{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43642db6",
   "metadata": {},
   "source": [
    "# Model Training & Evaluation \n",
    "\n",
    "This notebook serves understand better the steps needed for model training after data is split and scaled. We assume the dataset has already been prepared previously (label construction, feature engineering, one-hot encoding). \n",
    "\n",
    "Here we focus entirely on the **modeling stage** of the ML workflow: no data exploration or feature construction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb990c18",
   "metadata": {},
   "source": [
    "## Section 1 — Introduction\n",
    "\n",
    "**Session 1** built the dataset: we have features (RFM, one-hot encoded categoricals, etc.) and a target label (`future_revenue_90d`). We do **not** redo data exploration or feature engineering here.\n",
    "\n",
    "**Session 2** focuses on the **modeling pipeline**:\n",
    "\n",
    "1. **Split strategy** — Reserve a hold-out test set for final evaluation.\n",
    "2. **Baseline** — Establish simple benchmarks (e.g. mean prediction, linear model).\n",
    "3. **Metrics** — Use MAE, RMSE, and R² for regression evaluation.\n",
    "4. **Cross-validation** — Get more reliable performance estimates than a single train/test split.\n",
    "5. **Learning curves** — Plot train vs validation error as a function of training set size to diagnose underfitting/overfitting and whether more data would help.\n",
    "6. **Model comparison** — Compare several algorithms (Ridge, Lasso, tree-based) via CV.\n",
    "7. **Hyperparameter tuning** — Use grid search to select good hyperparameters (with care to avoid overfitting).\n",
    "8. **Train/validation development & early stopping** — For iterative models (e.g. gradient boosting), observe train vs validation error over iterations and stop at the best validation step instead of overfitting.\n",
    "9. **Feature selection** — After we have a chosen, tuned model, we select a subset of features (on the training set only) using that model's importance or a wrapper; this reduces overfitting and simplifies the pipeline.\n",
    "10. **Final test evaluation** — Evaluate the chosen model (trained on selected features) once on the held-out test set and interpret results (e.g. predictions vs actuals, feature importance)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f7eeae",
   "metadata": {},
   "source": [
    "## Section 2 — Load Dataset (pre-split, pre-scaled)\n",
    "\n",
    "We load the **already prepared training inputs** exported at the end of Session 1. The project root is set so the notebook works whether you run it from the repo root or from inside `notebooks/`.\n",
    "\n",
    "**What we load (from `data/`)**\n",
    "\n",
    "- `X_train_scaled.csv`, `y_train.csv`\n",
    "- `X_val_scaled.csv`, `y_val.csv`\n",
    "- `feature_columns.csv` (to enforce a stable feature order)\n",
    "\n",
    "**Why this is useful:** we can start model training immediately with a leakage-safe setup because scaling was fit on the training split only (Session 1) and then applied to both splits."
   ]
  },
  {
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   "id": "53ac0bed",
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    "execution": {
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   "source": [
    "# --- Imports and path setup ---\n",
    "# We need the project root so that data path works from repo root or from notebooks/\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "# Project root: if \"data\" not under cwd, assume we're in notebooks/ and go up\n",
    "PROJECT_ROOT = Path.cwd()\n",
    "if not (PROJECT_ROOT / \"data\").is_dir():\n",
    "    PROJECT_ROOT = PROJECT_ROOT.parent\n",
    "\n",
    "DATA_DIR = PROJECT_ROOT / \"data\"\n",
    "\n",
    "# Set seed for reproducibility \n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aab04a58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.860768Z",
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   "outputs": [],
   "source": [
    "# ---------------------------------------------------------------------------\n",
    "# Load the **pre-split and preprocessed** datasets produced in Session 1\n",
    "# ---------------------------------------------------------------------------\n",
    "# Session 1 exports:\n",
    "# - X_train_scaled.csv, X_val_scaled.csv (scaled feature matrices)\n",
    "# - y_train.csv, y_val.csv (targets)\n",
    "# - feature_columns.csv (feature order)\n",
    "\n",
    "feature_cols_path = DATA_DIR / \"feature_columns.csv\"\n",
    "X_train_path = DATA_DIR / \"X_train_scaled.csv\"\n",
    "X_test_path = DATA_DIR / \"X_val_scaled.csv\"\n",
    "y_train_path = DATA_DIR / \"y_train.csv\"\n",
    "y_test_path = DATA_DIR / \"y_val.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d675c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.863691Z",
     "iopub.status.busy": "2026-03-24T15:30:52.863591Z",
     "iopub.status.idle": "2026-03-24T15:30:52.873255Z",
     "shell.execute_reply": "2026-03-24T15:30:52.872790Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.863681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pre-split, scaled datasets from:\n",
      " - /Users/veit/cusy/trn/ai-tutorial/docs/1intro/data/X_train_scaled.csv\n",
      " - /Users/veit/cusy/trn/ai-tutorial/docs/1intro/data/X_val_scaled.csv\n",
      " - /Users/veit/cusy/trn/ai-tutorial/docs/1intro/data/y_train.csv\n",
      " - /Users/veit/cusy/trn/ai-tutorial/docs/1intro/data/y_val.csv\n",
      "Number of features: 17\n",
      "X_train: (960, 17) | y_train: (960,)\n",
      "X_test: (240, 17) | y_test: (240,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "FEATURE_COLS = pd.read_csv(feature_cols_path)[\"feature_name\"].tolist()\n",
    "\n",
    "X_train = pd.read_csv(X_train_path)\n",
    "X_test = pd.read_csv(X_test_path)\n",
    "\n",
    "y_train = pd.read_csv(y_train_path).iloc[:, 0]\n",
    "y_test = pd.read_csv(y_test_path).iloc[:, 0]\n",
    "\n",
    "# Enforce consistent column order (and protect against accidental reordering)\n",
    "X_train = X_train[FEATURE_COLS]\n",
    "X_test = X_test[FEATURE_COLS]\n",
    "\n",
    "TARGET = \"future_revenue_90d\"\n",
