloss:0.53062\n",
      "\n",
      "[391]\tvalidation_0-logloss:0.47378\tvalidation_1-logloss:0.50665\n",
      "[2065]\tvalidation_0-logloss:0.38992\tvalidation_1-logloss:0.53060\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2354]\tvalidation_0-logloss:0.38028\tvalidation_1-logloss:0.53469\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[680]\tvalidation_0-logloss:0.45565\tvalidation_1-logloss:0.50978\n",
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      "\n",
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      "\n",
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      "\n",
      "[703]\tvalidation_0-logloss:0.45444\tvalidation_1-logloss:0.51021\n",
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      "\n",
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      "\n",
      "[2432]\tvalidation_0-logloss:0.37769\tvalidation_1-logloss:0.53600\n",
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      "\n",
      "[2940]\tvalidation_0-logloss:0.01826\tvalidation_1-logloss:1.05710\n",
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      "\n",
      "\n",
      "[2945]\tvalidation_0-logloss:0.01825\tvalidation_1-logloss:1.05712\n",
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      "\n",
      "[2456]\tvalidation_0-logloss:0.37700\tvalidation_1-logloss:0.53631\n",
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      "\n",
      "[2956]\tvalidation_0-logloss:0.01819\tvalidation_1-logloss:1.05789\n",
      "[2464]\tvalidation_0-logloss:0.37674\tvalidation_1-logloss:0.53660\n",
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      "\n",
      "[2472]\tvalidation_0-logloss:0.37647\tvalidation_1-logloss:0.53677\n",
      "[741]\tvalidation_0-logloss:0.45260\tvalidation_1-logloss:0.51069\n",
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      "[2480]\tvalidation_0-logloss:0.37626\tvalidation_1-logloss:0.53693\n",
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      "[2493]\tvalidation_0-logloss:0.37581\tvalidation_1-logloss:0.53712\n",
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      "\n",
      "[2497]\tvalidation_0-logloss:0.37563\tvalidation_1-logloss:0.53701\n",
      "[2498]\tvalidation_0-logloss:0.37559\tvalidation_1-logloss:0.53700\n",
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      "[2981]\tvalidation_0-logloss:0.01810\tvalidation_1-logloss:1.05923\n",
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      "[766]\tvalidation_0-logloss:0.45145\tvalidation_1-logloss:0.51095\n",
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      "[2982]\tvalidation_0-logloss:0.01809\tvalidation_1-logloss:1.05934[767]\tvalidation_0-logloss:0.45140\tvalidation_1-logloss:0.51092\n",
      "\n",
      "[2505]\tvalidation_0-logloss:0.37541\tvalidation_1-logloss:0.53713\n",
      "[768]\tvalidation_0-logloss:0.45139\tvalidation_1-logloss:0.51093\n",
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      "[2984]\tvalidation_0-logloss:0.01808\tvalidation_1-logloss:1.05948\n",
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      "\n",
      "[773]\tvalidation_0-logloss:0.45107\tvalidation_1-logloss:0.51103\n",
      "[2512]\tvalidation_0-logloss:0.37519\tvalidation_1-logloss:0.53714\n",
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      "[2988]\tvalidation_0-logloss:0.01807\tvalidation_1-logloss:1.05985\n",
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      "[2990]\tvalidation_0-logloss:0.01806\tvalidation_1-logloss:1.06009\n",
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      "[2518]\tvalidation_0-logloss:0.37499\tvalidation_1-logloss:0.53717[779]\tvalidation_0-logloss:0.45075\tvalidation_1-logloss:0.51103\n",
      "\n",
      "[2991]\tvalidation_0-logloss:0.01805\tvalidation_1-logloss:1.06023\n",
      "[780]\tvalidation_0-logloss:0.45070\tvalidation_1-logloss:0.51105\n",
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      "[2992]\tvalidation_0-logloss:0.01805\tvalidation_1-logloss:1.06027\n",
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      "[2994]\tvalidation_0-logloss:0.01804\tvalidation_1-logloss:1.06029\n",
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      "[2995]\tvalidation_0-logloss:0.01804\tvalidation_1-logloss:1.06032\n",
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      "[2996]\tvalidation_0-logloss:0.01804\tvalidation_1-logloss:1.06029\n",
      "[787]\tvalidation_0-logloss:0.45035\tvalidation_1-logloss:0.51113\n",
      "[2526]\tvalidation_0-logloss:0.37481\tvalidation_1-logloss:0.53731\n",
      "[2997]\tvalidation_0-logloss:0.01803\tvalidation_1-logloss:1.06041\n",
      "[788]\tvalidation_0-logloss:0.45030\tvalidation_1-logloss:0.51115[2527]\tvalidation_0-logloss:0.37477\tvalidation_1-logloss:0.53731\n",
      "\n",
      "[2998]\tvalidation_0-logloss:0.01803\tvalidation_1-logloss:1.06044\n",
      "[2528]\tvalidation_0-logloss:0.37473\tvalidation_1-logloss:0.53730\n",
      "[789]\tvalidation_0-logloss:0.45025\tvalidation_1-logloss:0.51114\n",
      "[2529]\tvalidation_0-logloss:0.37468\tvalidation_1-logloss:0.53733\n",
      "[2999]\tvalidation_0-logloss:0.01803\tvalidation_1-logloss:1.06060\n",
      "[2530]\tvalidation_0-logloss:0.37465\tvalidation_1-logloss:0.53735\n",
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      "[2531]\tvalidation_0-logloss:0.37463\tvalidation_1-logloss:0.53736\n",
      "[3000]\tvalidation_0-logloss:0.01802\tvalidation_1-logloss:1.06078\n",
      "[791]\tvalidation_0-logloss:0.45015\tvalidation_1-logloss:0.51113\n",
      "[2532]\tvalidation_0-logloss:0.37458\tvalidation_1-logloss:0.53742\n",
      "[792]\tvalidation_0-logloss:0.45010\tvalidation_1-logloss:0.51112\n",
      "[3001]\tvalidation_0-logloss:0.01802\tvalidation_1-logloss:1.06082\n",
      "[2533]\tvalidation_0-logloss:0.37455\tvalidation_1-logloss:0.53746\n",
      "[793]\tvalidation_0-logloss:0.45007\tvalidation_1-logloss:0.51107\n",
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      "[2535]\tvalidation_0-logloss:0.37451\tvalidation_1-logloss:0.53747\n",
      "[3002]\tvalidation_0-logloss:0.01802\tvalidation_1-logloss:1.06081\n",
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      "[795]\tvalidation_0-logloss:0.44996\tvalidation_1-logloss:0.51115\n",
      "[3003]\tvalidation_0-logloss:0.01801\tvalidation_1-logloss:1.06082\n",
      "[2537]\tvalidation_0-logloss:0.37446\tvalidation_1-logloss:0.53739\n",
      "[796]\tvalidation_0-logloss:0.44994\tvalidation_1-logloss:0.51112\n",
      "[2538]\tvalidation_0-logloss:0.37444\tvalidation_1-logloss:0.53742[3004]\tvalidation_0-logloss:0.01801\tvalidation_1-logloss:1.06092\n",
      "\n",
      "[797]\tvalidation_0-logloss:0.44988\tvalidation_1-logloss:0.51110\n",
      "[2539]\tvalidation_0-logloss:0.37440\tvalidation_1-logloss:0.53740\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2572]\tvalidation_0-logloss:0.37333\tvalidation_1-logloss:0.53776\n",
      "[824]\tvalidation_0-logloss:0.44861\tvalidation_1-logloss:0.51163\n",
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      "\n",
      "[826]\tvalidation_0-logloss:0.44853\tvalidation_1-logloss:0.51166\n",
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      "\n",
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      "\n",
      "[2580]\tvalidation_0-logloss:0.37309\tvalidation_1-logloss:0.53789\n",
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      "\n",
      "[3035]\tvalidation_0-logloss:0.01791\tvalidation_1-logloss:1.06251\n",
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      "\n",
      "[2597]\tvalidation_0-logloss:0.37261\tvalidation_1-logloss:0.53793\n",
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      "\n",
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      "\n",
      "[2634]\tvalidation_0-logloss:0.37144\tvalidation_1-logloss:0.53878\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3092]\tvalidation_0-logloss:0.01770\tvalidation_1-logloss:1.06491\n",
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      "\n",
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      "\n",
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      "\n",
      "[3104]\tvalidation_0-logloss:0.01766\tvalidation_1-logloss:1.06557\n",
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      "\n",
      "[3107]\tvalidation_0-logloss:0.01765\tvalidation_1-logloss:1.06566\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3183]\tvalidation_0-logloss:0.01736\tvalidation_1-logloss:1.06963\n",
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      "\n",
      "[3193]\tvalidation_0-logloss:0.01734\tvalidation_1-logloss:1.06989\n",
      "[2838]\tvalidation_0-logloss:0.36532\tvalidation_1-logloss:0.54198\n",
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      "\n",
      "[2840]\tvalidation_0-logloss:0.36523\tvalidation_1-logloss:0.54203\n",
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      "\n",
      "[3200]\tvalidation_0-logloss:0.01732\tvalidation_1-logloss:1.07021\n",
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      "\n",
      "[1066]\tvalidation_0-logloss:0.43651\tvalidation_1-logloss:0.51469\n",
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      "\n",
      "[2855]\tvalidation_0-logloss:0.36476\tvalidation_1-logloss:0.54236\n",
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      "\n",
      "[3208]\tvalidation_0-logloss:0.01728\tvalidation_1-logloss:1.07053\n",
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      "\n",
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      "\n",
      "[1077]\tvalidation_0-logloss:0.43600\tvalidation_1-logloss:0.51477\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[1108]\tvalidation_0-logloss:0.43472\tvalidation_1-logloss:0.51501\n",
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      "\n",
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      "\n",
      "[2912]\tvalidation_0-logloss:0.36301\tvalidation_1-logloss:0.54332\n",
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      "\n",
      "[1122]\tvalidation_0-logloss:0.43416\tvalidation_1-logloss:0.51542\n",
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      "\n",
      "[3251]\tvalidation_0-logloss:0.01717\tvalidation_1-logloss:1.07220\n",
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      "\n",
      "[3255]\tvalidation_0-logloss:0.01716\tvalidation_1-logloss:1.07228\n",
      "[2933]\tvalidation_0-logloss:0.36237\tvalidation_1-logloss:0.54354\n",
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      "\n",
      "[1143]\tvalidation_0-logloss:0.43326\tvalidation_1-logloss:0.51561\n",
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      "\n",
      "[3266]\tvalidation_0-logloss:0.01713\tvalidation_1-logloss:1.07271\n",
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      "\n",
      "[1148]\tvalidation_0-logloss:0.43301\tvalidation_1-logloss:0.51552\n",
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      "\n",
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      "\n",
      "[3285]\tvalidation_0-logloss:0.01707\tvalidation_1-logloss:1.07351[2971]\tvalidation_0-logloss:0.36129\tvalidation_1-logloss:0.54408\n",
      "\n",
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      "\n",
      "[1188]\tvalidation_0-logloss:0.43128\tvalidation_1-logloss:0.51611\n",
      "[2989]\tvalidation_0-logloss:0.36076\tvalidation_1-logloss:0.54432\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3035]\tvalidation_0-logloss:0.35943\tvalidation_1-logloss:0.54518\n",
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      "[3332]\tvalidation_0-logloss:0.01691\tvalidation_1-logloss:1.07531\n",
      "\n",
      "[3043]\tvalidation_0-logloss:0.35915\tvalidation_1-logloss:0.54521\n",
      "[1236]\tvalidation_0-logloss:0.42923\tvalidation_1-logloss:0.51647\n",
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      "\n",
      "[3338]\tvalidation_0-logloss:0.01689\tvalidation_1-logloss:1.07556\n",
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      "\n",
      "[1244]\tvalidation_0-logloss:0.42893\tvalidation_1-logloss:0.51654\n",
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      "\n",
      "[1251]\tvalidation_0-logloss:0.42862\tvalidation_1-logloss:0.51675\n",
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      "\n",
      "[3348]\tvalidation_0-logloss:0.01687\tvalidation_1-logloss:1.07636\n",
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      "\n",
      "[1260]\tvalidation_0-logloss:0.42824\tvalidation_1-logloss:0.51690\n",
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      "\n",
      "[1267]\tvalidation_0-logloss:0.42793\tvalidation_1-logloss:0.51699\n",
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      "\n",
      "[3362]\tvalidation_0-logloss:0.01683\tvalidation_1-logloss:1.07686\n",
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      "\n",
      "[3094]\tvalidation_0-logloss:0.35772\tvalidation_1-logloss:0.54583\n",
      "[1280]\tvalidation_0-logloss:0.42744\tvalidation_1-logloss:0.51717\n",
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      "\n",
      "[1287]\tvalidation_0-logloss:0.42719\tvalidation_1-logloss:0.51733\n",
      "[3372]\tvalidation_0-logloss:0.01680\tvalidation_1-logloss:1.07718\n",
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      "\n",
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      "\n",
      "[3383]\tvalidation_0-logloss:0.01677\tvalidation_1-logloss:1.07771\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[3472]\tvalidation_0-logloss:0.34793\tvalidation_1-logloss:0.55123[1600]\tvalidation_0-logloss:0.41578\tvalidation_1-logloss:0.52071\n",
      "\n",
      "[3473]\tvalidation_0-logloss:0.34790\tvalidation_1-logloss:0.55118\n",
      "[1601]\tvalidation_0-logloss:0.41570\tvalidation_1-logloss:0.52080\n",
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      "\n",
      "[1607]\tvalidation_0-logloss:0.41547\tvalidation_1-logloss:0.52089\n",
      "[3620]\tvalidation_0-logloss:0.01611\tvalidation_1-logloss:1.08717\n",
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      "\n",
      "[1624]\tvalidation_0-logloss:0.41485\tvalidation_1-logloss:0.52105\n",
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      "\n",
      "[3508]\tvalidation_0-logloss:0.34704\tvalidation_1-logloss:0.55168\n",
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      "\n",
      "[3515]\tvalidation_0-logloss:0.34687\tvalidation_1-logloss:0.55175\n",
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      "\n",
      "[1639]\tvalidation_0-logloss:0.41430\tvalidation_1-logloss:0.52123\n",
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      "\n",
      "[1647]\tvalidation_0-logloss:0.41397\tvalidation_1-logloss:0.52148\n",
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      "\n",
      "[1649]\tvalidation_0-logloss:0.41391\tvalidation_1-logloss:0.52155\n",
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      "\n",
      "[3536]\tvalidation_0-logloss:0.34637\tvalidation_1-logloss:0.55179[3655]\tvalidation_0-logloss:0.01601\tvalidation_1-logloss:1.08906\n",
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      "\n",
      "[3537]\tvalidation_0-logloss:0.34635\tvalidation_1-logloss:0.55174\n",
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      "\n",
      "[1661]\tvalidation_0-logloss:0.41344\tvalidation_1-logloss:0.52171\n",
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      "\n",
      "[3557]\tvalidation_0-logloss:0.34595\tvalidation_1-logloss:0.55206\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[3757]\tvalidation_0-logloss:0.34079\tvalidation_1-logloss:0.55458\n",
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      "[3809]\tvalidation_0-logloss:0.01564\tvalidation_1-logloss:1.09468\n",
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      "[3810]\tvalidation_0-logloss:0.01564\tvalidation_1-logloss:1.09475[3766]\tvalidation_0-logloss:0.34058\tvalidation_1-logloss:0.55467\n",
      "\n",
      "[3767]\tvalidation_0-logloss:0.34056\tvalidation_1-logloss:0.55458\n",
      "[3811]\tvalidation_0-logloss:0.01564\tvalidation_1-logloss:1.09481\n",
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      "\n",
      "[1854]\tvalidation_0-logloss:0.40711\tvalidation_1-logloss:0.52369\n",
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      "\n",
      "[1868]\tvalidation_0-logloss:0.40671\tvalidation_1-logloss:0.52385\n",
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      "\n",
      "[3794]\tvalidation_0-logloss:0.33998\tvalidation_1-logloss:0.55490\n",
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      "\n",
      "[1871]\tvalidation_0-logloss:0.40664\tvalidation_1-logloss:0.52394\n",
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      "\n",
      "[3803]\tvalidation_0-logloss:0.33975\tvalidation_1-logloss:0.55493\n",
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      "[3850]\tvalidation_0-logloss:0.01556\tvalidation_1-logloss:1.09592[3824]\tvalidation_0-logloss:0.33929\tvalidation_1-logloss:0.55505\n",
      "[1893]\tvalidation_0-logloss:0.40595\tvalidation_1-logloss:0.52424\n",
      "\n",
      "[3825]\tvalidation_0-logloss:0.33926\tvalidation_1-logloss:0.55507\n",
      "[1894]\tvalidation_0-logloss:0.40591\tvalidation_1-logloss:0.52426\n",
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      "\n",
      "[3831]\tvalidation_0-logloss:0.33911\tvalidation_1-logloss:0.55522\n",
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      "\n",
      "[3844]\tvalidation_0-logloss:0.33874\tvalidation_1-logloss:0.55548\n",
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      "\n",
      "[3870]\tvalidation_0-logloss:0.01551\tvalidation_1-logloss:1.09682\n",
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      "\n",
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      "\n",
      "[3873]\tvalidation_0-logloss:0.01550\tvalidation_1-logloss:1.09696[1924]\tvalidation_0-logloss:0.40497\tvalidation_1-logloss:0.52468\n",
      "\n",
      "[3860]\tvalidation_0-logloss:0.33841\tvalidation_1-logloss:0.55567\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "[3919]\tvalidation_0-logloss:0.01541\tvalidation_1-logloss:1.09839\n",
      "[1983]\tvalidation_0-logloss:0.40323\tvalidation_1-logloss:0.52484\n",
      "[3924]\tvalidation_0-logloss:0.33688\tvalidation_1-logloss:0.55634\n",
      "[3925]\tvalidation_0-logloss:0.33686\tvalidation_1-logloss:0.55642\n",
      "[1984]\tvalidation_0-logloss:0.40319\tvalidation_1-logloss:0.52486\n",
      "[3920]\tvalidation_0-logloss:0.01541\tvalidation_1-logloss:1.09839\n",
      "[3926]\tvalidation_0-logloss:0.33684\tvalidation_1-logloss:0.55636\n",
      "[1985]\tvalidation_0-logloss:0.40316\tvalidation_1-logloss:0.52485\n",
      "[3921]\tvalidation_0-logloss:0.01541\tvalidation_1-logloss:1.09843[3927]\tvalidation_0-logloss:0.33680\tvalidation_1-logloss:0.55637\n",
      "\n",
      "[1986]\tvalidation_0-logloss:0.40311\tvalidation_1-logloss:0.52486\n",
      "[3928]\tvalidation_0-logloss:0.33678\tvalidation_1-logloss:0.55639\n",
      "[3922]\tvalidation_0-logloss:0.01540\tvalidation_1-logloss:1.09840\n",
      "[1987]\tvalidation_0-logloss:0.40309\tvalidation_1-logloss:0.52484\n",
      "[3929]\tvalidation_0-logloss:0.33676\tvalidation_1-logloss:0.55640\n",
      "[1988]\tvalidation_0-logloss:0.40308\tvalidation_1-logloss:0.52485\n",
      "[3930]\tvalidation_0-logloss:0.33675\tvalidation_1-logloss:0.55641\n",
      "[3923]\tvalidation_0-logloss:0.01540\tvalidation_1-logloss:1.09847\n",
      "[1989]\tvalidation_0-logloss:0.40306\tvalidation_1-logloss:0.52483[3931]\tvalidation_0-logloss:0.33673\tvalidation_1-logloss:0.55650\n",
      "\n",
      "[3932]\tvalidation_0-logloss:0.33671\tvalidation_1-logloss:0.55650\n",
      "[3924]\tvalidation_0-logloss:0.01540\tvalidation_1-logloss:1.09847\n",
      "[1990]\tvalidation_0-logloss:0.40304\tvalidation_1-logloss:0.52485\n",
      "[3933]\tvalidation_0-logloss:0.33670\tvalidation_1-logloss:0.55650\n",
      "[1991]\tvalidation_0-logloss:0.40300\tvalidation_1-logloss:0.52488\n",
      "[3925]\tvalidation_0-logloss:0.01540\tvalidation_1-logloss:1.09852[3934]\tvalidation_0-logloss:0.33668\tvalidation_1-logloss:0.55648\n",
      "\n",
      "[3935]\tvalidation_0-logloss:0.33667\tvalidation_1-logloss:0.55652\n",
      "[1992]\tvalidation_0-logloss:0.40298\tvalidation_1-logloss:0.52489\n",
      "[3936]\tvalidation_0-logloss:0.33664\tvalidation_1-logloss:0.55653\n",
      "[3926]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09859\n",
      "[1993]\tvalidation_0-logloss:0.40294\tvalidation_1-logloss:0.52486\n",
      "[3937]\tvalidation_0-logloss:0.33663\tvalidation_1-logloss:0.55646\n",
      "[1994]\tvalidation_0-logloss:0.40291\tvalidation_1-logloss:0.52486\n",
      "[3927]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09856\n",
      "[3938]\tvalidation_0-logloss:0.33661\tvalidation_1-logloss:0.55642\n",
      "[1995]\tvalidation_0-logloss:0.40285\tvalidation_1-logloss:0.52488[3928]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09856\n",
      "\n",
      "[3939]\tvalidation_0-logloss:0.33659\tvalidation_1-logloss:0.55643\n",
      "[1996]\tvalidation_0-logloss:0.40283\tvalidation_1-logloss:0.52491\n",
      "[3940]\tvalidation_0-logloss:0.33656\tvalidation_1-logloss:0.55648\n",
      "[3929]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09858\n",
      "[1997]\tvalidation_0-logloss:0.40280\tvalidation_1-logloss:0.52492\n",
      "[3941]\tvalidation_0-logloss:0.33654\tvalidation_1-logloss:0.55647\n",
      "[3930]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09860\n",
      "[1998]\tvalidation_0-logloss:0.40276\tvalidation_1-logloss:0.52493\n",
      "[3931]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09860[3942]\tvalidation_0-logloss:0.33653\tvalidation_1-logloss:0.55651\n",
      "\n",
      "[1999]\tvalidation_0-logloss:0.40274\tvalidation_1-logloss:0.52496\n",
      "[3943]\tvalidation_0-logloss:0.33651\tvalidation_1-logloss:0.55654\n",
      "[3932]\tvalidation_0-logloss:0.01539\tvalidation_1-logloss:1.09864\n",
      "[2000]\tvalidation_0-logloss:0.40271\tvalidation_1-logloss:0.52497\n",
      "[3944]\tvalidation_0-logloss:0.33648\tvalidation_1-logloss:0.55665\n",
      "[2001]\tvalidation_0-logloss:0.40268\tvalidation_1-logloss:0.52500\n",
      "[3933]\tvalidation_0-logloss:0.01538\tvalidation_1-logloss:1.09861\n",
      "[3945]\tvalidation_0-logloss:0.33644\tvalidation_1-logloss:0.55668\n",
      "[2002]\tvalidation_0-logloss:0.40265\tvalidation_1-logloss:0.52502\n",
      "[3946]\tvalidation_0-logloss:0.33642\tvalidation_1-logloss:0.55668\n",
      "[3934]\tvalidation_0-logloss:0.01538\tvalidation_1-logloss:1.09857\n",
      "[2003]\tvalidation_0-logloss:0.40264\tvalidation_1-logloss:0.52502\n",
      "[3947]\tvalidation_0-logloss:0.33639\tvalidation_1-logloss:0.55666\n",
      "[2004]\tvalidation_0-logloss:0.40257\tvalidation_1-logloss:0.52503\n",
      "[3948]\tvalidation_0-logloss:0.33638\tvalidation_1-logloss:0.55664\n",
      "[3935]\tvalidation_0-logloss:0.01538\tvalidation_1-logloss:1.09866\n",
      "[3949]\tvalidation_0-logloss:0.33635\tvalidation_1-logloss:0.55664\n",
      "[3936]\tvalidation_0-logloss:0.01538\tvalidation_1-logloss:1.09862\n",
      "[2005]\tvalidation_0-logloss:0.40253\tvalidation_1-logloss:0.52504\n",
      "[3950]\tvalidation_0-logloss:0.33632\tvalidation_1-logloss:0.55661\n",
      "[3937]\tvalidation_0-logloss:0.01538\tvalidation_1-logloss:1.09862\n",
      "[2006]\tvalidation_0-logloss:0.40248\tvalidation_1-logloss:0.52505\n",
      "[3951]\tvalidation_0-logloss:0.33631\tvalidation_1-logloss:0.55659\n",
      "[3952]\tvalidation_0-logloss:0.33629\tvalidation_1-logloss:0.55658[3938]\tvalidation_0-logloss:0.01537\tvalidation_1-logloss:1.09867\n",
      "\n",
      "[2007]\tvalidation_0-logloss:0.40245\tvalidation_1-logloss:0.52506\n",
      "[3953]\tvalidation_0-logloss:0.33628\tvalidation_1-logloss:0.55663\n",
      "[2008]\tvalidation_0-logloss:0.40242\tvalidation_1-logloss:0.52508\n",
      "[3939]\tvalidation_0-logloss:0.01537\tvalidation_1-logloss:1.09863\n",
      "[3954]\tvalidation_0-logloss:0.33624\tvalidation_1-logloss:0.55675\n",
      "[2009]\tvalidation_0-logloss:0.40237\tvalidation_1-logloss:0.52504\n",
      "[3940]\tvalidation_0-logloss:0.01537\tvalidation_1-logloss:1.09870\n",