    "\n",
    "print(\"Loaded pre-split, scaled datasets from:\")\n",
    "print(\" -\", X_train_path)\n",
    "print(\" -\", X_test_path)\n",
    "print(\" -\", y_train_path)\n",
    "print(\" -\", y_test_path)\n",
    "print(\"Number of features:\", len(FEATURE_COLS))\n",
    "print(\"X_train:\", X_train.shape, \"| y_train:\", y_train.shape)\n",
    "print(\"X_test:\", X_test.shape, \"| y_test:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce54cea4",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-03-24T15:30:52.873545Z",
     "iopub.status.idle": "2026-03-24T15:30:52.883313Z",
     "shell.execute_reply": "2026-03-24T15:30:52.882727Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.873603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (scaled) shape: (960, 17)\n",
      "X_test (scaled) shape: (240, 17)\n",
      "y_train shape: (960,)\n",
      "y_test shape: (240,)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>country_DE</th>\n",
       "      <th>country_ES</th>\n",
       "      <th>country_FR</th>\n",
       "      <th>country_IT</th>\n",
       "      <th>customer_segment_Premium</th>\n",
       "      <th>customer_segment_Standard</th>\n",
       "      <th>acquisition_channel_Organic</th>\n",
       "      <th>acquisition_channel_Paid Ads</th>\n",
       "      <th>acquisition_channel_Referral</th>\n",
       "      <th>recency_days</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_total</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>recent_30d_spend</th>\n",
       "      <th>prev_30d_spend</th>\n",
       "      <th>spend_trend</th>\n",
       "      <th>tenure_days</th>\n",
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       "      <td>0.361598</td>\n",
       "      <td>-0.202790</td>\n",
       "      <td>0.330898</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.858878</td>\n",
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       "      <td>0.166573</td>\n",
       "      <td>0.032016</td>\n",
       "      <td>-0.331379</td>\n",
       "      <td>0.234555</td>\n",
       "      <td>-0.348523</td>\n",
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       "      <td>0.057988</td>\n",
       "      <td>0.594713</td>\n",
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       "      <td>-0.506502</td>\n",
       "      <td>2.067541</td>\n",
       "      <td>-0.408</td>\n",
       "      <td>1.062357</td>\n",
       "      <td>-0.750678</td>\n",
       "      <td>1.523745</td>\n",
       "      <td>-0.477097</td>\n",
       "      <td>-1.215457</td>\n",
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       "      <td>-0.307036</td>\n",
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      "text/plain": [
       "   country_DE  country_ES  country_FR  country_IT  customer_segment_Premium  \\\n",
       "0   -0.574143   -0.521079    1.974325   -0.483666                    -0.408   \n",
       "1   -0.574143   -0.521079   -0.506502   -0.483666                    -0.408   \n",
       "2   -0.574143    1.919095   -0.506502   -0.483666                    -0.408   \n",
       "3   -0.574143   -0.521079   -0.506502    2.067541                    -0.408   \n",
       "4   -0.574143   -0.521079    1.974325   -0.483666                    -0.408   \n",
       "\n",
       "   customer_segment_Standard  acquisition_channel_Organic  \\\n",
       "0                   1.062357                     1.332129   \n",
       "1                  -0.941303                     1.332129   \n",
       "2                  -0.941303                     1.332129   \n",
       "3                   1.062357                    -0.750678   \n",
       "4                  -0.941303                    -0.750678   \n",
       "\n",
       "   acquisition_channel_Paid Ads  acquisition_channel_Referral  recency_days  \\\n",
       "0                     -0.656278                     -0.477097     -1.243983   \n",
       "1                     -0.656278                     -0.477097     -0.858878   \n",
       "2                     -0.656278                     -0.477097      0.168067   \n",
       "3                      1.523745                     -0.477097     -1.215457   \n",
       "4                     -0.656278                      2.096010      0.710066   \n",
       "\n",
       "   frequency  monetary_total  avg_order_value  recent_30d_spend  \\\n",
       "0   0.324130       -0.109856        -0.025927          0.361598   \n",
       "1   0.955295        0.166573         0.032016         -0.331379   \n",
       "2  -0.307036       -0.162731         0.423502         -0.331379   \n",
       "3   0.324130        0.208202         0.417439          2.039100   \n",
       "4  -0.307036       -0.331547        -0.047147         -0.331379   \n",
       "\n",
       "   prev_30d_spend  spend_trend  tenure_days  \n",
       "0       -0.202790     0.330898     0.451772  \n",
       "1        0.234555    -0.348523     1.695356  \n",
       "2       -0.202790     0.057988     0.594713  \n",
       "3       -0.017451     0.819264     0.189714  \n",
       "4       -0.202790     0.057988     0.532772  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    46.63\n",
       "1     0.00\n",
       "2     9.05\n",
       "3    76.17\n",
       "4     0.00\n",
       "Name: future_revenue_90d, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick inspection (these are already features-only matrices, scaled)\n",
    "print(\"X_train (scaled) shape:\", X_train.shape)\n",
    "print(\"X_test (scaled) shape:\", X_test.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"y_test shape:\", y_test.shape)\n",
    "\n",
    "display(X_train.head())\n",
    "display(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b2c35dd",
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 17\n",
      "Target stats (train) — min: 0.00, median: 18.66, max: 1070.01\n",
      "Percent of zeros in target (train): 45.6%\n",
      "Percent of zeros in target (test):  43.3%\n"
     ]
    }
   ],
   "source": [
    "# Targets are already loaded as y_train/y_test; feature matrices are already scaled.\n",
    "# We still report target stats for context (revenue is often right-skewed with many zeros).\n",