      "[3955]\tvalidation_0-logloss:0.33622\tvalidation_1-logloss:0.55673\n",
      "[2010]\tvalidation_0-logloss:0.40234\tvalidation_1-logloss:0.52504\n",
      "[3956]\tvalidation_0-logloss:0.33621\tvalidation_1-logloss:0.55678\n",
      "[3941]\tvalidation_0-logloss:0.01537\tvalidation_1-logloss:1.09882[2011]\tvalidation_0-logloss:0.40230\tvalidation_1-logloss:0.52508\n",
      "\n",
      "[3957]\tvalidation_0-logloss:0.33618\tvalidation_1-logloss:0.55673\n",
      "[2012]\tvalidation_0-logloss:0.40227\tvalidation_1-logloss:0.52511\n",
      "[3958]\tvalidation_0-logloss:0.33615\tvalidation_1-logloss:0.55673\n",
      "[3942]\tvalidation_0-logloss:0.01536\tvalidation_1-logloss:1.09888\n",
      "[3959]\tvalidation_0-logloss:0.33613\tvalidation_1-logloss:0.55671\n",
      "[2013]\tvalidation_0-logloss:0.40224\tvalidation_1-logloss:0.52510\n",
      "[3943]\tvalidation_0-logloss:0.01536\tvalidation_1-logloss:1.09894\n",
      "[3960]\tvalidation_0-logloss:0.33611\tvalidation_1-logloss:0.55676\n",
      "[2014]\tvalidation_0-logloss:0.40223\tvalidation_1-logloss:0.52511\n",
      "[3961]\tvalidation_0-logloss:0.33609\tvalidation_1-logloss:0.55684\n",
      "[3944]\tvalidation_0-logloss:0.01536\tvalidation_1-logloss:1.09897\n",
      "[3962]\tvalidation_0-logloss:0.33606\tvalidation_1-logloss:0.55688\n",
      "[2015]\tvalidation_0-logloss:0.40221\tvalidation_1-logloss:0.52513\n",
      "[3945]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09904\n",
      "[3963]\tvalidation_0-logloss:0.33603\tvalidation_1-logloss:0.55691\n",
      "[2016]\tvalidation_0-logloss:0.40220\tvalidation_1-logloss:0.52514\n",
      "[3964]\tvalidation_0-logloss:0.33601\tvalidation_1-logloss:0.55688\n",
      "[3946]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09906\n",
      "[2017]\tvalidation_0-logloss:0.40218\tvalidation_1-logloss:0.52512\n",
      "[3965]\tvalidation_0-logloss:0.33598\tvalidation_1-logloss:0.55693\n",
      "[3947]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09910\n",
      "[2018]\tvalidation_0-logloss:0.40214\tvalidation_1-logloss:0.52513\n",
      "[3966]\tvalidation_0-logloss:0.33595\tvalidation_1-logloss:0.55696\n",
      "[3967]\tvalidation_0-logloss:0.33591\tvalidation_1-logloss:0.55704\n",
      "[2019]\tvalidation_0-logloss:0.40213\tvalidation_1-logloss:0.52512\n",
      "[3948]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09910\n",
      "[3968]\tvalidation_0-logloss:0.33588\tvalidation_1-logloss:0.55702\n",
      "[2020]\tvalidation_0-logloss:0.40210\tvalidation_1-logloss:0.52512\n",
      "[3969]\tvalidation_0-logloss:0.33585\tvalidation_1-logloss:0.55702[2021]\tvalidation_0-logloss:0.40205\tvalidation_1-logloss:0.52515\n",
      "\n",
      "[3949]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09913\n",
      "[2022]\tvalidation_0-logloss:0.40201\tvalidation_1-logloss:0.52515\n",
      "[3970]\tvalidation_0-logloss:0.33583\tvalidation_1-logloss:0.55704\n",
      "[2023]\tvalidation_0-logloss:0.40196\tvalidation_1-logloss:0.52518\n",
      "[3971]\tvalidation_0-logloss:0.33583\tvalidation_1-logloss:0.55707\n",
      "[3950]\tvalidation_0-logloss:0.01535\tvalidation_1-logloss:1.09918\n",
      "[3972]\tvalidation_0-logloss:0.33581\tvalidation_1-logloss:0.55705\n",
      "[2024]\tvalidation_0-logloss:0.40190\tvalidation_1-logloss:0.52516\n",
      "[3951]\tvalidation_0-logloss:0.01534\tvalidation_1-logloss:1.09919\n",
      "[3973]\tvalidation_0-logloss:0.33578\tvalidation_1-logloss:0.55709\n",
      "[2025]\tvalidation_0-logloss:0.40187\tvalidation_1-logloss:0.52513\n",
      "[3974]\tvalidation_0-logloss:0.33576\tvalidation_1-logloss:0.55712[2026]\tvalidation_0-logloss:0.40184\tvalidation_1-logloss:0.52516\n",
      "\n",
      "[3952]\tvalidation_0-logloss:0.01534\tvalidation_1-logloss:1.09919\n",
      "[3975]\tvalidation_0-logloss:0.33574\tvalidation_1-logloss:0.55717\n",
      "[2027]\tvalidation_0-logloss:0.40181\tvalidation_1-logloss:0.52516\n",
      "[3953]\tvalidation_0-logloss:0.01534\tvalidation_1-logloss:1.09913\n",
      "[3976]\tvalidation_0-logloss:0.33572\tvalidation_1-logloss:0.55717\n",
      "[2028]\tvalidation_0-logloss:0.40179\tvalidation_1-logloss:0.52516\n",
      "[3977]\tvalidation_0-logloss:0.33570\tvalidation_1-logloss:0.55721\n",
      "[2029]\tvalidation_0-logloss:0.40176\tvalidation_1-logloss:0.52517\n",
      "[3954]\tvalidation_0-logloss:0.01534\tvalidation_1-logloss:1.09912\n",
      "[3978]\tvalidation_0-logloss:0.33569\tvalidation_1-logloss:0.55722\n",
      "[2030]\tvalidation_0-logloss:0.40173\tvalidation_1-logloss:0.52520\n",
      "[3979]\tvalidation_0-logloss:0.33567\tvalidation_1-logloss:0.55722\n",
      "[3955]\tvalidation_0-logloss:0.01534\tvalidation_1-logloss:1.09918\n",
      "[3980]\tvalidation_0-logloss:0.33565\tvalidation_1-logloss:0.55723\n",
      "[2031]\tvalidation_0-logloss:0.40171\tvalidation_1-logloss:0.52520\n",
      "[3956]\tvalidation_0-logloss:0.01533\tvalidation_1-logloss:1.09917[3981]\tvalidation_0-logloss:0.33565\tvalidation_1-logloss:0.55727\n",
      "[2032]\tvalidation_0-logloss:0.40169\tvalidation_1-logloss:0.52520\n",
      "\n",
      "[3982]\tvalidation_0-logloss:0.33563\tvalidation_1-logloss:0.55723\n",
      "[2033]\tvalidation_0-logloss:0.40163\tvalidation_1-logloss:0.52523\n",
      "[3957]\tvalidation_0-logloss:0.01533\tvalidation_1-logloss:1.09920\n",
      "[3983]\tvalidation_0-logloss:0.33561\tvalidation_1-logloss:0.55725\n",
      "[2034]\tvalidation_0-logloss:0.40161\tvalidation_1-logloss:0.52523\n",
      "[3984]\tvalidation_0-logloss:0.33557\tvalidation_1-logloss:0.55729\n",
      "[3958]\tvalidation_0-logloss:0.01533\tvalidation_1-logloss:1.09923\n",
      "[2035]\tvalidation_0-logloss:0.40157\tvalidation_1-logloss:0.52525\n",
      "[3985]\tvalidation_0-logloss:0.33556\tvalidation_1-logloss:0.55728\n",
      "[2036]\tvalidation_0-logloss:0.40154\tvalidation_1-logloss:0.52528\n",
      "[3959]\tvalidation_0-logloss:0.01533\tvalidation_1-logloss:1.09928\n",
      "[3986]\tvalidation_0-logloss:0.33555\tvalidation_1-logloss:0.55731\n",
      "[2037]\tvalidation_0-logloss:0.40152\tvalidation_1-logloss:0.52530\n",
      "[3987]\tvalidation_0-logloss:0.33552\tvalidation_1-logloss:0.55726\n",
      "[3960]\tvalidation_0-logloss:0.01533\tvalidation_1-logloss:1.09933\n",
      "[3988]\tvalidation_0-logloss:0.33551\tvalidation_1-logloss:0.55726\n",
      "[2038]\tvalidation_0-logloss:0.40150\tvalidation_1-logloss:0.52529\n",
      "[3989]\tvalidation_0-logloss:0.33547\tvalidation_1-logloss:0.55731[3961]\tvalidation_0-logloss:0.01532\tvalidation_1-logloss:1.09935\n",
      "\n",
      "[2039]\tvalidation_0-logloss:0.40149\tvalidation_1-logloss:0.52529\n",
      "[3990]\tvalidation_0-logloss:0.33545\tvalidation_1-logloss:0.55729\n",
      "[3962]\tvalidation_0-logloss:0.01532\tvalidation_1-logloss:1.09940\n",
      "[3991]\tvalidation_0-logloss:0.33544\tvalidation_1-logloss:0.55728\n",
      "[2040]\tvalidation_0-logloss:0.40147\tvalidation_1-logloss:0.52532\n",
      "[3992]\tvalidation_0-logloss:0.33542\tvalidation_1-logloss:0.55731\n",
      "[3963]\tvalidation_0-logloss:0.01532\tvalidation_1-logloss:1.09941\n",
      "[2041]\tvalidation_0-logloss:0.40140\tvalidation_1-logloss:0.52534\n",
      "[3993]\tvalidation_0-logloss:0.33539\tvalidation_1-logloss:0.55731\n",
      "[2042]\tvalidation_0-logloss:0.40139\tvalidation_1-logloss:0.52533\n",
      "[3964]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09950\n",
      "[3994]\tvalidation_0-logloss:0.33537\tvalidation_1-logloss:0.55731\n",
      "[3965]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09950\n",
      "[2043]\tvalidation_0-logloss:0.40139\tvalidation_1-logloss:0.52534\n",
      "[3995]\tvalidation_0-logloss:0.33535\tvalidation_1-logloss:0.55733\n",
      "[2044]\tvalidation_0-logloss:0.40135\tvalidation_1-logloss:0.52532\n",
      "[3996]\tvalidation_0-logloss:0.33533\tvalidation_1-logloss:0.55736\n",
      "[3966]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09957\n",
      "[3997]\tvalidation_0-logloss:0.33531\tvalidation_1-logloss:0.55740\n",
      "[2045]\tvalidation_0-logloss:0.40131\tvalidation_1-logloss:0.52533\n",
      "[3998]\tvalidation_0-logloss:0.33529\tvalidation_1-logloss:0.55746\n",
      "[3967]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09960\n",
      "[2046]\tvalidation_0-logloss:0.40126\tvalidation_1-logloss:0.52533\n",
      "[3999]\tvalidation_0-logloss:0.33526\tvalidation_1-logloss:0.55752\n",
      "[2047]\tvalidation_0-logloss:0.40123\tvalidation_1-logloss:0.52540\n",
      "[4000]\tvalidation_0-logloss:0.33524\tvalidation_1-logloss:0.55749\n",
      "[3968]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09965\n",
      "[2048]\tvalidation_0-logloss:0.40122\tvalidation_1-logloss:0.52541\n",
      "[4001]\tvalidation_0-logloss:0.33522\tvalidation_1-logloss:0.55749\n",
      "[3969]\tvalidation_0-logloss:0.01531\tvalidation_1-logloss:1.09965\n",
      "[2049]\tvalidation_0-logloss:0.40120\tvalidation_1-logloss:0.52542\n",
      "[4002]\tvalidation_0-logloss:0.33520\tvalidation_1-logloss:0.55749\n",
      "[4003]\tvalidation_0-logloss:0.33519\tvalidation_1-logloss:0.55747\n",
      "[2050]\tvalidation_0-logloss:0.40116\tvalidation_1-logloss:0.52545\n",
      "[3970]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.09983\n",
      "[4004]\tvalidation_0-logloss:0.33517\tvalidation_1-logloss:0.55747\n",
      "[2051]\tvalidation_0-logloss:0.40113\tvalidation_1-logloss:0.52545\n",
      "[3971]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.09986[4005]\tvalidation_0-logloss:0.33515\tvalidation_1-logloss:0.55747\n",
      "\n",
      "[2052]\tvalidation_0-logloss:0.40111\tvalidation_1-logloss:0.52544\n",
      "[4006]\tvalidation_0-logloss:0.33514\tvalidation_1-logloss:0.55746\n",
      "[3972]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.09995\n",
      "[2053]\tvalidation_0-logloss:0.40107\tvalidation_1-logloss:0.52548\n",
      "[4007]\tvalidation_0-logloss:0.33514\tvalidation_1-logloss:0.55742\n",
      "[3973]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.09997\n",
      "[2054]\tvalidation_0-logloss:0.40103\tvalidation_1-logloss:0.52549\n",
      "[4008]\tvalidation_0-logloss:0.33511\tvalidation_1-logloss:0.55740\n",
      "[3974]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.10002\n",
      "[4009]\tvalidation_0-logloss:0.33508\tvalidation_1-logloss:0.55738\n",
      "[2055]\tvalidation_0-logloss:0.40096\tvalidation_1-logloss:0.52552\n",
      "[4010]\tvalidation_0-logloss:0.33506\tvalidation_1-logloss:0.55739\n",
      "[3975]\tvalidation_0-logloss:0.01530\tvalidation_1-logloss:1.10003\n",
      "[2056]\tvalidation_0-logloss:0.40095\tvalidation_1-logloss:0.52553\n",
      "[4011]\tvalidation_0-logloss:0.33504\tvalidation_1-logloss:0.55739\n",
      "[2057]\tvalidation_0-logloss:0.40090\tvalidation_1-logloss:0.52552\n",
      "[4012]\tvalidation_0-logloss:0.33502\tvalidation_1-logloss:0.55740[3976]\tvalidation_0-logloss:0.01529\tvalidation_1-logloss:1.09993\n",
      "\n",
      "[4013]\tvalidation_0-logloss:0.33500\tvalidation_1-logloss:0.55744\n",
      "[3977]\tvalidation_0-logloss:0.01529\tvalidation_1-logloss:1.09994\n",
      "[2058]\tvalidation_0-logloss:0.40090\tvalidation_1-logloss:0.52551\n",
      "[4014]\tvalidation_0-logloss:0.33498\tvalidation_1-logloss:0.55745\n",
      "[2059]\tvalidation_0-logloss:0.40087\tvalidation_1-logloss:0.52554\n",
      "[3978]\tvalidation_0-logloss:0.01529\tvalidation_1-logloss:1.09999\n",
      "[4015]\tvalidation_0-logloss:0.33496\tvalidation_1-logloss:0.55749\n",
      "[4016]\tvalidation_0-logloss:0.33494\tvalidation_1-logloss:0.55750[2060]\tvalidation_0-logloss:0.40084\tvalidation_1-logloss:0.52551\n",
      "\n",
      "[3979]\tvalidation_0-logloss:0.01528\tvalidation_1-logloss:1.10002\n",
      "[4017]\tvalidation_0-logloss:0.33491\tvalidation_1-logloss:0.55751\n",
      "[2061]\tvalidation_0-logloss:0.40079\tvalidation_1-logloss:0.52555\n",
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      "\n",
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      "\n",
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      "\n",
      "[2080]\tvalidation_0-logloss:0.40038\tvalidation_1-logloss:0.52567\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4188]\tvalidation_0-logloss:0.33087\tvalidation_1-logloss:0.55996\n",
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      "\n",
      "[4102]\tvalidation_0-logloss:0.01502\tvalidation_1-logloss:1.10398\n",
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      "\n",
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      "\n",
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      "\n",
      "[4112]\tvalidation_0-logloss:0.01501\tvalidation_1-logloss:1.10436\n",
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      "\n",
      "[4222]\tvalidation_0-logloss:0.33014\tvalidation_1-logloss:0.56040\n",
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      "\n",
      "[2243]\tvalidation_0-logloss:0.39566\tvalidation_1-logloss:0.52768\n",
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      "\n",
      "[2251]\tvalidation_0-logloss:0.39551\tvalidation_1-logloss:0.52788\n",
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      "\n",
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      "\n",
      "[4160]\tvalidation_0-logloss:0.01492\tvalidation_1-logloss:1.10583\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4426]\tvalidation_0-logloss:0.01448\tvalidation_1-logloss:1.11359\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4492]\tvalidation_0-logloss:0.01439\tvalidation_1-logloss:1.11515\n",
      "[2677]\tvalidation_0-logloss:0.38407\tvalidation_1-logloss:0.53223[4754]\tvalidation_0-logloss:0.31856\tvalidation_1-logloss:0.56921\n",
      "\n",
      "[4493]\tvalidation_0-logloss:0.01438\tvalidation_1-logloss:1.11524\n",
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      "\n",
      "[4756]\tvalidation_0-logloss:0.31849\tvalidation_1-logloss:0.56926\n",
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      "\n",
      "[4511]\tvalidation_0-logloss:0.01436\tvalidation_1-logloss:1.11564\n",
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      "\n",
      "[4513]\tvalidation_0-logloss:0.01435\tvalidation_1-logloss:1.11569\n",
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      "\n",
      "[4794]\tvalidation_0-logloss:0.31775\tvalidation_1-logloss:0.56943\n",
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      "\n",
      "[2736]\tvalidation_0-logloss:0.38254\tvalidation_1-logloss:0.53291\n",
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      "\n",
      "[4823]\tvalidation_0-logloss:0.31714\tvalidation_1-logloss:0.56967\n",
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      "\n",
      "[4825]\tvalidation_0-logloss:0.31713\tvalidation_1-logloss:0.56961[2741]\tvalidation_0-logloss:0.38238\tvalidation_1-logloss:0.53302\n",
      "\n",
      "[4544]\tvalidation_0-logloss:0.01430\tvalidation_1-logloss:1.11662\n",
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      "\n",
      "[4832]\tvalidation_0-logloss:0.31701\tvalidation_1-logloss:0.56971\n",
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      "\n",
      "[4836]\tvalidation_0-logloss:0.31696\tvalidation_1-logloss:0.56975\n",
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      "[4838]\tvalidation_0-logloss:0.31692\tvalidation_1-logloss:0.56976\n",
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      "[4839]\tvalidation_0-logloss:0.31689\tvalidation_1-logloss:0.56979\n",
      "[4553]\tvalidation_0-logloss:0.01429\tvalidation_1-logloss:1.11704\n",
      "[2753]\tvalidation_0-logloss:0.38200\tvalidation_1-logloss:0.53306[4840]\tvalidation_0-logloss:0.31687\tvalidation_1-logloss:0.56983\n",
      "\n",
      "[4554]\tvalidation_0-logloss:0.01429\tvalidation_1-logloss:1.11706\n",
      "[4841]\tvalidation_0-logloss:0.31685\tvalidation_1-logloss:0.56983\n",
      "[2754]\tvalidation_0-logloss:0.38200\tvalidation_1-logloss:0.53304\n",
      "[4555]\tvalidation_0-logloss:0.01429\tvalidation_1-logloss:1.11713\n",
      "[4842]\tvalidation_0-logloss:0.31683\tvalidation_1-logloss:0.56990\n",
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      "[4843]\tvalidation_0-logloss:0.31683\tvalidation_1-logloss:0.56996\n",
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      "[4556]\tvalidation_0-logloss:0.01429\tvalidation_1-logloss:1.11715\n",
      "[4844]\tvalidation_0-logloss:0.31681\tvalidation_1-logloss:0.56997\n",
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      "[4557]\tvalidation_0-logloss:0.01428\tvalidation_1-logloss:1.11718\n",
      "[4845]\tvalidation_0-logloss:0.31680\tvalidation_1-logloss:0.56997\n",
      "[2758]\tvalidation_0-logloss:0.38189\tvalidation_1-logloss:0.53307\n",
      "[4846]\tvalidation_0-logloss:0.31678\tvalidation_1-logloss:0.56996\n",
      "[4558]\tvalidation_0-logloss:0.01428\tvalidation_1-logloss:1.11717\n",
      "[2759]\tvalidation_0-logloss:0.38188\tvalidation_1-logloss:0.53309\n",
      "[4847]\tvalidation_0-logloss:0.31675\tvalidation_1-logloss:0.56995\n",
      "[4559]\tvalidation_0-logloss:0.01428\tvalidation_1-logloss:1.11718\n",
      "[2760]\tvalidation_0-logloss:0.38184\tvalidation_1-logloss:0.53308\n",
      "[4848]\tvalidation_0-logloss:0.31673\tvalidation_1-logloss:0.56993\n",
      "[4560]\tvalidation_0-logloss:0.01428\tvalidation_1-logloss:1.11732\n",
      "[4849]\tvalidation_0-logloss:0.31670\tvalidation_1-logloss:0.56998\n",
      "[2761]\tvalidation_0-logloss:0.38183\tvalidation_1-logloss:0.53308\n",
      "[4561]\tvalidation_0-logloss:0.01427\tvalidation_1-logloss:1.11746\n",
      "[4850]\tvalidation_0-logloss:0.31668\tvalidation_1-logloss:0.56999\n",
      "[2762]\tvalidation_0-logloss:0.38181\tvalidation_1-logloss:0.53305\n",
      "[4562]\tvalidation_0-logloss:0.01427\tvalidation_1-logloss:1.11747\n",
      "[4851]\tvalidation_0-logloss:0.31667\tvalidation_1-logloss:0.56999\n",
      "[2763]\tvalidation_0-logloss:0.38177\tvalidation_1-logloss:0.53308\n",
      "[4852]\tvalidation_0-logloss:0.31665\tvalidation_1-logloss:0.56995[4563]\tvalidation_0-logloss:0.01427\tvalidation_1-logloss:1.11744\n",
      "\n",
      "[2764]\tvalidation_0-logloss:0.38173\tvalidation_1-logloss:0.53313\n",
      "[4853]\tvalidation_0-logloss:0.31662\tvalidation_1-logloss:0.56995\n",
      "[4564]\tvalidation_0-logloss:0.01427\tvalidation_1-logloss:1.11752\n",
      "[2765]\tvalidation_0-logloss:0.38171\tvalidation_1-logloss:0.53315\n",
      "[4854]\tvalidation_0-logloss:0.31661\tvalidation_1-logloss:0.56996\n",
      "[2766]\tvalidation_0-logloss:0.38168\tvalidation_1-logloss:0.53315\n",
      "[4855]\tvalidation_0-logloss:0.31659\tvalidation_1-logloss:0.56998\n",
      "[4565]\tvalidation_0-logloss:0.01427\tvalidation_1-logloss:1.11760\n",
      "[4856]\tvalidation_0-logloss:0.31657\tvalidation_1-logloss:0.56996\n",
      "[2767]\tvalidation_0-logloss:0.38165\tvalidation_1-logloss:0.53315\n",
      "[4857]\tvalidation_0-logloss:0.31656\tvalidation_1-logloss:0.56998\n",
      "[4566]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11764\n",
      "[4858]\tvalidation_0-logloss:0.31655\tvalidation_1-logloss:0.57001[2768]\tvalidation_0-logloss:0.38162\tvalidation_1-logloss:0.53315\n",
      "\n",
      "[4859]\tvalidation_0-logloss:0.31653\tvalidation_1-logloss:0.57006\n",
      "[2769]\tvalidation_0-logloss:0.38160\tvalidation_1-logloss:0.53315\n",
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      "[4567]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11767\n",
      "[2771]\tvalidation_0-logloss:0.38156\tvalidation_1-logloss:0.53319\n",
      "[4860]\tvalidation_0-logloss:0.31651\tvalidation_1-logloss:0.57006\n",
      "[4568]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11770\n",
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      "[4862]\tvalidation_0-logloss:0.31646\tvalidation_1-logloss:0.57005\n",
      "[4569]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11771[2773]\tvalidation_0-logloss:0.38149\tvalidation_1-logloss:0.53324\n",
      "\n",
      "[4863]\tvalidation_0-logloss:0.31645\tvalidation_1-logloss:0.57011\n",
      "[2774]\tvalidation_0-logloss:0.38148\tvalidation_1-logloss:0.53326\n",
      "[4570]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11771\n",
      "[4864]\tvalidation_0-logloss:0.31643\tvalidation_1-logloss:0.57017\n",
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      "[4571]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11779\n",
      "[4865]\tvalidation_0-logloss:0.31640\tvalidation_1-logloss:0.57023[2776]\tvalidation_0-logloss:0.38144\tvalidation_1-logloss:0.53322\n",
      "\n",
      "[4866]\tvalidation_0-logloss:0.31639\tvalidation_1-logloss:0.57025[4572]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11781\n",
      "\n",
      "[2777]\tvalidation_0-logloss:0.38142\tvalidation_1-logloss:0.53319\n",
      "[4867]\tvalidation_0-logloss:0.31637\tvalidation_1-logloss:0.57028\n",
      "[2778]\tvalidation_0-logloss:0.38142\tvalidation_1-logloss:0.53319\n",
      "[4573]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11783\n",
      "[4868]\tvalidation_0-logloss:0.31636\tvalidation_1-logloss:0.57029\n",
      "[2779]\tvalidation_0-logloss:0.38139\tvalidation_1-logloss:0.53320\n",
      "[4574]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11781\n",
      "[4869]\tvalidation_0-logloss:0.31635\tvalidation_1-logloss:0.57029\n",
      "[2780]\tvalidation_0-logloss:0.38138\tvalidation_1-logloss:0.53320\n",
      "[4870]\tvalidation_0-logloss:0.31631\tvalidation_1-logloss:0.57033\n",
      "[4871]\tvalidation_0-logloss:0.31629\tvalidation_1-logloss:0.57032\n",
      "[4872]\tvalidation_0-logloss:0.31627\tvalidation_1-logloss:0.57035\n",
      "[4575]\tvalidation_0-logloss:0.01426\tvalidation_1-logloss:1.11784\n",
      "[2781]\tvalidation_0-logloss:0.38136\tvalidation_1-logloss:0.53323\n",
      "[4873]\tvalidation_0-logloss:0.31625\tvalidation_1-logloss:0.57034\n",
      "[2782]\tvalidation_0-logloss:0.38134\tvalidation_1-logloss:0.53324\n",
      "[4874]\tvalidation_0-logloss:0.31624\tvalidation_1-logloss:0.57036\n",
      "[4576]\tvalidation_0-logloss:0.01425\tvalidation_1-logloss:1.11787\n",
      "[2783]\tvalidation_0-logloss:0.38131\tvalidation_1-logloss:0.53325\n",
      "[4875]\tvalidation_0-logloss:0.31620\tvalidation_1-logloss:0.57036\n",
      "[2784]\tvalidation_0-logloss:0.38129\tvalidation_1-logloss:0.53326[4577]\tvalidation_0-logloss:0.01425\tvalidation_1-logloss:1.11784\n",
      "\n",
      "[4876]\tvalidation_0-logloss:0.31618\tvalidation_1-logloss:0.57041\n",
      "[4578]\tvalidation_0-logloss:0.01425\tvalidation_1-logloss:1.11786\n",
      "[2785]\tvalidation_0-logloss:0.38128\tvalidation_1-logloss:0.53325\n",
      "[4877]\tvalidation_0-logloss:0.31617\tvalidation_1-logloss:0.57043\n",
      "[4579]\tvalidation_0-logloss:0.01425\tvalidation_1-logloss:1.11785\n",
      "[4878]\tvalidation_0-logloss:0.31616\tvalidation_1-logloss:0.57041\n",
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      "[4580]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11783\n",
      "[4879]\tvalidation_0-logloss:0.31614\tvalidation_1-logloss:0.57045\n",
      "[4581]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11783\n",
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      "[4582]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11783\n",
      "[4583]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11783[4881]\tvalidation_0-logloss:0.31608\tvalidation_1-logloss:0.57041\n",
      "[2788]\tvalidation_0-logloss:0.38121\tvalidation_1-logloss:0.53322\n",
      "\n",
      "[4584]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11783\n",
      "[2789]\tvalidation_0-logloss:0.38119\tvalidation_1-logloss:0.53323\n",
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      "[4585]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11786\n",
      "[4883]\tvalidation_0-logloss:0.31605\tvalidation_1-logloss:0.57042\n",
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      "[4586]\tvalidation_0-logloss:0.01424\tvalidation_1-logloss:1.11789\n",
      "[4885]\tvalidation_0-logloss:0.31600\tvalidation_1-logloss:0.57043\n",
      "[4886]\tvalidation_0-logloss:0.31598\tvalidation_1-logloss:0.57045\n",
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      "[2800]\tvalidation_0-logloss:0.38087\tvalidation_1-logloss:0.53337\n",
      "[4895]\tvalidation_0-logloss:0.31582\tvalidation_1-logloss:0.57045\n",
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      "[2801]\tvalidation_0-logloss:0.38085\tvalidation_1-logloss:0.53337\n",
      "[4896]\tvalidation_0-logloss:0.31580\tvalidation_1-logloss:0.57044\n",
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      "[4595]\tvalidation_0-logloss:0.01423\tvalidation_1-logloss:1.11816\n",
      "[4898]\tvalidation_0-logloss:0.31577\tvalidation_1-logloss:0.57046\n",
      "[4899]\tvalidation_0-logloss:0.31576\tvalidation_1-logloss:0.57048[2803]\tvalidation_0-logloss:0.38082\tvalidation_1-logloss:0.53344\n",
      "\n",
      "[4900]\tvalidation_0-logloss:0.31574\tvalidation_1-logloss:0.57042[4596]\tvalidation_0-logloss:0.01423\tvalidation_1-logloss:1.11820\n",
      "\n",
      "[2804]\tvalidation_0-logloss:0.38078\tvalidation_1-logloss:0.53340\n",
      "[4901]\tvalidation_0-logloss:0.31572\tvalidation_1-logloss:0.57042\n",
      "[4597]\tvalidation_0-logloss:0.01423\tvalidation_1-logloss:1.11828\n",
      "[2805]\tvalidation_0-logloss:0.38077\tvalidation_1-logloss:0.53339\n",
      "[4902]\tvalidation_0-logloss:0.31570\tvalidation_1-logloss:0.57042\n",
      "[2806]\tvalidation_0-logloss:0.38074\tvalidation_1-logloss:0.53342\n",
      "[4903]\tvalidation_0-logloss:0.31568\tvalidation_1-logloss:0.57043\n",
      "[4598]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11834\n",
      "[2807]\tvalidation_0-logloss:0.38071\tvalidation_1-logloss:0.53342\n",
      "[4904]\tvalidation_0-logloss:0.31565\tvalidation_1-logloss:0.57047\n",
      "[2808]\tvalidation_0-logloss:0.38069\tvalidation_1-logloss:0.53346\n",
      "[4599]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11843\n",
      "[4905]\tvalidation_0-logloss:0.31562\tvalidation_1-logloss:0.57049\n",
      "[4906]\tvalidation_0-logloss:0.31561\tvalidation_1-logloss:0.57052\n",
      "[2809]\tvalidation_0-logloss:0.38069\tvalidation_1-logloss:0.53346\n",
      "[4600]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11847\n",
      "[4907]\tvalidation_0-logloss:0.31558\tvalidation_1-logloss:0.57049\n",
      "[4601]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11847\n",
      "[2810]\tvalidation_0-logloss:0.38067\tvalidation_1-logloss:0.53346\n",
      "[4908]\tvalidation_0-logloss:0.31555\tvalidation_1-logloss:0.57053\n",
      "[4602]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11842\n",
      "[2811]\tvalidation_0-logloss:0.38066\tvalidation_1-logloss:0.53347\n",
      "[4909]\tvalidation_0-logloss:0.31553\tvalidation_1-logloss:0.57058\n",
      "[4603]\tvalidation_0-logloss:0.01422\tvalidation_1-logloss:1.11843[2812]\tvalidation_0-logloss:0.38064\tvalidation_1-logloss:0.53346\n",
      "\n",
      "[4910]\tvalidation_0-logloss:0.31550\tvalidation_1-logloss:0.57062\n",
      "[2813]\tvalidation_0-logloss:0.38061\tvalidation_1-logloss:0.53349\n",
      "[4911]\tvalidation_0-logloss:0.31549\tvalidation_1-logloss:0.57066\n",
      "[4604]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11840\n",
      "[4605]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11840\n",
      "[2814]\tvalidation_0-logloss:0.38059\tvalidation_1-logloss:0.53350\n",
      "[4912]\tvalidation_0-logloss:0.31545\tvalidation_1-logloss:0.57068\n",
      "[2815]\tvalidation_0-logloss:0.38056\tvalidation_1-logloss:0.53353\n",
      "[4913]\tvalidation_0-logloss:0.31544\tvalidation_1-logloss:0.57066\n",
      "[4606]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11844\n",
      "[2816]\tvalidation_0-logloss:0.38054\tvalidation_1-logloss:0.53358\n",
      "[4914]\tvalidation_0-logloss:0.31541\tvalidation_1-logloss:0.57069\n",
      "[2817]\tvalidation_0-logloss:0.38053\tvalidation_1-logloss:0.53361\n",
      "[4607]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11839\n",
      "[4915]\tvalidation_0-logloss:0.31540\tvalidation_1-logloss:0.57068\n",
      "[4608]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11844\n",
      "[2818]\tvalidation_0-logloss:0.38048\tvalidation_1-logloss:0.53362\n",
      "[4916]\tvalidation_0-logloss:0.31539\tvalidation_1-logloss:0.57066\n",
      "[4917]\tvalidation_0-logloss:0.31536\tvalidation_1-logloss:0.57071\n",
      "[2819]\tvalidation_0-logloss:0.38044\tvalidation_1-logloss:0.53362[4609]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11845\n",
      "\n",
      "[4610]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11845\n",
      "[4918]\tvalidation_0-logloss:0.31534\tvalidation_1-logloss:0.57075\n",
      "[2820]\tvalidation_0-logloss:0.38042\tvalidation_1-logloss:0.53365\n",
      "[4919]\tvalidation_0-logloss:0.31533\tvalidation_1-logloss:0.57076\n",
      "[4611]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11844\n",
      "[2821]\tvalidation_0-logloss:0.38039\tvalidation_1-logloss:0.53364\n",
      "[4920]\tvalidation_0-logloss:0.31531\tvalidation_1-logloss:0.57080\n",
      "[2822]\tvalidation_0-logloss:0.38037\tvalidation_1-logloss:0.53364\n",
      "[4921]\tvalidation_0-logloss:0.31530\tvalidation_1-logloss:0.57080\n",
      "[4612]\tvalidation_0-logloss:0.01421\tvalidation_1-logloss:1.11842\n",
      "[2823]\tvalidation_0-logloss:0.38035\tvalidation_1-logloss:0.53366\n",
      "[4922]\tvalidation_0-logloss:0.31527\tvalidation_1-logloss:0.57087\n",
      "[2824]\tvalidation_0-logloss:0.38032\tvalidation_1-logloss:0.53365\n",
      "[4613]\tvalidation_0-logloss:0.01420\tvalidation_1-logloss:1.11846\n",
      "[2825]\tvalidation_0-logloss:0.38031\tvalidation_1-logloss:0.53368\n",
      "[4923]\tvalidation_0-logloss:0.31524\tvalidation_1-logloss:0.57087\n",
      "[2826]\tvalidation_0-logloss:0.38029\tvalidation_1-logloss:0.53371\n",
      "[2827]\tvalidation_0-logloss:0.38027\tvalidation_1-logloss:0.53370\n",
      "[2828]\tvalidation_0-logloss:0.38023\tvalidation_1-logloss:0.53372\n",
      "[4924]\tvalidation_0-logloss:0.31522\tvalidation_1-logloss:0.57085\n",
      "[2829]\tvalidation_0-logloss:0.38021\tvalidation_1-logloss:0.53372\n",
      "[2830]\tvalidation_0-logloss:0.38017\tvalidation_1-logloss:0.53370\n",
      "[4614]\tvalidation_0-logloss:0.01420\tvalidation_1-logloss:1.11845\n",
      "[4925]\tvalidation_0-logloss:0.31521\tvalidation_1-logloss:0.57081\n",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:16:05,271] Trial 0 finished with value: 0.7324612164392383 and parameters: {'max_depth': 3, 'min_child_weight': 1, 'reg_alpha': 0.003840239840825237, 'reg_lambda': 2.290859732636261e-06, 'gamma': 0.05256769110802507, 'subsample': 0.7981363566199738, 'colsample_bytree': 0.8207757910147984, 'colsample_bylevel': 0.9895671973967499, 'learning_rate': 0.029980378757743585, 'max_bin': 128}. Best is trial 0 with value: 0.7324612164392383.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "[4925]\tvalidation_0-logloss:0.01381\tvalidation_1-logloss:1.12513[3203]\tvalidation_0-logloss:0.37136\tvalidation_1-logloss:0.53784\n",
      "\n",
      "[3204]\tvalidation_0-logloss:0.37133\tvalidation_1-logloss:0.53787\n",
      "[4926]\tvalidation_0-logloss:0.01381\tvalidation_1-logloss:1.12513\n",
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      "[4928]\tvalidation_0-logloss:0.01381\tvalidation_1-logloss:1.12516[3207]\tvalidation_0-logloss:0.37129\tvalidation_1-logloss:0.53788\n",
      "\n",
      "[3208]\tvalidation_0-logloss:0.37126\tvalidation_1-logloss:0.53789\n",
      "[4929]\tvalidation_0-logloss:0.01381\tvalidation_1-logloss:1.12519\n",
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      "[4967]\tvalidation_0-logloss:0.01375\tvalidation_1-logloss:1.12665[3256]\tvalidation_0-logloss:0.37011\tvalidation_1-logloss:0.53824\n",
      "\n",
      "[3257]\tvalidation_0-logloss:0.37009\tvalidation_1-logloss:0.53822\n",
      "[4968]\tvalidation_0-logloss:0.01375\tvalidation_1-logloss:1.12665\n",
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      "[4974]\tvalidation_0-logloss:0.01374\tvalidation_1-logloss:1.12683[3265]\tvalidation_0-logloss:0.36987\tvalidation_1-logloss:0.53840\n",
      "\n",
      "[3266]\tvalidation_0-logloss:0.36986\tvalidation_1-logloss:0.53842\n",
      "[4975]\tvalidation_0-logloss:0.01374\tvalidation_1-logloss:1.12689\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[4799]\tvalidation_0-logloss:0.34014\tvalidation_1-logloss:0.55385\n",
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      "\n",
      "[4812]\tvalidation_0-logloss:0.33989\tvalidation_1-logloss:0.55402\n",
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      "\n",
      "[4837]\tvalidation_0-logloss:0.33948\tvalidation_1-logloss:0.55432\n",
      "[1127]\tvalidation_0-logloss:0.04153\tvalidation_1-logloss:0.88680\n",
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      "\n",
      "[4872]\tvalidation_0-logloss:0.33884\tvalidation_1-logloss:0.55470\n",
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      "\n",
      "[4878]\tvalidation_0-logloss:0.33875\tvalidation_1-logloss:0.55472\n",
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      "\n",
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      "\n",
      "[4904]\tvalidation_0-logloss:0.33821\tvalidation_1-logloss:0.55495\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:16:23,204] Trial 2 finished with value: 0.7369039971017992 and parameters: {'max_depth': 4, 'min_child_weight': 7, 'reg_alpha': 2.241381331047004, 'reg_lambda': 4.476934714115417e-08, 'gamma': 0.00011286190232422962, 'subsample': 0.7252833359646541, 'colsample_bytree': 0.5585484713001434, 'colsample_bylevel': 0.8859256746891728, 'learning_rate': 0.021820651758079585, 'max_bin': 256}. Best is trial 2 with value: 0.7369039971017992.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:17:24,392] Trial 1 finished with value: 0.7272262609185686 and parameters: {'max_depth': 7, 'min_child_weight': 3, 'reg_alpha': 0.14369796212557015, 'reg_lambda': 0.00030482460930186133, 'gamma': 1.7623057914557492e-05, 'subsample': 0.9875778800134174, 'colsample_bytree': 0.6383784380302511, 'colsample_bylevel': 0.6163564931143626, 'learning_rate': 0.17239270546359142, 'max_bin': 128}. Best is trial 2 with value: 0.7369039971017992.\n",
      "[I 2026-05-05 06:17:25,344] A new study created in memory with name: shipping_delay_random_forest\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  CV AUC: 0.7369  |  Threshold: 0.585\n",
      "\n",
      "──────────────────────────────────────────────────  RANDOM_FOREST\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:17:39,907] Trial 0 finished with value: 0.7354277663728213 and parameters: {'n_estimators': 250, 'max_depth': 8, 'min_samples_split': 18, 'min_samples_leaf': 6, 'max_features': 0.7, 'bootstrap': True, 'class_weight': 'balanced_subsample'}. Best is trial 0 with value: 0.7354277663728213.\n",
      "[I 2026-05-05 06:17:41,440] Trial 2 finished with value: 0.7308579479128929 and parameters: {'n_estimators': 300, 'max_depth': 28, 'min_samples_split': 7, 'min_samples_leaf': 7, 'max_features': 0.7, 'bootstrap': False, 'class_weight': 'balanced'}. Best is trial 0 with value: 0.7354277663728213.\n",
      "[I 2026-05-05 06:17:42,360] Trial 1 finished with value: 0.7320776073743107 and parameters: {'n_estimators': 450, 'max_depth': 26, 'min_samples_split': 16, 'min_samples_leaf': 8, 'max_features': 0.5, 'bootstrap': False, 'class_weight': None}. Best is trial 0 with value: 0.7354277663728213.\n",
      "[I 2026-05-05 06:17:45,905] A new study created in memory with name: shipping_delay_logistic_regression\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  CV AUC: 0.7354  |  Threshold: 0.369\n",
      "\n",
      "──────────────────────────────────────────────────  LOGISTIC_REGRESSION\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2026-05-05 06:17:46,309] Trial 1 finished with value: 0.7194047417783681 and parameters: {'penalty_solver': 'l1_saga', 'C': 0.09575334507574831}. Best is trial 1 with value: 0.7194047417783681.\n",
      "[I 2026-05-05 06:17:46,457] Trial 2 finished with value: 0.7193272954152076 and parameters: {'penalty_solver': 'elasticnet_saga', 'C': 0.1375082115662957, 'l1_ratio': 0.9494909700048194}. Best is trial 1 with value: 0.7194047417783681.\n",
      "[I 2026-05-05 06:17:47,154] Trial 0 finished with value: 0.7191757839230366 and parameters: {'penalty_solver': 'l2_saga', 'C': 0.535667768221033}. Best is trial 1 with value: 0.7194047417783681.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  CV AUC: 0.7194  |  Threshold: 0.712\n",
      "\n",
      "All architectures trained!\n"
     ]
    }
   ],
   "source": [
    "architectures = ['xgb', 'random_forest', 'logistic_regression']\n",
    "\n",
    "# Seed the leaderboard with the LGBM model already trained above\n",
    "model_states = {'lgbm': lgbm_state}\n",
    "\n",
    "for arch in architectures:\n",
    "    print(f\"\\n{'─' * 50}  {arch.upper()}\")\n",
    "\n",
    "    config = TrainConfig(\n",
    "        name=f\"shipping_delay_{arch}\",\n",
    "        model_type=arch,\n",
    "        task=\"binary_classification\",\n",
    "        optimization_metric=\"roc_auc\",\n",
    "        n_trials=3,\n",
    "        cv_folds=3,\n",
    "        optimize_threshold=True,\n",
    "        verbose=False,\n",
    "        optuna_show_progress=False,\n",
    "        random_state=42\n",
    "    )\n",
    "\n",
    "    trainer = OptunaTrainer(config=config)\n",
    "    trainer.fit(X_train_transformed, y_train)\n",
    "    model_states[arch] = trainer.state\n",
    "    print(f\"  CV AUC: {trainer.state.best_score:.4f}  |  Threshold: {trainer.state.optimal_threshold:.3f}\")\n",
    "\n",
    "print(\"\\nAll architectures trained!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "x1y2z3a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "  ARCHITECTURE LEADERBOARD\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Architecture</th>\n",
       "      <th>CV AUC</th>\n",
       "      <th>Optimal Threshold</th>\n",
       "      <th>Train Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBM</td>\n",
       "      <td>0.7426</td>\n",
       "      <td>0.498</td>\n",
       "      <td>111.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGB</td>\n",
       "      <td>0.7369</td>\n",
       "      <td>0.585</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RANDOM_FOREST</td>\n",
       "      <td>0.7354</td>\n",
       "      <td>0.369</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOGISTIC_REGRESSION</td>\n",
       "      <td>0.7194</td>\n",
       "      <td>0.712</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Architecture  CV AUC  Optimal Threshold  Train Time (s)\n",
       "1                 LGBM  0.7426              0.498           111.3\n",
       "2                  XGB  0.7369              0.585           193.0\n",
       "3        RANDOM_FOREST  0.7354              0.369            20.6\n",
       "4  LOGISTIC_REGRESSION  0.7194              0.712             1.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Winner: LGBM — proceeding with LGBM for detailed analysis (SHAP available).\n"
     ]
    }
   ],
   "source": [
    "leaderboard = pd.DataFrame([\n",
    "    {\n",
    "        \"Architecture\"      : arch.upper(),\n",
    "        \"CV AUC\"            : round(state.best_score, 4),\n",
    "        \"Optimal Threshold\" : round(state.optimal_threshold or 0.5, 3),\n",
    "        \"Train Time (s)\"    : round(state.training_time_seconds, 1),\n",
    "    }\n",
    "    for arch, state in model_states.items()\n",
    "]).sort_values(\"CV AUC\", ascending=False).reset_index(drop=True)\n",
    "\n",
    "leaderboard.index += 1  # 1-based ranking\n",
    "\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"  ARCHITECTURE LEADERBOARD\")\n",
    "print(\"=\" * 60)\n",
    "display(leaderboard)\n",
    "\n",
    "best_arch = leaderboard.iloc[0]['Architecture'].lower()\n",
    "print(f\"\\nWinner: {best_arch.upper()} — proceeding with LGBM for detailed analysis (SHAP available).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "y1z2a3b4",
   "metadata": {},
   "source": [
    "## 8. Comprehensive Test Set Evaluation\n",
    "\n",
    "> **This is the moment of truth.**\n",
    "\n",
    "Cross-validation scores tell you how well the model generalised *during training*. The hold-out test set — never seen by any fitting step, including the transform pipeline — gives you an **unbiased** estimate of real-world performance. No optimism, no leakage.\n",
    "\n",
    "We use `bitbullet.evaluate.ModelEvaluator` for the notebook diagnostics and `evaluate_classification` for a JSON-ready metrics report that can be attached directly to model metadata. The threshold applied is the one discovered by Youden's Index during training — not a hand-tuned 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "z1a2b3c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877\n",
      "[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07\n",
      "[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796\n",
      "[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484\n",
      "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
      "============================================================\n",
      "Evaluation Results\n",
      "============================================================\n",
      "Threshold  : 0.4984\n",
      "\n",
      "Classification metrics:\n",
      "  Accuracy     : 0.6582\n",
      "  Precision    : 0.7913\n",
      "  Recall       : 0.5804\n",
      "  F1           : 0.6696\n",
      "  Specificity  : 0.7734\n",
      "\n",
      "Probabilistic metrics:\n",
      "  ROC AUC      : 0.7322\n",
      "  PR AUC       : 0.8479\n",
      "  Cohen's κ    : 0.3325\n",
      "\n",
      "Confusion matrix:\n",
      "  TN    686   FP    201\n",
      "  FN    551   TP    762\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# Score the sealed test set\n",
    "production_model   = lgbm_state.model\n",
    "y_pred_proba_test  = production_model.predict_proba(X_test_transformed)[:, 1]\n",
    "\n",
    "eval_config = EvaluationConfig(\n",
    "    name=\"shipping_delay_test_eval\",\n",
    "    threshold=lgbm_state.optimal_threshold,  # Youden's J threshold from training\n",
    "    optimize_threshold=False,\n",
    "    generate_plots=False,                    # We produce inline plots below\n",
    "    generate_report=False,\n",
    "    verbose=False\n",
    ")\n",
    "\n",
    "evaluator    = ModelEvaluator(config=eval_config)\n",
    "test_results = evaluator.evaluate(\n",
    "    y_true=y_test.values,\n",
    "    y_pred_proba=y_pred_proba_test\n",
    ")\n",
    "\n",
    "print(test_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2b3c4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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PwzAK9kbt37+/YTabDcPI7c1p3QPY09PTSElJMQyj5D1tJRk//PCDZZ31T/pDQkIsyz/77DObbebOnWtZd/jwYZted2XZ09YwDOPJJ5+0abd161bDMGyHvmjVqpXN43nw4EGbbTZs2GBZV1hP2zxnz541vv32W+Odd94xXnnlFWPWrFlGy5YtLduMHTvW0vZvf/ubZfnNN99sWT5hwgTL8ttuu61Ej0f+azZy5Ehj1qxZxvTp040hQ4bYrAsPDzfS0tLsbtejRw+bx8IwcnvPubq6Wtp89tlnNuvvuOMOy7obbrjBsrxJkyaW5Q0bNjTS09Mt6/I/Z0vS0/bbb7+12eahhx6yPJ/z5B8ixLq9vZ+av/766zZtrHtuHTlyxHB3d7ese+CBByzr8r8O3nnnnUKujOMV9f5l78/6NfjNN9/YrLMeYuTSpUtGSEiIZV2/fv0s6wq7hosXL7bZ3wcffGBZFxsba/N4l6an7UMPPWSz3127dhXa1vo93cfHp9iaDaPo98Si1pVWUT1td+/ebbNu48aNlnVms9mml/K4ceMMw8gdvidvWUBAQIHXttlsNo4ePWqz7EqHqMjzxRdfFPn8Cg4ONl5++WWb1671a9vDw8M4duyYZV1GRoZND1zrXt1X+9iXpDdpUdfb09PTMozOX3/9ZZhMJsu63bt3Fzje7t27jU8++cSYPXu2MWvWLGPatGk2x7Ee0qM8etoaRu4QFj/99JMxb94849VXXzVmzZpl9OzZ07JN3759bdqX5PlQWJuyen8tyecMAEDVQ09bAKji3N3dtWbNGk2bNk0LFy5UfHx8gTarV69W//799fvvvxc6YdODDz5oud2kSRObdZcuXSp1XSNHjrTpqXTPPffo/ffft9zfvn273R4w99xzj0wmk6TcnqF33323Vq5cKUnKyMjQ77//rk6dOpW4jtq1a2vw4MGW+9bnZn1e27dvt9nu3nvvtdxu2LChrrvuOv32228lPm5pGIZhd7l179m9e/cWOSnXxo0bi52IJi0tTQ8//LAWLVoks9lcaLu8HouS9Oijj+q9996TJH3//fc6e/aswsLCtHTpUkubMWPGFHncwnzxxRf64osvCiz38vLSRx99JC8vL7vbTZ48uUAvuC1btignJ8dy/6677iq0929eb9fk5GQdOnTIsvzWW2+1eYzzP2dLIn+P5xdeeMHyfM5Tkp7v9uqVpLCwMJteW1FRUbruuussPYwL6xEeEhKiv/3tb6U67vvvv6+EhIRSbZNfRUzYlv+c77nnHsvtoKAgDRs2TAsWLLDb1p78vRitn0eRkZE2j7e1FStW6Pfffy+wfODAgWrRokWB13n+58W1IP/zv6hrn3ctgoOD1aJFC+3bt0+JiYmqX7++OnbsqEaNGqlVq1a6/vrrVb9+/TKtc8SIEQoKCtK0adO0fv36Atfm0qVLevLJJ+Xh4aEJEyYUOLfMzExFRkYWe26O0qtXL9WvX1+xsbHKyMjQ119/rTFjxujzzz+3nGv79u3VunVryzY7duzQfffdp3379hW5b+v/P5Q1s9msp59+WrNnz1ZmZmaF1FAW768l/ZwBAKh6CG0BAAoMDNSsWbP073//W/v27dPmzZu1evVqLVu2TGlpaZKkuLg4ff311xo1apTdfURERFhu5w8Hiwr4ChMWFmZzPzw83OZ+YV9krnS7wlifl2R7btZf1PN+Ginlziae/6fjhf1Uvyz88ccfNvdr164tSbp48WKJ93H+/Pli2zzzzDNauHBhse0yMjIst1u2bKlevXppzZo1ys7O1sKFC3XddddZvjTXrFlTN954Y4nrLIy3t7ciIiLUt29fTZo0SQ0bNiy0bePGjQssK81jlZKSorS0NJtrLhX/3CsJ6zp8fHwK7PNKWD/n7e3Pus7CXh8NGjSQq6trqY47ffp0HT9+vFTb5DdlypRSh7ajRo2y+zyNjIy0W4/1Ofv5+cnHx8dmvfXjk5qaqszMzCKHe8n/XpD/p+CFPS8+//xzffTRRwWWV69eXS1atFCtWrVslh87dswmNLNmfZ41a9a02yZ/0Gj9unWUK33P+uyzz3TnnXdq//79OnXqlL799lvLOhcXF02YMEGvvfZamdbav39/9e/fX5cuXdKmTZu0adMmffvtt5ahHSTpjTfesIS2Zf1+fKVWr16t3r17F9nGZDJp9OjRmjJliqTcx3fMmDE2QyOMHTvWcjstLU033XSTTp8+Xezxr+R5VtLn6pw5czRr1qxyqaEwZfH+WtLPGQCAqofQFgBg4eLiolatWqlVq1YaN26cdu7cqfbt21vW//nnn4Vu6+7ubrldFj3A8sbEzXP27Fmb+0FBQWW6XWGsz0sq/Nys95uUlKS0tDSbsObMmTOlOm5JnTx5Uj///LPlfpMmTSzhTt64h5LUpk0bmx6E+XXs2LHYY1n3au3Tp4/ee+891a9fX66urhoxYoSWLFlid7tHH33UMq7khx9+qFOnTlnW3XfffaUOA/MsWLBAo0ePLvV2+UM5yfaxkqQnnniiyMDUzc3NMmZsnuKeeyUREhJiuZ2amqrz588rNDS01PuxZn1u+WuUbOvM/zjksfeYXSuszzk5OVmpqak252v9+Pj4+BQ7Pndx7wVX8ryQpB49etjc/+qrrzR06NAC7X755Reb4Nh6OxcXF8vtvH+Qy3P48OErqqss5X/+zZgxo8B7cB7ra9S6dWvt27dPe/fu1Y4dO3T48GHt2LFDy5cvl9ls1uuvv66hQ4cWG1Zeac2DBg3SoEGD9NJLL2n48OGW0Pj48ePKzs6Wm5ubzbn5+flZAlF7yvMf+Upq1KhReuGFF2QYhlavXq1169Zpx44dkv435neetWvX2gS2s2bN0v3336/g4GClpqZe0fjXJpPJElhaP1fNZrOOHj1qdxvr/0e1bNlSn332mZo2bSp3d3c9+eSTJQp0S6ss3l9L+jkDAFD1ENoCQBX30UcfKT09XXfddVeBoQ/8/Pxs7pc28LwaX3zxhZ5++mnLlxnriWYkWSZbyu+TTz6xDJFgGIY+/fRTyzoPDw+7k4CUhfyh5xdffGEJFP/880+tX7++zI95+vRp3XLLLZaJxqTcn/7n6datm7Zt22Zpe8899xQIA9LT07VkyRL16tWr2ONduHDBcvumm26y9GY9d+6c3Z975xk+fLhl4p7Dhw9bJvCRdEWha3no3LmzXF1dLUMkeHt76/HHHy/Qbv/+/bp48aLc3d3l7u6uJk2aWIZIWLp0qaZOnWrpJZX/OVsS3bt3t7k/depUvfXWWzbLjh8/btMzy83NzTLpVWpqaoF9duvWzRKonzt3TitXrrT8hPfo0aM2z82yHIrg2LFjZbav8pT/nD/55BPLUBCXL1+26bVZkscn/3vTZ599pvvvv19S7mNS2HvBwoULi+zJ3rNnTzVt2lQHDx6UlDsh1Lhx42xC2cTERD311FM221kPXWP9Hn7+/HkdPXpUUVFRSkpK0ty5cws9dv5Qyd7zrCzkf3xr1Khhd/iUrVu32vRG3LVrl9q2bWv5R8c8bdq00Z49eyTlDmGTF9pan8+VnMuoUaM0YcIEm3/UzGMdUPr6+srNza3AuSUnJ6t9+/bq27evzbaGYejXX3+1GQKloh77/PJ+ubBq1Srl5OTY/Mpm+PDhNgGk9f8bJFkm3pNye5BfiaCgIEvP1C1btujhhx+WlPs6sReO5q+jT58+ludCWlqavvvuu0KPdTXPB0e9vwIAqgZCWwCo4mJjYzV16lRNnDhRPXr0UNu2bRUcHKxz587Z9FoxmUwVOoPxvn371LVrVw0ePFi///67vv76a8u6Pn36FDqj88qVK3X99derZ8+eWr9+vVatWmVZd/fdd5dbj8GhQ4cqLCzM8mXywQcf1JYtWxQYGKhFixZZQrWrsXHjRr3yyitKSUnRvn379MMPP9j0QBoyZIgeeOABy/1HH31U8+bNU0ZGhs6dO6c2bdpoxIgRqlWrlhITE7V3716tWbNGycnJNmPwFqZJkyaWMTenTZums2fPymQy6eOPP7Y7FnIeV1dXjR8/Xs8995wkWULmrl27qmnTplf0WJS1atWqafTo0frwww8lSS+++KI2b96sLl26yN3dXXFxcdqwYYP279+vKVOm6LrrrpMk3X///XryyScl5YbzXbt21ZAhQwo8Z0tq8ODBlvE5Jentt9/Wjh071KdPH2VnZ1tmYbcOyWvXrm35Ofyrr76qCxcuyNvbW+3atdP111+vUaNG6aWXXrL8PPuWW26xmd08KytLUm74++ijj17hI1h53XTTTWrUqJGlp+kjjzyirVu3qkaNGvryyy9tftY+adKkYvc3dOhQhYaGWn7i/vDDD2vbtm0KDg7WJ598Ynm8S8vFxUXvvvuu+vXrp6ysLGVnZ6tv37667bbb1KpVK8XHx+vLL7/UyZMnLds88sgj6tKli+V+/kD5uuuuU+/evbV582bFxsYWeuy8IVfyPPzwwxowYIDc3Nw0dOhQu0OOXIm2bdvq+uuvt7xvjxs3Tt9//73atm0rKff/V2vWrFFsbKwWLFigNm3aSJK6dOmiWrVqqUePHqpVq5YCAgK0e/duS2Ar2QbW1uezfft2TZgwQXXr1pWHh4cee+yxYutctGiRFi1apIYNG6pHjx6W8Wm3bt2qH3/80dJuwIABlts33XSTzT/yDB48WLfeequaNm2q7Oxs/fHHH/rtt990+vRprV692jIOb1k/9l988UWBcZclqVGjRho2bJjNsjFjxliuhfXzI3+Qnn8c+0GDBmnw4ME6fPiwPvvss1LVlyc6Olq//PKLpNzH+8yZM3J3d9fy5csL3aZJkyaW1/H7778vk8mkgIAALVmyxGb88fyu5vnA+ysAoFw5ZPozAIDTsJ69uai/p556yma7/DNSW4uNjbVZt3r1asu6omZptt5m4MCBNrNU5/1Vq1bNOHToUKHHGjx4sN36o6KijPPnz1u2K2pG7qJqLOq8v//+e8P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",
      "text/plain": [
       "<Figure size 1400x1100 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  ROC AUC  0.7322   PR AUC  0.8479\n",
      "  F1       0.6696   κ (Kappa) 0.3325\n",
      "  Sens.    0.5804   Spec.     0.7734\n"
     ]
    }
   ],
   "source": [