    "\n",
    "print(\"Number of features:\", X_train.shape[1])\n",
    "print(\"Target stats (train) — min: {:.2f}, median: {:.2f}, max: {:.2f}\".format(\n",
    "    y_train.min(), y_train.median(), y_train.max()))\n",
    "\n",
    "pct_zeros_train = (y_train == 0).mean() * 100\n",
    "pct_zeros_test = (y_test == 0).mean() * 100\n",
    "print(\"Percent of zeros in target (train): {:.1f}%\".format(pct_zeros_train))\n",
    "print(\"Percent of zeros in target (test):  {:.1f}%\".format(pct_zeros_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370e6cf0",
   "metadata": {},
   "source": [
    "## Section 3 — Train/Test Split (already done upstream)\n",
    "\n",
    "In **Session 1**, we already:\n",
    "\n",
    "- built the feature matrix,\n",
    "- performed redundancy handling,\n",
    "- did the **train/validation split**, and\n",
    "- fit scaling on **training** data only, then transformed both splits.\n",
    "\n",
    "This notebook therefore **loads**:\n",
    "\n",
    "- `X_train_scaled.csv`, `y_train.csv`\n",
    "- `X_val_scaled.csv`, `y_val.csv`\n",
    "\n",
    "and treats `X_val_scaled`/`y_val` as the **held-out test set** for final evaluation.\n",
    "\n",
    "**Why this is good practice:** it keeps the test set untouched during model selection and avoids leakage from scaling/feature selection into evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd73eb3f",
   "metadata": {},
   "source": [
    "### Evaluation design in a nutshell\n",
    "\n",
    "Our evaluation follows a single, clear principle: **the test set is used exactly once**, at the end, to report final performance. Everything else happens on the training set:\n",
    "\n",
    "| Step | Where it happens | Purpose |\n",
    "|------|------------------|--------|\n",
    "| Train/test split | Once at the start | Reserve an untouched test set for final reporting. |\n",
    "| Baselines | Trained on train, evaluated on test | Establish a reference; we accept this one use of test for baselines so we can compare the final model to a trivial benchmark. |\n",
    "| Cross-validation | On training set only | Compare models and tune hyperparameters without touching the test set. |\n",
    "| Model selection & tuning | Via CV on train | Pick the best model and hyperparameters using only train/validation splits. |\n",
    "| Feature selection | On training set only (after tuning) | Choose a subset of features using the tuned model's importance (or a wrapper); refit and evaluate on test with selected features only. |\n",
    "| **Final evaluation** | **Once on test set** | Report MAE, RMSE, R² (and plots) as the unbiased estimate of generalization. |\n",
    "\n",
    "So: we **never** use the test set to choose a model or a hyperparameter. That way, test metrics are a valid estimate of how the chosen pipeline will perform on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64821aef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.886954Z",
     "iopub.status.busy": "2026-03-24T15:30:52.886879Z",
     "iopub.status.idle": "2026-03-24T15:30:52.889254Z",
     "shell.execute_reply": "2026-03-24T15:30:52.888758Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.886947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (960, 17) | y_train: (960,)\n",
      "X_test: (240, 17) | y_test: (240,)\n"
     ]
    }
   ],
   "source": [
    "# Train/test split has already been performed in Session 1; we loaded X_train/X_test above.\n",
    "# We just re-print shapes here for convenience.\n",
    "print(\"X_train:\", X_train.shape, \"| y_train:\", y_train.shape)\n",
    "print(\"X_test:\", X_test.shape, \"| y_test:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f40c8b09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.889822Z",
     "iopub.status.busy": "2026-03-24T15:30:52.889667Z",
     "iopub.status.idle": "2026-03-24T15:30:52.892944Z",
     "shell.execute_reply": "2026-03-24T15:30:52.892582Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.889809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pre-scaled feature matrices.\n",
      "X_train dtype summary:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "float64    17\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------------------------------------------------------------\n",
    "# Scaling note\n",
    "# ---------------------------------------------------------------------------\n",
    "# Scaling has already been applied in Session 1 and exported as X_train_scaled.csv / X_val_scaled.csv.\n",
    "# In this notebook we therefore train directly on X_train (scaled) and evaluate on X_test (scaled).\n",
    "#\n",
    "# If you want to re-fit preprocessing inside this notebook instead, load the raw splits\n",
    "# (X_train.csv / X_val.csv) and fit the scaler on X_train only.\n",
    "\n",
    "print(\"Using pre-scaled feature matrices.\")\n",
    "print(\"X_train dtype summary:\")\n",
    "display(X_train.dtypes.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479aac46",
   "metadata": {},
   "source": [
    "## Section 4 — Baseline Models\n",
    "\n",
    "**Why baselines matter:** Before using complex models, we need a reference. A baseline (e.g. predicting the mean, or a simple linear model) tells us whether our features and models add value. If a sophisticated model cannot beat a trivial baseline, we should reconsider the problem or the features.\n",
    "\n",
    "**Why these metrics (MAE, RMSE, R²)?** We report three regression metrics so that we can interpret both *magnitude* and *scale-free* performance: **MAE** (mean absolute error) is the average error in the same units as the target (e.g. euros)—easy to communicate and robust to outliers. **RMSE** (root mean squared error) also has target units but penalizes large errors more; it is useful when big mistakes are especially costly. **R²** (coefficient of determination) is the fraction of variance in the target explained by the model (0 = no better than predicting the mean; 1 = perfect). Reporting all three gives a fuller picture than a single number.\n",
    "\n",
    "**Interpreting the table:** The **Dummy (mean)** regressor predicts the average of `y_train` for every test point; its R² is typically near 0 (or slightly negative). **LinearRegression** should do a bit better (lower MAE/RMSE, higher R²) if the features carry signal. If LinearRegression is only marginally better than the dummy, that suggests either limited predictive signal in the features or that the problem is inherently hard (e.g. noisy target)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a49fc1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.893464Z",
     "iopub.status.busy": "2026-03-24T15:30:52.893395Z",
     "iopub.status.idle": "2026-03-24T15:30:52.901164Z",
     "shell.execute_reply": "2026-03-24T15:30:52.900823Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.893458Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline results on test set:\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>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dummy (mean)</th>\n",
       "      <td>46.247892</td>\n",
       "      <td>66.807927</td>\n",
       "      <td>-0.002711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>45.174727</td>\n",
       "      <td>65.755370</td>\n",
       "      <td>0.028636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        MAE       RMSE        R2\n",
       "Dummy (mean)      46.247892  66.807927 -0.002711\n",
       "LinearRegression  45.174727  65.755370  0.028636"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Baseline A: predict the training mean for every test sample (no use of X_test features)\n",
    "dummy = DummyRegressor(strategy=\"mean\")\n",
    "dummy.fit(X_train, y_train)\n",
    "y_pred_dummy = dummy.predict(X_test)\n",
    "\n",
    "# Baseline B: ordinary least-squares linear regression using all features\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "\n",
    "def eval_metrics(y_true, y_pred):\n",
    "    return {\n",
    "        \"MAE\": mean_absolute_error(y_true, y_pred),\n",
    "        \"RMSE\": np.sqrt(mean_squared_error(y_true, y_pred)),\n",
    "        \"R2\": r2_score(y_true, y_pred),\n",
    "    }\n",
    "\n",
    "results_baseline = pd.DataFrame({\n",
    "    \"Dummy (mean)\": eval_metrics(y_test, y_pred_dummy),\n",
    "    \"LinearRegression\": eval_metrics(y_test, y_pred_lr),\n",
    "}).T\n",
    "print(\"Baseline results on test set:\")\n",
    "display(results_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1318334e",
   "metadata": {},
   "source": [
    "## Section 5 — Cross-Validation\n",
    "\n",
    "A single train/test split can be noisy. **Cross-validation** (e.g. 5-fold) trains and evaluates the model on different splits and reports the mean and standard deviation of the metric. This gives a more reliable estimate of performance and reduces the risk of overfitting to one particular split. \n",
    "\n",
    "**Why 5-fold?** Five folds are a common default, esp. for a smaller dataset: each fold leaves 20% out for validation, so we get five different train/validation splits and average the metric—more stable than a single split, without the higher cost of 10-fold CV.\n",
    "\n",
    "**Outcome:** The printed CV RMSE is the average over 5 folds. Note that this is computed on the **training** set only (each fold has its own train/validation split). The mean gives a more stable performance estimate than the single test split we did for baselines; the standard deviation indicates how much the metric varies across folds. If std is high, performance is sensitive to the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdfb843c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.901623Z",
     "iopub.status.busy": "2026-03-24T15:30:52.901454Z",
     "iopub.status.idle": "2026-03-24T15:30:52.914329Z",
     "shell.execute_reply": "2026-03-24T15:30:52.913886Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.901615Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression — 5-fold CV RMSE: 81.2037 ± 8.8965\n"
     ]
    }
   ],
   "source": [
    "# 5-fold CV on training data only; sklearn uses neg RMSE so we negate to get positive RMSE\n",
    "kfold = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "scores_rmse = cross_val_score(\n",
    "    LinearRegression(), X_train, y_train,\n",
    "    cv=kfold, scoring=\"neg_root_mean_squared_error\"\n",
    ")\n",
    "rmse_cv = -scores_rmse  # convert to positive RMSE\n",
    "print(\"LinearRegression — 5-fold CV RMSE: {:.4f} ± {:.4f}\".format(\n",
    "    rmse_cv.mean(), rmse_cv.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa63f0d",
   "metadata": {},
   "source": [
    "## Section 5b — Learning Curves: Train vs Validation by Data Size\n",
    "\n",
    "So far we have looked at a single metric (e.g. CV RMSE) for a fixed training set. **Learning curves** show how **training error** and **validation error** change as we vary the **size of the training set**. This helps answer:\n",
    "\n",
    "- **Underfitting**: If both train and validation error are high and close, the model may be too simple or need more data.\n",
    "- **Overfitting**: If training error is much lower than validation error, the model is fitting the training data too closely.\n",
    "- **More data?**: If validation error is still decreasing at the right end of the curve, more data might help.\n",
    "\n",
    "We define a helper that wraps sklearn's `learning_curve`: it trains on increasing subsets of the data and reports mean train and validation score (here, RMSE) with standard deviation bands.\n",
    "\n",
    "**Interpreting the plot:** Typically, train RMSE decreases as we use more data (the model fits the training set better), while validation RMSE often starts high and then decreases. A large gap between the two curves at the right suggests overfitting; curves that are close and high suggest underfitting or that the model is already at its capacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b3061e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.914720Z",
     "iopub.status.busy": "2026-03-24T15:30:52.914641Z",
     "iopub.status.idle": "2026-03-24T15:30:52.921457Z",
     "shell.execute_reply": "2026-03-24T15:30:52.920797Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.914712Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Helpers for plotting training vs validation behaviour (learning curves and\n",