    "# ─── Evaluation visualisation ────────────────────────────────────────────────\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 11))\n",
    "fig.suptitle(\"Shipping Delay Predictor — Hold-Out Test Set Evaluation\",\n",
    "             fontsize=14, fontweight=\"bold\", y=1.01)\n",
    "\n",
    "BLUE, RED, GREY = \"#2563EB\", \"#DC2626\", \"#9CA3AF\"\n",
    "\n",
    "# 1. Confusion matrix\n",
    "ax = axes[0, 0]\n",
    "cm = test_results.confusion_matrix\n",
    "sns.heatmap(\n",
    "    cm, annot=True, fmt='d', cmap='Blues', ax=ax, cbar=False, linewidths=0.5,\n",
    "    xticklabels=['Predicted\\nDelayed', 'Predicted\\nOn Time'],\n",
    "    yticklabels=['Actual\\nDelayed', 'Actual\\nOn Time']\n",
    ")\n",
    "ax.set_title(f\"Confusion Matrix  (τ = {test_results.threshold:.3f})\", fontweight=\"bold\")\n",
    "\n",
    "# 2. ROC curve\n",
    "ax = axes[0, 1]\n",
    "fpr_arr, tpr_arr, _ = roc_curve(y_test, y_pred_proba_test)\n",
    "ax.plot(fpr_arr, tpr_arr, color=BLUE, lw=2, label=f'ROC AUC = {test_results.roc_auc:.4f}')\n",
    "ax.plot([0, 1], [0, 1], color=GREY, ls='--', lw=1, label='Random')\n",
    "ax.scatter([test_results.fpr], [test_results.sensitivity],\n",
    "           color=RED, s=80, zorder=5, label=\"Operating point (Youden's J)\")\n",
    "ax.set(xlabel='False Positive Rate', ylabel='True Positive Rate', title='ROC Curve')\n",
    "ax.set_title('ROC Curve', fontweight=\"bold\")\n",
    "ax.legend(loc='lower right', fontsize=9)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# 3. Precision-Recall curve\n",
    "ax = axes[1, 0]\n",
    "prec_arr, rec_arr, _ = precision_recall_curve(y_test, y_pred_proba_test)\n",
    "baseline_pr = float(y_test.mean())\n",
    "ax.plot(rec_arr, prec_arr, color=BLUE, lw=2, label=f'PR AUC = {test_results.pr_auc:.4f}')\n",
    "ax.axhline(baseline_pr, color=GREY, ls='--', lw=1,\n",
    "           label=f'Baseline (prevalence = {baseline_pr:.2f})')\n",
    "ax.set(xlabel='Recall', ylabel='Precision', title='Precision-Recall Curve')\n",
    "ax.set_title('Precision-Recall Curve', fontweight=\"bold\")\n",
    "ax.legend(loc='upper right', fontsize=9)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# 4. Score distributions\n",
    "ax = axes[1, 1]\n",
    "pos_scores = y_pred_proba_test[y_test.values == 1]\n",
    "neg_scores = y_pred_proba_test[y_test.values == 0]\n",
    "n_bins = max(30, int(np.sqrt(len(y_pred_proba_test))))\n",
    "ax.hist(pos_scores, bins=n_bins, color=BLUE, alpha=0.55, density=True,\n",
    "        label=f'On Time  (n={len(pos_scores):,})')\n",
    "ax.hist(neg_scores, bins=n_bins, color=RED, alpha=0.55, density=True,\n",
    "        label=f'Delayed  (n={len(neg_scores):,})')\n",
    "ax.axvline(test_results.threshold, color='black', ls='--', lw=2,\n",
    "           label=f'Threshold = {test_results.threshold:.3f}')\n",
    "ax.set(xlabel='P(On Time)', ylabel='Density', title='Score Distributions by True Class')\n",
    "ax.set_title('Score Distributions by True Class', fontweight=\"bold\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n  ROC AUC  {test_results.roc_auc:.4f}   PR AUC  {test_results.pr_auc:.4f}\")\n",
    "print(f\"  F1       {test_results.f1_score:.4f}   κ (Kappa) {test_results.kappa:.4f}\")\n",
    "print(f\"  Sens.    {test_results.sensitivity:.4f}   Spec.     {test_results.specificity:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c3d4e5",
   "metadata": {},
   "source": [
    "## 9. Model Serialisation with Full Provenance\n",
    "\n",
    "Saving a model file is not enough. Production systems need to answer: *What features was this model trained on? What were its hyperparameters? What test-set metrics did it achieve? Who trained it and when?*\n",
    "\n",
    "`ModelMetadata` captures all of that, plus the fitted feature schema, preprocessing pointer, search metadata, target mapping, and a lightweight inference contract. `ModelSerializer` bundles it with the model object and optionally the training/test datasets into a single, self-describing package — everything needed for audit, rollback, debugging, and exact inference replay.\n",
    "\n",
    "Because we evaluated first (Section 8), we can embed the test metrics directly into the metadata before saving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2d3e4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: shipping_delay_predictor_v1 (v1.0.0)\n",
      "Type: LGBMClassifier (lightgbm)\n",
      "Task: binary_classification\n",
      "Features: 19\n",
      "Trained: 2026-05-05 06:35\n",
      "\n",
      "Performance Metrics:\n",
      "  test_roc_auc: 0.7322\n",
      "  test_pr_auc: 0.8479\n",
      "  test_f1: 0.6696\n",
      "  test_accuracy: 0.6582\n",
      "  test_kappa: 0.3325\n",
      "\n",
      "Cross-Validation:\n",
      "  roc_auc: 0.7426 ± 0.0130\n",
      "\n",
      "Training Data: 8799 samples\n",
      "Test Data: 2200 samples\n"
     ]
    }
   ],
   "source": [
    "metadata = ModelMetadata(\n",
    "    name=\"shipping_delay_predictor_v1\",\n",
    "    version=\"1.0.0\",\n",
    "    model_type=\"LGBMClassifier\",\n",
    "    framework=\"lightgbm\",\n",
    "    task=\"binary_classification\",\n",
    "    trained_at=datetime.now(),\n",
    "    feature_names=lgbm_state.feature_names,\n",
    "    n_features=lgbm_state.n_features_selected,\n",
    "    hyperparameters=lgbm_state.best_params,\n",
    "    optimal_threshold=lgbm_state.optimal_threshold,\n",
    "    training_time_seconds=lgbm_state.training_time_seconds,\n",
    "    tags=[\"binary_classification\", \"logistics\", \"lightgbm\", \"v1\"],\n",
    "    notes=\"Trained on e-commerce shipping data. 80/20 stratified split. Youden threshold.\"\n",
    ")\n",
    "\n",
    "# CV performance from the best Optuna trial\n",
    "if lgbm_state.study:\n",
    "    fold_scores = lgbm_state.study.best_trial.user_attrs.get('cv_scores', [lgbm_state.best_score])\n",
    "    metadata.add_cv_scores(\"roc_auc\", fold_scores)\n",
    "\n",
    "# Test-set performance — locked in from Section 8\n",
    "metadata.add_metric(\"test_roc_auc\",  test_results.roc_auc)\n",
    "metadata.add_metric(\"test_pr_auc\",   test_results.pr_auc)\n",
    "metadata.add_metric(\"test_f1\",       test_results.f1_score)\n",
    "metadata.add_metric(\"test_accuracy\", test_results.accuracy)\n",
    "metadata.add_metric(\"test_kappa\",    test_results.kappa)\n",
    "\n",
    "# Dataset provenance\n",
    "metadata.add_dataset(X_train_transformed, y_train, \"train\")\n",
    "metadata.add_dataset(X_test_transformed,  y_test,  \"test\")\n",
    "\n",
    "print(metadata.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2e3f4g5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model package  → models/shipping_delay_predictor_v1.pkl\n",
      "Metadata JSON  → models/shipping_delay_predictor_v1_metadata.json\n",
      "\n",
      "Package loaded:\n",
      "Model: shipping_delay_predictor_v1 (v1.0.0)\n",
      "Type: LGBMClassifier (lightgbm)\n",
      "Task: binary_classification\n",
      "Features: 19\n",
      "Trained: 2026-05-05 06:35\n",
      "\n",
      "Performance Metrics:\n",
      "  test_roc_auc: 0.7322\n",
      "  test_pr_auc: 0.8479\n",
      "  test_f1: 0.6696\n",
      "  test_accuracy: 0.6582\n",
      "  test_kappa: 0.3325\n",
      "\n",
      "Cross-Validation:\n",
      "  roc_auc: 0.7426 ± 0.0130\n",
      "\n",
      "Training Data: 8799 samples\n",
      "Test Data: 2200 samples\n",
      "\n",
      "Training data included: 8799 samples\n",
      "Test data included: 2200 samples\n"
     ]
    }
   ],
   "source": [
    "model_path = \"models/shipping_delay_predictor_v1.pkl\"\n",
    "\n",
    "ModelSerializer.save(\n",
    "    model=lgbm_state.model,\n",
    "    path=model_path,\n",
    "    metadata=metadata,\n",
    "    train_data=(X_train_transformed, y_train),\n",
    "    test_data=(X_test_transformed, y_test),\n",
    "    include_datasets=True\n",
    ")\n",
    "print(f\"Model package  → {model_path}\")\n",
    "print(f\"Metadata JSON  → models/shipping_delay_predictor_v1_metadata.json\")\n",
    "\n",
    "# Verify the round-trip\n",
    "package = ModelSerializer.load(model_path)\n",
    "print(f\"\\nPackage loaded:\")\n",
    "print(package.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f3g4h5",
   "metadata": {},
   "source": [
    "## 10. Explainability with SHAP\n",
    "\n",
    "Because we set `generate_shap=True` in `TrainConfig`, the trainer automatically computed SHAP values during the training run and stored them in `lgbm_state`. No separate explainer setup, no additional data passing — the results are waiting.\n",
    "\n",
    "We compare **native tree feature importance** (based on information gain split counts) against **SHAP mean |value|** (a theoretically grounded Shapley attribution). Divergences reveal features frequently used by the tree but with disproportionately small marginal contributions — candidates for removal in a future feature engineering cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2g3h4i5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CQrSO9FfeKEzV0KFDERUVpbG96hy27du3F1+Dr7/+WpwPe9++fXjy5Il41UBOjI2N0apVK8ydOzfX2xAREdHHY+GWiIhIRzLf5Cc/kpOTAWRc9q8s2BkaGiIkJAT16tWDVCrFr7/+ivXr1wMAFApFvo/14cMH8efc3AwIAJydnfN9PGXfCoK1tbX4s/JS/szLVQmZbvxWkHKz7/yct499D9ja2qo9NzL6vzRRGbNq7MopJz5WQb7OBUkmk2HChAkYOnQoDh48qDXOuXPnioVFX19fjBkzBk5OTkhPT0dAQIDY7mN+91T16dMHBw8eBJBReHz27Bnevn0LAOjcuXO+isOf6jX4mN9/IO9x5aZ9cHAwJk6ciPT0dGzatAnffvut+CVa2bJlxZvBARlTDLi5uWHTpk24dOkSYmJi8PTpU/GLiOPHj2PHjh25is3BwQGDBw/G4MGDAWR8IderVy9cuHABgPpNzDJT3ihMlZWVlca27969w9atW8Xnq1evxurVq7O0+/DhAzZs2KB2IztVymKxgYEBLC0t4e7unqupIYiIiKhgcY5bIiIiPVS+fHnx5zp16kAQBI2PpKQkAFC7Q3rlypUxadIkNGrUCPXq1cODBw80HkO1gAloLiwp5yIFMuZ3Vcrt/Iiainuqffvll1809islJQW//vprro5RmG7cuIHnz5+Lz0+ePCn+LJFI4O7uDk9PTxgaGmpsk5KSgkuXLonPvby8NB5HW1FU9TXL/Hrl5z2QV8q5UwHg/v37uHjxYpY2yuKu6uvctWtXja+zQqEQ5y7Njrb3f24feR1tq9S/f3+4uroCAM6cOaOxjep5nzhxIjp27JjjCOHsXsectG3bFnZ2dgCAXbt2YeXKleI61dGimd+HN2/e1Po35FPNc5vT73+pUqWQlpamNS5XV9cC70fx4sXFOYYfPHiAQYMGie/Z3r17q8UsCALq1q2LpUuX4vTp03j27Blu3boFuVwOIOP8K4vm2ly7dg23b9/OsrxixYro0KGD+LygCvt//PGHOH9wTlRH5mamLBbXqVMHFStWZNGWiIhIRzjiloiISA917twZEyZMwOvXr3Hq1Cl06NAB3bp1g5WVFR49eoTo6Gjs3LkTXbt2RWhoqDjFApBRKFiyZAnc3d3x+++/IyIiQuMxbG1tIZFIxKLF/PnzUbNmTRgYGKBu3boAMoo/ypFgQ4YMwaBBg3Dx4kVx9GZ+9OnTRyxejho1Ck+fPkWNGjXw/v17PHjwACdPnkR4eDiuXr2a74Lbp5KSkoK2bdti9OjRSExMxPjx48V1jRs3FkettmnTRhyJN3ToUCQnJ8PR0RHLli0TRyubm5ujU6dOeTq+nZ0d7t27ByDj5kMGBgYwMjKCj49Pvt4DedW4cWO4ubkhNjYWgiCgRYsWGDduHLy9vfHkyRPs3LkTI0aMgJ+fH4KDg7FgwQIoFAr8/vvvsLCwQMuWLWFqaooHDx7g6tWr2LFjB6ZOnYrg4OACia+gmZiYICQkBH379tXapkyZMuKUEj///DOMjY3x6NGjbG9qpiy8AsCePXtQr149mJmZwdXVFS4uLjnG9PXXX2PhwoV4/fq1OMray8sLderUEdtZW1ujXbt24g22mjdvju+//x7u7u54+fIl7t+/j/379+Pu3buIiYnJ+WQUkCZNmsDFxQUPHjxAXFwcAgMD0a9fPzg6OuLx48eIiYnBrl27UKVKFaxZs+aT9KNPnz7YvXs3gIzzD2SMUs9c+O3YsSMkEgkaNmyIEiVKwMLCAhcvXhSLtQqFAqmpqTAzM9N6rLNnz2LAgAHw8/NDUFAQvL29IZPJcOvWLcyfP19spzpty8dQLcY2b95c7UZ4QMZI21GjRolfmly8eBHVq1cvkGMTERHRJ1BAc+USERFRDjLfnCwnO3fuFExNTbO9UYzyxkYKhUKoX79+lvWGhoZqyzPfxKZ27doat1HauHGjxuNWqlRJ/Dm7m5Npkp6ernYzJW0P1ZuhaZPbm5OpatCggcYbK2m7IZTqcjc3N8HGxiZLrFZWVsL169fFbR4/fqx2c6/MDxMTE2Hr1q15Om+CIAjjx4/XuL/IyMh8vQdUb06W+b2h7TydP39esLOz09o31ZumLVmyROMN0lQfmW9upSuqMf3111/i8rS0NMHDw0Ntver748CBAxr71bBhQ63v5/3792vcZurUqYIgaH8vKl29ejXLtnPmzMnSLiEhQahYsWK251/19zc/5yq7m5NpuvGaIGTcPMva2jrbuFTfjwXdj7S0NKFYsWJq27do0SJLu8DAwGyP2aZNmxyPtXLlyhz/1jk5OQn3798Xt8n890DTjQhVfz+V5+rhw4dqv2/Hjx/XGFPdunXFNkOGDMn1MYmIiKjwcaoEIiIiPdW2bVtcvnwZ/fv3F+cXNDc3h7u7O1q2bIkVK1Zg0KBBADIuSd61axf69u0LZ2dnmJmZoU6dOjhw4AAaNmyo9Rjr169H8+bNtc6L2a1bN8yZMweurq4wNjaGp6cnFixYgAULFuS7XwYGBti0aRO2bt2KZs2awcHBAUZGRrC3t4ePjw8GDBiAPXv25DjyUBdcXV1x+vRp/O9//4OlpSXMzc0RFBSEEydOqF0CXqxYMVy4cAHTpk1DtWrVYG5uDmNjY7i4uOCbb77B+fPn0bFjxzwf/8cff8SAAQPg6OiY5TL0/L4H8srX1xf//PMPxo4di0qVKsHMzAxSqRSurq7o3LkzSpcuLbYdPHgwzpw5g+7du6NUqVIwMTGBpaUlypUrh44dO2LdunVo165dgcX2KRgZGYk3dNKkSZMm+PPPP1GtWjXIZDIUL14cI0aMQHh4uNZtmjZtip9//hlly5ZVmwYgtypVqoQaNWqIz42NjdGjR48s7RwcHHDu3DnMnTsXtWrVgpWVFYyNjVG8eHHUqlULP/zwQ67naC1ItWvXxrVr1zBy5Eh4e3vDzMwMMpkMpUuXRpMmTTB//nxMmTLlk/XDyMgoy/lSnWZCadCgQejRowfKly8PW1tbGBoawsLCAtWrV8dPP/2E33//PcdjtW3bFmvXrkVwcDAqV64MJycnGBsbw8zMDBUrVsTIkSMRFRWFUqVK5Tp+bdavXy9OueDk5CReOZGZ6s0Pf//9d7x///6jj01ERESfhkQQPuHdN4iIiIg+c2FhYejVqxcAoEGDBgU27QAREREREVF2OOKWiIiIiIiIiIiISM+wcEtERERERERERESkZ1i4JSIiIiIiIiIiItIznOOWiIiIiIiIiIiISM9wxC0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHBLREREREREREREpGdYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9AwLt0RERERERERERER6hoVbIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHBLREREREREREREpGdYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9AwLt0RERERERERERER6hoVbIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhlog+a6GhoZBIJOIjPDxcbX1wcLC4bsWKFR91nNDQUCxYsCDLurCwMPEYoaGh+T7Gx1A9Bzk9IiIidBKjNhEREWJsbm5uug6nwC1YsEB8/3zpYmJiIJVKIZFIsGPHDnF5VFSUeA705f33/v17jB07FgEBAbC0tBTfg/7+/hrbL168GO3bt4eHhwesrKxgYWGBihUrYvTo0UhISFBre/78eXF/x48fL4TeEBERaXb37l307t0brq6uMDExgYWFBUqVKoWAgAAMHz4cKSkpYlvVnFbT/4eqebW2vKZPnz5qeefkyZM1tlPdl/JhYGAAGxsbBAQEYMuWLbnqn7+/v9o+pkyZkqXNrFmz1NrUqlUrV/vWF8rXRXnO4+LiYGBgAIlEAlNTUyQmJmbZRhAEuLq6in0+duxYno6pel5jY2MLoBc5U34mCA4OzvU2L168gL29PSQSCebNmycuj42NFXPPXbt2FXyw+TRt2jQ0a9YMdnZ2OX7+2bBhA7p37w4vLy/Y2trCzMwMXl5e6N+/f5bX5OHDhzA1NYVEIsG6des+fUeocAlERJ+xSZMmCQDER8WKFYX09HRxfc+ePcV1y5cvz/dxlPtwdXXNsm7NmjXi+kmTJuX7GB9D9Rzk9Dh69KhOYtTm6NGj2Z7fz52rq6vYvy9dy5YtBQBCpUqVBIVCIS7Xh9+RzBITEzX+fjRo0EBje6lUqvV3ysnJSXjw4IFa++bNmwsAhMqVK6v9TSIiIiosd+7cEWxtbbPNC58+fSq2V/3/WtP/h6p5tab/z9+8eSNYWFio7b906dJqOYGmfWl7jBs3Lsc+NmjQQG0bZ2dn4f379+L6Dx8+qOViAISvvvoqdydQTyhfF9Vz3rBhQ7E/K1asyLKNan6t7TXIjup5vXfv3kf2IHeUMffs2TPX2wwZMkQAIDg6Ogpv3rzJsq+87u9Ts7KyyvI+1/b5p1y5clp/N+RyuXDp0iW19oMGDRJ/B5KTkwuhN1RYOOKWiL4o//zzDzZt2lSoxwwODoYgCBAEQWejKpXHVz5U3bt3T22dthGFb9++LYRIiwaFQoHU1FRdh1Gorly5gt27dwMAvv32W0gkEh1HlD1jY2N8++23WL16NRYtWpRjezs7O0yaNAnXr19HSkoKjh8/DmdnZwDA48ePMXfuXLX23377LQD180JERFSY5s+fjxcvXgAAJkyYgKdPnyIlJQU3btzAunXr0LJlSxgaGhbY8Xbs2IHk5GQAEPOAe/fu5Xj1yaRJkyAIApKTk9Vy6dmzZyMuLi5PMcTHx6td9RMeHo779+/naR+fA9VRqevXr8+yXnVZz5499T4vy4+EhASsXLkSQMb5MDMz03FEOevZsyeWL1+OrVu35tjW3Nwc3333HS5duoSUlBRcvnwZFSpUAAC8fv06y2h2Ze4ZHx+P1atXF3zwpDMs3BLRF0OZeE6aNAlpaWnZtj1+/Dhat26NsmXLwsrKCkZGRrC3t0eTJk3ULqdRTsWgdP/+/SyXtWiaKqFdu3bislOnTqkdu1+/fuI61YLO8ePH0bZtWzg5OcHExASOjo5o3749Ll68+BFnRV1sbKzaJXC7d++Gr68vTE1NMWjQoHzFkpCQgFGjRsHLywsymQzm5uaoUaMGfvnllyxF5LxQnUIhODgYv/32Gzw9PSGTyVC7dm2cOXMGqampGD9+PJydnWFjY4OgoCDcuXNHbT9ubm7ifm7fvo02bdrA0tISFhYWaNeuncbLv3bs2IHGjRvD1tYWJiYmKF68ODp37oxLly6ptVOdquOXX37BuHHj4OLiAmNjY/z++++QSCRqHxZUL9MDgHfv3qFXr16oUqUKHBwcYGJiAnNzc/j4+GDixIl48+aN2vFU33vnz59HkyZNYG5ujhIlSmDgwIF4/fq1Wvv09HSsWLEC9erVg7W1NUxMTFCiRAm0bt0a8fHxYjtl8ufj4wNzc3PIZDJUqlQJM2fOxPv373P1ei1btgwAYGRkhC5duqid/169eonPJ0+erPEyy0uXLqFLly4oUaIETExMYGtri0aNGql9+AKyvi82bdoEHx8fmJqaolSpUpgyZQoUCkWO8Zqbm2PZsmXo1asXPDw8cmwfHR2N0NBQlC9fHqampqhfvz6+++47cf2NGzfU2isvg1M9N0RERIXp1q1b4s/NmzeHvb09TE1NUa5cOXzzzTf466+/YGNjU2DHCwsLE38eOHCgxuXZkcvlmDhxIiwsLABkfBF+7ty5XB+/dOnSADKmN1JS/qxcp0laWhoWLFiAmjVrwsLCAlKpFOXKlcO4ceOQlJSk1vbXX39Fo0aNULJkSZibm8PExAQlS5ZEly5dcPXqVbW2qtNBhIeHY9iwYXB2doaFhQXq16//UTl++/btxfN08uRJ3L17V1z37t07bN++HUBG7tijRw8AwE8//YT69eujePHikMlkMDU1RZkyZdCnT59cTYeQOQdTpe2y/4LIMbX57bffxIESX3/9tbjc398fAQEB4vO1a9dqjPv27dvo06cP3NzcYGJiAktLS9SpUwcrV65U+wyT+fPTgQMH8NVXX0Emk8HJyQkjRoxQm3IkOwsXLsTAgQNRqVKlHNsePXoUP//8M6pWrQpTU1NUqVJFLXfOnHtWrFgRPj4+AJh7fnF0M9CXiKhgqE6V0K1bN/Fy5mXLlgmCoH2qhPnz52d7adamTZuy7D/zQ3lZi6bLwPfu3SsuGzhwoHjcd+/eiZfIlCxZUvjw4YMgCIKwbNkyQSKRaDyOsbGx8Ndff+XpvKhur3p5071798TlNjY2goGBQZbLiPISy507dwRnZ2et56hLly45xqptqgTV5Q4ODln2bWlpKTRr1izL8vLly4vnVRDUpyrQtJ+SJUsKz549E9uPGjVKa3+MjY2FP/74Q2yr+v6wt7dXa6v6vtD0EATtl+srH02bNtX4uspkMo2X7vfv319sm5qaKjRu3Fjrvi9fviwIgiA8f/5cqFChgtZ2fn5+Qmpqao6vY8mSJQUAQrVq1dSWZ748UfWh/H3ZuXOnYGxsrLXd6NGjc/2+ACB8++23OcarSvX3VdtUCZqEhoaK2/Xr1y/L+v/9738CAMHExER4+/ZtnmIiIiL6WH379hX/nzI3Nxc6dOggzJs3Tzh16pTadAJKHzNVwv3798W8snjx4kJSUpIgk8kEIOOy7tevX+dqXwqFQm26hW3btmXbR9VL+ufMmSP+fPHiReGff/7RuE51qoR3795lmW4hc1754sULsX3r1q21tpXL5cKtW7c09tHGxiZLe3t7e+HVq1fZ9k/1dcl8znv37i3ua/LkyeLyzZs3i8v9/f3F5ZUrV9Yau7Ozs/D8+XON51X5WSK76QeUy1Vz+bzmmHmdKqFu3boCAMHOzk5teXavp3Lfp0+fFuRyudZ2HTt2FKeXUP38ZGtrKxgaGmZp37x581zFrBQdHa3xnOUkLCxM3K5JkyZZ1g8bNkxcf/v27TzFRPqLI26J6Ivh4uIiXiIybdq0bL/59Pf3x+HDh/H48WOkpqbizZs3+Ouvv8T1ysueQ0ND1b5xdXV1FaccyO6b6aZNm4rfOG/ZskX8Rjk8PByvXr0CkHHzBkNDQzx69AjfffcdBEFAtWrVEB0djdTUVFy4cAEODg5IS0tD//798eHDh3ydF20SExPRsWNHPHjwAElJSZgwYUKeYxk+fDji4+NhZGSEbdu24e3bt3jy5Ak6duwIANi8eTP+/vvvj4716dOnWL9+PZKSktCmTRsAQFJSEvbv348dO3bgxYsX8PX1BZAxMvL8+fMa9/PVV1/h8ePHiI2NFW9M8fDhQ/H1Pn/+vHhjA2traxw5cgRJSUniaI20tDT069dP43vr1atXCAsLQ1JSEmJjY9GqVSvxxhBKyveO8j0lk8mwceNG3LlzB8nJyXj//j1u376NKlWqAAAOHDiAa9euZTlWSkoK2rZti6dPn+L06dOQSqUAgHXr1on7XrJkCQ4dOgQAcHJywu7du5GcnIy4uDgsXrwYVlZWACBe/q/cJikpCS9fvsSwYcMAZIy+Vl6Gps3Dhw/x8OFDAEDlypXV1sXGxmLNmjXic+XlkML/n1okJSUF/fv3F0fJL126FElJSThy5AgsLS0BZPw+anpNnz59it9++w3Jycn4+++/xfOwYsWKLKMQClpsbKw4xYKhoaHayCKlatWqAci4EVrm0dpERESf2rBhw8T/G9+8eYPt27dj1KhRqFOnDpydnTFjxgytV0cdO3Ysy83D1q5dq/VYa9euFa946dixIywsLNC8eXMAGaMulSNAs/PmzRtMnjxZnG7BwMAANWvWzHV/K1asKE4JtnjxYjF/q1WrlpgnZrZkyRLxxl3jx4/H8+fP8ebNG8yaNQtARl45ffp0sf2gQYNw4cIFPHv2DGlpaXj+/Dl+/PFHsZ/aboZsZmaGc+fO4dmzZ2KMz549w549e3Ldv8xUR49u2LBB/Fl1mgTVNqGhobh69SpevHiBtLQ0PHnyRLwqKj4+Hhs3bsx3LJoURI6pjUKhEHND5ShTpYiICBw9elR83rNnTzH3VI7+7tOnj3il2vjx4/Hy5UtcvHgRLi4uAIBt27ZpfM++ePECEydOxKtXr3Dq1CnY29sDAPbs2YP9+/fnqy+59eLFC0ybNk18PmTIkCxtlLknAJw5c+aTxkOFh4VbIvqiTJgwAXK5HP/995/aZVKZlSxZEn/99Rf8/f1hbW0Nc3NztGrVSlyvTDLyy8DAAH369AGQUSBVTomgvMunoaGhuH7v3r3iZT6XLl1C+fLlIZVK4evri6dPnwLISKauXLnyUTFlZmlpid9++w0lS5aEhYUFPD098xTLu3fvsG/fPgDAhw8f0LFjR5iZmaFYsWLYtm2beBxlm4/x1Vdf4euvv4aFhQWaNm0qLq9duzbatWsHGxsbNG7cWFyurag+b948FCtWDK6urmrzQh04cAAA8Oeff4rLevXqhYCAAFhYWGDIkCFiQfLZs2dZpr8AgG+++QY9e/aEhYUFXF1dxcvksyOVSvHu3Tv07NkTpUqVgqmpKdzd3REVFSW20fReNDAwwLJly2Bvb49atWqhYsWKADIujXvy5AkA4I8//hDbz5w5Ey1atIBcLoeLiwuGDBkiXjKo2m7IkCGwtLSEtbW12ryvOb2GqtMuODg45NhvVSdPnsSzZ88AAFWrVsWgQYNgYWGBgIAAtSkWwsPDs2xbu3Zt9O7dG3K5HM2bN0e7du0AZBTIDx48mKc48iI6Ohr+/v548eIFJBIJli9frpYoK6meC9VzREREVBgqVaqES5cuoUOHDpDL5Wrrnj9/jgkTJmDp0qUFcizVO9l37txZ7V8g++kSlNMoyeVytfxs9OjRKFWqVJ7iUBYFN2/eLBYzhw4dqrW9ah40Y8YM2NnZwdzcHGPHjhWXq+ZBTk5OmDt3LqpWrQpzc3PY2dmpFdO0fYYYM2YMatSoATs7O7Rv315cnpspCrSpX78+3N3dAQAxMTE4c+YMnj59KhYQ5XI5OnToILa3s7PDDz/8AG9vbzFnV/1y/WM//2RWEDmmNs+fPxcHxuQ197x9+7bYV3t7e0ydOhVWVlaoVq0aRo4cKbbTlHsWL14cISEhsLS0RO3atdGvXz9xnfLzxKfw33//oWHDhrh9+zYA4IcffsD//ve/LO2Ye36ZWLgloi+Kg4ODOO/krFmzxNGtqhQKBRo1aoQFCxbgxo0bGkdPvnv37qNj6d27tzjv7vr16/H06VMxOQkKChK/0VUW2nKiLG4VlHLlysHc3FxtWV5ief78ea5GARdE3MqkFMgYpaqkOl+ZckQJoP31Ux39qvpzQkICAPX+q64HoDZnl6bzVL16da3xazNv3jz06dMHJ06cQGJiosa5WTW9P52cnNTmpFN9HZV9f/z4sbgsu3m0cvOaF/R7T9vx83rOM7fX9JoWtMjISNStWxf379+HkZER1qxZo5a0ExER6ZMKFSpg27Zt4hfP06dPVyuGbt68WeN2DRo0yHLz2549e2psGxkZKRaUbGxsYGpqiqioKJQoUQJGRkYAMkbw5lSklEgksLKygp+fHzZs2CCOes2L//3vf3B1dcW7d+/w5s0bODk5qRUvM8tLHnT//n3UqVMHmzdvxoMHDzTO0artir/y5cuLP2vK2/JLOX8tkPF54/fffxfz8w4dOojHOnv2LAICAvDXX38hPj5e4/1AcjtPKwC1kdraPg/oOsfURjWukiVLqt2gL6fcs1SpUmr3PymM3PP69euoXbu2OIhn2rRpal8W0JePhVsi+uKMHj0adnZ2ePHihdr0B0rXrl0Tbx5QrFgxXLt2DR8+fMhy84GPVbx4cbRo0QJAxuUzS5cuFROb/v37i+2KFSsm/jxgwIAsSbIgCFAoFAgMDCzQ+DTdeTUvsdjZ2YnJuIWFBVJTUzW237Rp00fHqjxObpdro3qjMNWfHR0dAaj3P/MdiFU/bKi2U9J2J9vs7uKrelnbwoUL8fbtWwiCII4c1cbY2DjHYzg5OYk///PPP1r3peyLRCLBf//9p/E11DTCWJWzs7P4s3Jkdk7xZT4+kPdznrm9pte0IG3btg1NmjRBYmIiLC0tsWfPHq0fYgH1c6H6ehARERUG1QEMUqkUtWvXxvjx49Vys+fPn3/0cVRH0yYmJqJatWqoWrUq6tatK+a+giBonWpBOY2SQqHAy5cvcezYMXTv3j1fsRgaGqrdcHfAgAEwMTHR2l41vzh9+rTGPOi///4DAOzatUu8cWzDhg3x6NEjCIKgcWRmZqq5W3Z5UV717NlT3N+WLVvURtCqTpOwefNmpKenAwC6d++OZ8+eQRAEtdGvOTE1NRV/fvv2rfizsmifWUHkmNrY2dmJ5/Rjcs+HDx+K5wXIOfd88OCBWtH6U+eex48fR7169RAXFwdjY2OsW7cOP/zwg9b2zD2/TCzcEtEXx9LSUry8SfU/YiXVYp+hoSHkcjlevXqldmlMZsrL3p89e4ZHjx7lOhZlgfb9+/f46aefAGR8s6uc8wvIGH2rHC26Zs0arFu3Dq9evUJKSgqioqLw448/ok6dOrk+5sfISyympqZo1qwZACA5ORm9e/dGbGws0tLS8ODBA6xduxZ169bF8ePHCyX23BgzZgyePHmCuLg4TJo0SVyunH5B9ZKjsLAwHDt2DK9fv8ayZcvEb7nt7e3z9HqoTpmgOgUCoP5elMvlkEgk+PPPPwtkXmDV4u+4ceOwd+9evH79Go8ePcLy5ctx7949AEDbtm0BQBxJEx0djbS0NDx+/Bjbt29Hs2bN1OZK06RkyZIoUaKExj4C6ucgOjpabYRKnTp1xPWXL1/GihUr8Pr1axw7dkztg6DqVCZKp0+fRlhYGF6/fo29e/di586dADKS9SZNmmQbM5Dx+/zs2TO1L23S0tLE5aofSubPn4/OnTsjNTUVJUqUQGRkZI7HUM5ra2JionEqBSIiok9p6NChCAoKwoYNG3D//n2kpaUhISFBrXDr7e39Ucd4+/at2hRZ2Vm7dq3WOXULUt++fdGmTRu0bt0aAwYMyLatMg8CgMGDB+PixYtITU3F8+fPsWfPHnTs2BEzZswAoJ63mZiYwNzcHHfu3NHp6MdSpUohICAAQEYRXpmHlS5dGn5+fmI71dhNTU0hk8lw5coVLFy4MNfHUh2Nevr0aTx//hypqalaC4kFkWNqY2BggBo1agAArl69muV9pZp7xsTEiAV3IONKPuUI6GfPnmHSpEl49eoVoqKiMH/+fLGdpqkIHj16hJ9++glJSUk4c+aM2hy9qtO5aZOYmIhnz57h5cuX4jKFQiHmnsp5dwFg69ataNq0KRITE2FlZYV9+/bhm2++yXb/qvdUUN7Pg74ABXKLMyIiHZk0aZJ458yxY8eKy9++fSsUL15c7W6fy5cvFwRBED58+CBUrFgxy91APT091Z6ratWqlda7kqregTfz3V7T09MFFxcXte0mTpyYpR/Lly8XJBKJ1jub5uVuo4IgqG2rvBOsIKjfFVXT3YLzGsvdu3eFEiVKaG0LQDh69Gi2sareoVZ139ruXKt6vlWXq74X1qxZIy53dXUVl2uKtWTJksKzZ8/E9iNGjNDaFyMjI2H79u05HlPV0KFDs+xHee5nzpyZZZ2BgYFQtmxZjfvV9n7QdOff1NRUoXHjxlr7cvnyZUEQMu746+3tne1rqK1vqvr16ycAEAwNDdXuviwIgvDo0SNBKpVqfW9s375dMDIy0nr8ESNGiPtSfV9k/h1XPgYOHJhjvKrnU9tD9fc5p7aZX5O0tDTBzs5OACA0bdo0V/EQEREVpO7du2f7f5dMJhMuXLggtlfNsTTliT179szyf+S6devEZVWrVs2yzYcPH9Tyr4iICK37yg/VHGjv3r1a26nmD1999ZW4/N27d4K/v3+u8oG7d+8KZmZmWdarfoZQPW+qfVTNh7P77KCJsr22tqqvgfIRGhqq1ubUqVOCgYFBtrGr5tWacktBEIQmTZqIy01MTASpVKp2TlTzobzmmMrXSDWO7Pz000/ifq5evaq2LiUlRXBwcNB6vJMnT2p8LZWPdu3aCQqFQhAE9c9PDg4OgrGxcZb2QUFBYvvsqH4u0fRQ7XtObYGs5TwfHx/xdaUvB0fcEtEXSSaTYeLEiRrXGRoa4q+//kKbNm1gY2MDS0tLtG/fHkeOHNG6v8WLF6NVq1a5uuGUKtWblCmf9+3bN0u7gQMHIjIyEh06dICzszOMjIxga2uLSpUqYeDAgfj111/zdNyPkZdYSpcujaioKIwZMwYVKlQQv8EvU6YMWrVqpfWmTbpy8uRJtGvXDhYWFpDL5WjTpg0iIyPVXtf58+djy5YtCAgIgLW1NYyMjMT50U6dOqV2Q4ncCA0NRffu3VGsWLEsl22NHj0aU6ZMgZubG6RSKSpXrow//vgD9erV++i+mpiYYN++fVi2bBnq1q0LKysrGBsbw9nZGa1atRIv/7K1tcXZs2cxdepU8UYbUqkUrq6uaNKkCebNm4egoKAcj6e8LDE9PR1btmxRW1e8eHFs3LgRlSpVUpujWKl9+/Y4ffo0OnbsCCcnJxgZGcHKygr+/v7YvHmz2ugHVU2aNMH27dtRuXJlmJiYoGTJkpg8eTKWLFmS19NV4Pbt2ydefqp6ySYREVFh+e677zBhwgTUr18fpUqVgpmZGYyNjeHi4oJu3brhzJkz+ZqjX5Xq9Ae9e/fOst7Q0FBtWqHsblKmC1KpFAcPHsTixYtRu3ZtWFpaijmFn58fpk2bJsZfunRp7NmzB7Vq1YKZmRmcnZ0xevToPE038Cm0b98elpaW4nOJRKI29y2QcUPXbdu2wcfHB6ampnB1dcX06dMxbty4PB1rw4YN6Ny5M+zt7WFiYoImTZrg7NmzGtsWVI6pTZ8+fcRpMDZu3Ki2ztTUFFu3bkXNmjWz3JgPyLji6/LlywgODoaLiwuMjY0hl8vx1VdfYfny5di2bZvG6RYqVKiAAwcOoHbt2pBKpXB0dMTw4cOxffv2Ap0CIz/+/fdfcTpA5p5fFokgFMK1CkRERDri5uYmzj/F//I+rZYtW+Lvv/9G5cqVcfny5U+SwEZERIiXBPbs2VPvPgAqtWjRAnv27IGPjw8uX74MAwN+V05ERER5FxYWhl69emHSpEkIDQ3VdTifjDLHy0t+N2TIECxduhTFihXDvXv3NA4Q+FixsbHiDZEbNGiAiIiIAj9GQRg8eDCWLVsGJycnxMTEaCxY0+eJnyKIiIioQMyfPx8mJia4cuWKON9sUXThwgXs2bMHALBo0SIWbYmIiIg+gSlTpsDOzg5PnjzBsmXLdB2Ozjx69Ai//fYbAGDWrFks2n5h8nY7biIiIiItPDw8kJqaquswdM7X15eju4mIiIg+MVtbWzx79kzXYehciRIl8O7dO12HQZ8IC7dERPRFi42N1XUIVID8/f1ZFCUiIiKiQuHm5sbck3SKc9wSERERERERERER6RlOukZERERERERERESkZzhVApEOKBQK/Pfff7CwsPgkd10nIiL6UgmCgOTkZBQvXpw3fiPSMea0REREeZeXfJaFWyId+O+//+Di4qLrMIiIiD5bDx48QMmSJXUdBlGRxpyWiIgo/3KTz7JwS6QDFhYWAID79+/D2tpat8EUMoVCgadPn8LBwaFIjpQqyv1n39l39r3o+JR9T0pKgouLi/h/KRHpTlHLaYva3/Wi1N+i1FeA/f3Ssb/6Ly/5LAu3RDqgvJTM0tISlpaWOo6mcCkUCrx79w6WlpafzR/VglSU+8++s+/se9FRGH3nZdlEulfUctqi9ne9KPW3KPUVYH+/dOzv5yM3+ezn1SMiIiIiIiIiIiKiIoCFWyIiIiIiIiIiIiI9w8ItERERERERERERkZ5h4ZaIiIiIiIiIiIhIz7BwS0RERERERERERKRnWLglIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQMC7dEREREREREREREeoaFWyIiIiIiIiIiIiI9w8ItERERERERERERkZ5h4ZaIiIiIiIiIiIhIz7BwS0RERERERERERKRnWLglIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQMC7dEREREREREREREeoaFWyIiIiIiIiIiIiI9w8ItERERERERERERkZ4x0nUAREVZ0JwUGJpKdR1GoZJAgTIWqbibnAKhCH53VJT7z76z7+x70SGBAtv66ToKIiosZY7MhiA31XUYn5yBAPgoLHHVIAkKia6j+fSKUn+LUl8B9vdLx/4WrMSmEwt+p3lQtD5FEBEREREREREREX0GWLglIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQMC7dEREREREREREREeoaFWyIiIiIiIiIiIiI9w8ItERERERERERERkZ5h4ZaIiIiIiIiIiIhIz7BwS0RERERERERERKRnWLj9wu3atQvu7u4wNDTEiBEj8rUPNzc3LFiwoEDj0pWwsDBYW1vrOgwiIiKiIksikWDXrl26DkPnNOXpBZG7f4wvKe8nIiL6ErBwmwePHz/G0KFDUaZMGUilUri4uKBVq1Y4fPjwR+/7UxUUBwwYgA4dOuDBgweYOnWqTmLISWhoKCQSCSQSCQwNDeHi4oK+ffvi6dOnhR5LXsXGxkIikSAqKkrXoRARERHpVHBwsJjTGRsbo1ixYmjSpAlWr14NhUIhtouPj0dQUJAOI82bwszT85K7ExER0ZfPSNcBfC5iY2NRt25dWFtbY/bs2fDx8UFaWhr279+PwYMH48aNG7oOMYvXr18jISEBgYGBKF68uK7DyZa3tzcOHTqE9PR0XL58GX369MGjR4+wd+/eLG3T09MhkUhgYMDvHYiIiIj0SbNmzbBmzRqkp6fjyZMn2LdvH4YPH47t27cjPDwcRkZGcHJy0nWYOqcpTy+o3P39+/cwMTEpqFCJiIhIh1j5yqVBgwZBIpHg3Llz6NChAzw9PeHt7Y2RI0fizJkzAIC4uDi0bt0acrkclpaW6NSpE548eSLu48qVKwgICICFhQUsLS1RvXp1XLhwAREREejVqxdevXoljlIIDQ3NMabExET06NEDNjY2MDMzQ1BQEGJiYgAAERERsLCwAAA0bNgQEokEERERWveVUwxv375F7969YWFhgVKlSuHXX39V2/7Ro0fo3LkzbGxsYGdnh9atWyM2NjZ3JxcQk/gSJUqgZcuWGDZsGA4cOICUlBRxlMPu3btRoUIFSKVS3L9/P9v+K4WFhaFUqVIwMzND27Zt8fz5c7X1wcHBaNOmjdqyESNGwN/fX3yuUCgwa9YsuLu7QyqVolSpUvjpp58AAKVLlwYAVK1aFRKJRG07VampqUhKSlJ7EBEREX1ppFKpmNNVq1YNEyZMwJ9//om9e/ciLCwMgPpUCe/fv8eQIUPg7OwMU1NTuLm5YcaMGeL+Xr58if79+6NYsWIwNTVFxYoVsXv3bnH9jh074O3tDalUCjc3N8ybN08tHk3TMlhbW4uxKK+e2rlzJwICAmBmZobKlSvj9OnTAHLOkbXJT56uLXc/deoU/Pz8IJPJ4OLigmHDhuHNmzfisdzc3DBt2jQEBwfDysoK/fr1y9V2CQkJaNWqFWQyGUqXLo2NGzfm2C/mtERERIWLhdtcePHiBfbt24fBgwfD3Nw8y3pra2sIgoA2bdrgxYsXOHbsGA4ePIg7d+6gc+fOYrvu3bujZMmSOH/+PC5evIhx48bB2NgYderUwYIFC2BpaYn4+HjEx8dj9OjROcYVHByMCxcuIDw8HKdPn4YgCGjevDnS0tJQp04d3Lx5E0BGQhsfH486depo3VdOMcybNw++vr64fPkyBg0ahG+//VYcZfz27VsEBARALpfj+PHjOHHiBORyOZo1a4b379/n+jyrkslkUCgU+PDhg3iMGTNmYNWqVfj333/h6OiYbf8B4OzZs+jduzcGDRqEqKgoBAQEYNq0aXmOZfz48Zg1axZCQkJw/fp1bNq0CcWKFQMAnDt3DgBw6NAhxMfHY+fOnRr3MWPGDFhZWYkPFxeX/JwWIiIios9Ow4YNUblyZY150qJFixAeHo6tW7fi5s2b2LBhA9zc3ABkfHkeFBSEU6dOYcOGDbh+/TpmzpwJQ0NDAMDFixfRqVMndOnSBdeuXUNoaChCQkLEomxe/PDDDxg9ejSioqLg6emJrl274sOHD4Wap2tadu3aNQQGBqJdu3a4evUqtmzZghMnTmDIkCFqx5szZw4qVqyIixcvIiQkJFfbBQcHIzY2FkeOHMH27duxbNkyJCQkZNsv5rRERESFi1Ml5MLt27chCAK8vLy0tjl06BCuXr2Ke/fuiQnM+vXr4e3tjfPnz6NGjRqIi4vD999/L+7Hw8ND3N7KygoSiSTXl47FxMQgPDwcJ0+eFAuyGzduhIuLC3bt2oWOHTvC0dERAGBra5vjfk1MTLKNoXnz5hg0aBAAYOzYsZg/fz4iIiLg5eWFzZs3w8DAAKtWrYJEIgEArFmzBtbW1oiIiEDTpk1z1SelGzduYPny5ahZs6Y48iAtLQ3Lli1D5cqVc93/hQsXIjAwEOPGjQMAeHp64tSpU9i3b1+uY0lOTsbChQuxZMkS9OzZEwBQtmxZ1KtXDwDg4OAAALCzs8v2HI8fPx4jR44UnyclJTHRJSIioiLDy8sLV69ezbI8Li4OHh4eqFevHiQSCVxdXcV1hw4dwrlz5xAdHQ1PT08AQJkyZcT1P//8Mxo1aoSQkBAAGbne9evXMWfOHAQHB+cpvtGjR6NFixYAgMmTJ8Pb2xu3b9+Gl5dXoeXpmpbNmTMH3bp1E29U5uHhgUWLFqFBgwZYvnw5TE1NAWQUx1ULyj169Mh2u7i4OOzduxdnzpzBV199BQD47bffUL58+Wz7xpyWiIiocHHEbS4IggAAYlFSk+joaLi4uKglLhUqVIC1tTWio6MBACNHjkTfvn3RuHFjzJw5E3fu3Ml3TNHR0TAyMhITLSCjeFiuXDnxeAXJx8dH/FmZuCq/kb948SJu374NCwsLyOVyyOVy2Nra4t27d7nu47Vr1yCXyyGTyVChQgW4uLioXa5lYmKiFkNu+h8dHY3atWurHSfz85xER0cjNTUVjRo1ytN2mUmlUlhaWqo9iIiIiIoKQRA05tLBwcGIiopCuXLlxKmylKKiolCyZEmxaJtZdHQ06tatq7asbt26iImJQXp6ep7iU80znZ2dASDH0afaFGSefvHiRYSFhYk5tlwuR2BgIBQKBe7duye28/X1zdN2yhhVt/Py8srxJmzMaYmIiAoXR9zmgoeHByQSCaKjo7PMh6qkLRlVXR4aGopu3brh77//xt69ezFp0iRs3rwZbdu2zXNMymJybuP4WMbGxmrPJRKJeHdghUKB6tWra5wXSzkiNSflypVDeHg4DA0NUbx4cUilUrX1MplMrV+56b+2NqoMDAyytFNOtaA8LhERERF9nOjoaPHeAKqqVauGe/fuYe/evTh06BA6deqExo0bY/v27TnmYZry3sx5nUQiyTbXU1LNdZX7VOa6eVWQebpCocCAAQMwbNiwLOtKlSol/px5OrectlNOy/ApPjcQERFRweGI21ywtbVFYGAgli5dqjahv9LLly9RoUIFxMXF4cGDB+Ly69ev49WrV2qXHHl6euK7777DgQMH0K5dO6xZswZAxojSvIwMqFChAj58+ICzZ8+Ky54/f45bt27leImTNnmNQalatWqIiYmBo6Mj3N3d1R5WVla5Pra7uztKly6dpWirSW76X6FCBfHGcUqZnzs4OCA+Pl5tWVRUlPizh4cHZDIZDh8+rDVuAPk6b0RERERFwZEjR3Dt2jW0b99e43pLS0t07twZK1euxJYtW7Bjxw68ePECPj4+ePjwIW7duqVxuwoVKuDEiRNqy06dOgVPT09xHtzMuV5MTAzevn2bp/h1madXq1YN//77b5Yc293dXcxD87Nd+fLl8eHDB1y4cEHc5ubNm3j58mWe4iMiIqJPi4XbXFq2bBnS09NRs2ZN7NixAzExMYiOjsaiRYtQu3ZtNG7cGD4+PujevTsuXbqEc+fOoUePHmjQoAF8fX2RkpKCIUOGICIiAvfv38fJkydx/vx5MXlzc3PD69evcfjwYTx79izHhNLDwwOtW7dGv379cOLECVy5cgVff/01SpQogdatW+erj3mNQal79+6wt7dH69atERkZiXv37uHYsWMYPnw4Hj58mK9YcpKb/g8bNgz79u3D7NmzcevWLSxZsiTL/LYNGzbEhQsXsG7dOsTExGDSpEn4559/xPWmpqYYO3YsxowZg3Xr1uHOnTs4c+YMfvvtNwAZc5HJZDLs27cPT548watXrz5Jf4mIiIg+B6mpqXj8+DEePXqES5cuYfr06WjdujVatmyJHj16ZGk/f/58bN68GTdu3MCtW7ewbds2ODk5wdraGg0aNICfnx/at2+PgwcPiiNzlfncqFGjcPjwYUydOhW3bt3C2rVrsWTJErW5Xhs2bIglS5bg0qVLuHDhAgYOHJjlSrKc6DJPHzt2LE6fPo3BgwcjKipKnD936NChH7VduXLl0KxZM/Tr1w9nz57FxYsX0bdvX15tRkREpGdYuM2l0qVL49KlSwgICMCoUaNQsWJFNGnSBIcPH8by5cshkUiwa9cu2NjYwM/PD40bN0aZMmWwZcsWAIChoSGeP3+OHj16wNPTE506dUJQUBAmT54MAKhTpw4GDhyIzp07w8HBAbNnz84xpjVr1qB69epo2bIlateuDUEQsGfPnjwno0r5iQEAzMzMcPz4cZQqVQrt2rVD+fLl0bt3b6SkpHzSea9y6n+tWrWwatUqLF68GFWqVMGBAwfw448/qu0jMDAQISEhGDNmDGrUqIHk5OQsHypCQkIwatQoTJw4EeXLl0fnzp3FOc+MjIywaNEi/PLLLyhevHi+i+ZEREREX4J9+/bB2dkZbm5uaNasGY4ePYpFixbhzz//FEfBqpLL5Zg1axZ8fX1Ro0YNxMbGYs+ePTAwyPiYsmPHDtSoUQNdu3ZFhQoVMGbMGHH0a7Vq1bB161Zs3rwZFStWxMSJEzFlyhS1G5PNmzcPLi4u8PPzQ7du3TB69GiYmZnlqU+6zNN9fHxw7NgxxMTEoH79+qhatSpCQkLEeXg/Zrs1a9bAxcUFDRo0QLt27dC/f3/xBmlERESkHyRCbiYCJaIClZSUBCsrK9Sa8B8MTa11HU6hkkCBMhbPcDfZHkIR/O6oKPeffWff2feiQwIFtvVLhqOjo1iAKyjK/0NfvXrFGyMR6Zjy99Fmx3gIclNdh/PJGQiAj8ISVw2SoCgC0wMXpf4Wpb4C7O+Xjv0tWIlNJxb4PvOSzxatTxFEREREREREREREnwEWbvVUZGQk5HK51kd+BAUFad3f9OnTC7gH/ye7fkRGRn6y4xIRERERFbRPkacTERERaWKk6wBIM19fX0RFRRXoPletWoWUlBSN62xtbQv0WKqy60eJEiU+2XGJiIiIiArap8jTiYiIiDRh4VZPyWQyuLu7F+g+dVUkLeh+EBERERHpyqfI04mIiIg04VQJRERERERERERERHqGhVsiIiIiIiIiIiIiPcPCLREREREREREREZGe4Ry3RDq093sZrK1lug6jUCkUCiQkSOHoKIOBQdH77qgo9599Z9/Z96Ijo+/Jug6DiArJ3YZjYG1treswPrmMv20JcHR0LBJ/14tSf4tSXwH290vH/n5ZvrweEREREREREREREX3mWLglIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQMC7dEREREREREREREeoaFWyIiIiIiIiIiIiI9Y6TrAIiKsqA5KTA0leo6jEIlgQJlLFJxNzkFQhH87qgo9599Z9/Z9y/XiRCZrkMgIh0qc2Q2BLmprsP45AwEwEdhiasGSVBIdB3Np1eU+vsxfU1sOvHTBEVEBI64JSIiIiIiIiIiItI7LNwSERERERERERER6RkWbomIiIiIiIiIiIj0DAu3RERERERERERERHqGhVsiIiIiIiIiIiIiPcPCLREREREREREREZGeYeGWiIiIiIiIiIiISM+wcEtERERERERERESkZ1i4JSIiIiIiIiIiItIzLNwSACAsLAzW1tZ52iY4OBht2rTReRxERERERABzWiIiIvqysHD7GVqxYgUsLCzw4cMHcdnr169hbGyM+vXrq7WNjIyERCLBrVu3st1n586dc2yTH25ubliwYEGu23+qOIiIiIhIvzCnJSIiIsoeC7efoYCAALx+/RoXLlwQl0VGRsLJyQnnz5/H27dvxeUREREoXrw4PD09s92nTCaDo6PjJ4s5t/QlDiIiIiL6tJjTEhEREWWPhdvPULly5VC8eHFERESIyyIiItC6dWuULVsWp06dUlseEBCA9+/fY8yYMShRogTMzc3x1VdfqW2v6XKuadOmwdHRERYWFujbty/GjRuHKlWqZIln7ty5cHZ2hp2dHQYPHoy0tDQAgL+/P+7fv4/vvvsOEokEEokkx75ljiM0NBRVqlTB+vXr4ebmBisrK3Tp0gXJycm5OlfJycno3r07zM3N4ezsjPnz58Pf3x8jRowQ27i5uWHatGno0aMH5HI5XF1d8eeff+Lp06do3bo15HI5KlWqpPah4v79+2jVqhVsbGxgbm4Ob29v7NmzJ1cxERERERFzWua0RERElBMWbj9T/v7+OHr0qPj86NGj8Pf3R4MGDcTl79+/x+nTpxEQEIBevXrh5MmT2Lx5M65evYqOHTuiWbNmiImJ0bj/jRs34qeffsKsWbNw8eJFlCpVCsuXL8/S7ujRo7hz5w6OHj2KtWvXIiwsDGFhYQCAnTt3omTJkpgyZQri4+MRHx+fr77euXMHu3btwu7du7F7924cO3YMM2fOzNW2I0eOxMmTJxEeHo6DBw8iMjISly5dytJu/vz5qFu3Li5fvowWLVrgm2++QY8ePfD111/j0qVLcHd3R48ePSAIAgBg8ODBSE1NxfHjx3Ht2jXMmjULcrlcaxypqalISkpSexAREREVdcxpmdMSERGRdizcfqb8/f1x8uRJfPjwAcnJybh8+TL8/PzQoEEDcdTBmTNnkJKSAn9/f/z+++/Ytm0b6tevj7Jly2L06NGoV68e1qxZo3H/ixcvRp8+fdCrVy94enpi4sSJqFSpUpZ2NjY2WLJkCby8vNCyZUu0aNEChw8fBgDY2trC0NAQFhYWcHJygpOTU776qlAoEBYWhooVK6J+/fr45ptvxGNkJzk5GWvXrsXcuXPRqFEjVKxYEWvWrEF6enqWts2bN8eAAQPg4eGBiRMnIjk5GTVq1EDHjh3h6emJsWPHIjo6Gk+ePAEAxMXFoW7duqhUqRLKlCmDli1bws/PT2ssM2bMgJWVlfhwcXHJ17kgIiIi+pIwp2VOS0RERNqxcPuZCggIwJs3b3D+/HlERkbC09MTjo6OaNCgAc6fP483b94gIiICpUqVwqVLlyAIAjw9PSGXy8XHsWPHcOfOHY37v3nzJmrWrKm2LPNzAPD29oahoaH43NnZGQkJCQXaVzc3N1hYWOT5GHfv3kVaWppa3FZWVihXrlyWtj4+PuLPxYoVAwC1pF65THncYcOGYdq0aahbty4mTZqEq1evZhvL+PHj8erVK/Hx4MGDHOMnIiIi+tIxp2VOS0RERNoZ6ToAyh93d3eULFkSR48eRWJiIho0aAAAcHJyQunSpXHy5EkcPXoUDRs2hEKhgKGhIS5evKiWkALI9lKozPN3KS+pUmVsbJxlG4VCkd9uaZTfYyjjzWs/lO01LVMet2/fvggMDMTff/+NAwcOYMaMGZg3bx6GDh2qMRapVAqpVJpjzERERERFCXNa5rRERESkHUfcfsYCAgIQERGBiIgI+Pv7i8sbNGiA/fv348yZMwgICEDVqlWRnp6OhIQEuLu7qz20XepVrlw5nDt3Tm2Z6o0McsvExETjZVyFoWzZsjA2NlbrR1JSktY50PLKxcUFAwcOxM6dOzFq1CisXLmyQPZLREREVJQwp80ec1oiIqKiiyNuP2MBAQHiHW+VoxOAjCT322+/xbt37xAQEAAXFxd0794dPXr0wLx581C1alU8e/YMR44cQaVKldC8efMs+x46dCj69esHX19f1KlTB1u2bMHVq1dRpkyZPMXo5uaG48ePo0uXLpBKpbC3t//ofueWhYUFevbsie+//x62trZwdHTEpEmTYGBgkKu7AWdnxIgRCAoKgqenJxITE3HkyBGUL1++gCInIiIiKjqY02aPOS0REVHRxRG3n7GAgACkpKTA3d1dnK8KyEhyk5OTUbZsWfGGAWvWrEGPHj0watQolCtXDv/73/9w9uxZrTcU6N69O8aPH4/Ro0ejWrVquHfvHoKDg2FqapqnGKdMmYLY2FiULVsWDg4O+e9sPv3888+oXbs2WrZsicaNG6Nu3booX758nvuRWXp6OgYPHozy5cujWbNmKFeuHJYtW1ZAURMREREVHcxpc8acloiIqGiSCJomRyLSoEmTJnBycsL69et1HUq+vXnzBiVKlMC8efPQp08fncWRlJQEKysr1JrwHwxNrXUWhy5IoEAZi2e4m2wPoQh+d1SU+8++s+/s+5frRIhM7blCoUBCQgIcHR1hYFCwfVf+H/rq1StYWloW6L6paGBOW3CUv482O8ZDkH9cEflzYCAAPgpLXDVIguLjBjt/FopSfz+mr4lNJ36aoD6hT/n/tD5if79sn2N/85LPcqoE0ujt27dYsWIFAgMDYWhoiN9//x2HDh3CwYMHdR1anly+fBk3btxAzZo18erVK0yZMgUA0Lp1ax1HRkRERESfGnNaIiIi+px9HqVoKnQSiQR79uxB/fr1Ub16dfz111/YsWMHGjdu/FH7DQoKglwu1/iYPn16nvYVFxendV9yuRxxcXEAgLlz56Jy5cpo3Lgx3rx5g8jIyEKdl4yIiIiIdIM5LREREX3OOOKWNJLJZDh06FCB73fVqlVISUnRuM7W1jZP+ypevDiioqKyXV+qVClcvHgxT/slIiIioi8Dc1oiIiL6nLFwS4WqRIkSBbYvIyMjuLu7F9j+iIiIiIhygzktERERFQZOlUBERERERERERESkZ1i4JSIiIiIiIiIiItIzLNwSERERERERERER6RnOcUukQ3u/l8HaWqbrMAqVQqFAQoIUjo4yGBgUve+OinL/2Xf2nX0nIvoy3W04BtbW1roO45PL+LueAEdHxyLxd70o9bco9ZWIPi/8i0RERERERERERESkZ1i4JSIiIiIiIiIiItIzLNwSERERERERERER6RkWbomIiIiIiIiIiIj0DAu3RERERERERERERHqGhVsiIiIiIiIiIiIiPcPCLREREREREREREZGeMdJ1AERFWdCcFBiaSnUdRqGSQIEyFqm4m5wCoQh+d1SU+/859v1EiEzXIRAREem9MkdmQ5Cb6jqMbCU2najrEIiIiPLs8/jkTERERERERERERFSEsHBLREREREREREREpGdYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9AwLt0RERERERERERER6hoVbIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHD7GZFIJNi1a5euw9C5Xbt2wd3dHYaGhhgxYoTWZYXJzc0NCxYsKPTjEhEREX2p/P39dZLXFRbmj0RERJQTFm71QHBwMCQSCSQSCYyNjVGsWDE0adIEq1evhkKhENvFx8cjKChIh5HmTVhYGKytrQt8vwMGDECHDh3w4MEDTJ06VesyIiIiItIPmfPdMmXKYPTo0Xjz5o3WbXbu3Mm8joiIiIo0Fm71RLNmzRAfH4/Y2Fjs3bsXAQEBGD58OFq2bIkPHz4AAJycnCCVSnUcqW69fv0aCQkJCAwMRPHixWFhYaFxWX68f/++gKMlIiIiIiVlvnv37l1MmzYNy5Ytw+jRo7O0S0tLAwDY2trmO68DgPT0dLVBEIVJ2QciIiKij8HCrZ6QSqVwcnJCiRIlUK1aNUyYMAF//vkn9u7di7CwMADqUyW8f/8eQ4YMgbOzM0xNTeHm5oYZM2aI+3v58iX69++PYsWKwdTUFBUrVsTu3bvF9Tt27IC3tzekUinc3Nwwb948tXg0TctgbW0txhIbGwuJRIKdO3ciICAAZmZmqFy5Mk6fPg0AiIiIQK9evfDq1StxdEVoaGiO5yExMRE9evSAjY0NzMzMEBQUhJiYGHGfyuS9YcOGkEgkWpcBwKlTp+Dn5weZTAYXFxcMGzZMbVSHm5sbpk2bhuDgYFhZWaFfv3652i4hIQGtWrWCTCZD6dKlsXHjxhz7lZqaiqSkJLUHERERUVGizHddXFzQrVs3dO/eHbt27UJoaCiqVKmC1atXo0yZMpBKpRAEIctUCdnlicD/Xe21e/duVKhQAVKpFPfv3882puDgYLRp0waTJ0+Go6MjLC0tMWDAALUv9DVNaVClShW13FYikWDFihVo3bo1zM3NMW3aNABAeHg4fH19YWpqCnt7e7Rr105tP2/fvkXv3r1hYWGBUqVK4ddff1VbP3bsWHh6esLMzAxlypRBSEiIWlH4ypUrCAgIgIWFBSwtLVG9enVcuHBBXJ9TXrts2TJ4eHjA1NQUxYoVQ4cOHbI9X8xpiYiIChcLt3qsYcOGqFy5Mnbu3Jll3aJFixAeHo6tW7fi5s2b2LBhA9zc3AAACoUCQUFBOHXqFDZs2IDr169j5syZMDQ0BABcvHgRnTp1QpcuXXDt2jWEhoYiJCRELMrmxQ8//IDRo0cjKioKnp6e6Nq1Kz58+IA6depgwYIFsLS0RHx8POLj4zWOqMgsODgYFy5cQHh4OE6fPg1BENC8eXOkpaWhTp06uHnzJoCMwnN8fLzWZdeuXUNgYCDatWuHq1evYsuWLThx4gSGDBmidrw5c+agYsWKuHjxIkJCQnK1XXBwMGJjY3HkyBFs374dy5YtQ0JCQrb9mjFjBqysrMSHi4tLXk81ERER0RdFJpOJRcjbt29j69at2LFjB6KiojS2zy5PVHr79i1mzJiBVatW4d9//4Wjo2OOcRw+fBjR0dE4evQofv/9d/zxxx+YPHlynvszadIktG7dGteuXUPv3r3x999/o127dmjRogUuX76Mw4cPw9fXV22befPmwdfXF5cvX8agQYPw7bff4saNG+J6CwsLhIWF4fr161i4cCFWrlyJ+fPni+u7d++OkiVL4vz587h48SLGjRsHY2NjAMgxr71w4QKGDRuGKVOm4ObNm9i3bx/8/Pyy7SNzWiIiosJlpOsAKHteXl64evVqluVxcXHw8PBAvXr1IJFI4OrqKq47dOgQzp07h+joaHh6egIAypQpI67/+eef0ahRI4SEhAAAPD09cf36dcyZMwfBwcF5im/06NFo0aIFAGDy5Mnw9vbG7du34eXlBSsrK0gkEjg5OeVqXzExMQgPD8fJkydRp04dAMDGjRvh4uKCXbt2oWPHjmLybWtrK+5X07I5c+agW7du4igNDw8PLFq0CA0aNMDy5cthamoKIKM4rlpQ7tGjR7bbxcXFYe/evThz5gy++uorAMBvv/2G8uXLZ9u38ePHY+TIkeLzpKQkJrpERERUZJ07dw6bNm1Co0aNAGRcTbZ+/Xo4ODhobJ+bPBHImKJg2bJlqFy5cq5jMTExwerVq2FmZgZvb29MmTIF33//PaZOnQoDg9yPc+nWrRt69+4tPu/atSu6dOmiVgTOHFfz5s0xaNAgABmja+fPn4+IiAh4eXkBAH788UexrZubG0aNGoUtW7ZgzJgxADI+E3z//fdiew8PD7F9TvlwXFwczM3N0bJlS1hYWMDV1RVVq1bNto/MaYmIiAoXR9zqOUEQIJFIsiwPDg5GVFQUypUrh2HDhuHAgQPiuqioKJQsWVIs2mYWHR2NunXrqi2rW7cuYmJikJ6enqf4fHx8xJ+dnZ0BIMfRp9pER0fDyMhILIgCgJ2dHcqVK4fo6Og87evixYsICwuDXC4XH4GBgVAoFLh3757YLvOoh5y2U8aoup2Xl1eON2GTSqWwtLRUexAREREVJbt374ZcLoepqSlq164NPz8/LF68GADg6uqqtWgL5D5PNDExUctPc6Ny5cowMzMTn9euXRuvX7/GgwcP8rSfzHllVFSUWJjWRjVW5YAH1Vx6+/btqFevHpycnCCXyxESEoK4uDhx/ciRI9G3b180btwYM2fOxJ07d8R1OeW1TZo0gaurK8qUKYNvvvkGGzduxNu3b7ONlzktERFR4WLhVs9FR0ejdOnSWZZXq1YN9+7dw9SpU5GSkoJOnTqJc1LJZLJs96mpGCwIgtpziUSSZZmmmywoL8VSbgMg3zeByHy87OLNiUKhwIABAxAVFSU+rly5gpiYGJQtW1ZsZ25unqftlDHmNR4iIiKioi4gIABRUVG4efMm3r17h507d4pXTmXOyTLLbZ4ok8kKLE9T7sfAwCBXeXHmPuSUkwPqubTymMpc+syZM+jSpQuCgoKwe/duXL58GT/88IPa/LuhoaH4999/0aJFCxw5cgQVKlTAH3/8ASDnvNbCwgKXLl3C77//DmdnZ0ycOBGVK1fGy5cvcz45REREVCg4VYIeO3LkCK5du4bvvvtO43pLS0t07twZnTt3RocOHdCsWTO8ePECPj4+ePjwIW7duqVx1G2FChVw4sQJtWWnTp2Cp6enOA+ug4MD4uPjxfUxMTE5fgOfmYmJSZ5G8FaoUAEfPnzA2bNnxUvgnj9/jlu3buU4FUFm1apVw7///gt3d/cC3a58+fL48OEDLly4gJo1awIAbt68yQSXiIiIKAfm5uZ5zs2UCjJPzOzKlStISUkRC61nzpyBXC5HyZIlAWTNi5OSktSu4NLGx8cHhw8fRq9evfIV18mTJ+Hq6ooffvhBXKbpZmuenp7w9PTEd999h65du2LNmjVo27ZtrvJhIyMjNG7cGI0bN8akSZNgbW2NI0eOZLmJGhEREekGR9zqidTUVDx+/BiPHj3CpUuXMH36dLRu3RotW7ZEjx49srSfP38+Nm/ejBs3buDWrVvYtm0bnJycYG1tjQYNGsDPzw/t27fHwYMHce/ePezduxf79u0DAIwaNQqHDx/G1KlTcevWLaxduxZLlixRm+u1YcOGWLJkCS5duoQLFy5g4MCBWUYE5MTNzQ2vX7/G4cOH8ezZsxwLvx4eHmjdujX69euHEydO4MqVK/j6669RokQJtG7dOk/HHjt2LE6fPo3BgwcjKipKnBdt6NChH7VduXLl0KxZM/Tr1w9nz57FxYsX0bdv31yNqCAiIiKi/CnIPDGz9+/fo0+fPrh+/Tr27t2LSZMmYciQIeL8tg0bNsT69esRGRmJf/75Bz179hQHO2Rn0qRJ+P333zFp0iRER0fj2rVrmD17dq7jcnd3R1xcHDZv3ow7d+5g0aJF4mhaAEhJScGQIUMQERGB+/fv4+TJkzh//rxYyM4pr929ezcWLVqEqKgo3L9/H+vWrYNCoUC5cuXycvqIiIjoE2LhVk/s27cPzs7OcHNzQ7NmzXD06FEsWrQIf/75p8bEUC6XY9asWfD19UWNGjUQGxuLPXv2iAnmjh07UKNGDXTt2hUVKlTAmDFjxNGv1apVw9atW7F582ZUrFgREydOxJQpU9RuTDZv3jy4uLjAz88P3bp1w+jRo9Xm/sqNOnXqYODAgejcuTMcHBxylaiuWbMG1atXR8uWLVG7dm0IgoA9e/bkuWjs4+ODY8eOISYmBvXr10fVqlUREhIizsP7MdutWbMGLi4uaNCgAdq1a4f+/fvn6o7FRERERJR/BZUnZtaoUSN4eHjAz88PnTp1QqtWrRAaGiquHz9+PPz8/NCyZUs0b94cbdq0UZt6Sxt/f39s27YN4eHhqFKlCho2bIizZ8/mOq7WrVvju+++w5AhQ1ClShWcOnVKvLkwABgaGuL58+fo0aMHPD090alTJwQFBYk3Q8spr7W2tsbOnTvRsGFDlC9fHitWrMDvv/8Ob2/vXMdIREREn5ZE0DZhFBF9MklJSbCyskKtCf/B0NRa1+EUKgkUKGPxDHeT7SEUwe+OinL/P8e+nwgpmNH0CoUCCQkJcHR0zNMdyr8E7Dv7XtB9V/4f+urVK94YiT5acHAwXr58iV27duk6lM+S8vfRZsd4CHJTXYeTrcSmEz96H0Xt73pR6m9R6ivA/n7p2F/9l5d89vPoEREREREREREREVERwpuTUaGJjIxEUFCQ1vWvX78uxGiIiIiI6Esnl8u1rtu7d28hRkJERESUdyzcUqHx9fVFVFSUrsMgIiIioiIiu9yzRIkSqF+/fuEFQ0RERJRHLNxSoZHJZHB3d9d1GERERERURDD3JCIios8Z57glIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQM57gl0qG938tgbS3TdRiFSqFQICFBCkdHGQwMit53R0W5/0W570RERF+yuw3HwNraWtdhEBERfXH4yZmIiIiIiIiIiIhIz7BwS0RERERERERERKRnWLglIiIiIiIiIiIi0jMs3BIRERERERERERHpGRZuiYiIiIiIiIiIiPQMC7dEREREREREREREesZI1wEQFWVBc1JgaCrVdRiFSgIFylik4m5yCoQv5LujEyEyXYdAREREpDNljsyGIDfVdRhqEptO1HUIREREH+3LqJoQERERERERERERfUFYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9AwLt0RERERERERERER6hoVbIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHBLREREREREREREpGdYuKUiLTQ0FFWqVNF1GERERESUjV27dsHd3R2GhoYYMWJEvvbh5uaGBQsWFGhcuhIWFgZra2tdh0FERESfGAu3eubx48cYOnQoypQpA6lUChcXF7Rq1QqHDx/+6H3HxsZCIpEgKirq4wP9DEkkEuzatUtt2ejRowvk3BIRERF9Lj5lvvmpCooDBgxAhw4d8ODBA0ydOlUnMeQkNDQUEokEEokEhoaGcHFxQd++ffH06dNCjyWvivrnBCIiIn1lpOsA6P/Exsaibt26sLa2xuzZs+Hj44O0tDTs378fgwcPxo0bN3Qd4ieXlpYGY2PjXLdPT0+HRCKBgUH+voOQy+WQy+X52paIiIjoc/M55puvX79GQkICAgMDUbx4cV2Hky1vb28cOnQI6enpuHz5Mvr06YNHjx5h7969Wdp+bB5LREREXz5mCXpk0KBBkEgkOHfuHDp06ABPT094e3tj5MiROHPmjMZvwl++fAmJRIKIiAgAQGJiIrp37w4HBwfIZDJ4eHhgzZo1AIDSpUsDAKpWrQqJRAJ/f38AgEKhwJQpU1CyZElIpVJUqVIF+/btE4+hPO7WrVtRv359yGQy1KhRA7du3cL58+fh6+sLuVyOZs2aZRlRsGbNGpQvXx6mpqbw8vLCsmXLNO7X398fpqam2LBhQ7bnSDmCYvfu3ahQoQKkUinu37+P8+fPo0mTJrC3t4eVlRUaNGiAS5cuidu5ubkBANq2bQuJRCI+zzxVQnBwMNq0aYO5c+fC2dkZdnZ2GDx4MNLS0sQ28fHxaNGiBWQyGUqXLo1NmzZ9UZfeERER0Zcrp3wTAOLi4tC6dWvI5XJYWlqiU6dOePLkibiPK1euICAgABYWFrC0tET16tVx4cIFREREoFevXnj16pU48jQ0NDTHmBITE9GjRw/Y2NjAzMwMQUFBiImJAQBERETAwsICANCwYUO1vFeTnGJ4+/YtevfuDQsLC5QqVQq//vqr2vaPHj1C586dYWNjAzs7O7Ru3RqxsbG5O7kAjIyM4OTkhBIlSqBly5YYNmwYDhw4gJSUFK15bHb9VwoLC0OpUqVgZmaGtm3b4vnz52rrlTmsqhEjRoj5PpCR88+aNQvu7u6QSqUoVaoUfvrpJwDaPycQERGRbrFwqydevHiBffv2YfDgwTA3N8+yPreXe4WEhOD69evYu3cvoqOjsXz5ctjb2wMAzp07BwA4dOgQ4uPjsXPnTgDAwoULMW/ePMydOxdXr15FYGAg/ve//2VJGCdNmoQff/wRly5dgpGREbp27YoxY8Zg4cKFiIyMxJ07dzBx4kSx/cqVK/HDDz/gp59+QnR0NKZPn46QkBCsXbtWbb9jx47FsGHDEB0djcDAwBz7+PbtW8yYMQOrVq3Cv//+C0dHRyQnJ6Nnz56IjIzEmTNn4OHhgebNmyM5ORkAcP78eQAZheT4+HjxuSZHjx7FnTt3cPToUaxduxZhYWEICwsT1/fo0QP//fcfIiIisGPHDvz6669ISEjINubU1FQkJSWpPYiIiIgKU27yTUEQ0KZNG7x48QLHjh3DwYMHcefOHXTu3Fls1717d5QsWRLnz5/HxYsXMW7cOBgbG6NOnTpYsGABLC0tER8fj/j4eIwePTrHuIKDg3HhwgWEh4fj9OnTEAQBzZs3R1paGurUqYObN28CAHbs2IH4+HjUqVNH675yimHevHnw9fXF5cuXMWjQIHz77bfiKOO3b98iICAAcrkcx48fx4kTJ8TBCe/fv8/1eVYlk8mgUCjw4cMH8RiZ89js+g8AZ8+eRe/evTFo0CBERUUhICAA06ZNy3Ms48ePx6xZs8TPC5s2bUKxYsUAaP+ckBlzWiIiosLFqRL0xO3btyEIAry8vD5qP3FxcahatSp8fX0B/N9IUwBwcHAAANjZ2cHJyUlcPnfuXIwdOxZdunQBAMyaNQtHjx7FggULsHTpUrHd6NGjxcLq8OHD0bVrVxw+fBh169YFAPTp00etwDl16lTMmzcP7dq1A5DxTf7169fxyy+/oGfPnmK7ESNGiG1yIy0tDcuWLUPlypXFZQ0bNlRr88svv8DGxgbHjh1Dy5Ytxb5bW1ur9V0TGxsbLFmyBIaGhvDy8kKLFi1w+PBh9OvXDzdu3MChQ4fEkcYAsGrVKnh4eGS7zxkzZmDy5Mm57iMRERFRQctNvnno0CFcvXoV9+7dg4uLCwBg/fr18Pb2xvnz51GjRg3ExcXh+++/F/ejmgdZWVlBIpHkmG8pxcTEIDw8HCdPnhQLshs3boSLiwt27dqFjh07wtHREQBga2ub435NTEyyjaF58+YYNGgQgIzBA/Pnz0dERAS8vLywefNmGBgYYNWqVZBIJAAyvvS3trZGREQEmjZtmqs+Kd24cQPLly9HzZo1xVHDmfPY3PR/4cKFCAwMxLhx4wAAnp6eOHXqlNoVcjlJTk7GwoULsWTJEjEPL1u2LOrVqwdA++eEzJjTEhERFS6OuNUTgiAAgJgk5te3336LzZs3o0qVKhgzZgxOnTqVbfukpCT8999/YvFVqW7duoiOjlZb5uPjI/6s/Ha+UqVKasuUI0+fPn2KBw8eoE+fPuI8snK5HNOmTcOdO3fU9qssgOaWiYmJWiwAkJCQgIEDB8LT0xNWVlawsrLC69evERcXl6d9AxlzkxkaGorPnZ2dxX7dvHkTRkZGqFatmrje3d0dNjY22e5z/PjxePXqlfh48OBBnuMiIiIi+hi5yTejo6Ph4uIiFm0BoEKFCrC2thZzw5EjR6Jv375o3LgxZs6cmSW3y4vo6GgYGRnhq6++EpfZ2dmhXLlyWXLRgqCaQyqLu8o87+LFi7h9+zYsLCzE3NXW1hbv3r3LdR+vXbsGuVwOmUyGChUqwMXFBRs3bhTXZ85jc9P/6Oho1K5dW+04mZ/nJDo6GqmpqWjUqFGetsuMOS0REVHh4ohbPeHh4QGJRILo6Ogs81MpKW9coEy6AajNvQoAQUFBuH//Pv7++28cOnQIjRo1wuDBgzF37txsj585gRcEIcsy1ZuGKddlXqZQKABA/HflypVqiSgAtaIoAI2X6mVHJpNliS04OBhPnz7FggUL4OrqCqlUitq1a+frsrbMN0dT7ZfquVelbbmSVCqFVCrNcyxEREREBSU3+aamHDDz8tDQUHTr1g1///039u7di0mTJmHz5s1o27ZtnmPKLrf62AENmmSX5ykUClSvXl2t0KqkHJGak3LlyiE8PByGhoYoXrx4lvwvcx6bm/7nlGcCGZ8TMrdT/Zwgk8lyFX9OmNMSEREVLo641RO2trYIDAzE0qVL8ebNmyzrX758KSaM8fHx4nLVG5UpOTg4IDg4GBs2bMCCBQvEmy6YmJgAyLiDrZKlpSWKFy+OEydOqO3j1KlTKF++fL77U6xYMZQoUQJ3796Fu7u72kN584OCFBkZiWHDhqF58+bw9vaGVCrFs2fP1NoYGxur9T0/vLy88OHDB1y+fFlcdvv2bbx8+fKj9ktERET0qeUm36xQoQLi4uLURlJev34dr169UssNPT098d133+HAgQNo166deDNcExOTPOVbFSpUwIcPH3D27Flx2fPnz3Hr1q1856J5jUGpWrVqiImJgaOjY5b81crKKtfHVua7uSlw5qb/FSpUEG8cp5T5uYODg9pnBED9c4KHhwdkMhkOHz6sNW4AH50rExERUcFi4VaPLFu2DOnp6ahZsyZ27NiBmJgYREdHY9GiRahduzZkMhlq1aqFmTNn4vr16zh+/Dh+/PFHtX1MnDgRf/75J27fvo1///0Xu3fvFpM+R0dHyGQy7Nu3D0+ePMGrV68AAN9//z1mzZqFLVu24ObNmxg3bhyioqIwfPjwj+pPaGgoZsyYgYULF+LWrVu4du0a1qxZg59//vmj9quJu7s71q9fj+joaJw9exbdu3fPMrLAzc0Nhw8fxuPHj5GYmJiv43h5eaFx48bo378/zp07h8uXL6N///4aRwETERER6Zuc8s3GjRvDx8cH3bt3x6VLl3Du3Dn06NEDDRo0gK+vL1JSUjBkyBBERETg/v37OHnyJM6fPy/mm25ubnj9+jUOHz6MZ8+e4e3bt9nG4+HhgdatW6Nfv344ceIErly5gq+//holSpRA69at89XHvMag1L17d9jb26N169aIjIzEvXv3cOzYMQwfPhwPHz7MVyw5yU3/hw0bhn379mH27Nm4desWlixZkmV+24YNG+LChQtYt24dYmJiMGnSJPzzzz/ielNTU4wdOxZjxozBunXrcOfOHZw5cwa//fYbAO2fE4iIiEi3WLjVI6VLl8alS5cQEBCAUaNGoWLFimjSpAkOHz6M5cuXAwBWr16NtLQ0+Pr6Yvjw4VnuKGtiYoLx48fDx8cHfn5+MDQ0xObNmwEARkZGWLRoEX755RcUL15cLRkcNWoURo0ahUqVKmHfvn0IDw/P8YZbOenbty9WrVqFsLAwVKpUCQ0aNEBYWNgnGXG7evVqJCYmomrVqvjmm28wbNgw8UYWSvPmzcPBgwfh4uKCqlWr5vtY69atQ7FixeDn54e2bduiX79+sLCwgKmp6cd2g4iIiOiTyinflEgk2LVrF2xsbODn54fGjRujTJky2LJlC4CMKa+eP3+OHj16wNPTE506dUJQUJB4w6o6depg4MCB6Ny5MxwcHDB79uwcY1qzZg2qV6+Oli1bonbt2hAEAXv27MkyrUFu5ScGADAzM8Px48dRqlQptGvXDuXLl0fv3r2RkpICS0vLfMWSGzn1v1atWli1ahUWL16MKlWq4MCBA1kGbwQGBiIkJARjxoxBjRo1kJycjB49eqi1CQkJwahRozBx4kSUL18enTt3Fuf31fY5gYiIiHRLIuRm0iQi0urhw4dwcXER5xTOjaSkJFhZWaHWhP9gaGr9aQPUMxIoUMbiGe4m20P4Qr47OhGS+3njFAoFEhIS4OjoKM5bXVSw7+w7+150fMq+K/8PffXq1SctphFRzpS/jzY7xkOQ69cghsSmEwt8n0Xt73pR6m9R6ivA/n7p2F/9l5d8ljcnI8qjI0eO4PXr16hUqRLi4+MxZswYuLm5wc/PT9ehERERERERERHRF+LzKEVTkREUFAS5XK7xMX36dF2HByDjDr0TJkyAt7c32rZtCwcHB0REROT7cj4iIiKiL1VkZKTW3E4ul+drn7rKF7PrR2Rk5Cc7LhERERVdHHFLemXVqlVISUnRuM7W1raQo9EsMDAQgYGBug6DiIiISO/5+voiKiqqQPepq3wxu36UKFHikx2XiIiIii4WbkmvMOklIiIi+nLIZDK4u7sX6D51lS8WdD+IiIiIcsKpEoiIiIiIiIiIiIj0DAu3RERERERERERERHqGhVsiIiIiIiIiIiIiPcM5bol0aO/3Mlhby3QdRqFSKBRISJDC0VEGAwN+d0RERET0ubvbcAysra11HQYREdEXh1UTIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHBLREREREREREREpGdYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9IyRrgMgKsqC5qTA0FSq6zAKlQQKlLFIxd3kFAif+XdHJ0Jkug6BiIiISOfKHJkNQW6a6/aJTSd+wmiIiIi+HJ931YSIiIiIiIiIiIjoC8TCLREREREREREREZGeYeGWiIiIiIiIiIiISM+wcEtERERERERERESkZ1i4JSIiIiIiIiIiItIzLNwSERERERERERER6RkWbomIiIiIiIiIiIj0DAu3RERERERERERERHqGhVsiIiIiIiIiIiIiPcPCLVE23NzcsGDBAvG5RCLBrl27dBYPEREREVFmoaGhqFKliq7DICIiogLGwm0uPX78GEOHDkWZMmUglUrh4uKCVq1a4fDhwx+979jYWEgkEkRFRX18oEREREREOsbc+dPRNJBg9OjRBXJuiYiISL8Y6TqAz0FsbCzq1q0La2trzJ49Gz4+PkhLS8P+/fsxePBg3LhxQ9chfnJpaWkwNjb+4o9JRERERB+HuXPe89j09HRIJBIYGORvXI1cLodcLs/XtkRERKS/OOI2FwYNGgSJRIJz586hQ4cO8PT0hLe3N0aOHIkzZ85o/Nb/5cuXkEgkiIiIAAAkJiaie/fucHBwgEwmg4eHB9asWQMAKF26NACgatWqkEgk8Pf3BwAoFApMmTIFJUuWhFQqRZUqVbBv3z7xGMrjbt26FfXr14dMJkONGjVw69YtnD9/Hr6+vpDL5WjWrBmePn2q1qc1a9agfPnyMDU1hZeXF5YtW6Zxv/7+/jA1NcWGDRtyPE8nT55EgwYNYGZmBhsbGwQGBiIxMREAsG/fPtSrVw/W1taws7NDy5YtcefOnVwdM7tYc/Lw4UN06dIFtra2MDc3h6+vL86ePQsAuHPnDlq3bo1ixYpBLpejRo0aOHToUK73/f79ewwZMgTOzs4wNTWFm5sbZsyYobFtamoqkpKS1B5EREREXyLmzjnnzmFhYbC2tsbu3btRoUIFSKVS3L9/H+fPn0eTJk1gb28PKysrNGjQAJcuXRK3c3NzAwC0bdsWEolEfJ55qoTg4GC0adMGc+fOhbOzM+zs7DB48GCkpaWJbeLj49GiRQvIZDKULl0amzZtyjJNWGbMaYmIiAoXC7c5ePHiBfbt24fBgwfD3Nw8y3pra+tc7SckJATXr1/H3r17ER0djeXLl8Pe3h4AcO7cOQDAoUOHEB8fj507dwIAFi5ciHnz5mHu3Lm4evUqAgMD8b///Q8xMTFq+540aRJ+/PFHXLp0CUZGRujatSvGjBmDhQsXIjIyEnfu3MHEiRPF9itXrsQPP/yAn376CdHR0Zg+fTpCQkKwdu1atf2OHTsWw4YNQ3R0NAIDA7PtX1RUFBo1agRvb2+cPn0aJ06cQKtWrZCeng4AePPmDUaOHInz58/j8OHDMDAwQNu2baFQKLI9Zm5j1eT169do0KAB/vvvP4SHh+PKlSsYM2aMeMzXr1+jefPmOHToEC5fvozAwEC0atUKcXFxOe4bABYtWoTw8HBs3boVN2/exIYNG8TkObMZM2bAyspKfLi4uOTqGERERESfE+bOucudAeDt27eYMWMGVq1ahX///ReOjo5ITk5Gz549ERkZiTNnzsDDwwPNmzdHcnIyAOD8+fMAMgrJ8fHx4nNNjh49ijt37uDo0aNYu3YtwsLCEBYWJq7v0aMH/vvvP0RERGDHjh349ddfkZCQkG3MzGmJiIgKF6dKyMHt27chCAK8vLw+aj9xcXGoWrUqfH19AUCtwOfg4AAAsLOzg5OTk7h87ty5GDt2LLp06QIAmDVrFo4ePYoFCxZg6dKlYrvRo0eLyeHw4cPRtWtXHD58GHXr1gUA9OnTRy1Jmzp1KubNm4d27doByBi1cP36dfzyyy/o2bOn2G7EiBFim5zMnj0bvr6+aqMPvL29xZ/bt2+v1v63336Do6Mjrl+/jooVK2o9Zm5j1WTTpk14+vQpzp8/D1tbWwCAu7u7uL5y5cqoXLmy+HzatGn4448/EB4ejiFDhuTY57i4OHh4eKBevXqQSCRwdXXV2nb8+PEYOXKk+DwpKYmJLhEREX1xmDvnLncGMqZTWLZsmVo+2rBhQ7U2v/zyC2xsbHDs2DG0bNlS7Lu1tbVa3zWxsbHBkiVLYGhoCC8vL7Ro0QKHDx9Gv379cOPGDRw6dEgcaQwAq1atgoeHR7b7ZE5LRERUuFi4zYEgCAAybgLwMb799lu0b98ely5dQtOmTdGmTRvUqVNHa/ukpCT8999/YgKpVLduXVy5ckVtmY+Pj/hzsWLFAACVKlVSW6b89vzp06d48OAB+vTpg379+oltPnz4ACsrK7X9KpO43IiKikLHjh21rr9z5w5CQkJw5swZPHv2TBz1GhcXp1a4VT1mXmLVFlPVqlXFom1mb968weTJk7F79278999/+PDhA1JSUnI94jY4OBhNmjRBuXLl0KxZM7Rs2RJNmzbV2FYqlUIqleZqv0RERESfK+bOuWdiYqIWCwAkJCRg4sSJOHLkCJ48eYL09HS8ffs21/mpKm9vbxgaGorPnZ2dce3aNQDAzZs3YWRkhGrVqonr3d3dYWNjk+0+mdMSEREVLhZuc+Dh4QGJRILo6Gi0adNGYxvlTQSUiSoAtfmjACAoKAj379/H33//jUOHDqFRo0YYPHgw5s6dm+3xMye9giBkWaZ64wPluszLlIVS5b8rV67EV199pbYf1cQOgMbL27SRyWTZrm/VqhVcXFywcuVKFC9eHAqFAhUrVsT79++1HjMvseYnpu+//x779+/H3Llz4e7uDplMhg4dOmSJSZtq1arh3r172Lt3Lw4dOoROnTqhcePG2L59e662JyIiIvrSMHfOPZlMliW24OBgPH36FAsWLICrqyukUilq166d6/xUVeabo6n2S/Xcq9K2nIiIiHSDc9zmwNbWFoGBgVi6dCnevHmTZf3Lly/FS5bi4+PF5ao3W1BycHBAcHAwNmzYgAULFuDXX38FkPFtOwBxPlgAsLS0RPHixXHixAm1fZw6dQrly5fPd3+KFSuGEiVK4O7du3B3d1d7KG/0kB8+Pj44fPiwxnXPnz9HdHQ0fvzxRzRq1Ajly5cXb1r2KWP18fFBVFQUXrx4oXF9ZGQkgoOD0bZtW1SqVAlOTk6IjY3Ncb+qLC0t0blzZ6xcuRJbtmzBjh07tB6PiIiI6EvH3PnjREZGYtiwYWjevDm8vb0hlUrx7NkztTbGxsZqfc8PLy8vfPjwAZcvXxaX3b59Gy9fvvyo/RIREVHB4ojbXFi2bBnq1KmDmjVrYsqUKfDx8cGHDx9w8OBBLF++HNHR0ahVqxZmzpwJNzc3PHv2DD/++KPaPiZOnIjq1avD29sbqamp2L17t5hEOjo6QiaTYd++fShZsiRMTU1hZWWF77//HpMmTULZsmVRpUoVrFmzBlFRUdi4ceNH9Sc0NBTDhg2DpaUlgoKCkJqaigsXLiAxMVFtzqq8GD9+PCpVqoRBgwZh4MCBMDExwdGjR9GxY0fY2trCzs4Ov/76K5ydnREXF4dx48Z98li7du2K6dOno02bNpgxYwacnZ1x+fJlFC9eHLVr14a7uzt27tyJVq1aQSKRICQkJMvN0rIzf/58ODs7o0qVKjAwMMC2bdvg5OSU65tuEBEREX2JmDvnn7u7O9avXw9fX18kJSXh+++/z3IVmZubmzgnr1QqzXF6A028vLzQuHFj9O/fH8uXL4exsTFGjRqlcRQwERER6Q5H3OZC6dKlcenSJQQEBGDUqFGoWLEimjRpgsOHD2P58uUAgNWrVyMtLQ2+vr4YPnw4pk2bprYPExMTjB8/Hj4+PvDz84OhoSE2b94MADAyMsKiRYvwyy+/oHjx4mjdujUAYNiwYRg1ahRGjRqFSpUqYd++fQgPD8/xpgE56du3L1atWoWwsDBUqlQJDRo0QFhY2EeNGvD09MSBAwdw5coV1KxZE7Vr18aff/4JIyMjGBgYYPPmzbh48SIqVqyI7777DnPmzPnksZqYmODAgQNwdHRE8+bNUalSJcycOVO8rG3+/PmwsbFBnTp10KpVKwQGBqrN85UTuVyOWbNmwdfXFzVq1EBsbCz27NkjXv5HREREVBQxd86/1atXIzExEVWrVsU333yDYcOGwdHRUa3NvHnzcPDgQbi4uKBq1ar5Pta6detQrFgx+Pn5oW3btujXrx8sLCxgamr6sd0gIiKiAiIROJERUaFLSkqClZUVak34D4am1roOp1BJoEAZi2e4m2wP4TP/7uhESPbzKGuiUCiQkJAAR0fHIlfkZ9/Zd/a96PiUfVf+H/rq1StYWloW6L6JirKHDx/CxcVFnFM4N5S/jzY7xkOQ577gm9h0Yn7D1Kmi9ne9KPW3KPUVYH+/dOyv/stLPsupEoiIiIiIiIqYI0eO4PXr16hUqRLi4+MxZswYuLm5wc/PT9ehERER0f/3eZSiSeeCgoIgl8s1PqZPn66TmKZPn641pqCgIJ3ERERERESkj7lzZmlpaZgwYQK8vb3Rtm1bODg4ICIiAsbGxroOjYiIiP4/jrilXFm1ahVSUlI0rrO1tS3kaDIMHDgQnTp10rgu800ciIiIiIgKiz7mzpkFBgYiMDBQ12EQERFRNli4pVwpUaKErkPIwtbWVm8SXyIiIiIiJX3MnYmIiOjzw6kSiIiIiIiIiIiIiPQMC7dEREREREREREREeoaFWyIiIiIiIiIiIiI9wzluiXRo7/cyWFsXrRupKRQKJCRI4egog4EBvzsiIiIi+tzdbTgG1tbWug6DiIjoi8OqCREREREREREREZGeYeGWiIiIiIiIiIiISM+wcEtERERERERERESkZ1i4JSIiIiIiIiIiItIzLNwSERERERERERER6RkWbomIiIiIiIiIiIj0jJGuAyAqyoLmpMDQVKrrMAqVBAqUsUjF3eQUCJ/hd0cnQmS6DoGIiIhIr5Q5MhuC3DRXbRObTvzE0RAREX05Pr+qCREREREREREREdEXjoVbIiIiIiIiIiIiIj3Dwi0RERERERERERGRnmHhloiIiIiIiIiIiEjPsHBLREREREREREREpGdYuCUiIiIiIiIiIiLSMyzcEhEREREREREREekZFm6JiIiIiIiIiIiI9AwLt0RERERERERERER6hoXbL5S/vz9GjBih6zA+GTc3NyxYsEDXYRARERER6bXMebNEIsGuXbt0Fg8RERHlHgu3n4Hg4GBIJBJIJBIYGxujTJkyGD16NN68eaN1m507d2Lq1KmFGCURERER6bPHjx9j6NChKFOmDKRSKVxcXNCqVSscPnz4o/cdGxsLiUSCqKiojw+UiIiIiAAARroOgHKnWbNmWLNmDdLS0hAZGYm+ffvizZs3WL58uVq7tLQ0GBsbw9bW9qOOl56eDolEAgODwq/tK/tARERERAUjNjYWdevWhbW1NWbPng0fHx+kpaVh//79GDx4MG7cuKHrED85XeSYzGuJiIjoY3DE7WdCKpXCyckJLi4u6NatG7p3745du3YhNDQUVapUwerVq8XRE4IgZJkqITExET169ICNjQ3MzMwQFBSEmJgYcX1YWBisra2xe/duVKhQAVKpFPfv3882puDgYLRp0waTJ0+Go6MjLC0tMWDAALx//15so2lKgypVqiA0NFR8LpFIsGLFCrRu3Rrm5uaYNm0aACA8PBy+vr4wNTWFvb092rVrp7aft2/fonfv3rD4f+zdd1gV1/o+/HvTN2yaKE0RRQQBAQsWbIglqInBoxGDBbBFLLELehTBbjQeSxLRWMAkRjRRiS1GJeARxQKKsexYsOD5hgSjiMFCXe8fvszPTQdFUO7Pdc0VZ2bNWs8advRh7TVr9PXRuHFjfP311yrng4ODYWdnB11dXdjY2CAkJAS5ubnS+YsXL8LT0xP6+vowMDBA27ZtkZiYKJ0/deoUunXrBrlcDisrK0yePFlllvP69evRvHlz6OjowMzMDB999FGZ94uIiIiopkyYMAEymQxnz57FRx99BDs7Ozg5OWH69Ok4ffp0iTNmHz16BJlMhri4OAAv8slhw4ahQYMGkMvlaN68OSIiIgAATZs2BQC0bt0aMpkM3bt3BwAUFBRg4cKFaNSoEbS1tdGqVSscPnxYaqOw3V27dqFr166Qy+Vo164drl+/jnPnzsHNzQ0KhQJ9+vTB/fv3VfoUEREBBwcH6OjooEWLFli/fn2J9Xbv3h06Ojr47rvvyr1PJ0+ehIeHB3R1dWFsbAwvLy9kZGQAAA4fPowuXbrAyMgIJiYm+OCDD5CSklKhNsuKtTz/+9//8PHHH6NevXrQ09ODm5sbzpw5AwBISUmBt7c3zMzMoFAo0K5dOxw7dqzCdefk5GDSpEmwsLCAjo4OmjRpgmXLllX4eiIiIqpeHLh9S8nlcmkQ8ubNm9i1axd2795d6uNpAQEBSExMxL59+5CQkAAhBPr166cykPn06VMsW7YMmzdvxpUrV2BqalpuHDExMVAqlYiNjcWOHTuwd+9eLFiwoNL9CQ0Nhbe3Ny5duoRRo0bh4MGDGDhwIN5//31cuHABMTExcHNzU7lm1apVcHNzw4ULFzBhwgSMHz9eZbaIvr4+IiMjcfXqVaxduxabNm3C6tWrpfPDhg1Do0aNcO7cOSQlJWH27NnSjIhLly7By8sLAwcOxG+//YadO3ciPj4ekyZNAgAkJiZi8uTJWLhwIa5du4bDhw+jW7dupfYvOzsbjx8/VtmIiIiI3oSHDx/i8OHDmDhxIvT09IqdNzIyqlA9ISEhuHr1Kn7++WcolUqEh4ejfv36AICzZ88CAI4dO4a0tDTs2bMHALB27VqsWrUKn3/+OX777Td4eXnhww8/VJlAALzIBefNm4fz589DQ0MDvr6+CAoKwtq1a3HixAmkpKRg/vz5UvlNmzZh7ty5WLJkCZRKJZYuXYqQkBBs27ZNpd7g4GBMnjwZSqUSXl5eZfYvOTkZPXv2hJOTExISEhAfH4/+/fsjPz8fAPDkyRNMnz4d586dQ0xMDNTU1PCvf/0LBQUFZbZZ0VhLkpWVBQ8PD/zxxx/Yt28fLl68iKCgIKnNrKws9OvXD8eOHcOFCxfg5eWF/v37IzU1tdy6AWDdunXYt28fdu3ahWvXruG7775DkyZNSi3PnJaIiOjN4lIJb6GzZ8/i+++/R8+ePQG8+Kb822+/RYMGDUosf+PGDezbtw8nT55Ep06dAADbt2+HlZUVoqOjMXjwYAAvHuVav349XF1dKxyLlpYWtm7dCl1dXTg5OWHhwoWYNWsWFi1aVKllFoYOHYpRo0ZJ+76+vvj4449VBoGLxtWvXz9MmDABwIsEefXq1YiLi0OLFi0AAPPmzZPKNmnSBDNmzMDOnTsRFBQEAEhNTcWsWbOk8s2bN5fKr1y5EkOHDpVmLTdv3hzr1q2Dh4cHwsPDkZqaCj09PXzwwQfQ19eHtbU1WrduXWr/li1bVqUBbSIiIqJXdfPmTQghpJynqlJTU9G6dWvpy/SXB/gK81ATExOYm5tLxz///HMEBwfj448/BgB89tlniI2NxZo1a/DVV19J5WbOnCkNrE6ZMgW+vr6IiYlB586dAQCjR49GZGSkVH7RokVYtWqV9ERW06ZNcfXqVWzcuBH+/v5SualTpxZ7aqs0K1asgJubm8psWCcnJ+nPgwYNUim/ZcsWmJqa4urVq2jZsmWpbVY01pJ8//33uH//Ps6dOycthWZrayudd3V1VcmRFy9ejL1792Lfvn3ShIOypKamonnz5ujSpQtkMhmsra3LLM+cloiI6M3ijNu3xIEDB6BQKKCjowN3d3d069YNX3zxBQDA2tq61EFbAFAqldDQ0ECHDh2kYyYmJrC3t4dSqZSOaWlpwcXFpVJxubq6QldXV9p3d3dHVlYW7t27V6l6is6mLZzxUJaXY5XJZDA3N0d6erp07Mcff0SXLl1gbm4OhUKBkJAQldkH06dPx5gxY9CrVy8sX75c5VG3pKQkREZGQqFQSJuXlxcKCgpw+/Zt9O7dG9bW1rCxscGIESOwfft2PH36tNRY58yZg8zMTGmr7P0hIiIiqiohBIAX+dKrGD9+PKKiotCqVSsEBQXh1KlTZZZ//Pgx/vjjD2nwtVDnzp1VclBANa8zMzMDADg7O6scK8zz7t+/j3v37mH06NEqudrixYtV8jmgeI5ZlvLyz5SUFAwdOhQ2NjYwMDCQlocoOrv15TYrE2tpMbVu3brU91c8efIEQUFBcHR0hJGRERQKBX7//fcKz7gNCAhAcnIy7O3tMXnyZBw5cqTM8sxpiYiI3iwO3L4lPD09kZycjGvXruH58+fYs2ePtJRBSY+8vawwWS/p+MsJvFwuf+WEvlBhPWpqasXaf3l5hkJF+yCXy8tto+iLHmQymfTY2OnTp/Hxxx+jb9++OHDgAC5cuIC5c+eqrL8bFhaGK1eu4P3338evv/4KR0dH7N27F8CL9djGjRuH5ORkabt48SJu3LiBZs2aQV9fH+fPn8eOHTtgYWGB+fPnw9XVFY8ePSoxVm1tbRgYGKhsRERERG9C8+bNIZPJig2WvqzwSamX87aiOVvfvn1x9+5dTJ06FX/88Qd69uyJmTNnltt+0fyyaA4KqOZ1heeKHivM8wr/u2nTJpVc7fLlyzh9+rRKveXlyS8rL//s378/Hjx4gE2bNuHMmTPSOrMv55dF26xMrFWJadasWdi9ezeWLFmCEydOIDk5Gc7OzsViKk2bNm1w+/ZtLFq0CM+ePYOPj0+Z721gTktERPRmceD2LaGnpwdbW1tYW1tX+s20jo6OyMvLk5JLAHjw4AGuX78OBweHV4rr4sWLePbsmbR/+vRpKBQKNGrUCMCLx+bS0tKk848fP8bt27fLrdfFxQUxMTFVjuvkyZOwtrbG3Llz4ebmhubNm5f4sjU7OztMmzYNR44cwcCBA6UXbLRp0wZXrlyBra1tsU1LSwsAoKGhgV69emHFihX47bffcOfOHfz6669VjpmIiIioOtSrVw9eXl746quvVF60WujRo0fS01sv520lvTuhQYMGCAgIwHfffYc1a9ZIL4ctzI8K14MFAAMDA1haWiI+Pl6ljlOnTr1SDmpmZoaGDRvi1q1bxfK0wlmwVVFW/vngwQMolUrMmzcPPXv2hIODg/TSsuqM1cXFBcnJyXj48GGJ50+cOIGAgAD861//grOzM8zNzXHnzp1y632ZgYEBhgwZgk2bNmHnzp3YvXt3qe0RERHRm8U1buuA5s2bw9vbG2PHjsXGjRuhr6+P2bNno2HDhvD29n6lunNycjB69GjMmzcPd+/eRWhoKCZNmiTN2ujRowciIyPRv39/GBsbIyQkBOrq6uXWGxoaip49e6JZs2b4+OOPkZeXh59//llan7Y8tra2SE1NRVRUFNq1a4eDBw9Ks2kB4NmzZ5g1axY++ugjNG3aFP/73/9w7tw5ae2y4OBgdOzYERMnTsTYsWOhp6cHpVKJo0eP4osvvsCBAwdw69YtdOvWDcbGxjh06BAKCgpgb29fhbtIREREVL3Wr1+PTp06oX379li4cCFcXFyQl5eHo0ePIjw8HEqlEh07dsTy5cvRpEkT/P333yrvCwCA+fPno23btnByckJ2djYOHDggDcCamppCLpfj8OHDaNSoEXR0dGBoaIhZs2YhNDQUzZo1Q6tWrRAREYHk5GRs3779lfoTFhaGyZMnw8DAAH379kV2djYSExORkZGB6dOnV6nOOXPmwNnZGRMmTEBgYCC0tLQQGxuLwYMHo169ejAxMcHXX38NCwsLpKamYvbs2dUeq6+vL5YuXYoBAwZg2bJlsLCwwIULF2BpaQl3d3fY2tpiz5496N+/P2QyGUJCQoq9LK0sq1evhoWFBVq1agU1NTX88MMPMDc3r/AL64iIiKh6ccZtHREREYG2bdvigw8+gLu7O4QQOHToUKVn7xbVs2dPNG/eHN26dYOPjw/69++PsLAw6fycOXPQrVs3fPDBB+jXrx8GDBiAZs2alVtv9+7d8cMPP2Dfvn1o1aoVevTooTJjuDze3t6YNm0aJk2ahFatWuHUqVMICQmRzqurq+PBgwfw8/ODnZ0dfHx80LdvX+llCy4uLjh+/Dhu3LiBrl27onXr1ggJCYGFhQWAF29f3rNnD3r06AEHBwds2LABO3bsUHmBBREREVFt0bRpU5w/fx6enp6YMWMGWrZsid69eyMmJgbh4eEAgK1btyI3Nxdubm6YMmUKFi9erFKHlpYW5syZAxcXF3Tr1g3q6uqIiooC8OJJpHXr1mHjxo2wtLSUJgdMnjwZM2bMwIwZM+Ds7IzDhw9j3759Ki+FrYoxY8Zg8+bNiIyMhLOzMzw8PBAZGflKM27t7Oxw5MgRXLx4Ee3bt4e7uzt++uknaGhoQE1NDVFRUUhKSkLLli0xbdo0rFy5stpj1dLSwpEjR2Bqaop+/frB2dkZy5cvlyZCrF69GsbGxujUqRP69+8PLy8vtGnTpsJ9VigU+Oyzz+Dm5oZ27drhzp07OHToUKVeMkxERETVRyZKWwCVqBwBAQF49OgRoqOjazqUt87jx49haGiIjv/+A+o6RjUdzhslQwFs9P/GrX/qQ7yF3x3Fh5S//nJZCgoKkJ6eDlNT0zr3SxH7zr6z73VHdfa98N/QzMxMrq9JVMMK/3803j0HQqFToWsy3ptfzVFVn7r293pd6m9d6ivA/r7r2N/arzL57NvRIyIiIiIiIiIiIqI6hAO3VCqFQlHqduLEiZoOj4iIiIiowvr27Vtqbrt06dIaiWnp0qWlxtS3b98aiYmIiIhqD76cjEpV0puECzVs2BBdu3Z9c8EQEREREb2CzZs349mzZyWeq1ev3huO5oXAwED4+PiUeE4uf7XlmYiIiOjtx4FbKpWtrW1Nh0BERERE9Fo0bNiwpkMopl69ejU2aExERES1H5dKICIiIiIiIiIiIqplOHBLREREREREREREVMtw4JaIiIiIiIiIiIioluEat0Q16OdZchgZ1a0XTxQUFCA9XRumpnKoqfG7IyIiIqK33a0eQTAyMqrpMIiIiN45HDUhIiIiIiIiIiIiqmU4cEtERERERERERERUy3DgloiIiIiIiIiIiKiW4cAtERERERERERERUS3DgVsiIiIiIiIiIiKiWoYDt0RERERERERERES1jEZNB0BUl/Vd+QzqOtqVvi4+RF4N0RARERERVZ7NrysgFDolnst4b/4bjoaIiOjdwRm3RERERERERERERLUMB26JiIiIiIiIiIiIahkO3BIRERERERERERHVMhy4JSIiIiIiIiIiIqplOHBLREREREREREREVMtw4JaIiIiIiIiIiIioluHALREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cvsXi4uIgk8nw6NGjmg6lygICAjBgwIAyy3Tv3h1Tp059bW2+7vqIiIiIShIWFoZWrVrVaAzMe95+RXP+yMhIGBkZ1WhMRERE9GZw4LaSNmzYAH19feTl5UnHsrKyoKmpia5du6qUPXHiBGQyGa5fv/6mw6RqEhYWBplMJm2Ghobo2rUrjh8/XtOhERERUQUFBARI/5ZramrCxsYGM2fOxJMnT2o6tDJV5Uv7PXv2YNGiRdUX1Ct6FyYivGlDhgzh7xdERER1BAduK8nT0xNZWVlITEyUjp04cQLm5uY4d+4cnj59Kh2Pi4uDpaUl7OzsKtWGEEJlYJhqFycnJ6SlpSEtLQ0JCQlo3rw5PvjgA2RmZtZ0aERERFRBffr0QVpaGm7duoXFixdj/fr1mDlzZrFyubm5NRDd61OvXj3o6+vXdBgletvvbU2Ry+UwNTWt6TCIiIjoDeDAbSXZ29vD0tIScXFx0rG4uDh4e3ujWbNmOHXqlMpxT09PfPfdd3Bzc4O+vj7Mzc0xdOhQpKenq5STyWT45Zdf4ObmBm1tbZw4cQJCCKxYsQI2NjaQy+VwdXXFjz/+WCympKQkuLm5QVdXF506dcK1a9dUzoeHh6NZs2bQ0tKCvb09vv32W+ncnTt3IJPJkJycLB179OgRZDKZ1MeMjAwMGzYMDRo0gFwuR/PmzRERESGV/7//+z8MGTIExsbGMDExgbe3N+7cuVOp+7pgwQKYmprCwMAA48aNQ05OTqllMzIy4OfnB2NjY+jq6qJv3764ceOGSpmTJ0/Cw8MDurq6MDY2hpeXFzIyMkqs7/DhwzA0NMQ333xToVg1NDRgbm4Oc3NzODo6YsGCBcjKyipz5kN2djYeP36sshEREVHN0dbWhrm5OaysrDB06FAMGzYM0dHR0vIGW7duhY2NDbS1tSGEQGpqKry9vaFQKGBgYAAfHx/89ddfKnUuX74cZmZm0NfXx+jRo/H8+XOV8yUtWzBgwAAEBARI+9nZ2QgKCoKVlRW0tbXRvHlzbNmyBXfu3IGnpycAwNjYGDKZTOW60hRts0mTJli8eDH8/PygUChgbW2Nn376Cffv35f65+zsrDJJofDR/OjoaNjZ2UFHRwe9e/fGvXv3VNoqK+cEAJlMhg0bNsDb2xt6enoYM2ZMqX06fPgwunTpAiMjI5iYmOCDDz5ASkqKVFdhDrtnzx54enpCV1cXrq6uSEhIUGmzrJyworl2SV7O31u3bg25XI4ePXogPT0dP//8MxwcHGBgYABfX1+ViR0VafPQoUOws7ODXC6Hp6dnsby66FIJJS09NnXqVHTv3l3a7969Oz799FNMnToVxsbGMDMzw9dff40nT55g5MiR0NfXR7NmzfDzzz+X2W/mtERERG8WB26roHv37oiNjZX2Y2Nj0b17d3h4eEjHc3JykJCQAE9PT+Tk5GDRokW4ePEioqOjcfv27RIT7aCgICxbtgxKpRIuLi6YN28eIiIiEB4ejitXrmDatGkYPnx4scfy586di1WrViExMREaGhoYNWqUdG7v3r2YMmUKZsyYgcuXL2PcuHEYOXKkSvzlCQkJwdWrV/Hzzz9DqVQiPDwc9evXBwA8ffoUnp6eUCgU+O9//4v4+HgoFAr06dOnzMHXl8XExECpVCI2NhY7duzA3r17sWDBglLLBwQEIDExEfv27UNCQgKEEOjXr580ayM5ORk9e/aEk5MTEhISEB8fj/79+yM/P79YXVFRUfDx8cE333wDPz+/Ct+TQtnZ2VLybG9vX2q5ZcuWwdDQUNqsrKwq3RYRERFVH7lcLuUSN2/exK5du7B7927py+0BAwbg4cOHOH78OI4ePYqUlBQMGTJEun7Xrl0IDQ3FkiVLkJiYCAsLC6xfv77Scfj5+SEqKgrr1q2DUqnEhg0boFAoYGVlhd27dwMArl27hrS0NKxdu7ZKfV29ejU6d+6MCxcu4P3338eIESPg5+eH4cOH4/z587C1tYWfnx+EENI1T58+xZIlS7Bt2zacPHkSjx8/xscffyydr2jOGRoaCm9vb1y6dAkLFy4stU9PnjzB9OnTce7cOcTExEBNTQ3/+te/UFBQoFLf3LlzMXPmTCQnJ8POzg6+vr7Sk2vl5YQVzbXLEhYWhi+//BKnTp3CvXv34OPjgzVr1uD777/HwYMHcfToUXzxxRdS+fLavHfvHgYOHIh+/fohOTkZY8aMwezZsyscT1m2bduG+vXr4+zZs/j0008xfvx4DB48GJ06dcL58+fh5eWFESNGqAw0F8WcloiI6A0TVGlff/210NPTE7m5ueLx48dCQ0ND/PXXXyIqKkp06tRJCCHE8ePHBQCRkpJS7PqzZ88KAOKff/4RQggRGxsrAIjo6GipTFZWltDR0RGnTp1SuXb06NHC19dX5bpjx45J5w8ePCgAiGfPngkhhOjUqZMYO3asSh2DBw8W/fr1E0IIcfv2bQFAXLhwQTqfkZEhAIjY2FghhBD9+/cXI0eOLPFebNmyRdjb24uCggLpWHZ2tpDL5eKXX34p/Sb+//z9/UW9evXEkydPpGPh4eFCoVCI/Px8IYQQHh4eYsqUKUIIIa5fvy4AiJMnT0rl//77byGXy8WuXbuEEEL4+vqKzp07l9pmYX1fffWVMDQ0FL/++mu5cRYKDQ0VampqQk9PT+jp6QmZTCYMDAzEzz//XOZ1z58/F5mZmdJ27949AUB0/PcfovPCp5Xe3mb5+fkiLS1N+vnWNXW5/+w7+17XsO/V0/fMzEwBQGRmZla5Dn9/f+Ht7S3tnzlzRpiYmAgfHx8RGhoqNDU1RXp6unT+yJEjQl1dXaSmpkrHrly5IgCIs2fPCiGEcHd3F4GBgSrtdOjQQbi6ukr7L+c0hby9vYW/v78QQohr164JAOLo0aMlxl2Y+2VkZFS4r0XbtLa2FsOHD5f209LSBAAREhIiHUtISBAARFpamhBCiIiICAFAnD59WiqjVCoFAHHmzBkhRPk5pxBCABBTp06tUp/S09MFAHHp0iUhxP/LYTdv3iyVKfyZKJVKIUTZOWFFcu2ylJSHL1u2rFj+P27cOOHl5VXhNufMmSMcHBxUcuvg4GCVexQRESEMDQ2l80U/z0IIMWXKFOHh4SHte3h4iC5dukj7eXl5Qk9PT4wYMUI6VvhZSEhIKLXfpeW0xrvnCKNfFpS4vUvq2t/rdam/damvQrC/7zr2t/arTD7LGbdV4OnpiSdPnuDcuXM4ceIE7OzsYGpqCg8PD5w7dw5PnjxBXFwcGjduDBsbG1y4cAHe3t6wtraGvr6+9NhSamqqSr1ubm7Sn69evYrnz5+jd+/eUCgU0vbNN9+oPCYGAC4uLtKfLSwsAEBaikGpVKJz584q5Tt37gylUlnh/o4fPx5RUVFo1aoVgoKCVJaDSEpKws2bN6Gvry/FWK9ePTx//rxYnKVxdXWFrq6utO/u7o6srKxij98V9kdDQwMdOnSQjpmYmMDe3l7qU+HsirLs3r0bU6dOxZEjR6RH9CrK3t4eycnJSE5ORlJSkjRb4eVHCovS1taGgYGBykZEREQ158CBA1AoFNDR0YG7uzu6desmzYy0trZGgwYNpLJKpRJWVlYqswsdHR1hZGQk5R9KpRLu7u4qbRTdL09ycjLU1dXh4eFR1W5VyMu5o5mZGQDA2dm52LGXl/bS0NBQyVVbtGhRrP8VyTlfrqMsKSkpGDp0KGxsbGBgYICmTZsCKJ4/l5UHl5UTVibXLkvRe6mrqwsbGxuVY4XxVKRNpVKJjh07QiaTSXVU9nNUkVjV1dVhYmJS7s+9KOa0REREb5ZGTQfwNrK1tUWjRo0QGxuLjIwMKbk2NzdH06ZNcfLkScTGxqJHjx548uQJ3nvvPbz33nv47rvv0KBBA6SmpsLLy6vYUgJ6enrSnwsfAzt48CAaNmyoUk5bW1tlX1NTU/pzYZL38mNkLyd+wIu1tQqPqampSccKFX1RRN++fXH37l0cPHgQx44dQ8+ePTFx4kR8/vnnKCgoQNu2bbF9+/Zi9+nlX3iqomjcReMserywvFwuL7fuVq1a4fz584iIiEC7du1KbKs0WlpasLW1lfZbt26N6OhorFmzBt99912F6yEiIqKa4+npifDwcGhqasLS0lIln3o5JwNU84yKHC+NmppasVzm5byrIjnM61BS7lhePvny8dKOlZVzFip6b0vTv39/WFlZYdOmTbC0tERBQQFatmxZLH8uK+6y7mdlcu2yFG3/5f3CY4VtVaTN0nLdspT3uSop1pLiLe3nTkRERDWHM26ryNPTE3FxcYiLi1NZ+N/DwwO//PILTp8+DU9PT/z+++/4+++/sXz5cnTt2hUtWrQo81vsQo6OjtDW1kZqaipsbW1VtsqsJeXg4ID4+HiVY6dOnYKDgwOA/ze4mpaWJp1/+UVlhRo0aICAgAB89913WLNmDb7++msAQJs2bXDjxg2YmpoWi9PQ0LBCMV68eBHPnj2T9k+fPg2FQoFGjRoVK+vo6Ii8vDycOXNGOvbgwQNcv35d6pOLiwtiYmLKbLNZs2aIjY3FTz/9hE8//bRCcZZFXV1dpQ9ERERUu+np6cHW1hbW1tbFBrSKcnR0RGpqqsrTQFevXkVmZqaUfzg4OOD06dMq1xXdb9CggUrOlZ+fj8uXL0v7zs7OKCgoKHWNVS0tLem6Ny0vL0/l6aJr167h0aNHaNGiBYDyc87SlNSnBw8eQKlUYt68eejZsyccHBxKfclsWcrKCV9Xrl0ZFWnT0dGx3M9RUUU/V0DJ+TwRERG9fTjjtoo8PT0xceJE5ObmqjzO5uHhgfHjx+P58+fw9PSEjo4OtLS08MUXXyAwMBCXL1/GokWLyq1fX18fM2fOxLRp01BQUIAuXbrg8ePHOHXqFBQKBfz9/SsU56xZs+Dj44M2bdqgZ8+e2L9/P/bs2YNjx44BeDEToWPHjli+fDmaNGmCv//+G/PmzVOpY/78+Wjbti2cnJyQnZ2NAwcOSEn4sGHDsHLlSnh7e2PhwoVo1KgRUlNTsWfPHsyaNavEwdeicnJyMHr0aMybNw93795FaGgoJk2aJM0Gflnz5s3h7e2NsWPHYuPGjdDX18fs2bPRsGFDeHt7AwDmzJkDZ2dnTJgwAYGBgdDS0kJsbCwGDx4svVQNAOzs7KQXy2loaGDNmjUVuqd5eXn4888/AQD//PMPdu7ciatXryI4OLhC1xMREdHbpVevXnBxccGwYcOwZs0a5OXlYcKECfDw8JAe/Z8yZQr8/f3h5uaGLl26YPv27bhy5YrKY/M9evTA9OnTcfDgQTRr1gyrV6/Go0ePpPNNmjSBv78/Ro0ahXXr1sHV1RV3795Feno6fHx8YG1tDZlMhgMHDqBfv36Qy+VQKBRv5B5oamri008/xbp166CpqYlJkyahY8eOaN++PYDyc87SlNQnY2NjmJiY4Ouvv4aFhQVSU1Or9IKu8nLC15FrV0ZF8vvAwECsWrUK06dPx7hx45CUlITIyMgy6+3RowdWrlyJb775Bu7u7vjuu+9w+fJltG7d+rX3gYiIiN4szritIk9PTzx79gy2trbSelDAi4Hbf/75B82aNYOVlRUaNGiAyMhI/PDDD3B0dMTy5cvx+eefV6iNRYsWYf78+Vi2bBkcHBzg5eWF/fv3S2t8VcSAAQOwdu1arFy5Ek5OTti4cSMiIiJUZglv3boVubm5cHNzw5QpU7B48WKVOrS0tDBnzhy4uLigW7duUFdXR1RUFABAV1cX//3vf9G4cWMMHDgQDg4OGDVqFJ49e1bhNa969uyJ5s2bo1u3bvDx8UH//v0RFhZWavmIiAi0bdsWH3zwAdzd3SGEwKFDh6TZMnZ2djhy5AguXryI9u3bw93dHT/99BM0NIp/T2Fvb49ff/0VO3bswIwZMyoU75UrV2BhYQELCwu0atUKu3btQnh4OPz8/Cp0PREREb1dZDIZoqOjYWxsjG7duqFXr16wsbHBzp07pTJDhgzB/PnzERwcjLZt2+Lu3bsYP368Sj2jRo2Cv78//Pz84OHhgaZNmxZbaz88PBwfffQRJkyYgBYtWmDs2LF48uQJAKBhw4ZYsGABZs+eDTMzM0yaNKn6O///09XVRXBwMIYOHQp3d3fI5XIpHwQqlnOWpKQ+qampISoqCklJSWjZsiWmTZuGlStXVjrm8nLC15FrV1Z5bTZu3Bi7d+/G/v374erqig0bNmDp0qVl1unl5YWQkBAEBQWhXbt2+Oeff5iXEhERvSNkoioLKRHRK3n8+DEMDQ3R8d9/QF3HqNLXx4e8mTXwqkNBQQHS09Nhampa4qzqd11d7j/7zr6z73VHdfa98N/QzMxMvhjpDYmMjMTUqVNVZgcTAf/v/0fj3XMgFDollsl4b/4bjqr61LW/1+tSf+tSXwH2913H/tZ+lcln344eEREREREREREREdUhHLilaqVQKErdTpw4UdPhFfO2xUtERER1V2pqapm5S2pqak2H+NYKDAws9b4GBgbWdHhERERUR/DlZFStynqjbcOGDd9cIBX0tsVLREREdZelpWWZuYulpeVraScgIAABAQGvpa63xcKFCzFz5swSz3GJDiIiInpTOHBL1crW1ramQ6iUty1eIiIiqrs0NDSYu1QTU1NTmJqa1nQYREREVMdxqQQiIiIiIiIiIiKiWoYDt0RERERERERERES1DAduiYiIiIiIiIiIiGoZrnFLVIN+niWHkZG8psMgIiIiIqqyWz2CYGRkVNNhEBERvXM445aIiIiIiIiIiIioluHALREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyGjUdAFFd1nflM6jraFf6uvgQeTVEQ0RERERUeTa/roBQ6AAAMt6bX8PREBERvTs445aIiIiIiIiIiIioluHALREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLglIiIiIiIiIiIiqmU4cEtERERERERERERUy3DgloiIiIiIiIiIiKiWqZUDt2FhYWjVqlWNxtC9e3dMnTq1RmOgVxMXFweZTIZHjx4BACIjI2FkZFSjMRERERFVB+bP766iOW1JqvLz58+LiIio9qvUwG1AQABkMhlkMhk0NTVhY2ODmTNn4smTJ9UV32tRkWSnqD179mDRokXVF9Qrqkqf6rohQ4bg+vXrNR0GERER1SHMn2uPdzl/njlzJmJiYmo6DCIiInrNNCp7QZ8+fRAREYHc3FycOHECY8aMwZMnTxAeHq5SLjc3F5qamq8t0DetXr16NR1CqXJzc2s6hLeSXC6HXC6v6TCIiIiojmH+XPPe9fxZoVBAoVDUdBhERET0mlV6qQRtbW2Ym5vDysoKQ4cOxbBhwxAdHS09nrN161bY2NhAW1sbQgikpqbC29sbCoUCBgYG8PHxwV9//aVS5/Lly2FmZgZ9fX2MHj0az58/Vzlf0mM8AwYMQEBAgLSfnZ2NoKAgWFlZQVtbG82bN8eWLVtw584deHp6AgCMjY0hk8lUritN0TabNGmCxYsXw8/PDwqFAtbW1vjpp59w//59qX/Ozs5ITEyUril8ND86Ohp2dnbQ0dFB7969ce/ePZW2wsPD0axZM2hpacHe3h7ffvutynmZTIYNGzbA29sbenp6GDNmTKl9Onz4MLp06QIjIyOYmJjggw8+QEpKilTXnTt3IJPJsGfPHnh6ekJXVxeurq5ISEhQafPkyZPw8PCArq4ujI2N4eXlhYyMDACAEAIrVqyAjY0N5HI5XF1d8eOPP5Z7T4H/N9Phl19+QevWrSGXy9GjRw+kp6fj559/hoODAwwMDODr64unT59K11WkzUOHDsHOzg5yuRyenp64c+eOyvmiSyUEBARgwIABKmWmTp2K7t27S/vdu3fHp59+iqlTp8LY2BhmZmb4+uuv8eTJE4wcORL6+vpo1qwZfv755zL7nZ2djcePH6tsREREVDcwf2b+/Cr5c6GkpCS4ublBV1cXnTp1wrVr16RzRZdKyMvLw+TJk6U+BQcHw9/fv1juW1BQgKCgINSrVw/m5uYICwsrMwbmtERERG/WK69xK5fLpW+wb968iV27dmH37t1ITk4G8CJBfPjwIY4fP46jR48iJSUFQ4YMka7ftWsXQkNDsWTJEiQmJsLCwgLr16+vdBx+fn6IiorCunXroFQqsWHDBigUClhZWWH37t0AgGvXriEtLQ1r166tUl9Xr16Nzp0748KFC3j//fcxYsQI+Pn5Yfjw4Th//jxsbW3h5+cHIYR0zdOnT7FkyRJs27YNJ0+exOPHj/Hxxx9L5/fu3YspU6ZgxowZuHz5MsaNG4eRI0ciNjZWpe3Q0FB4e3vj0qVLWLhwYal9evLkCaZPn45z584hJiYGampq+Ne//oWCggKV+ubOnYuZM2ciOTkZdnZ28PX1RV5eHgAgOTkZPXv2hJOTExISEhAfH4/+/fsjPz8fADBv3jxEREQgPDwcV65cwbRp0zB8+HAcP368wvcyLCwMX375JU6dOoV79+7Bx8cHa9aswffff4+DBw/i6NGj+OKLL6Ty5bV57949DBw4EP369UNycjLGjBmD2bNnVziesmzbtg3169fH2bNn8emnn2L8+PEYPHgwOnXqhPPnz8PLywsjRoxQGWguatmyZTA0NJQ2Kyur1xIbERERvX2YPzN/rkr+PHfuXKxatQqJiYnQ0NDAqFGjSi372WefYfv27YiIiJDuYXR0dLFy27Ztg56eHs6cOYMVK1Zg4cKFOHr0aKn1MqclIiJ6w0Ql+Pv7C29vb2n/zJkzwsTERPj4+IjQ0FChqakp0tPTpfNHjhwR6urqIjU1VTp25coVAUCcPXtWCCGEu7u7CAwMVGmnQ4cOwtXVVdr38PAQU6ZMUSnj7e0t/P39hRBCXLt2TQAQR48eLTHu2NhYAUBkZGRUuK9F27S2thbDhw+X9tPS0gQAERISIh1LSEgQAERaWpoQQoiIiAgBQJw+fVoqo1QqBQBx5swZIYQQnTp1EmPHjlVpe/DgwaJfv37SPgAxderUKvUpPT1dABCXLl0SQghx+/ZtAUBs3rxZKlP4M1EqlUIIIXx9fUXnzp1LrC8rK0vo6OiIU6dOqRwfPXq08PX1LTOWl+M+duyYdGzZsmUCgEhJSZGOjRs3Tnh5eVW4zTlz5ggHBwdRUFAgnQ8ODla5RxEREcLQ0FA6X/TzLIQQU6ZMER4eHtK+h4eH6NKli7Sfl5cn9PT0xIgRI6RjhZ+FhISEUvv9/PlzkZmZKW337t0TAETHf/8hOi98WuntbZafny/S0tJEfn5+TYdSI+py/9l39r2uYd+rp++ZmZkCgMjMzHztdVcH5s/Mn6sjfz548KAAIJ49eyaEECI0NFTl529mZiZWrlwp7efl5YnGjRurfBaL5rlCCNGuXTsRHBxcaiyl5bTGu+cIo18WCKNfFpTbn7dZXft7vS71ty71VQj2913H/tZ+lclnKz3j9sCBA1AoFNDR0YG7uzu6desmzYy0trZGgwYNpLJKpRJWVlYq38Q6OjrCyMgISqVSKuPu7q7SRtH98iQnJ0NdXR0eHh6V7U6luLi4SH82MzMDADg7Oxc7lp6eLh3T0NCAm5ubtN+iRYti/e/cubNKO507d5bOF3q5jrKkpKRg6NChsLGxgYGBAZo2bQoASE1NLbUvFhYWKnEXzhgoydWrV/H8+XP07t1bWktLoVDgm2++UXmkrDxF76Wuri5sbGxUjhXGU5E2lUolOnbsCJlMJtVR2c9RRWJVV1eHiYlJuT/3orS1tWFgYKCyERERUd3A/PkF5s+vL38u2v7LMjMz8ddff6F9+/bSMXV1dbRt27bMOgvrZU5LRERUe1T65WSenp4IDw+HpqYmLC0tVV6goKenp1JWCKEykFbe8dKoqampPD4FqL5g4E29cOrlvhbGX9Kxoo9VldTXl48VPV/S/Sl6b0vTv39/WFlZYdOmTbC0tERBQQFatmyJnJyccvtSGHdZ97OwzMGDB9GwYUOVc9ra2hWKsaT2i76IQyaTSW1VpM2in4+KKO9zVVKsJcVb2s+diIiICGD+XIj58+vNn1+uuyQl3aOy6iy8hjktERFR7VHpGbd6enqwtbWFtbV1uW+9dXR0RGpqqsrLBK5evYrMzEw4ODgAABwcHHD69GmV64ruN2jQAGlpadJ+fn4+Ll++LO07OzujoKCg1DWitLS0pOvetLy8PJUXLly7dg2PHj1CixYtALzof3x8vMo1p06dku5PaUrq04MHD6BUKjFv3jz07NkTDg4O0gsRKsPFxQUxMTElnnN0dIS2tjZSU1Nha2urslXXGlcVadPR0bHcz1FRRT9XAKS15YiIiIheF+bPlcP8+dUYGhrCzMwMZ8+elY7l5+fjwoULr70tIiIiql6VnnFbGb169YKLiwuGDRuGNWvWIC8vDxMmTICHh4f06NKUKVPg7+8PNzc3dOnSBdu3b8eVK1dUHpvv0aMHpk+fjoMHD6JZs2ZYvXo1Hj16JJ1v0qQJ/P39MWrUKKxbtw6urq64e/cu0tPT4ePjA2tra8hkMhw4cAD9+vWDXC6HQqGozq5LNDU18emnn2LdunXQ1NTEpEmT0LFjR+nRpVmzZsHHxwdt2rRBz549sX//fuzZswfHjh0rs96S+mRsbAwTExN8/fXXsLCwQGpqapVe0DVnzhw4OztjwoQJCAwMhJaWFmJjYzF48GDUr18fM2fOxLRp01BQUIAuXbrg8ePHOHXqFBQKBfz9/at0n8qir69fbpuBgYFYtWoVpk+fjnHjxiEpKQmRkZFl1tujRw+sXLkS33zzDdzd3fHdd9/h8uXLaN269WvvAxEREVFFMH9m/vw6fPrpp1i2bBlsbW3RokULfPHFF8jIyKjUrG0iIiKqeZWecVsZMpkM0dHRMDY2Rrdu3dCrVy/Y2Nhg586dUpkhQ4Zg/vz5CA4ORtu2bXH37l2MHz9epZ5Ro0bB398ffn5+8PDwQNOmTeHp6alSJjw8HB999BEmTJiAFi1aYOzYsXjy5AkAoGHDhliwYAFmz54NMzMzTJo0qTq7rUJXVxfBwcEYOnQo3N3dIZfLERUVJZ0fMGAA1q5di5UrV8LJyQkbN25EREQEunfvXma9JfVJTU0NUVFRSEpKQsuWLTFt2jSsXLmy0jHb2dnhyJEjuHjxItq3bw93d3f89NNP0NB4Mc6/aNEizJ8/H8uWLYODgwO8vLywf/9+aT2w6lBem40bN8bu3buxf/9+uLq6YsOGDVi6dGmZdXp5eSEkJARBQUFo164d/vnnH/j5+VVbH4iIiIjKw/yZ+fPrEBwcDF9fX/j5+cHd3R0KhQJeXl7Q0dGplvaIiIioeshEVRYHpQqJjIzE1KlTVWY3EAHA48ePYWhoiI7//gPqOkaVvj4+5M2sS1cdCgoKkJ6eDlNTU6ipVet3R7VSXe4/+86+s+91R3X2vfDf0MzMTL4Y6R3E/Ll6FBQUwMHBAT4+Pli0aNFrq7fw/0fj3XMgFC8GhTPem//a6q9t6trf63Wpv3WprwD7+65jf2u/yuSz1bpUAhEREREREb1Zd+/exZEjR+Dh4YHs7Gx8+eWXuH37NoYOHVrToREREVElvB1D0a9ZamoqFApFqVtqampNh/jWCgwMLPW+BgYG1nR4RERERFQFzJ+rT3Xkz2pqaoiMjES7du3QuXNnXLp0CceOHSv3BW5ERERUu9TJGbeWlpZITk4u8/zrEBAQgICAgNdS19ti4cKFmDlzZonn+DgjERER0duJ+XP1qY782crKCidPnnyVsIiIiKgWqJMDtxoaGrC1ta3pMN5JpqamMDU1rekwiIiIiOg1Yv5cfZg/ExERUWnq5FIJRERERERERERERLUZB26JiIiIiIiIiIiIahkO3BIRERERERERERHVMnVyjVui2uLnWXIYGclrOgwiIiIioiq71SMIRkZGNR0GERHRO4czbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLglIiIiIiIiIiIiqmU4cEtERERERERERERUy3DgloiIiIiIiIiIiKiW4cAtERERERERERERUS2jUdMBENVlfVc+g7qOdqWviw+RV0M0RERERESVZ/PrCgiFDgAg4735NRwNERHRu4MzbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLglIiIiIiIiIiIiqmU4cEtERERERERERERUy3DgloiIiIiIiIiIiKiW4cAtERERERERERERUS3DgVsiIiIiIiIiIiKiWoYDt0RERERERERERES1DAduiYiIiIiIiIiIiGoZDtzSO6d79+6YOnVqTYdBRERERBUQFxcHmUyGR48e1XQoVRYQEIABAwaUWeZ156jMeYmIiN59HLilavHnn39iypQpsLW1hY6ODszMzNClSxds2LABT58+renwXllYWBhkMlmx7dixYzUdGhEREVG5NmzYAH19feTl5UnHsrKyoKmpia5du6qUPXHiBGQyGa5fv/6mw6Rq9r///Q9aWlpo0aJFTYdCREREJdCo6QDo3XPr1i107twZRkZGWLp0KZydnZGXl4fr169j69atsLS0xIcffljTYZYqPz8fMpkMamplf6/h5ORUbKC2Xr161RkaERER0Wvh6emJrKwsJCYmomPHjgBeDNCam5vj3LlzePr0KXR1dQG8mBFraWkJOzu7SrUhhEB+fj40NPgrR20VGRkJHx8f/Pe//8XJkyfRuXPnmg6JiIiIXsIZt/TaTZgwARoaGkhMTISPjw8cHBzg7OyMQYMG4eDBg+jfvz8AIDMzE5988glMTU1hYGCAHj164OLFi1I9YWFhaNWqFb799ls0adIEhoaG+Pjjj/HPP/9IZZ48eQI/Pz8oFApYWFhg1apVxeLJyclBUFAQGjZsCD09PXTo0AFxcXHS+cjISBgZGeHAgQNwdHSEtrY27t69W24/NTQ0YG5urrJpaWm9wp0jIiIiejPs7e1haWmpkhPFxcXB29sbzZo1w6lTp1SOe3p64rvvvoObmxv09fVhbm6OoUOHIj09XaWcTCbDL7/8Ajc3N2hra+PEiRMQQmDFihWwsbGBXC6Hq6srfvzxx2IxJSUlwc3NDbq6uujUqROuXbumcj48PBzNmjWDlpYW7O3t8e2330rn7ty5A5lMhuTkZOnYo0ePIJPJpD5mZGRg2LBhaNCgAeRyOZo3b46IiAip/P/93/9hyJAhMDY2homJCby9vXHnzp1K3dcFCxZIue24ceOQk5NTatmMjAz4+fnB2NgYurq66Nu3L27cuKFS5uTJk/Dw8ICuri6MjY3h5eWFjIyMEus7fPgwDA0N8c0331QoViEEIiIiMGLECAwdOhRbtmypeEeJiIjojeDALb1WDx48wJEjRzBx4kTo6emVWEYmk0EIgffffx9//vknDh06hKSkJLRp0wY9e/bEw4cPpbIpKSmIjo7GgQMHcODAARw/fhzLly+Xzs+aNQuxsbHYu3cvjhw5gri4OCQlJam0N3LkSJw8eRJRUVH47bffMHjwYPTp00clMX769CmWLVuGzZs348qVKzA1NX2t9yU7OxuPHz9W2YiIiIhqUvfu3REbGyvtx8bGonv37vDw8JCO5+TkICEhAZ6ensjJycGiRYtw8eJFREdH4/bt2wgICChWb1BQEJYtWwalUgkXFxfMmzcPERERCA8Px5UrVzBt2jQMHz4cx48fV7lu7ty5WLVqFRITE6GhoYFRo0ZJ5/bu3YspU6ZgxowZuHz5MsaNG4eRI0eqxF+ekJAQXL16FT///DOUSiXCw8NRv359AC9yQU9PTygUCvz3v/9FfHw8FAoF+vTpU+bg68tiYmKgVCoRGxuLHTt2YO/evViwYEGp5QMCApCYmIh9+/YhISEBQgj069cPubm5AIDk5GT07NkTTk5OSEhIQHx8PPr374/8/PxidUVFRcHHxwfffPMN/Pz8KhRvbGwsnj59il69emHEiBHYtWuXygSJkjCnJSIierP43BK9Vjdv3oQQAvb29irH69evj+fPnwMAJk6cCC8vL1y6dAnp6enQ1tYGAHz++eeIjo7Gjz/+iE8++QQAUFBQgMjISOjr6wMARowYgZiYGCxZsgRZWVnYsmULvvnmG/Tu3RsAsG3bNjRq1EhqNyUlBTt27MD//vc/WFpaAgBmzpyJw4cPIyIiAkuXLgUA5ObmYv369XB1da1wXy9dugSFQiHtOzo64uzZsyWWXbZsWZmJOxEREdGb1r17d0ybNg15eXl49uwZLly4gG7duiE/Px/r1q0DAJw+fRrPnj2Dp6cnbGxspGttbGywbt06tG/fHllZWSo50cKFC6Xc7MmTJ/jPf/6DX3/9Fe7u7tK18fHx2LhxIzw8PKTrlixZIu3Pnj0b77//Pp4/fw4dHR18/vnnCAgIwIQJEwAA06dPx+nTp/H555/D09OzQv1NTU1F69at4ebmBgBo0qSJdC4qKgpqamrYvHkzZDIZACAiIgJGRkaIi4vDe++9V279Wlpa2Lp1K3R1deHk5ISFCxdi1qxZWLRoUbEluG7cuIF9+/bh5MmT6NSpEwBg+/btsLKyQnR0NAYPHowVK1bAzc0N69evl65zcnIq1u769evx73//Gz/99FOF7wUAbNmyBR9//DHU1dXh5OQEW1tb7Ny5E2PGjCn1Gua0REREbxYHbqlaFCa8hc6ePYuCggIMGzYM2dnZSEpKQlZWFkxMTFTKPXv2DCkpKdJ+kyZNpEFbALCwsJAeyUtJSUFOTo70SwDwYo3ZlweNz58/DyFEsTXZsrOzVdrW0tKCi4tLpfpob2+Pffv2SfuFA9AlmTNnDqZPny7tP378GFZWVpVqj4iIiOh18vT0xJMnT3Du3DlkZGTAzs4Opqam8PDwwIgRI/DkyRPExcWhcePGsLGxwYULFxAWFobk5GQ8fPgQBQUFAF4MiDo6Okr1Fg6MAsDVq1fx/PlzaSC3UE5ODlq3bq1y7OVczMLCAgCQnp6Oxo0bQ6lUSl/sF+rcuTPWrl1b4f6OHz8egwYNwvnz5/Hee+9hwIAB0qBpUlISbt68qZJ3AsDz589VctOyuLq6SusCA4C7uzuysrJw7949WFtbq5RVKpXQ0NBAhw4dpGMmJiawt7eHUqkE8GLG7eDBg8tsc/fu3fjrr78QHx+P9u3bVyhO4MUyEnv27EF8fLx0bPjw4di6dWuZA7fMaYmIiN4sDtzSa2VrawuZTIbff/9d5XjhDA25XA7gxUxaCwsLlXXVChkZGUl/1tTUVDknk8mkXxKEEOXGU1BQAHV1dSQlJUFdXV3l3MszQ+RyebHB5vJoaWnB1ta2QmW1tbXLHNglIiIietNsbW3RqFEjxMbGIiMjQ5rtam5ujqZNm+LkyZOIjY1Fjx498OTJE7z33nt477338N1336FBgwZITU2Fl5dXsaUEXl4uqzBvO3jwIBo2bKhSrmhu9HLeV5iXFV7/8rFCQgjpWOGM1pfzw8IlBwr17dsXd+/excGDB3Hs2DH07NkTEydOxOeff46CggK0bdsW27dvL3afGjRoUOxYZZSUY5aWx77cp8K8uSytWrXC+fPnERERgXbt2lU4n/3+++/x/PlzlYFjIQQKCgpw9epVlYH4lzGnJSIierO4xi29ViYmJujduze+/PJLPHnypNRybdq0wZ9//gkNDQ3Y2tqqbIVrjZXH1tYWmpqaOH36tHQsIyMD169fl/Zbt26N/Px8pKenF2vH3Ny86h0lIiIiegd4enoiLi4OcXFx6N69u3Tcw8MDv/zyC06fPg1PT0/8/vvv+Pvvv7F8+XJ07doVLVq0UHkxWWkKX/yamppaLBerzExNBwcHldmhAHDq1Ck4ODgA+H+Dq2lpadL5l19UVqhBgwYICAjAd999hzVr1uDrr78G8CI3vXHjBkxNTYvFaWhoWKEYL168iGfPnkn7p0+fhkKhUFnGq5CjoyPy8vJw5swZ6diDBw9w/fp1qU8uLi6IiYkps81mzZohNjYWP/30Ez799NMKxQm8WCZhxowZSE5OlraLFy/C09MTW7durXA9REREVL04cEuv3fr165GXlwc3Nzfs3LkTSqUS165dw3fffYfff/8d6urq6NWrF9zd3TFgwAD88ssvuHPnDk6dOoV58+YhMTGxQu0oFAqMHj0as2bNQkxMDC5fvoyAgACVNcTs7OwwbNgw+Pn5Yc+ePbh9+zbOnTuHzz77DIcOHaquW0BERET0VvD09ER8fDySk5NV1pv18PDApk2b8Pz5c3h6eqJx48bQ0tLCF198gVu3bmHfvn1YtGhRufXr6+tj5syZmDZtGrZt24aUlBRcuHABX331FbZt21bhOGfNmoXIyEhs2LABN27cwH/+8x/s2bMHM2fOBPBidmrHjh2xfPlyXL16Ff/9738xb948lTrmz5+Pn376CTdv3sSVK1dw4MABaZB02LBhqF+/Pry9vXHixAncvn0bx48fx5QpU/C///2vQjHm5ORg9OjR0gvQQkNDMWnSpGLr2wJA8+bN4e3tjbFjxyI+Ph4XL17E8OHD0bBhQ3h7ewN4sSzBuXPnMGHCBPz222/4/fffER4ejr///lulLjs7O8TGxmL37t2YOnVquXEmJyfj/PnzGDNmDFq2bKmy+fr64ptvvik2W5mIiIhqBgdu6bVr1qwZLly4gF69emHOnDlwdXWFm5sbvvjiC8ycOROLFi2CTCbDoUOH0K1bN4waNQp2dnb4+OOPcefOHZiZmVW4rZUrV6Jbt2748MMP0atXL3Tp0gVt27ZVKRMREQE/Pz/MmDED9vb2+PDDD3HmzBmux0VERER1nqenJ549ewZbW1uVHMzDwwP//PMPmjVrBisrKzRo0ACRkZH44Ycf4OjoiOXLl+Pzzz+vUBuLFi3C/PnzsWzZMjg4OMDLywv79+9H06ZNKxzngAEDsHbtWqxcuRJOTk7YuHEjIiIiVGYJb926Fbm5uXBzc8OUKVOwePFilTq0tLQwZ84cuLi4oFu3blBXV0dUVBQAQFdXF//973/RuHFjDBw4EA4ODhg1ahSePXsGAwODCsXYs2dPNG/eHN26dYOPjw/69++PsLCwUstHRESgbdu2+OCDD+Du7g4hBA4dOiQtGWFnZ4cjR47g4sWLaN++Pdzd3fHTTz9BQ6P4anf29vb49ddfsWPHDsyYMaPMOLds2QJHR0e0aNGi2LkBAwbg4cOH2L9/f4X6TERERNVLJiqyUCgRvVaPHz+GoaEhOv77D6jrGFX6+viQ8tc8q60KCgqQnp4OU1PTEmegvOvqcv/Zd/adfa87qrPvhf+GZmZmVnhAjYiqR+H/j8a750AodAAAGe/Nr+Goqk9d+3u9LvW3LvUVYH/fdexv7VeZfPbt6BERERERERERERFRHcKBW6ISKBSKUrcTJ07UdHhERERE9Aa9bbnh2xYvERERlaz4AklEVOJbiAs1bNjwzQVCRERERDXubcsN37Z4iYiIqGQcuCUqga2tbU2HQERERES1xNuWG75t8RIREVHJuFQCERERERERERERUS3DgVsiIiIiIiIiIiKiWoYDt0RERERERERERES1DNe4JapBP8+Sw8hIXtNhEBERERFV2a0eQTAyMqrpMIiIiN45nHFLREREREREREREVMtw4JaIiIiIiIiIiIioluHALREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyGjUdAFFd1nflM6jraFf6uvgQeTVEQ0RERERUeTa/roBQ6CDjvfk1HQoREdE7hTNuiYiIiIiIiIiIiGoZDtwSERERERERERER1TIcuCUiIiIiIiIiIiKqZThwS0RERERERERERFTLcOCWiIiIiIiIiIiIqJbhwC0RERERERERERFRLcOBWyIiIiIiIiIiIqJahgO3RERERERERERERLUMB26JiIiIiIiIiIiIahkO3JYiLi4OMpkMjx49emNtRkdHw9bWFurq6pg6deor1xcQEIABAwaUWaZJkyZYs2bNK7f1trpz5w5kMhmSk5NLLRMZGQkjI6M3FhMRERERUffu3V/L7wRERET09nprB24DAgIgk8kQGBhY7NyECRMgk8kQEBDw5gN7BePGjcNHH32Ee/fuYdGiReWW37hxI1xdXaGnpwcjIyO0bt0an332WaXaPHfuHD755JOqhvxGVWUA9datW/D19YWlpSV0dHTQqFEjeHt74/r16xWuY8iQIZUqT0RERETvhj///BNTpkyBra0tdHR0YGZmhi5dumDDhg14+vRpTYf3ysLCwiCTyYptx44dq+nQiIiICIBGTQfwKqysrBAVFYXVq1dDLpcDAJ4/f44dO3agcePGNRxd5WRlZSE9PR1eXl6wtLQst/yWLVswffp0rFu3Dh4eHsjOzsZvv/2Gq1evVqrdBg0aVDXkWi8nJwe9e/dGixYtsGfPHlhYWOB///sfDh06hMzMzArXI5fLpc8XEREREdUNt27dQufOnWFkZISlS5fC2dkZeXl5uH79OrZu3QpLS0t8+OGHNR1mqfLz8yGTyaCmVvZcHScnp2IDtfXq1avO0IiIiKiC3toZtwDQpk0bNG7cGHv27JGO7dmzB1ZWVmjdurV0LDs7G5MnT4apqSl0dHTQpUsXnDt3TqWuQ4cOwc7ODnK5HJ6enrhz506x9k6dOoVu3bpBLpfDysoKkydPxpMnTyoUa0ZGBvz8/GBsbAxdXV307dsXN27cAPBiWQZ9fX0AQI8ePSCTyRAXF1dmffv374ePjw9Gjx4NW1tbODk5wdfXt8SZup9//jksLCxgYmKCiRMnIjc3VzpXdKkEmUyG8PBw9O3bF3K5HE2bNsUPP/wgnS9cWmDXrl3o2rUr5HI52rVrh+vXr+PcuXNwc3ODQqFAnz59cP/+fZU4IiIi4ODgAB0dHbRo0QLr168vVu+ePXvg6ekJXV1duLq6IiEhQbpHI0eORGZmpjQTICwsrMx7dPXqVdy6dQvr169Hx44dYW1tjc6dO2PJkiVo166dStlbt26V2C5QfKZvWFgYWrVqhY0bN8LKygq6uroYPHhwmctqZGdn4/HjxyobEREREdVeEyZMgIaGBhITE+Hj4wMHBwc4Oztj0KBBOHjwIPr37w8AyMzMxCeffAJTU1MYGBigR48euHjxolRPYe747bffokmTJjA0NMTHH3+Mf/75Ryrz5MkT+Pn5QaFQwMLCAqtWrSoWT05ODoKCgtCwYUPo6emhQ4cOKr8zFOasBw4cgKOjI7S1tXH37t1y+6mhoQFzc3OVTUtLq8SyzGmJiIjerLd64BYARo4ciYiICGl/69atGDVqlEqZoKAg7N69G9u2bcP58+dha2sLLy8vPHz4EABw7949DBw4EP369UNycjLGjBmD2bNnq9Rx6dIleHl5YeDAgfjtt9+wc+dOxMfHY9KkSRWKMyAgAImJidi3bx8SEhIghEC/fv2Qm5uLTp064dq1awCA3bt3Iy0tDZ06dSqzPnNzc5w+fbrcZCw2NhYpKSmIjY3Ftm3bEBkZicjIyDKvCQkJwaBBg3Dx4kUMHz4cvr6+UCqVKmVCQ0Mxb948nD9/HhoaGvD19UVQUBDWrl2LEydOICUlBfPnz5fKb9q0CXPnzsWSJUugVCqxdOlShISEYNu2bSr1zp07FzNnzkRycjLs7Ozg6+uLvLw8dOrUCWvWrIGBgQHS0tKQlpaGmTNnltmPBg0aQE1NDT/++CPy8/PLLFtau6W5efMmdu3ahf379+Pw4cNITk7GxIkTSy2/bNkyGBoaSpuVlVWZ8RARERFRzXnw4AGOHDmCiRMnQk9Pr8QyMpkMQgi8//77+PPPP3Ho0CEkJSWhTZs26Nmzp/S7BgCkpKQgOjoaBw4cwIEDB3D8+HEsX75cOj9r1izExsZi7969OHLkCOLi4pCUlKTS3siRI3Hy5ElERUXht99+w+DBg9GnTx9pMggAPH36FMuWLcPmzZtx5coVmJqavtb7wpyWiIjoDRNvKX9/f+Ht7S3u378vtLW1xe3bt8WdO3eEjo6OuH//vvD29hb+/v4iKytLaGpqiu3bt0vX5uTkCEtLS7FixQohhBBz5swRDg4OoqCgQCoTHBwsAIiMjAwhhBAjRowQn3zyiUoMJ06cEGpqauLZs2dlxnr9+nUBQJw8eVI69vfffwu5XC527dolhBAiIyNDABCxsbEV6v8ff/whOnbsKAAIOzs74e/vL3bu3Cny8/NV7pG1tbXIy8uTjg0ePFgMGTJE2re2tharV6+W9gGIwMBAlbY6dOggxo8fL4QQ4vbt2wKA2Lx5s3R+x44dAoCIiYmRji1btkzY29tL+1ZWVuL7779XqXfRokXC3d291HqvXLkiAAilUimEECIiIkIYGhpW6P4U+vLLL4Wurq7Q19cXnp6eYuHChSIlJUU6X5V2Q0NDhbq6urh375507OeffxZqamoiLS2txDieP38uMjMzpe3evXsCgOj47z9E54VPK729zfLz80VaWprKZ7Uuqcv9Z9/Z97qGfa+evmdmZgoAIjMz87XXTVTo9OnTAoDYs2ePynETExOhp6cn9PT0RFBQkIiJiREGBgbi+fPnKuWaNWsmNm7cKIR4kTvq6uqKx48fS+dnzZolOnToIIQQ4p9//hFaWloiKipKOv/gwQMhl8vFlClThBBC3Lx5U8hkMvF///d/Ku307NlTzJkzRwjxImcFIJKTkyvcz9DQUKGmpib1SU9PT7Rr167U8qXltMa75wijXxZUuN23VV37e70u9bcu9VUI9vddx/7WfpXJZ9/qNW4BoH79+nj//fexbds26Rvv+vXrS+dTUlKQm5uLzp07S8c0NTXRvn17aRapUqlEx44dIZPJpDLu7u4q7SQlJeHmzZvYvn27dEwIgYKCAty+fRsODg6lxqhUKqGhoYEOHTpIx0xMTGBvb19sJmtFWVhYICEhAZcvX8bx48dx6tQp+Pv7Y/PmzTh8+LC0lpWTkxPU1dVVrrt06VKZdRftu7u7O5KTk1WOubi4SH82MzMDADg7O6scS09PBwDcv38f9+7dw+jRozF27FipTF5eHgwNDUut18LCAgCQnp6OFi1alBlzaSZOnAg/Pz/ExsbizJkz+OGHH7B06VLs27cPvXv3rnK7jRs3RqNGjaR9d3d3FBQU4Nq1azA3Ny9WXltbG9ra2lXqAxERERHVjJd/PwCAs2fPoqCgAMOGDUN2djaSkpKQlZUFExMTlXLPnj1DSkqKtN+kSRNpaTTgRb5ZmCunpKQgJydHJQevV68e7O3tpf3z589DCAE7OzuVdrKzs1Xa1tLSUslrK8Le3h779u2T9svKWZnTEhERvVlv/cAtAIwaNUpasuCrr75SOSeEAFA86RJCSMcKy5SloKAA48aNw+TJk4udK+9FaKXV/3IMVdWyZUu0bNkSEydORHx8PLp27Yrjx4/D09MTwItB6pfJZDIUFBRUup2icb5cb+G5oscK2yn876ZNm1QGrwGoDCqXVm9V4n2Zvr4+PvzwQ3z44YdYvHgxvLy8sHjxYpWB21dtt/CaV/15EhEREVHNs7W1hUwmw++//65y3MbGBgCkF9cWFBTAwsKixPdTvPyOhLJy8or+LqKuro6kpKRi+bNCoZD+LJfLK52PamlpwdbWtlLXEBER0Zvx1q9xCwB9+vRBTk4OcnJy4OXlpXLO1tYWWlpaiI+Pl47l5uYiMTFRmiXr6OiI06dPq1xXdL9Nmza4cuUKbG1ti22lLd5fyNHREXl5eThz5ox07MGDB7h+/XqZM3Ury9HREQAq/MK00pR0L6o64xV4Mfu2YcOGuHXrVrF717Rp0wrXo6WlVe5ateWRyWRo0aLFK9+j1NRU/PHHH9J+QkIC1NTUis2CICIiIqK3j4mJCXr37o0vv/yyzLyxTZs2+PPPP6GhoVEsz335KcCy2NraQlNTUyUHz8jIwPXr16X91q1bIz8/H+np6cXaKelpLyIiIno3vBMzbtXV1aUlB4p+A62np4fx48dj1qxZqFevHho3bowVK1bg6dOnGD16NAAgMDAQq1atwvTp0zFu3DgkJSUVe4FXcHAwOnbsiIkTJ2Ls2LHQ09ODUqnE0aNH8cUXX5QZX/PmzeHt7Y2xY8di48aN0NfXx+zZs9GwYUN4e3tXqc/jx4+HpaUlevTogUaNGiEtLQ2LFy9GgwYNii11UFk//PAD3Nzc0KVLF2zfvh1nz57Fli1bXqnOsLAwTJ48GQYGBujbty+ys7ORmJiIjIwMTJ8+vUJ1NGnSBFlZWYiJiYGrqyt0dXWhq6tbavnk5GSEhoZixIgRcHR0hJaWFo4fP46tW7ciODj4lfqjo6MDf39/fP7553j8+DEmT54MHx8fJs5ERERE74j169ejc+fOcHNzQ1hYGFxcXKCmpoZz587h999/R9u2bdGrVy+4u7tjwIAB+Oyzz2Bvb48//vgDhw4dwoABA+Dm5lZuOwqFAqNHj8asWbNgYmICMzMzzJ07V1r6DADs7OwwbNgw+Pn5YdWqVWjdujX+/vtv/Prrr3B2dka/fv2q81YQERFRDXknBm4BwMDAoNRzy5cvR0FBAUaMGIF//vkHbm5u+OWXX2BsbAzgxVIHu3fvxrRp07B+/Xq0b98eS5cuxahRo6Q6XFxccPz4ccydOxddu3aFEALNmjXDkCFDKhRfREQEpkyZgg8++AA5OTno1q0bDh06VOyxqYrq1asXtm7divDwcDx48AD169eHu7s7YmJiiq2xVVkLFixAVFQUJkyYAHNzc2zfvl2azVtVY8aMga6uLlauXImgoCDo6enB2dkZU6dOrXAdnTp1QmBgIIYMGYIHDx4gNDQUYWFhpZZv1KgRmjRpggULFuDOnTuQyWTS/rRp016pP7a2thg4cCD69euHhw8fol+/fli/fv0r1UlEREREtUezZs1w4cIFLF26FHPmzMH//vc/aGtrw9HRETNnzsSECRMgk8lw6NAhzJ07F6NGjcL9+/dhbm6Obt26Se+BqIiVK1ciKysLH374IfT19TFjxgxkZmaqlImIiMDixYsxY8YM/N///R9MTEzg7u7OQVsiIqJ3mExUZFElqjNkMhn27t2LAQMG1HQotVZYWBiio6OLvbCtMh4/fgxDQ0N0/PcfUNcxqvT18SHyKrdd0woKCpCeng5TU1OVmSR1RV3uP/vOvrPvdUd19r3w39DMzMwyv7gnoupX+P+j8e45EAodZLw3v6ZDqlZ17e/1utTfutRXgP1917G/tV9l8tm3o0dEREREREREREREdQgHbl+DEydOQKFQlLpVRd++fUutb+nSpa+5B2+n6rjvRERERETvirJy5RMnTtR0eERERFSOd2aN25rk5ub2So/Nl2Tz5s149uxZiefq1av3Wtt62du0ckZ13PeKCAsLK3NtXSIiIiKi2qCsXLlhw4ZvLhAiIiKqEg7cvgZyuRy2travtU4mUuWrjvtORERERPSuYK5MRET0duNSCURERERERERERES1DAduiYiIiIiIiIiIiGoZDtwSERERERERERER1TJc45aoBv08Sw4jI3lNh0FEREREVGW3egTByMiopsMgIiJ653DGLREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLglIiIiIiIiIiIiqmU0ajoAorqs78pnUNfRrtQ18SHyaoqGiIiIiKjybH5dAaHQQcZ782s6FCIioncKZ9wSERERERERERER1TIcuCUiIiIiIiIiIiKqZThwS0RERERERERERFTLcOCWiIiIiIiIiIiIqJbhwC0RERERERERERFRLcOBWyIiIiIiIiIiIqJahgO3RERERERERERERLUMB26JiIiIiIiIiIiIahkO3BIRERERERERERHVMnV+4DYsLAytWrWq0Ri6d++OqVOn1mgM76K4uDjIZDI8evSo1DJV+fnz50VERES1QUVyndctOjoatra2UFdXfy35UEBAAAYMGFBmmSZNmmDNmjWv3Nbbqmj/ZTIZoqOjK3x9TXxOiIiI6PWotQO3AQEBkMlkkMlk0NTUhI2NDWbOnIknT57UdGhlqkpitGfPHixatKj6gnpF73KyN3PmTMTExNR0GERERPQOKsxnAwMDi52bMGECZDIZAgIC3nxgr2DcuHH46KOPcO/evQrlrxs3boSrqyv09PRgZGSE1q1b47PPPqtUm+fOncMnn3xS1ZDfqMjISBgZGVXqmu7du0u/97y85eXllVg+LS0Nffv2fQ3R/j+1YTILERERFadR0wGUpU+fPoiIiEBubi5OnDiBMWPG4MmTJwgPD1cpl5ubC01NzRqK8tXVq1evpkMoVW5ubk2HUK0UCgUUCkVNh0FERETvKCsrK0RFRWH16tWQy+UAgOfPn2PHjh1o3LhxDUdXOVlZWUhPT4eXlxcsLS3LLb9lyxZMnz4d69atg4eHB7Kzs/Hbb7/h6tWrlWq3QYMGVQ35rTF27FgsXLhQ5ZiGRsm/qpmbm7+JkIiIiKgWqLUzbgFAW1sb5ubmsLKywtChQzFs2DBER0dL3whv3boVNjY20NbWhhACqamp8Pb2hkKhgIGBAXx8fPDXX3+p1Ll8+XKYmZlBX18fo0ePxvPnz1XOl/QY/IABA1RmQ2RnZyMoKAhWVlbQ1tZG8+bNsWXLFty5cweenp4AAGNj4wrPoijaZpMmTbB48WL4+flBoVDA2toaP/30E+7fvy/1z9nZGYmJidI1hd/uR0dHw87ODjo6Oujduzfu3bun0lZ4eDiaNWsGLS0t2Nvb49tvv1U5L5PJsGHDBnh7e0NPTw9jxowptU+HDx9Gly5dYGRkBBMTE3zwwQdISUmR6rpz5w5kMhn27NkDT09P6OrqwtXVFQkJCSptnjx5Eh4eHtDV1YWxsTG8vLyQkZEBABBCYMWKFbCxsYFcLoerqyt+/PHHcu/py5KSkuDm5gZdXV106tQJ165dk84VnV2Ql5eHyZMnS30KDg6Gv79/sUf4CgoKEBQUhHr16sHc3BxhYWGViomIiIjqhjZt2qBx48bYs2ePdGzPnj2wsrJC69atpWPZ2dmYPHkyTE1NoaOjgy5duuDcuXMqdR06dAh2dnaQy+Xw9PTEnTt3irV36tQpdOvWDXK5HFZWVpg8eXKFn1jLyMiAn58fjI2Noauri759++LGjRsAXjyBpa+vDwDo0aMHZDIZ4uLiyqxv//798PHxwejRo2FrawsnJyf4+vqWOFP3888/h4WFBUxMTDBx4kSVyQMlLRUQHh6Ovn37Qi6Xo2nTpvjhhx+k84U56K5du9C1a1fI5XK0a9cO169fx7lz5+Dm5gaFQoE+ffrg/v37KnFERETAwcEBOjo6aNGiBdavX1+s3tJy27i4OIwcORKZmZnSrNmK5oi6urowNzdX2UpTdKmEU6dOoVWrVtDR0YGbmxuio6Mhk8mQnJyscl1pOXFkZCQWLFiAixcvSnFHRkZWKG4iIiKqXrV64LYouVwuJXE3b97Erl27sHv3bikpGTBgAB4+fIjjx4/j6NGjSElJwZAhQ6Trd+3ahdDQUCxZsgSJiYmwsLBQScYqys/PD1FRUVi3bh2USiU2bNgAhUIBKysr7N69GwBw7do1pKWlYe3atVXq6+rVq9G5c2dcuHAB77//PkaMGAE/Pz8MHz4c58+fh62tLfz8/CCEkK55+vQplixZgm3btuHkyZN4/PgxPv74Y+n83r17MWXKFMyYMQOXL1/GuHHjMHLkSMTGxqq0HRoaCm9vb1y6dAkLFy4stU9PnjzB9OnTce7cOcTExEBNTQ3/+te/UFBQoFLf3LlzMXPmTCQnJ8POzg6+vr7So1/Jycno2bMnnJyckJCQgPj4ePTv3x/5+fkAgHnz5iEiIgLh4eG4cuUKpk2bhuHDh+P48eMVvpdz587FqlWrkJiYCA0NDYwaNarUsp999hm2b9+OiIgI6R6WtIbYtm3boKenhzNnzmDFihVYuHAhjh49Wmq92dnZePz4scpGREREdcPIkSMREREh7W/durVYPhIUFITdu3dj27ZtUq7n5eWFhw8fAgDu3buHgQMHol+/fkhOTsaYMWMwe/ZslTouXboELy8vDBw4EL/99ht27tyJ+Ph4TJo0qUJxBgQEIDExEfv27UNCQgKEEOjXrx9yc3NVBvp2796NtLQ0dOrUqcz6zM3Ncfr0ady9e7fMcrGxsUhJSUFsbCy2bduGyMjIcgcOQ0JCMGjQIFy8eBHDhw+Hr68vlEqlSpnQ0FDMmzcP58+fh4aGBnx9fREUFIS1a9fixIkTSElJwfz586XymzZtwty5c7FkyRIolUosXboUISEh2LZtm0q9peW2nTp1wpo1a2BgYIC0tDSkpaVh5syZZfbjVf3zzz/o378/nJ2dcf78eSxatAjBwcElli0tJx4yZAhmzJgBJycnKe6Xf4d6GXNaIiKiN0zUUv7+/sLb21vaP3PmjDAxMRE+Pj4iNDRUaGpqivT0dOn8kSNHhLq6ukhNTZWOXblyRQAQZ8+eFUII4e7uLgIDA1Xa6dChg3B1dZX2PTw8xJQpU1TKeHt7C39/fyGEENeuXRMAxNGjR0uMOzY2VgAQGRkZFe5r0Tatra3F8OHDpf20tDQBQISEhEjHEhISBACRlpYmhBAiIiJCABCnT5+WyiiVSgFAnDlzRgghRKdOncTYsWNV2h48eLDo16+ftA9ATJ06tUp9Sk9PFwDEpUuXhBBC3L59WwAQmzdvlsoU/kyUSqUQQghfX1/RuXPnEuvLysoSOjo64tSpUyrHR48eLXx9fcuM5eW4jx07Jh07ePCgACCePXsmhBAiNDRU5edvZmYmVq5cKe3n5eWJxo0bq3wWPTw8RJcuXVTaateunQgODi41ltDQUAGg2Nbx33+IzgufVmp72+Xn54u0tDSRn59f06HUiLrcf/adfa9r2Pfq6XtmZqYAIDIzM1973a9bYT57//59oa2tLW7fvi3u3LkjdHR0xP3796UcMysrS2hqaort27dL1+bk5AhLS0uxYsUKIYQQc+bMEQ4ODqKgoEAqExwcrJKjjRgxQnzyyScqMZw4cUKoqalJuU9prl+/LgCIkydPSsf+/vtvIZfLxa5du4QQQmRkZAgAIjY2tkL9/+OPP0THjh0FAGFnZyf8/f3Fzp07VT4X/v7+wtraWuTl5UnHBg8eLIYMGSLtW1tbi9WrV0v7AErM6cePHy+EKDkH3bFjhwAgYmJipGPLli0T9vb20r6VlZX4/vvvVepdtGiRcHd3L7XeorltRESEMDQ0rND9KeTh4SE0NTWFnp6etE2fPr3M/u/du1cIIUR4eLgwMTFR+flu2rRJABAXLlwQQlQtJy5NaTmt8e45wuiXBZXq99uorv29Xpf6W5f6KgT7+65jf2u/yuSztXrG7YEDB6BQKKCjowN3d3d069YNX3zxBQDA2tpaZb0rpVIJKysrWFlZScccHR1hZGQkffuuVCrh7u6u0kbR/fIkJydDXV0dHh4eVe1Whbi4uEh/NjMzAwA4OzsXO5aeni4d09DQgJubm7TfokWLYv3v3LmzSjudO3cuNjvh5TrKkpKSgqFDh8LGxgYGBgZo2rQpACA1NbXUvlhYWKjEXTjjtiRXr17F8+fP0bt3b2ktWoVCgW+++UZlSYbylNX+yzIzM/HXX3+hffv20jF1dXW0bdu2zDoL6y2pzkJz5sxBZmamtBVdwoKIiIjeXfXr18f777+Pbdu2ISIiAu+//z7q168vnU9JSUFubq5KnqapqYn27dur5HEdO3aETCaTyhTNY5OSkhAZGamSN3l5eaGgoAC3b98uM0alUgkNDQ106NBBOmZiYgJ7e/tiuWJFWVhYICEhAZcuXcLkyZORm5sLf39/9OnTR+UJLScnJ6irq6tcV1ZeBRTvu7u7e7E4K5JPF7Zz//593Lt3D6NHj1a5f4sXLy6Wd1Y0t6yMYcOGITk5WdrmzJlToeuuXbsGFxcX6OjoSMdezmVf9jriZk5LRET0ZtXql5N5enoiPDwcmpqasLS0VHkBmZ6enkpZIYRKIlve8dKoqampLD8AqL6gq/ClEtXt5b4Wxl/SsaLLEpTU15ePFT1f0v0pem9L079/f1hZWWHTpk2wtLREQUEBWrZsiZycnHL7Uhh3WfezsMzBgwfRsGFDlXPa2toVirG89ktS0j0qq87Ca8qqU1tbu1IxExER0btl1KhR0pIFX331lcq5wlyjrDytpHykqIKCAowbNw6TJ08udq68F6GVVn9lc+mStGzZEi1btsTEiRMRHx+Prl274vjx49J7FCqbV5WmaJwVyacL2yn876ZNm1QGrwGoDCqXVm9V4n2ZoaEhbG1tK31dST+f0n6WryNu5rRERERvVq2ecaunpwdbW1tYW1sXS+iKcnR0RGpqqsq3vlevXkVmZiYcHBwAAA4ODjh9+rTKdUX3GzRogLS0NGk/Pz8fly9flvadnZ1RUFBQ6hqrWlpa0nVvWl5ensoLy65du4ZHjx6hRYsWAF70Pz4+XuWaU6dOSfenNCX16cGDB1AqlZg3bx569uwJBwcH6YVileHi4oKYmJgSzzk6OkJbWxupqamwtbVV2V6eWf26GBoawszMDGfPnpWO5efn48KFC6+9LSIiIqpb+vTpg5ycHOTk5MDLy0vlnK2tLbS0tFTytNzcXCQmJkp5mqOjY7l5bJs2bXDlypVieVNh/WVxdHREXl4ezpw5Ix178OABrl+/Xm6uWBmOjo4AUOEXppWmpHtRmPNWhZmZGRo2bIhbt24Vu3eFT5VVhJaW1hv9PaBFixb47bffkJ2dLR17+feBinrTcRMREVHF1OoZt5XRq1cvuLi4YNiwYVizZg3y8vIwYcIEeHh4SI/+T5kyBf7+/nBzc0OXLl2wfft2XLlyBTY2NlI9PXr0wPTp03Hw4EE0a9YMq1evxqNHj6TzTZo0gb+/P0aNGoV169bB1dUVd+/eRXp6Onx8fGBtbQ2ZTIYDBw6gX79+kMvlUCgUb+QeaGpq4tNPP8W6deugqamJSZMmoWPHjtLjUrNmzYKPjw/atGmDnj17Yv/+/dizZw+OHTtWZr0l9cnY2BgmJib4+uuvYWFhgdTU1GIvyKiIOXPmwNnZGRMmTEBgYCC0tLQQGxuLwYMHo379+pg5cyamTZuGgoICdOnSBY8fP8apU6egUCjg7+9fpftUlk8//RTLli2Dra0tWrRogS+++AIZGRmvPNOEiIiI6jZ1dXXpUf6iMzj19PQwfvx4zJo1C/Xq1UPjxo2xYsUKPH36FKNHjwYABAYGYtWqVZg+fTrGjRsnLYvwsuDgYHTs2BETJ07E2LFjoaenB6VSiaNHj0rLjZWmefPm8Pb2xtixY7Fx40bo6+tj9uzZaNiwIby9vavU5/Hjx8PS0hI9evRAo0aNkJaWhsWLF6NBgwaVXq6sqB9++EElpz979iy2bNnySnWGhYVh8uTJMDAwQN++fZGdnY3ExERkZGRg+vTpFaqjSZMmyMrKQkxMDFxdXaGrqwtdXd1XiqssQ4cOxdy5c/HJJ59g9uzZSE1Nxeeffw6g5CfxStOkSRPcvn0bycnJaNSoEfT19TmzloiIqBao1TNuK0MmkyE6OhrGxsbo1q0bevXqBRsbG+zcuVMqM2TIEMyfPx/BwcFo27Yt7t69i/Hjx6vUM2rUKPj7+8PPzw8eHh5o2rSp9BhXofDwcHz00UeYMGECWrRogbFjx0qzBho2bIgFCxZg9uzZMDMzq/BbfF8HXV1dBAcHY+jQoXB3d4dcLkdUVJR0fsCAAVi7di1WrlwJJycnbNy4EREREejevXuZ9ZbUJzU1NURFRSEpKQktW7bEtGnTsHLlykrHbGdnhyNHjuDixYto37493N3d8dNPP0FD48V3CosWLcL8+fOxbNkyODg4wMvLC/v376/UzIfKCA4Ohq+vL/z8/ODu7i6tDffyumFEREREVWFgYAADA4MSzy1fvhyDBg3CiBEj0KZNG9y8eRO//PILjI2NAbxY6mD37t3Yv38/XF1dsWHDBixdulSlDhcXFxw/fhw3btxA165d0bp1a4SEhEjrmZYnIiICbdu2xQcffAB3d3cIIXDo0KFyn3wrTa9evXD69GkMHjwYdnZ2GDRoEHR0dBATEwMTE5Mq1VlowYIFiIqKgouLC7Zt24bt27dLs3mrasyYMdi8eTMiIyPh7OwMDw8PREZGVirv7NSpEwIDAzFkyBA0aNAAK1aseKWYymNgYID9+/cjOTkZrVq1wty5czF//nwAqFT+OmjQIPTp0weenp5o0KABduzYUV0hExERUSXIREUWzKJaLzIyElOnTlWZHUyvrqCgAA4ODvDx8cGiRYteW72PHz+GoaEhOv77D6jrGFXq2viQN7POcnUpKChAeno6TE1Noab2znx3VGF1uf/sO/vOvtcd1dn3wn9DMzMzSx0EpXebTCbD3r17MWDAgJoOpVbavn07Ro4ciczMzGp/P0fh/4/Gu+dAKHSQ8d78am2vptW1v9frUn/rUl8B9vddx/7WfpXJZ9+ZpRKIXoe7d+/iyJEj8PDwQHZ2Nr788kvcvn0bQ4cOrenQiIiIiIiK+eabb2BjY4OGDRvi4sWLCA4Oho+Pzxt7qTIRERFVn7djKPotlpqaCoVCUeqWmppa0yG+tQIDA0u9r4GBgVWqU01NDZGRkWjXrh06d+6MS5cu4dixY6/1pRxEREREb9qJEyfKzEmrom/fvqXWV3QZh7qqOu57UX/++SeGDx8OBwcHTJs2DYMHD8bXX3/9WuomIiKimsUZt9XM0tISycnJZZ5/HQICAhAQEPBa6npbLFy4EDNnzizxXFUfnbSyssLJkydfJSwiIiKiWsfNza3MnLQqNm/ejGfPnpV4rl69eq+1rZe9TSu9Vcd9LyooKAhBQUHV2gYRERHVDA7cVjMNDQ3Y2trWdBjvJFNTU5iamtZ0GERERES1nlwuf+05acOGDV9rfe+i6rjvREREVHdwqQQiIiIiIiIiIiKiWoYDt0RERERERERERES1DAduiYiIiIiIiIiIiGoZrnFLVIN+niWHkZG8psMgIiIiIqqyWz2CYGRkVNNhEBERvXM445aIiIiIiIiIiIioluHALREREREREREREVEtw4FbIiIiIiIiIiIiolqGA7dEREREREREREREtQwHbomIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLglIiIiIiIiIiIiqmU0ajoAorqs78pnUNfRrlDZ+BB5NUdDRERERFR5Nr+uwMOBS2s6DCIioncOZ9wSERERERERERER1TIcuCUiIiIiIiIiIiKqZThwS0RERERERERERFTLcOCWiIiIiIiIiIiIqJbhwC0RERERERERERFRLcOBWyIiIiIiIiIiIqJahgO3RERERERERERERLUMB26JiIiIiIiIiIiIahkO3BIRERERERERERHVMhy4BRAXFweZTIZHjx69sTajo6Nha2sLdXV1TJ069ZXrCwgIwIABA8os06RJE6xZs+aV26LiKnL/iYiIiN60sLAwtGrVqkZj6N69+2vJd0lVTfwOQ0RERG/WWzFwGxAQAJlMhsDAwGLnJkyYAJlMhoCAgDcf2CsYN24cPvroI9y7dw+LFi0qt/zGjRvh6uoKPT09GBkZoXXr1vjss88q1ea5c+fwySefVDXkNyoyMhJGRkaVuqZ79+6QyWSQyWTQ0tJCs2bNMGfOHGRnZ1dPkERERERVVJjfymQyaGpqwsbGBjNnzsSTJ09qOrQyVWWwcM+ePRXKd2sKB0CJiIiottKo6QAqysrKClFRUVi9ejXkcjkA4Pnz59ixYwcaN25cw9FVTlZWFtLT0+Hl5QVLS8tyy2/ZsgXTp0/HunXr4OHhgezsbPz222+4evVqpdpt0KBBVUN+a4wdOxYLFy5ETk4Ozp07h5EjRwIAli1bVsOREREREanq06cPIiIikJubixMnTmDMmDF48uQJwsPDVcrl5uZCU1OzhqJ8dfXq1avpEEqVm5tb0yEQERERleqtmHELAG3atEHjxo2xZ88e6diePXtgZWWF1q1bS8eys7MxefJkmJqaQkdHB126dMG5c+dU6jp06BDs7Owgl8vh6emJO3fuFGvv1KlT6NatG+RyOaysrDB58uQKz4DIyMiAn58fjI2Noauri759++LGjRsAXnyjr6+vDwDo0aMHZDIZ4uLiyqxv//798PHxwejRo2FrawsnJyf4+vqWOHPh888/h4WFBUxMTDBx4kSVZLToUgkymQzh4eHo27cv5HI5mjZtih9++EE6f+fOHchkMuzatQtdu3aFXC5Hu3btcP36dZw7dw5ubm5QKBTo06cP7t+/rxJHREQEHBwcoKOjgxYtWmD9+vXF6t2zZw88PT2hq6sLV1dXJCQkSPdo5MiRyMzMlGaihIWFVeje6+rqwtzcHI0bN8agQYPQu3dvHDlyRDpf3ucjPz8fo0ePRtOmTSGXy2Fvb4+1a9eqtJGfn4/p06fDyMgIJiYmCAoKghCizLiys7Px+PFjlY2IiIjqNm1tbZibm8PKygpDhw7FsGHDEB0dLS1vsHXrVtjY2EBbWxtCCKSmpsLb2xsKhQIGBgbw8fHBX3/9pVLn8uXLYWZmBn19fYwePRrPnz9XOV/SsgUDBgxQeXotOzsbQUFBsLKygra2Npo3b44tW7bgzp078PT0BAAYGxtX+Km3om02adIEixcvhp+fHxQKBaytrfHTTz/h/v37Uv+cnZ2RmJgoXVP4NFZ0dDTs7Oygo6OD3r174969eypthYeHo1mzZtDS0oK9vT2+/fZblfMymQwbNmyAt7c39PT0MGbMmFL7dPjwYXTp0kXK+T744AOkpKRIdZWX0xY6efIkPDw8oKurC2NjY3h5eSEjIwMAIITAihUrYGNjA7lcDldXV/z444/l3tPS7N69G05OTtDW1kaTJk2watUq6dwXX3wBZ2dnaT86OhoymQxfffWVdMzLywtz5swptX7mtERERG/WWzNwCwAjR45ERESEtL9161aMGjVKpUxQUBB2796Nbdu24fz587C1tYWXlxcePnwIALh37x4GDhyIfv36ITk5GWPGjMHs2bNV6rh06RK8vLwwcOBA/Pbbb9i5cyfi4+MxadKkCsUZEBCAxMRE7Nu3DwkJCRBCoF+/fsjNzUWnTp1w7do1AC8Sq7S0NHTq1KnM+szNzXH69GncvXu3zHKxsbFISUlBbGwstm3bhsjISERGRpZ5TUhICAYNGoSLFy9i+PDh8PX1hVKpVCkTGhqKefPm4fz589DQ0ICvry+CgoKwdu1anDhxAikpKZg/f75UftOmTZg7dy6WLFkCpVKJpUuXIiQkBNu2bVOpd+7cuZg5cyaSk5NhZ2cHX19f5OXloVOnTlizZg0MDAyQlpaGtLQ0zJw5s8x+lOTixYs4efKkygyV8j4fBQUFaNSoEXbt2oWrV69i/vz5+Pe//41du3ZJdaxatQpbt27Fli1bEB8fj4cPH2Lv3r1lxrJs2TIYGhpKm5WVVaX7Q0RERO82uVwufel+8+ZN7Nq1C7t370ZycjKAFwOsDx8+xPHjx3H06FGkpKRgyJAh0vW7du1CaGgolixZgsTERFhYWKh8eV5Rfn5+iIqKwrp166BUKrFhwwYoFApYWVlh9+7dAIBr164hLS2t2BfcFbV69Wp07twZFy5cwPvvv48RI0bAz88Pw4cPl3I0Pz8/lS/Hnz59iiVLlmDbtm04efIkHj9+jI8//lg6v3fvXkyZMgUzZszA5cuXMW7cOIwcORKxsbEqbYeGhsLb2xuXLl3CwoULS+3TkydPMH36dJw7dw4xMTFQU1PDv/71LxQUFKjUV1pOCwDJycno2bMnnJyckJCQgPj4ePTv3x/5+fkAgHnz5iEiIgLh4eG4cuUKpk2bhuHDh+P48eOVvqdJSUnw8fHBxx9/jEuXLiEsLAwhISHS7wPdu3fHlStX8PfffwMAjh8/jvr160tt5eXl4dSpU/Dw8Ci1Dea0REREb5h4C/j7+wtvb29x//59oa2tLW7fvi3u3LkjdHR0xP3794W3t7fw9/cXWVlZQlNTU2zfvl26NicnR1haWooVK1YIIYSYM2eOcHBwEAUFBVKZ4OBgAUBkZGQIIYQYMWKE+OSTT1RiOHHihFBTUxPPnj0rM9br168LAOLkyZPSsb///lvI5XKxa9cuIYQQGRkZAoCIjY2tUP//+OMP0bFjRwFA2NnZCX9/f7Fz506Rn5+vco+sra1FXl6edGzw4MFiyJAh0r61tbVYvXq1tA9ABAYGqrTVoUMHMX78eCGEELdv3xYAxObNm6XzO3bsEABETEyMdGzZsmXC3t5e2reyshLff/+9Sr2LFi0S7u7updZ75coVAUAolUohhBARERHC0NCwQvenkIeHh9DU1BR6enpCS0tLABBqamrixx9/FEKICn0+SjJhwgQxaNAgad/CwkIsX75c2s/NzRWNGjUS3t7epdbx/PlzkZmZKW337t0TAETHf/8hOi98WqHtXZGfny/S0tJUPr91SV3uP/vOvtc17Hv19D0zM1MAEJmZma+97jepML8tdObMGWFiYiJ8fHxEaGio0NTUFOnp6dL5I0eOCHV1dZGamiodK8yfzp49K4QQwt3dvcTcztXVVdr38PAQU6ZMUSlTmEsLIcS1a9cEAHH06NES446NjVXJmyuiaJvW1tZi+PDh0n5aWpoAIEJCQqRjCQkJAoBIS0sTQrzIDQGI06dPS2WUSqUAIM6cOSOEEKJTp05i7NixKm0PHjxY9OvXT9oHIKZOnVqlPqWnpwsA4tKlS0KIiuW0vr6+onPnziXWl5WVJXR0dMSpU6dUjo8ePVr4+vqWGUtJcQ8dOlT07t1bpcysWbOEo6OjEEKIgoICUb9+fSk3btWqlVi2bJkwNTUVQghx6tQpoaGhIf75559S2ywtpzXePafceN8Fde3v9brU37rUVyHY33cd+1v7VSaffatm3NavXx/vv/8+tm3bhoiICLz//vuoX7++dD4lJQW5ubno3LmzdExTUxPt27eXZpEqlUp07NgRMplMKuPu7q7STlJSEiIjI6FQKKTNy8sLBQUFuH37dpkxKpVKaGhooEOHDtIxExMT2NvbF5vJWlEWFhZISEjApUuXMHnyZOTm5sLf3x99+vRR+cbfyckJ6urqKtelp6eXWXfRvru7uxeL08XFRfqzmZkZAKg8ZmVmZia1c//+fdy7dw+jR49WuX+LFy9WebSsaL0WFhYAUG685Rk2bBiSk5ORkJAAHx8fjBo1CoMGDQJQsc8HAGzYsAFubm5o0KABFAoFNm3ahNTUVABAZmYm0tLSVO6bhoYG3NzcyoxLW1sbBgYGKhsRERHVbQcOHIBCoYCOjg7c3d3RrVs3fPHFFwAAa2trlfcTKJVKWFlZqcxwdHR0hJGRkUqeW1JuVxnJyclQV1cvc9bl61CR/BJQzQ2L5lwtWrQo1v+X8zwA6Ny5c7Hctry8rVBKSgqGDh0KGxsbGBgYoGnTpgAg5YUl9aVoTls447YkV69exfPnz9G7d2+VvPmbb74pljdXRGn9v3HjBvLz8yGTydCtWzfExcXh0aNHuHLlCgIDA5Gfnw+lUom4uDi0adMGCoWi1DaY0xIREb1Zb83LyQqNGjVKWrLg5fWYAEiPUr08KFt4vPCYKGctUuDF4/Ljxo3D5MmTi50r70VopdX/cgxV1bJlS7Rs2RITJ05EfHw8unbtiuPHj0vrchV9aYVMJiv2KFdFFI3z5XoLzxU9VthO4X83bdqkMngNQGVQubR6qxLvywwNDWFrawsA+O677+Dk5IQtW7Zg9OjRFfp87Nq1C9OmTcOqVavg7u4OfX19rFy5EmfOnHmluIiIiIiK8vT0RHh4ODQ1NWFpaamSG+np6amULS2XrGyOqaamVixfffmdCIUvAa5uFckvgeK5YUl9fflYWXleoaL3tjT9+/eHlZUVNm3aBEtLSxQUFKBly5bIyckpty+FcZd1PwvLHDx4EA0bNlQ5p62tXaEYX1ZSX4v+rLt3746vv/4aJ06cgKurK4yMjNCtWzccP34ccXFx6N69e6XbJSIiourzVs24BV68fTcnJwc5OTnw8vJSOWdrawstLS3Ex8dLx3Jzc5GYmAgHBwcAL2YmnPUxX1wAACiUSURBVD59WuW6ovtt2rTBlStXYGtrW2zT0tIqMz5HR0fk5eWpDPQ9ePAA169fl2J4HRwdHQGgwi9MK01J96JFixZVrs/MzAwNGzbErVu3it27wlkKFaGlpSWt/VVVmpqa+Pe//4158+bh6dOnFfp8nDhxAp06dcKECRPQunVr2Nraqsx4MDQ0hIWFhcp9y8vLQ1JS0ivFSkRERHWPnp4ebG1tYW1tXewL+KIcHR2Rmpqq8jKuq1evIjMzU8pjHBwcys1zGzRogLS0NGk/Pz8fly9flvadnZ1RUFBQ6hqrhbnwq+ZpVZGXl6fywrJr167h0aNHUu7q4OCgkucBL144XF4OXlKfHjx4AKVSiXnz5qFnz55wcHCQXihWGS4uLoiJiSnxnKOjI7S1tZGamlosb67K2rGOjo4l9t/Ozk6aQFG4zu2PP/4oDdJ6eHjg2LFj5a5vS0RERG/eWzdwq66uDqVSCaVSWWwGp56eHsaPH49Zs2bh8OHDuHr1KsaOHYunT59i9OjRAIDAwECkpKRg+vTpuHbtGr7//vtiL/AKDg5GQkICJk6ciOTkZNy4cQP79u3Dp59+Wm58zZs3h7e3N8aOHYv4+HjppV8NGzaEt7d3lfo8fvx4LFq0CCdPnsTdu3dx+vRp+Pn5oUGDBpV+/K2oH374AVu3bsX169cRGhqKs2fPVvglbKUJCwvDsmXLsHbtWly/fh2XLl1CREQE/vOf/1S4jiZNmiArKwsxMTH4+++/8fTp0yrFMnToUMhkMqxfv75Cnw9bW1skJibil19+wfXr1xESEoJz586p1DllyhQsX74ce/fuxe+//44JEybg0aNHVYqPiIiIqCJ69eoFFxcXDBs2DOfPn8fZs2fh5+cHDw8P6dH/KVOmYOvWrSq53ZUrV1Tq6dGjBw4ePIiDBw+WmMc0adIE/v7+GDVqFKKjo3H79m3ExcVJL2q1traGTCbDgQMHcP/+fWRlZb2xe6CpqYlPP/0UZ86cwfnz5zFy5Eh07NgR7du3BwDMmjULkZGR2LBhA27cuIH//Oc/2LNnT7kvuS2pT8bGxjAxMcHXX3+Nmzdv4tdff8X06dMrHfOcOXNw7tw5TJgwAb/99ht+//13hIeH4++//4a+vj5mzpyJadOmYdu2bUhJScGFCxfw1VdfFXupb0XMmDEDMTExWLRoEa5fv45t27bhyy+/VOl/y5YtYWJigu3bt0sDt927d0d0dDSePXuGLl26VLpdIiIiqj5v3cAtgDLXU1q+fDkGDRqEESNGoE2bNrh58yZ++eUXGBsbA3ix1MHu3buxf/9+uLq6YsOGDVi6dKlKHS4uLjh+/Dhu3LiBrl27onXr1ggJCZHWrCpPREQE2rZtiw8++ADu7u4QQuDQoUPlzqQoTa9evXD69GkMHjwYdnZ2GDRoEHR0dBATEwMTE5Mq1VlowYIFiIqKgouLC7Zt24bt27dLs3mrasyYMdi8eTMiIyPh7OwMDw8PREZGVmrGbadOnRAYGIghQ4agQYMGWLFiRZVi0dLSwqRJk7BixQpkZWWV+/kIDAzEwIEDMWTIEHTo0AEPHjzAhAkTVOqcMWMG/Pz8EBAQIC2n8K9//atK8RERERFVhEwmQ3R0NIyNjdGtWzf06tULNjY22Llzp1RmyJAhmD9/PoKDg9G2bVvcvXsX48ePV6ln1KhR8Pf3lwZ9mzZtKi27VSg8PBwfffQRJkyYgBYtWmDs2LHSU14NGzbEggULMHv2bJiZmb3yF/6Voauri+DgYAwdOhTu7u6Qy+WIioqSzg8YMABr167FypUr4eTkhI0bNyIiIqLcx/9L6pOamhqioqKQlJSEli1bYtq0aVi5cmWlY7azs8ORI0dw8eJFtG/fHu7u7vjpp5+gofFixbpFixZh/vz5WLZsGRwcHODl5YX9+/dXKm8u1KZNG+zatQtRUVFo2bIl5s+fj4ULFyIgIEAqI5PJpFm1Xbt2BfDidx9DQ0O0bt2aa9YSERHVMjJRkUVf6Z0kk8mwd+9eDBgwoKZDqXMeP34MQ0NDdPz3H1DXMarQNfEhb2bNuepWUFCA9PR0mJqaQk3trfzu6JXU5f6z7+w7+153VGffC/8NzczM5CBTHRIZGYmpU6fyKadapvD/R+Pdc/Bw4NLyL/j/2rvzuKiq/3/gr4FBQJYRUXYD1JCIIBUXMMUlRMXtk+YSKeRS5JZmWmooLpVpWrnQJih9UtEU/FJqZSkoLqkISUhqgqKFmeYCqCBwfn/0834cWWQGmPX1fDx4PJh7z9x5v+6p45nDnTt6ztjGdWPKa0xZAeY1dMyr+1SZz+pHIiIiIiIiIiIiIiIjwoVbFR04cADW1tY1/qijf//+NR7v4ds4GKvGOO9EREREVD8FBQW1ztEKCgq0XaLeioqKqvG8RkVFabs8IiIi0gC5tgvQNwEBAcjKymrQY65btw537typdl/z5s0b9LUepE93yWiM805ERERE9ePi4lLrHM3FxaVBXicyMlLpXq3GYNGiRTV+sRpvE0JERGQcuHCrIktLS7Rt27ZBj+nq6tqgxzNEjXHeiYiIiKh+5HI552iNxMHBAQ4ODtoug4iIiLSIt0ogIiIiIiIiIiIi0jFcuCUiIiIiIiIiIiLSMVy4JSIiIiIiIiIiItIxvMctkRbtnmWJZs0stV0GEREREZHa8nrP1nYJREREBolX3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGLm2CyAyRkIIAMCtW7dgYmJcfz+prKxEUVERLCwsjC47YNz5mZ3Zmd14NGb2W7duAfjfv6VEpD3GNqc1tnHdmPIaU1aAeQ0d8+o+VeazXLgl0oJr164BANzd3bVcCRERkX4qKiqCQqHQdhlERo1zWiIiIvXVZT7LhVsiLWjevDkAoKCgwOjedN66dQutWrXCxYsXYWtrq+1yNM6Y8zM7szO78WjM7EIIFBUVwcXFpUGPS0SqM7Y5rbGN68aU15iyAsxr6JhX96kyn+XCLZEW3L98X6FQ6M3A0tBsbW2NNjtg3PmZndmNDbM3fHZjWCAi0gfGOqc1tnHdmPIaU1aAeQ0d8+q2us5n9ePmD0RERERERERERERGhAu3RERERERERERERDqGC7dEWmBubo4FCxbA3Nxc26VonDFnB4w7P7Mzu7FhduPMTmRMjO3/deY1XMaUFWBeQ8e8hkUmhBDaLoKIiIiIiIiIiIiI/odX3BIRERERERERERHpGC7cEhEREREREREREekYLtwSERERERERERER6Rgu3BIRERERERERERHpGC7cEhEREREREREREekYLtwSaUFsbCw8PT1hYWGBjh074sCBA9ouqcHFxMRAJpMp/Tg5OUn7hRCIiYmBi4sLLC0t0bNnT+Tk5GixYvXt378fgwYNgouLC2QyGXbs2KG0vy5ZS0tLMXXqVLRo0QJWVlYYPHgwLl26pMEU6nlU9sjIyCr/HXTt2lWpjb5mf++999CpUyfY2NjAwcEBQ4cOxenTp5XaGGrf1yW7ofb9J598Aj8/P9ja2sLW1haBgYHYvXu3tN9Q+xx4dHZD7XMiY6fqvDUtLQ0dO3aEhYUFWrdujU8//VRDldafKlmTkpIQEhKCli1bSmPi999/r8Fq60/d9yQHDx6EXC7H008/3bgFNjBV85aWlmLevHlwd3eHubk52rRpg/j4eA1VW3+q5t24cSP8/f3RtGlTODs746WXXsK1a9c0VG39POo9SXX0eaxSNa8+j1fq9O19+jpWPYwLt0QatmXLFkyfPh3z5s1DZmYmunfvjv79+6OgoEDbpTW4J598EoWFhdJPdna2tG/ZsmVYuXIl1qxZg2PHjsHJyQkhISEoKirSYsXqKSkpgb+/P9asWVPt/rpknT59OpKTk5GYmIj09HQUFxdj4MCBqKio0FQMtTwqOwD069dP6b+DXbt2Ke3X1+xpaWmYPHkyjhw5gj179qC8vBx9+/ZFSUmJ1MZQ+74u2QHD7Hs3NzcsXboUx48fx/Hjx9G7d28MGTJEWpw11D4HHp0dMMw+JzJmqs5b8/PzMWDAAHTv3h2ZmZmYO3cupk2bhu3bt2u4ctWpmnX//v0ICQnBrl27kJGRgV69emHQoEHIzMzUcOXqUfc9yc2bNzF27Fj06dNHQ5U2DHXyjhgxAj/99BPi4uJw+vRpbN68Gd7e3hqsWn2q5k1PT8fYsWMxfvx45OTk4Ouvv8axY8cwYcIEDVeunrq8J3mQPo9VgOp59Xm8UjXrffo6VlVLEJFGde7cWURFRSlt8/b2Fm+99ZaWKmocCxYsEP7+/tXuq6ysFE5OTmLp0qXStrt37wqFQiE+/fRTDVXYOACI5ORk6XFdst64cUOYmZmJxMREqc0ff/whTExMxHfffaex2uvr4exCCBERESGGDBlS43MMJbsQQly5ckUAEGlpaUII4+r7h7MLYVx9b2dnJ9atW2dUfX7f/exCGFefExkLVeets2fPFt7e3krbXnnlFdG1a9dGq7GhNMQc3cfHRyxcuLChS2sU6uYdOXKkePvtt2ud6+siVfPu3r1bKBQKce3aNU2U1+BUzbt8+XLRunVrpW2rVq0Sbm5ujVZjY6nuPcnD9Hmselhd8lZHn8ar+1TJqq9jVXV4xS2RBpWVlSEjIwN9+/ZV2t63b18cOnRIS1U1nrNnz8LFxQWenp4YNWoU8vLyAPz7F87Lly8rnQdzc3MEBwcb3HmoS9aMjAzcu3dPqY2Liwt8fX0N4nykpqbCwcEBXl5emDhxIq5cuSLtM6TsN2/eBAA0b94cgHH1/cPZ7zP0vq+oqEBiYiJKSkoQGBhoVH3+cPb7DL3PiYyJOvPWw4cPV2kfGhqK48eP4969e41Wa301xBy9srISRUVFVf4t1EXq5l2/fj3OnTuHBQsWNHaJDUqdvCkpKQgICMCyZcvg6uoKLy8vvPHGG7hz544mSq4XdfIGBQXh0qVL2LVrF4QQ+Ouvv7Bt2zaEhYVpomSN09exqqHo03ilDn0dq2oi13YBRMbk6tWrqKiogKOjo9J2R0dHXL58WUtVNY4uXbrgyy+/hJeXF/766y8sWbIEQUFByMnJkbJWdx4uXLigjXIbTV2yXr58GU2aNIGdnV2VNvr+30X//v3x/PPPw93dHfn5+YiOjkbv3r2RkZEBc3Nzg8kuhMDrr7+OZ555Br6+vgCMp++ryw4Ydt9nZ2cjMDAQd+/ehbW1NZKTk+Hj4yO9GTLkPq8pO2DYfU5kjNSZt16+fLna9uXl5bh69SqcnZ0brd76aIg5+ooVK1BSUoIRI0Y0RokNSp28Z8+exVtvvYUDBw5ALtevZQR18ubl5SE9PR0WFhZITk7G1atXMWnSJPzzzz86f59bdfIGBQVh48aNGDlyJO7evYvy8nIMHjwYq1ev1kTJGqevY1VD0afxSlX6PFbVxDBSEOkZmUym9FgIUWWbvuvfv7/0+1NPPYXAwEC0adMGCQkJ0pfVGMN5uE+drIZwPkaOHCn97uvri4CAALi7u2Pnzp147rnnanyevmWfMmUKTp48ifT09Cr7DL3va8puyH3frl07ZGVl4caNG9i+fTsiIiKQlpYm7TfkPq8pu4+Pj0H3OZExU3VMq659ddt1kbpz082bNyMmJgb/93//BwcHh8Yqr8HVNW9FRQVeeOEFLFy4EF5eXpoqr8Gp0r+VlZWQyWTYuHEjFAoFAGDlypUYPnw41q5dC0tLy0avt75UyXvq1ClMmzYN8+fPR2hoKAoLCzFr1ixERUUhLi5OE+VqnD6PVfWhr+NVXRjKWPUw3iqBSINatGgBU1PTKn/pvHLlSpW/+BkaKysrPPXUUzh79iycnJwAwCjOQ12yOjk5oaysDNevX6+xjaFwdnaGu7s7zp49C8Awsk+dOhUpKSnYt28f3NzcpO3G0Pc1Za+OIfV9kyZN0LZtWwQEBOC9996Dv78/Pv74Y6Po85qyV8eQ+pzIGKkzb3Vycqq2vVwuh729faPVWl/1maNv2bIF48ePx9atW/Hss882ZpkNRtW8RUVFOH78OKZMmQK5XA65XI5Fixbhl19+gVwux969ezVVulrU6V9nZ2e4urpKi7YA8MQTT0AIgUuXLjVqvfWlTt733nsP3bp1w6xZs+Dn54fQ0FDExsYiPj4ehYWFmihbo/R1rKovfRyvVKHvY1VNuHBLpEFNmjRBx44dsWfPHqXte/bsQVBQkJaq0ozS0lLk5ubC2dkZnp6ecHJyUjoPZWVlSEtLM7jzUJesHTt2hJmZmVKbwsJC/PrrrwZ3Pq5du4aLFy9KHz/S5+xCCEyZMgVJSUnYu3cvPD09lfYbct8/Knt1DKnvHyaEQGlpqUH3eU3uZ6+OIfc5kTFQZ94aGBhYpf0PP/yAgIAAmJmZNVqt9aXuHH3z5s2IjIzEpk2b9OpeoKrmtbW1RXZ2NrKysqSfqKgo6VMYXbp00VTpalGnf7t164Y///wTxcXF0rYzZ87AxMTkkX+s1jZ18t6+fRsmJsrLQ6ampgD+dyWqIdHXsao+9HW8UoW+j1U10tCXoBHR/5eYmCjMzMxEXFycOHXqlJg+fbqwsrIS58+f13ZpDWrmzJkiNTVV5OXliSNHjoiBAwcKGxsbKefSpUuFQqEQSUlJIjs7W4wePVo4OzuLW7duably1RUVFYnMzEyRmZkpAIiVK1eKzMxMceHCBSFE3bJGRUUJNzc38eOPP4oTJ06I3r17C39/f1FeXq6tWHVSW/aioiIxc+ZMcejQIZGfny/27dsnAgMDhaurq0Fkf/XVV4VCoRCpqamisLBQ+rl9+7bUxlD7/lHZDbnv58yZI/bv3y/y8/PFyZMnxdy5c4WJiYn44YcfhBCG2+dC1J7dkPucyJg9at761ltviTFjxkjt8/LyRNOmTcWMGTPEqVOnRFxcnDAzMxPbtm3TVoQ6UzXrpk2bhFwuF2vXrlX6t/DGjRvaiqASVfM+TN++qV3VvEVFRcLNzU0MHz5c5OTkiLS0NPH444+LCRMmaCuCSlTNu379eiGXy0VsbKw4d+6cSE9PFwEBAaJz587aiqCSR70fM6SxSgjV8+rzeKVq1ofp21hVHS7cEmnB2rVrhbu7u2jSpIno0KGDSEtL03ZJDW7kyJHC2dlZmJmZCRcXF/Hcc8+JnJwcaX9lZaVYsGCBcHJyEubm5qJHjx4iOztbixWrb9++fQJAlZ+IiAghRN2y3rlzR0yZMkU0b95cWFpaioEDB4qCggItpFFNbdlv374t+vbtK1q2bCnMzMzEY489JiIiIqrk0tfs1eUGINavXy+1MdS+f1R2Q+77cePGSeN3y5YtRZ8+faRFWyEMt8+FqD27Ifc5kbGrbd4aEREhgoODldqnpqaK9u3biyZNmggPDw/xySefaLhi9amSNTg4uNb5nz5QtW8fpI+LIarmzc3NFc8++6ywtLQUbm5u4vXXX1f6A72uUzXvqlWrhI+Pj7C0tBTOzs4iPDxcXLp0ScNVq+dR78cMbaxSNa8+j1fq9O2D9HGsephMCAO87p2IiIiIiIiIiIhIj/Eet0REREREREREREQ6hgu3RERERERERERERDqGC7dEREREREREREREOoYLt0REREREREREREQ6hgu3RERERERERERERDqGC7dEREREREREREREOoYLt0REREREREREREQ6hgu3RERERERERERERDqGC7dERPRIkZGRGDp0qLbLqNH58+chk8mQlZWl7VLq5MqVK3jllVfw2GOPwdzcHE5OTggNDcXhw4e1XRoRERERaZhMJsP58+e1XYbGxcTE4Omnn27w43p4eCA1NbXBj0ukDVy4JSIivVZWVqbtElQ2bNgw/PLLL0hISMCZM2eQkpKCnj174p9//mm019TH80RERESkrsjISMhkMkRFRVXZN2nSJMhkMkRGRmq+sDrIy8vD6NGj4eLiAgsLC7i5uWHIkCE4c+aM1EYmk2HHjh1VnlvTBReHDh2Cqakp+vXrV2Xf/Ysg7v/Y2dmhR48eSEtLa8hYRKQGLtwSEZHKevbsialTp2L69Omws7ODo6MjPv/8c5SUlOCll16CjY0N2rRpg927d0vPSU1NhUwmw86dO+Hv7w8LCwt06dIF2dnZSsfevn07nnzySZibm8PDwwMrVqxQ2u/h4YElS5YgMjISCoUCEydOhKenJwCgffv2kMlk6NmzJwDg2LFjCAkJQYsWLaBQKBAcHIwTJ04oHU8mk2HdunX4z3/+g6ZNm+Lxxx9HSkqKUpucnByEhYXB1tYWNjY26N69O86dOyftX79+PZ544glYWFjA29sbsbGxNZ67GzduID09He+//z569eoFd3d3dO7cGXPmzEFYWJhSu5dffhmOjo6wsLCAr68vvv3223qdJ+DfSXuPHj1gaWmJVq1aYdq0aSgpKamxXiIiIiJ91apVKyQmJuLOnTvStrt372Lz5s147LHHtFhZzcrKyhASEoJbt24hKSkJp0+fxpYtW+Dr64ubN2+qfdz4+HhMnToV6enpKCgoqLbNjz/+iMLCQqSlpcHW1hYDBgxAfn6+2q9JRPXHhVsiIlJLQkICWrRogaNHj2Lq1Kl49dVX8fzzzyMoKAgnTpxAaGgoxowZg9u3bys9b9asWfjggw9w7NgxODg4YPDgwbh37x4AICMjAyNGjMCoUaOQnZ2NmJgYREdHY8OGDUrHWL58OXx9fZGRkYHo6GgcPXoUwP8mm0lJSQCAoqIiRERE4MCBAzhy5Agef/xxDBgwAEVFRUrHW7hwIUaMGIGTJ09iwIABCA8Pl65+/eOPP9CjRw9YWFhg7969yMjIwLhx41BeXg4A+OKLLzBv3jy88847yM3Nxbvvvovo6GgkJCRUe96sra1hbW2NHTt2oLS0tNo2lZWV6N+/Pw4dOoSvvvoKp06dwtKlS2Fqalqv85SdnY3Q0FA899xzOHnyJLZs2YL09HRMmTKltq4mIiIi0ksdOnTAY489Js0NASApKQmtWrVC+/btldoKIbBs2TK0bt0alpaW8Pf3x7Zt26T9FRUVGD9+PDw9PWFpaYl27drh448/VjrG/atdP/jgAzg7O8Pe3h6TJ0+W5rp1cerUKeTl5SE2NhZdu3aFu7s7unXrhnfeeQedOnVS6zyUlJRg69atePXVVzFw4MAqc8b77O3t4eTkBD8/P3z22We4ffs2fvjhhyrtbt68CUtLS3z33XdK25OSkmBlZYXi4mIAwJtvvgkvLy80bdoUrVu3RnR0dK3nomfPnpg+fbrStqFDhypdGV1WVobZs2fD1dUVVlZW6NKlC2+LQIZNEBERPUJERIQYMmSI9Dg4OFg888wz0uPy8nJhZWUlxowZI20rLCwUAMThw4eFEELs27dPABCJiYlSm2vXrglLS0uxZcsWIYQQL7zwgggJCVF67VmzZgkfHx/psbu7uxg6dKhSm/z8fAFAZGZm1pqjvLxc2NjYiG+++UbaBkC8/fbb0uPi4mIhk8nE7t27hRBCzJkzR3h6eoqysrJqj9mqVSuxadMmpW2LFy8WgYGBNdaxbds2YWdnJywsLERQUJCYM2eO+OWXX6T933//vTAxMRGnT5+u9vnqnqcxY8aIl19+WWnbgQMHhImJibhz506N9RIRERHpm/vz15UrV4o+ffpI2/v06SM+/PBDMWTIEBERESFtnzt3rvD29hbfffedOHfunFi/fr0wNzcXqampQgghysrKxPz588XRo0dFXl6e+Oqrr0TTpk2leez917S1tRVRUVEiNzdXfPPNN6Jp06bi888/r7VWACI/P18IIcSlS5eEiYmJ+OCDD0R5eXmtz0lOTq4x94Pi4uJEQECAEEKIb775Rnh4eIjKykppf3Vz6X/++UcAEKtXr6729YcNGyZefPHFKttGjx4tPV68eLE4ePCgyM/PFykpKcLR0VG8//770v4FCxYIf39/6XFwcLB47bXXlI75cD+98MILIigoSOzfv1/8/vvvYvny5cLc3FycOXNGauPu7i727dtXbd1E+oZX3BIRkVr8/Pyk301NTWFvb4+nnnpK2ubo6Ajg3y/ielBgYKD0e/PmzdGuXTvk5uYCAHJzc9GtWzel9t26dcPZs2dRUVEhbQsICKhTjVeuXEFUVBS8vLygUCigUChQXFxc5eNhD2axsrKCjY2NVHdWVha6d+8OMzOzKsf/+++/cfHiRYwfP166ktba2hpLlixRupXCw4YNG4Y///wTKSkpCA0NRWpqKjp06CBd/ZCVlQU3Nzd4eXlV+3x1z1NGRgY2bNigVGtoaCgqKyv5MTgiIiIySGPGjEF6ejrOnz+PCxcu4ODBg3jxxReV2pSUlGDlypWIj49HaGgoWrdujcjISLz44ov47LPPAABmZmZYuHAhOnXqBE9PT4SHhyMyMhJbt25VOpadnR3WrFkDb29vDBw4EGFhYfjpp5/qXK+rqytWrVqF+fPnw87ODr1798bixYuRl5dXpe3o0aOV5nXW1tbYuHFjlXZxcXFS5n79+qG4uLjWmkpKSjBnzhyYmpoiODi42jbh4eHYsWOH9Om6W7duYefOnUrn9u2330ZQUBA8PDwwaNAgzJw5s8r5UsW5c+ewefNmfP311+jevTvatGmDN954A8888wzWr1+v9nGJdJlc2wUQEZF+enghUyaTKW2TyWQA/v3Y/6PcbyuEkH6/TwhRpb2VlVWdaoyMjMTff/+Njz76CO7u7jA3N0dgYGCVL+qqLsv9ui0tLWs8/v02X3zxBbp06aK07/5tDWpiYWGBkJAQhISEYP78+ZgwYQIWLFiAyMjIWl8TUP88VVZW4pVXXsG0adOqtNXV+7wRERER1UeLFi0QFhaGhIQECCEQFhaGFi1aKLU5deoU7t69i5CQEKXtZWVlSrdU+PTTT7Fu3TpcuHABd+7cQVlZGZ5++mml5zz55JNK80BnZ+cq3+nwKJMnT8bYsWOxb98+/Pzzz/j666/x7rvvIiUlRanGDz/8EM8++6zSc998802lP+SfPn0aR48elW4XIZfLMXLkSMTHx1d5blBQEExMTHD79m04Oztjw4YNShdmPCgsLAxyuRwpKSkYNWoUtm/fDhsbG/Tt21dqs23bNnz00Uf4/fffUVxcjPLyctja2qp0Lh504sQJCCGqXNxQWloKe3t7tY9LpMu4cEtERBp15MgRaZHw+vXrOHPmDLy9vQEAPj4+SE9PV2p/6NAheHl51boQ2qRJEwBQmqQCwIEDBxAbG4sBAwYAAC5evIirV6+qVK+fnx8SEhJw7969Kgu8jo6OcHV1RV5eHsLDw1U67sN8fHykbwb28/PDpUuXcObMmWqvulX3PHXo0AE5OTlo27ZtvWolIiIi0ifjxo2T7um/du3aKvvv/zF+586dcHV1Vdpnbm4OANi6dStmzJiBFStWIDAwEDY2Nli+fDl+/vlnpfa1XRCgChsbGwwePBiDBw/GkiVLEBoaiiVLligt3Do5OVWZ19nY2ODGjRvS47i4OJSXlyvlEkLAzMwM169fh52dnbR9y5Yt8PHxQbNmzR65ENqkSRMMHz4cmzZtwqhRo7Bp0yaMHDkScvm/y0xHjhzBqFGjsHDhQoSGhkKhUCAxMbHKF+o+yMTEpMrFCA/eE7eyshKmpqbIyMioMue1trautV4ifcWFWyIi0qhFixbB3t4ejo6OmDdvHlq0aIGhQ4cCAGbOnIlOnTph8eLFGDlyJA4fPow1a9YgNja21mM6ODhIX5Dg5uYGCwsLKBQKtG3bFv/9738REBCAW7duYdasWY+8mvVhU6ZMwerVqzFq1CjMmTMHCoUCR44cQefOndGuXTvExMRg2rRpsLW1Rf/+/VFaWorjx4/j+vXreP3116sc79q1a3j++ecxbtw4+Pn5wcbGBsePH8eyZcswZMgQAEBwcDB69OiBYcOGYeXKlWjbti1+++03yGQy9OvXT+3z9Oabb6Jr166YPHkyJk6cCCsrK+Tm5mLPnj1YvXq1SueFiIiISF/069dP+sRVaGholf0+Pj4wNzdHQUFBjbcGOHDgAIKCgjBp0iRpW223xmpIMpkM3t7eOHTokErPKy8vx5dffokVK1YoXQkL/Hvrro0bNyp9SW2rVq3Qpk2bOh8/PDwcffv2RU5ODvbt24fFixdL+w4ePAh3d3fMmzdP2nbhwoVaj9eyZUsUFhZKjysqKvDrr7+iV69eAID27dujoqICV65cQffu3etcJ5E+4z1uiYhIo5YuXYrXXnsNHTt2RGFhIVJSUqQrZjt06ICtW7ciMTERvr6+mD9/PhYtWqT0TbLVkcvlWLVqFT777DO4uLhIC6Dx8fG4fv062rdvjzFjxmDatGlwcHBQqV57e3vs3bsXxcXFCA4ORseOHfHFF19IV1NMmDAB69atkz5KFhwcjA0bNsDT07Pa41lbW6NLly748MMP0aNHD/j6+iI6OhoTJ07EmjVrpHbbt29Hp06dMHr0aPj4+GD27NnSFcXqnic/Pz+kpaXh7Nmz6N69O9q3b4/o6Gg4OzurdE6IiIiI9ImpqSlyc3ORm5tb7aeTbGxs8MYbb2DGjBlISEjAuXPnkJmZibVr1yIhIQEA0LZtWxw/fhzff/89zpw5g+joaBw7dqzBa83KysKQIUOwbds2nDp1Cr///jvi4uIQHx8vzXHr6ttvv8X169cxfvx4+Pr6Kv0MHz4ccXFx9ao1ODgYjo6OCA8Ph4eHB7p27Srta9u2LQoKCpCYmIhz585h1apVSE5OrvV4vXv3xs6dO7Fz50789ttvmDRpktLVw15eXggPD8fYsWORlJSE/Px8HDt2DO+//z527dpVryxEukomqrspHhERUQNLTU1Fr169cP36dTRr1kzb5RARERGRAYuMjMSNGzekW1E9bOjQoWjWrJn05bBCCKxevRqxsbHIy8tDs2bN0KFDB8ydOxc9evRAaWkpoqKikJycDJlMhtGjR0OhUGD37t3Iysqq8TWnT5+OrKwspKam1lirTCZDfn4+PDw8cPXqVSxevBh79+7F+fPnIZPJ4OHhgYiICMyYMQMmJibSc5KTk6VPrlWXe9CgQaisrMTOnTurvOaJEyfQsWNHZGRkoHnz5vD09ERmZmaVe/Y+yuzZs7F8+XLMnz8fCxcurLIvPj4epaWlCAsLQ9euXRETEyMtxsbExGDHjh3S+bt37x5ee+01bNmyBXK5HDNmzMCRI0eU+unevXtYsmQJvvzyS/zxxx+wt7dHYGAgFi5cKN2P18PDAxs2bEDPnj1VykKki7hwS0REGsGFWyIiIiKiqh5cuKX648ItGRLeKoGIiIiIiIiIiIhIx/DLyYiISCN69uxZ5VtiiYiIiIiIiKh6vOKWiIiIiIiIiEhLFixYwFuJNaDp06fzthNkMHiPWyIiIiIiIiIiIiIdwytuiYiIiIiIiIiIiHQMF26JiIiIiIiIiIiIdAwXbomIiIiIiIiIiIh0DBduiYiIiIiIiIiIiHQMF26JiIiIiIiIiIiIdAwXbomIiIiIiIiIiIh0DBduiYiIiIiIiIiIiHTM/wMqf1R6XGsBkwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature ranking comparison (sorted by SHAP rank):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>shap_rank</th>\n",
       "      <th>rank_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Discount_offered</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weight_in_gms</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prior_purchases</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cost_of_the_Product</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Customer_rating</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Customer_care_calls</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Product_importance_medium</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Product_importance_high</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Warehouse_block_A</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gender_F</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mode_of_Shipment_Flight</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Product_importance_low</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      feature  tree_rank  shap_rank  rank_delta\n",
       "2            Discount_offered          3          1           2\n",
       "1               Weight_in_gms          2          2           0\n",
       "5             Prior_purchases          6          3           3\n",
       "0         Cost_of_the_Product          1          4           3\n",
       "3             Customer_rating          4          5           1\n",
       "4         Customer_care_calls          5          6           1\n",
       "7   Product_importance_medium          8          7           1\n",
       "10    Product_importance_high         11          8           3\n",
       "13          Warehouse_block_A         14          9           5\n",
       "8                    Gender_F          9         10           1\n",
       "15    Mode_of_Shipment_Flight         16         11           5\n",
       "14     Product_importance_low         15         12           3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "fig.suptitle(\"Feature Importance — Native Tree vs SHAP\", fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "# Native tree importance\n",
    "ax = axes[0]\n",
    "if lgbm_state.feature_importance is not None:\n",
    "    top_tree = lgbm_state.feature_importance.head(12)\n",
    "    ax.barh(top_tree['feature'][::-1], top_tree['importance'][::-1], color=\"#3B82F6\")\n",
    "    ax.set_title(\"Native Tree Importance (top 12)\", fontweight=\"bold\")\n",
    "    ax.set_xlabel(\"Importance Score\")\n",
    "    ax.grid(axis='x', alpha=0.3)\n",
    "else:\n",
    "    ax.text(0.5, 0.5, \"Not available\", ha='center', va='center', transform=ax.transAxes)\n",
    "\n",
    "# SHAP importance\n",
    "ax = axes[1]\n",
    "if lgbm_state.shap_feature_importance is not None:\n",
    "    top_shap = lgbm_state.shap_feature_importance.head(12)\n",
    "    ax.barh(top_shap['feature'][::-1], top_shap['importance'][::-1], color=\"#10B981\")\n",
    "    ax.set_title(\"SHAP Mean |Value| (top 12)\", fontweight=\"bold\")\n",
    "    ax.set_xlabel(\"Mean |SHAP value|\")\n",
    "    ax.grid(axis='x', alpha=0.3)\n",
    "else:\n",
    "    ax.text(0.5, 0.5, \"SHAP not computed\\n(set generate_shap=True)\",\n",
    "            ha='center', va='center', transform=ax.transAxes)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Rank comparison — where do the two methods agree and diverge?\n",
    "if lgbm_state.feature_importance is not None and lgbm_state.shap_feature_importance is not None:\n",
    "    tree_rank = lgbm_state.feature_importance.assign(\n",
    "        tree_rank=range(1, len(lgbm_state.feature_importance) + 1)\n",
    "    ).rename(columns={'importance': 'tree_importance'})\n",
    "\n",
    "    shap_rank = lgbm_state.shap_feature_importance.assign(\n",
    "        shap_rank=range(1, len(lgbm_state.shap_feature_importance) + 1)\n",
    "    ).rename(columns={'importance': 'shap_importance'})\n",
    "\n",
    "    comparison = (\n",
    "        tree_rank.merge(shap_rank, on='feature')\n",
    "        .assign(rank_delta=lambda d: (d['tree_rank'] - d['shap_rank']).abs())\n",
    "        .sort_values('shap_rank')\n",
    "        .head(12)\n",
    "    )\n",
    "    print(\"\\nFeature ranking comparison (sorted by SHAP rank):\")\n",
    "    display(comparison[['feature', 'tree_rank', 'shap_rank', 'rank_delta']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "g2h3i4j5",
   "metadata": {},
   "source": [
    "## 11. Formal Training Report\n",
    "\n",
    "`TrainingReportGenerator` consolidates the Optuna study, trainer state, and configuration into a structured, printable report — suitable for model cards, peer review, or automated audit trails. It also surfaces **hyperparameter importance** via fANOVA: which hyperparameters moved the needle most during the search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "h2i3j4k5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Training Report: shipping_delay_lgbm\n",
      "================================================================================\n",
      "\n",
      "Model Configuration:\n",
      "  Type: lgbm\n",
      "  Task: binary_classification\n",
      "\n",
      "Optimization Results:\n",
      "  Best CV Score: 0.742580 ± 0.000000\n",
      "  CV Scores: []\n",
      "  Best Iteration: 0\n",
      "  Total Trials: 5\n",
      "\n",
      "Best Hyperparameters:\n",
      "  num_leaves: 125\n",
      "  max_depth: 7\n",
      "  min_child_samples: 53\n",
      "  lambda_l1: 0.000001\n",
      "  lambda_l2: 0.000001\n",
      "  min_gain_to_split: 0.168427\n",
      "  feature_fraction: 0.946029\n",
      "  bagging_fraction: 0.564919\n",
      "  bagging_freq: 2\n",
      "  learning_rate: 0.091531\n",
      "  max_bin: 63\n",
      "  n_estimators: 48\n",
      "\n",
      "Threshold Optimization:\n",
      "  Optimal Threshold: 0.4984\n",
      "  youden_index: 0.5163\n",
      "  tpr: 0.6636\n",
      "  fpr: 0.1474\n",
      "  sensitivity: 0.6636\n",
      "  specificity: 0.8526\n",
      "\n",
      "Feature Selection:\n",
      "  Original Features: 19\n",
      "  Selected Features: 19\n",
      "  Reduction: 0.0%\n",
      "================================================================================\n",
      "\n",
      "================================================================================\n",
      "HYPERPARAMETER IMPORTANCE  (fANOVA — higher = greater impact on AUC)\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>max_depth</td>\n",
       "      <td>0.187103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bagging_fraction</td>\n",
       "      <td>0.178320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>num_leaves</td>\n",
       "      <td>0.169172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bagging_freq</td>\n",
       "      <td>0.119174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max_bin</td>\n",
       "      <td>0.111460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>min_child_samples</td>\n",
       "      <td>0.071306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>learning_rate</td>\n",
       "      <td>0.065942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lambda_l1</td>\n",
       "      <td>0.033099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           parameter  importance\n",
       "0          max_depth    0.187103\n",
       "1   bagging_fraction    0.178320\n",
       "2         num_leaves    0.169172\n",
       "3       bagging_freq    0.119174\n",
       "4            max_bin    0.111460\n",
       "5  min_child_samples    0.071306\n",
       "6      learning_rate    0.065942\n",
       "7          lambda_l1    0.033099"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = TrainingReportGenerator.from_trainer_state(\n",
    "    trainer_state=lgbm_state,\n",
    "    config=lgbm_config,\n",
    "    study=lgbm_state.study\n",
    ")\n",
    "\n",
    "print(report)\n",
    "\n",
    "# Which hyperparameters actually drove performance?\n",
    "if lgbm_state.study:\n",
    "    print(\"\\n\" + \"=\" * 80)\n",
    "    print(\"HYPERPARAMETER IMPORTANCE  (fANOVA — higher = greater impact on AUC)\")\n",
    "    print(\"=\" * 80)\n",
    "    hp_imp = TrainingReportGenerator.generate_optuna_importance_report(\n",
    "        lgbm_state.study, top_n=8\n",
    "    )\n",
    "    if not hp_imp.empty:\n",
    "        display(hp_imp)\n",
    "    else:\n",
    "        print(\"(Needs ≥ 3 completed trials for fANOVA)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "i2j3k4l5",
   "metadata": {},
   "source": [
    "## 12. Production Inference — The Complete End-to-End Pipeline\n",
    "\n",
    "In a real deployment, the training environment and the inference service are completely separate processes. The inference service needs exactly two artefacts — the **serialised pipeline** and the **model package** — and must apply them in the correct order.\n",
    "\n",
    "The cell below simulates what your inference microservice executes on a batch of new shipments arriving in production:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "j2k3l4m5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline loaded — fitted : True\n",
      "Model loaded    — name   : shipping_delay_predictor_v1 v1.0.0\n",
      "Decision threshold       : 0.4984\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877\n",
      "[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07\n",
      "[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796\n",
      "[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484\n",
      "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
      "\n",
      "============================================================\n",
      "  INFERENCE RESULTS\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Warehouse_block</th>\n",
       "      <th>Mode_of_Shipment</th>\n",
       "      <th>Weight_in_gms</th>\n",
       "      <th>on_time_probability</th>\n",
       "      <th>status</th>\n",
       "      <th>ground_truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>2083</td>\n",
       "      <td>0.9936</td>\n",
       "      <td>On Time</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6542</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>1823</td>\n",
       "      <td>0.5791</td>\n",
       "      <td>On Time</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6428</th>\n",
       "      <td>A</td>\n",
       "      <td>Road</td>\n",
       "      <td>5839</td>\n",
       "      <td>0.4485</td>\n",
       "      <td>Delayed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8060</th>\n",
       "      <td>A</td>\n",
       "      <td>Road</td>\n",
       "      <td>4287</td>\n",
       "      <td>0.3439</td>\n",
       "      <td>Delayed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9822</th>\n",
       "      <td>D</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5282</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>Delayed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4478</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4291</td>\n",
       "      <td>0.2371</td>\n",
       "      <td>Delayed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Warehouse_block Mode_of_Shipment  Weight_in_gms  on_time_probability  \\\n",
       "1666               C             Ship           2083               0.9936   \n",
       "6542               A             Ship           1823               0.5791   \n",
       "6428               A             Road           5839               0.4485   \n",
       "8060               A             Road           4287               0.3439   \n",
       "9822               D             Ship           5282               0.3525   \n",
       "4478               A             Ship           4291               0.2371   \n",
       "\n",
       "       status  ground_truth  \n",
       "1666  On Time             1  \n",
       "6542  On Time             1  \n",
       "6428  Delayed             0  \n",
       "8060  Delayed             0  \n",
       "9822  Delayed             0  \n",
       "4478  Delayed             1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Inference complete: two artefacts, zero leakage, full reproducibility.\n"
     ]
    }
   ],
   "source": [
    "# ── Step 1: Load artefacts from disk ─────────────────────────────────────────\n",
    "inference_pipeline = TransformPipeline.load(\"pipeline_binary.pkl\")\n",
    "loaded_package     = ModelSerializer.load(\"models/shipping_delay_predictor_v1.pkl\")\n",
    "inference_model    = loaded_package.model\n",
    "threshold          = loaded_package.metadata.optimal_threshold\n",
    "\n",
    "print(f\"Pipeline loaded — fitted : {inference_pipeline.is_fitted}\")\n",
    "print(f\"Model loaded    — name   : {loaded_package.metadata.name} v{loaded_package.metadata.version}\")\n",
    "print(f\"Decision threshold       : {threshold:.4f}\")\n",
    "\n",
    "# ── Step 2: Simulate incoming raw records ─────────────────────────────────────\n",
    "# In production these would be live records. We use held-out test rows as a proxy.\n",
    "new_shipments_raw = X_test.iloc[:6].copy()\n",
    "\n",
    "# ── Step 3: Apply the serialised transform pipeline ──────────────────────────\n",
    "new_shipments_transformed = inference_pipeline.transform(new_shipments_raw)\n",
    "\n",
    "# ── Step 4: Score and apply threshold ────────────────────────────────────────\n",
    "on_time_scores = inference_model.predict_proba(new_shipments_transformed)[:, 1]\n",
    "predictions    = (on_time_scores >= threshold).astype(int)\n",
    "\n",
    "# ── Step 5: Format output ─────────────────────────────────────────────────────\n",
    "results_df = new_shipments_raw.assign(\n",
    "    on_time_probability=on_time_scores.round(4),\n",
    "    prediction=predictions,\n",
    "    status=pd.Series(predictions).map({1: 'On Time', 0: 'Delayed'}).values,\n",
    "    ground_truth=y_test.iloc[:6].values\n",
    ")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"  INFERENCE RESULTS\")\n",
    "print(\"=\" * 60)\n",
    "display(\n",
    "    results_df[[\n",
    "        'Warehouse_block', 'Mode_of_Shipment', 'Weight_in_gms',\n",
    "        'on_time_probability', 'status', 'ground_truth'\n",
    "    ]]\n",
    ")\n",
    "\n",
    "print(\"\\nInference complete: two artefacts, zero leakage, full reproducibility.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "k2l3m4n5",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## What You Built\n",
    "\n",
    "| You Wrote | BitBullet Handled |\n",
    "|-----------|-------------------|\n",
    "| Feature descriptions in `generate_feature_stats` | Full statistical audit across all columns |\n",
    "| Declared transforms in `pipeline.add()` | Fit, chain, state management, serialisation |\n",
    "| Called `pipeline.fit_transform(X_train)` | Learned parameters on training data only |\n",
    "| Called `pipeline.save()` / `TransformPipeline.load()` | Binary serialisation with exact parameter reload |\n",
    "| Declared a `TrainConfig` | Optuna TPE search, Stratified K-Fold CV, early stopping |\n",
    "| Called `trainer.fit()` | Optimisation, final training, threshold tuning, feature importance, SHAP |\n",
    "| Changed `model_type` string | Full multi-architecture benchmark with zero boilerplate |\n",
    "| Called `ModelEvaluator.evaluate(...)` and `evaluate_classification(...)` | Human diagnostics plus JSON-ready metric payloads |\n",
    "| Populated `ModelMetadata` | JSON-exportable provenance with CV metrics, test metrics, schema, preprocessing, search, and inference-support metadata |\n",
    "| Called `ModelSerializer.save()` | Self-describing package with datasets and metadata |\n",
    "| Called `TrainingReportGenerator.from_trainer_state()` | Structured report + hyperparameter importance |\n",
    "\n",
    "**Continue the Academy:**\n",
    "- `02_Multi_Class_Classification.ipynb` — extend this workflow to a 4-class target\n",
    "- `03_Clustering.ipynb` — unsupervised segmentation with `bitbullet.cluster`\n",
    "- `04_Data_Transformations.ipynb` — deep dive into every transform in the library\n",
    "- `05_Model_Management.ipynb` — versioning, comparison, and model governance"
   ]
  }
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