    "train/val development over iterations). Used in the Session 2 notebook to\n",
    "teach overfitting, underfitting, and early stopping.\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "def plot_learning_curve(\n",
    "    estimator,\n",
    "    X,\n",
    "    y,\n",
    "    *,\n",
    "    cv=5,\n",
    "    train_sizes=None,\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    ax=None,\n",
    "    title=\"Learning curve (train vs validation)\",\n",
    "):\n",
    "    \"\"\"\n",
    "    Plot learning curve: mean train and validation score vs training set size.\n",
    "\n",
    "    Uses sklearn's learning_curve. For RMSE we use neg_root_mean_squared_error;\n",
    "    the plot shows positive RMSE (converted from negative scores).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : sklearn estimator\n",
    "    X, y : feature matrix and target\n",
    "    cv : int or CV splitter\n",
    "    train_sizes : array-like, optional\n",
    "        Relative or absolute numbers of training examples. Default: np.linspace(0.1, 1.0, 10).\n",
    "    scoring : str\n",
    "        Sklearn scorer name. Default neg_root_mean_squared_error (we convert to positive for plot).\n",
    "    ax : matplotlib axes, optional\n",
    "    title : str\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax\n",
    "    \"\"\"\n",
    "    if train_sizes is None:\n",
    "        train_sizes = np.linspace(0.1, 1.0, 10)\n",
    "    train_sizes_abs, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, train_sizes=train_sizes, scoring=scoring, n_jobs=-1\n",
    "    )\n",
    "    # sklearn returns negative RMSE; convert to positive for display\n",
    "    sign = -1 if scoring.startswith(\"neg_\") else 1\n",
    "    train_scores = sign * train_scores\n",
    "    test_scores = sign * test_scores\n",
    "    train_mean = train_scores.mean(axis=1)\n",
    "    train_std = train_scores.std(axis=1)\n",
    "    test_mean = test_scores.mean(axis=1)\n",
    "    test_std = test_scores.std(axis=1)\n",
    "\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 5))\n",
    "    ax.fill_between(\n",
    "        train_sizes_abs, train_mean - train_std, train_mean + train_std, alpha=0.2\n",
    "    )\n",
    "    ax.fill_between(\n",
    "        train_sizes_abs, test_mean - test_std, test_mean + test_std, alpha=0.2\n",
    "    )\n",
    "    ax.plot(train_sizes_abs, train_mean, \"o-\", label=\"Train (RMSE)\")\n",
    "    ax.plot(train_sizes_abs, test_mean, \"o-\", label=\"Validation (RMSE)\")\n",
    "    ax.set_xlabel(\"Training set size\")\n",
    "    ax.set_ylabel(\"RMSE\")\n",
    "    ax.set_title(title)\n",
    "    ax.legend(loc=\"best\")\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    return ax\n",
    "\n",
    "\n",
    "def compute_gbr_staged_rmse(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    X_val,\n",
    "    y_val,\n",
    "    n_estimators=200,\n",
    "    random_state=42,\n",
    "    **kwargs,\n",
    "):\n",
    "    \"\"\"\n",
    "    Fit a GradientBoostingRegressor with n_estimators and return train/val RMSE\n",
    "    at each boosting stage (1, 2, ..., n_estimators).\n",
    "\n",
    "    Uses staged_predict for efficiency (single fit, then iterate predictions).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X_train, y_train : training data\n",
    "    X_val, y_val : validation data\n",
    "    n_estimators : int\n",
    "    random_state : int\n",
    "    **kwargs : passed to GradientBoostingRegressor (e.g. max_depth, learning_rate).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    steps : np.ndarray of shape (n_estimators,), 1..n_estimators\n",
    "    train_rmse : np.ndarray of shape (n_estimators,)\n",
    "    val_rmse : np.ndarray of shape (n_estimators,)\n",
    "    model : fitted GradientBoostingRegressor (with n_estimators trees)\n",
    "    \"\"\"\n",
    "    from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "    model = GradientBoostingRegressor(\n",
    "        n_estimators=n_estimators, random_state=random_state, **kwargs\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    train_rmse = []\n",
    "    for y_pred in model.staged_predict(X_train):\n",
    "        train_rmse.append(np.sqrt(mean_squared_error(y_train, y_pred)))\n",
    "    val_rmse = []\n",
    "    for y_pred in model.staged_predict(X_val):\n",
    "        val_rmse.append(np.sqrt(mean_squared_error(y_val, y_pred)))\n",
    "\n",
    "    steps = np.arange(1, n_estimators + 1, dtype=int)\n",
    "    return steps, np.array(train_rmse), np.array(val_rmse), model\n",
    "\n",
    "\n",
    "def early_stop_index(val_scores, patience=0):\n",
    "    \"\"\"\n",
    "    Index at which validation score is best (for early stopping).\n",
    "\n",
    "    For RMSE/loss, \"best\" is minimum. If patience > 0, returns the first index\n",
    "    after which the validation score did not improve for `patience` consecutive\n",
    "    steps (optional more realistic early stopping).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    val_scores : array-like, e.g. validation RMSE per step\n",
    "    patience : int\n",
    "        If > 0: stop when no improvement for this many steps. If 0: simply argmin.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    int : index (0-based) at which to stop\n",
    "    \"\"\"\n",
    "    val_scores = np.asarray(val_scores)\n",
    "    best_idx = 0\n",
    "    best_val = val_scores[0]\n",
    "    for i in range(1, len(val_scores)):\n",
    "        if val_scores[i] < best_val:\n",
    "            best_val = val_scores[i]\n",
    "            best_idx = i\n",
    "        if patience > 0 and (i - best_idx) >= patience:\n",
    "            return best_idx\n",
    "    return best_idx\n",
    "\n",
    "\n",
    "def plot_train_val_development(\n",
    "    steps,\n",
    "    train_scores,\n",
    "    val_scores,\n",
    "    early_stop_idx=None,\n",
    "    *,\n",
    "    xlabel=\"Number of boosting iterations\",\n",
    "    ylabel=\"RMSE\",\n",
    "    title=\"Train vs validation error development\",\n",
    "    ax=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Plot train and validation score (e.g. RMSE) vs step (e.g. n_estimators).\n",
    "    Optionally mark the early-stopping step.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    steps : array-like, e.g. [1, 2, ..., 200]\n",
    "    train_scores, val_scores : array-like, same length as steps\n",
    "    early_stop_idx : int or None\n",
    "        Index (0-based) at which early stopping would occur; draw a vertical line.\n",
    "    xlabel, ylabel, title : str\n",
    "    ax : matplotlib axes, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax\n",
    "    \"\"\"\n",
    "    steps = np.asarray(steps)\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 5))\n",
    "    ax.plot(steps, train_scores, label=\"Train\", color=\"C0\")\n",
    "    ax.plot(steps, val_scores, label=\"Validation\", color=\"C1\")\n",
    "    if early_stop_idx is not None and 0 <= early_stop_idx < len(steps):\n",
    "        ax.axvline(\n",
    "            steps[early_stop_idx],\n",
    "            color=\"gray\",\n",
    "            linestyle=\"--\",\n",
    "            alpha=0.8,\n",
    "            label=f\"Early stop (step {steps[early_stop_idx]})\",\n",
    "        )\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(loc=\"best\")\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1137828",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:52.922326Z",
     "iopub.status.busy": "2026-03-24T15:30:52.922242Z",
     "iopub.status.idle": "2026-03-24T15:30:54.944184Z",
     "shell.execute_reply": "2026-03-24T15:30:54.943552Z",
     "shell.execute_reply.started": "2026-03-24T15:30:52.922318Z"
    }
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure project root is on path so we can import from utils\n",
    "import sys\n",
    "sys.path.insert(0, str(PROJECT_ROOT))\n",
    "\n",
    "# Learning curve for LinearRegression on the training set only (no test set used)\n",
    "# train_sizes from 20% to 100% of X_train in 9 steps\n",
    "plot_learning_curve(\n",
    "    LinearRegression(),\n",
    "    X_train,\n",
    "    y_train,\n",
    "    cv=KFold(5, shuffle=True, random_state=SEED),\n",
    "    train_sizes=np.linspace(0.2, 1.0, 9),\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    title=\"Learning curve — LinearRegression (5-fold CV)\",\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee024b97",
   "metadata": {},
   "source": [
    "## Section 6 — Model Comparison\n",
    "\n",
    "We compare several models using 5-fold CV on the **training set** only. Included:\n",
    "\n",
    "- **Ridge** — L2 regularization; shrinks coefficients.\n",
    "- **Lasso** — L1 regularization; can drive some coefficients to zero (sparsity), which helps interpretability and can reduce overfitting.\n",
    "- **DecisionTreeRegressor** — Non-linear, interpretable splits; prone to overfitting without tuning.\n",
    "- **RandomForestRegressor** — Ensemble of trees; typically stronger and more stable than a single tree.\n",
    "\n",
    "**Interpreting the table:** Compare `mean_RMSE` (and optionally `mean_MAE`) across models. The model with the lowest mean RMSE is the best performer in CV. Random forest often wins on tabular data; if DecisionTree is much worse than RandomForest, that suggests the single tree is overfitting. Ridge and Lasso help when features are correlated or numerous relative to sample size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55f18d0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:54.944760Z",
     "iopub.status.busy": "2026-03-24T15:30:54.944619Z",
     "iopub.status.idle": "2026-03-24T15:30:56.949601Z",
     "shell.execute_reply": "2026-03-24T15:30:56.949196Z",
     "shell.execute_reply.started": "2026-03-24T15:30:54.944746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model comparison (5-fold CV on train set):\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>model_name</th>\n",
       "      <th>mean_RMSE</th>\n",
       "      <th>std_RMSE</th>\n",
       "      <th>mean_MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>81.181349</td>\n",
       "      <td>8.908445</td>\n",
       "      <td>49.383988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>81.190558</td>\n",
       "      <td>8.902085</td>\n",
       "      <td>49.392256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>111.892680</td>\n",
       "      <td>10.583362</td>\n",
       "      <td>64.544156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>84.959778</td>\n",
       "      <td>5.981569</td>\n",
       "      <td>51.976574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model_name   mean_RMSE   std_RMSE   mean_MAE\n",
       "0         Ridge   81.181349   8.908445  49.383988\n",
       "1         Lasso   81.190558   8.902085  49.392256\n",
       "2  DecisionTree  111.892680  10.583362  64.544156\n",
       "3  RandomForest   84.959778   5.981569  51.976574"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define models with fixed seeds for reproducibility. Ridge/Lasso use default or mild regularization.\n",
    "models = {\n",
    "    \"Ridge\": Ridge(alpha=1.0),\n",
    "    \"Lasso\": Lasso(alpha=0.01),\n",
    "    \"DecisionTree\": DecisionTreeRegressor(random_state=SEED),\n",
    "    \"RandomForest\": RandomForestRegressor(n_estimators=100, random_state=SEED),\n",
    "}\n",
    "kfold = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "rows = []\n",
    "for name, model in models.items():\n",
    "    rmse_scores = -cross_val_score(\n",
    "        model, X_train, y_train, cv=kfold, scoring=\"neg_root_mean_squared_error\"\n",
    "    )\n",
    "    mae_scores = -cross_val_score(\n",
    "        model, X_train, y_train, cv=kfold, scoring=\"neg_mean_absolute_error\"\n",
    "    )\n",
    "    rows.append({\n",
    "        \"model_name\": name,\n",
    "        \"mean_RMSE\": rmse_scores.mean(),\n",
    "        \"std_RMSE\": rmse_scores.std(),\n",
    "        \"mean_MAE\": mae_scores.mean(),\n",
    "    })\n",
    "comparison = pd.DataFrame(rows)\n",
    "print(\"Model comparison (5-fold CV on train set):\")\n",
    "display(comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98e8ad1",
   "metadata": {},
   "source": [
    "## Section 7 — Hyperparameter Tuning\n",
    "\n",
    "We use **GridSearchCV** to search over a small grid of hyperparameters for `RandomForestRegressor`. The best model is chosen by CV performance on the training set. **Important:** The test set must remain untouched until the final evaluation. Tuning too aggressively on validation folds can lead to *hyperparameter overfitting*—the chosen hyperparameters may work well on the CV splits but not generalize. Always report final performance on the held-out test set.\n",
    "\n",
    "**Outcome:** The first cell prints `best_params_` and the best CV RMSE (positive). The second cell shows the top 5 parameter combinations by rank; you can see how small changes in `max_depth`, `n_estimators`, etc. affect CV score. The best configuration is then used for the final model in Section 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62799da2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:30:56.949955Z",
     "iopub.status.busy": "2026-03-24T15:30:56.949872Z",
     "iopub.status.idle": "2026-03-24T15:31:00.693318Z",
     "shell.execute_reply": "2026-03-24T15:31:00.692930Z",
     "shell.execute_reply.started": "2026-03-24T15:30:56.949948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_depth': 5, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "Best CV RMSE (positive): 81.9548\n"
     ]
    }
   ],
   "source": [
    "# Small grid for speed; in practice you might use RandomizedSearchCV or a larger grid\n",
    "param_grid = {\n",
    "    \"n_estimators\": [100, 200],\n",
    "    \"max_depth\": [None, 5, 10],\n",
    "    \"min_samples_split\": [2, 5],\n",
    "    \"min_samples_leaf\": [1, 2],\n",
    "}\n",
    "grid = GridSearchCV(\n",
    "    RandomForestRegressor(random_state=SEED),\n",
    "    param_grid,\n",
    "    cv=KFold(5, shuffle=True, random_state=SEED),\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    n_jobs=-1,\n",
    ")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Best params:\", grid.best_params_)\n",
    "print(\"Best CV RMSE (positive): {:.4f}\".format(-grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "551d36a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:00.693905Z",
     "iopub.status.busy": "2026-03-24T15:31:00.693815Z",
     "iopub.status.idle": "2026-03-24T15:31:00.702310Z",
     "shell.execute_reply": "2026-03-24T15:31:00.701903Z",
     "shell.execute_reply.started": "2026-03-24T15:31:00.693896Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 configurations:\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>params</th>\n",
       "      <th>mean_RMSE</th>\n",
       "      <th>std_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
       "      <td>81.954780</td>\n",
       "      <td>7.653798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
       "      <td>81.984885</td>\n",
       "      <td>7.612004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
       "      <td>81.990217</td>\n",
       "      <td>7.649633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
       "      <td>82.036437</td>\n",
       "      <td>7.522941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
       "      <td>82.228796</td>\n",
       "      <td>7.332110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               params  mean_RMSE  std_RMSE\n",
       "15  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  81.954780  7.653798\n",
       "12  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  81.984885  7.612004\n",
       "13  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  81.990217  7.649633\n",
       "14  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  82.036437  7.522941\n",
       "11  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  82.228796  7.332110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show top 5 configurations from grid search (by rank_test_score); convert negative score to RMSE\n",
    "cv_results = pd.DataFrame(grid.cv_results_)\n",
    "top5 = cv_results.nsmallest(5, \"rank_test_score\")[\n",
    "    [\"params\", \"mean_test_score\", \"std_test_score\"]\n",
    "].copy()\n",
    "top5[\"mean_test_score\"] = -top5[\"mean_test_score\"]\n",
    "top5 = top5.rename(columns={\"mean_test_score\": \"mean_RMSE\", \"std_test_score\": \"std_RMSE\"})\n",
    "print(\"Top 5 configurations:\")\n",
    "display(top5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a01d6a2",
   "metadata": {},
   "source": [
    "## Section 7b — Train vs Validation Development and Early Stopping\n",
    "\n",
    "When we **tune an iterative model** (e.g. number of trees in gradient boosting), we can record **training error** and **validation error** at each step. This gives a clear picture of overfitting: training error keeps decreasing while validation error may flatten or increase after a point. **Early stopping** means we choose the number of iterations where validation performance is best (or stop when it has not improved for a few steps), instead of always using the maximum.\n",
    "\n",
    "We use **GradientBoostingRegressor**, which builds trees one at a time. Sklearn provides `staged_predict()` so we can get predictions after 1, 2, …, N trees without refitting. Our helper `utils.model_curves` computes train and validation RMSE at each stage and plots them; we then mark the recommended early-stopping step (argmin of validation RMSE). Optionally, **patience**: stop only when validation has not improved for the last K steps (e.g. `patience=5`), which can avoid stopping at a noisy minimum.\n",
    "\n",
    "**Interpreting the plot:** Train RMSE usually decreases monotonically as we add trees; validation RMSE often improves then levels off or worsens. The vertical dashed line shows where we would stop (best validation step). Using that number of trees instead of the maximum can reduce overfitting and save computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39d4cf4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:00.702751Z",
     "iopub.status.busy": "2026-03-24T15:31:00.702651Z",
     "iopub.status.idle": "2026-03-24T15:31:00.922571Z",
     "shell.execute_reply": "2026-03-24T15:31:00.921958Z",
     "shell.execute_reply.started": "2026-03-24T15:31:00.702716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at iteration: 32 | Best validation RMSE: 95.3662\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Split training set into train/val for the development curve (test set stays untouched)\n",
    "X_tr, X_val, y_tr, y_val = train_test_split(\n",
    "    X_train, y_train, test_size=0.2, random_state=SEED\n",
    ")\n",
    "n_estimators = 150  # max number of trees\n",
    "steps, train_rmse, val_rmse, gbr_model = compute_gbr_staged_rmse(\n",
    "    X_tr, y_tr, X_val, y_val,\n",
    "    n_estimators=n_estimators,\n",
    "    max_depth=3,\n",
    "    learning_rate=0.1,\n",
    "    random_state=SEED,\n",
    ")\n",
    "# patience=0 means \"best val step\"; set patience=5 to stop when no improvement for 5 steps\n",
    "stop_idx = early_stop_index(val_rmse, patience=0)\n",
    "print(\"Early stop at iteration:\", steps[stop_idx], \"| Best validation RMSE: {:.4f}\".format(val_rmse[stop_idx]))\n",
    "\n",
    "plot_train_val_development(\n",
    "    steps, train_rmse, val_rmse,\n",
    "    early_stop_idx=stop_idx,\n",
    "    xlabel=\"Number of boosting iterations\",\n",
    "    ylabel=\"RMSE\",\n",
    "    title=\"Gradient Boosting: train vs validation RMSE (early stop = best val)\",\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edb6510",
   "metadata": {},
   "source": [
    "## Section 7c — Feature Selection\n",
    "\n",
    "After we have a **chosen, tuned model**, we perform **feature selection on the training set only**: we use the tuned model's feature importances (or coefficients) to keep only the most useful features. This reduces overfitting and simplifies the pipeline. We fit a selector on `X_train` and `y_train`, then apply it to both train and test to get `X_train_sel` and `X_test_sel`. The final model (Section 8) is trained and evaluated using these selected features only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3407671b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:00.923093Z",
     "iopub.status.busy": "2026-03-24T15:31:00.922981Z",
     "iopub.status.idle": "2026-03-24T15:31:01.163680Z",
     "shell.execute_reply": "2026-03-24T15:31:01.163082Z",
     "shell.execute_reply.started": "2026-03-24T15:31:00.923079Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 9 of 17 features: ['country_ES', 'customer_segment_Premium', 'recency_days', 'frequency', 'monetary_total', 'avg_order_value', 'prev_30d_spend', 'spend_trend', 'tenure_days']\n"
     ]
    }
   ],
   "source": [
    "# Use the best estimator from grid search; select features by importance (e.g. threshold='median')\n",
    "best_estimator = grid.best_estimator_\n",
    "selector = SelectFromModel(best_estimator, threshold='median')\n",
    "selector.fit(X_train, y_train)\n",
    "\n",
    "# Get selected feature names and subset train/test (use .loc to keep column names)\n",
    "selected = selector.get_support()\n",
    "X_train_sel = X_train.loc[:, selected]\n",
    "X_test_sel = X_test.loc[:, selected]\n",
    "print(f\"Selected {X_train_sel.shape[1]} of {X_train.shape[1]} features: {list(X_train_sel.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50c9f00",
   "metadata": {},
   "source": [
    "## Section 8 — Final Evaluation on Held-out Test Set\n",
    "\n",
    "We fit the **best estimator** from GridSearchCV on the full training set **using the selected features only** (`X_train_sel`, `y_train`) and evaluate **once** on the test set (`X_test_sel`). This gives an unbiased estimate of generalization. We also plot predicted vs actual: points close to the diagonal indicate good predictions; systematic deviations suggest bias or underfitting/overfitting.\n",
    "\n",
    "**Why \"once\"?** By design, we have not used the test set for model choice or tuning. So this is the **only** place we use it for performance—and these test metrics are exactly what we report as our generalization estimate. Using the test set repeatedly (e.g. to pick among models) would leak information and make the estimate optimistic.\n",
    "\n",
    "**Outcome:** The printed MAE, RMSE, and R² are the *final* numbers you would report. They should be in a similar range to the baseline and CV results; if test performance is much worse than CV, that may indicate overfitting to the validation folds or an unlucky split. The scatter plot shows each test point’s actual value vs predicted value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ceb6fe5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:01.164219Z",
     "iopub.status.busy": "2026-03-24T15:31:01.164123Z",
     "iopub.status.idle": "2026-03-24T15:31:01.365420Z",
     "shell.execute_reply": "2026-03-24T15:31:01.364853Z",
     "shell.execute_reply.started": "2026-03-24T15:31:01.164211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final test set metrics:\n",
      "  MAE:  45.3921\n",
      "  RMSE: 66.0375\n",
      "  R²:   0.0203\n"
     ]
    }
   ],
   "source": [
    "# Refit best model on full training set (selected features only), then evaluate once on test set\n",
    "best_model = grid.best_estimator_\n",
    "best_model.fit(X_train_sel, y_train)\n",
    "y_pred = best_model.predict(X_test_sel)\n",
    "\n",
    "print(\"Final test set metrics:\")\n",
    "print(\"  MAE:  {:.4f}\".format(mean_absolute_error(y_test, y_pred)))\n",
    "print(\"  RMSE: {:.4f}\".format(np.sqrt(mean_squared_error(y_test, y_pred))))\n",
    "print(\"  R²:   {:.4f}\".format(r2_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20cd4311",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:01.366138Z",
     "iopub.status.busy": "2026-03-24T15:31:01.366003Z",
     "iopub.status.idle": "2026-03-24T15:31:01.411449Z",
     "shell.execute_reply": "2026-03-24T15:31:01.410873Z",
     "shell.execute_reply.started": "2026-03-24T15:31:01.366125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HoJVBXq0J5s6da+jbt6++ZvGawXNep04dbY8RHR190/Utec9dunRJW4Dgctw/WxM4Phf8Y+3gjYiIiMgesMaJiIiIyEIMnIiIiIgsxMCJiIiIyEIMnIiIiIgsxMCJiIiIyEIMnIiIiIgsxAaY/62dhAZ9aN7H9vlERETOxWAwSExMjDbSzbqmaVYMnP7raozFPYmIiMh5nT17VjvV54aBk4hpmQjsMCw3QERERM4jOjpaEyg5LRtljoGT2erWCJoYOBERETknl//igdywOJyIiIjIQgyciIiIiCzEwImIiIjIQqxxyoe0tDRJSUnJz38hKlYeHh7i5ubGvU5EVEQYOFnY3+HSpUsSGRlZVM8DUaEJDg6WMmXKsCcZEVERYOBkAWPQVKpUKfH19eUBiWw2wI+Pj5crV67o6bJly1p7k4iIHA4DJwuG54xBU2hoaPE8K0QF5OPjo78RPOE1y2E7IqLCxeLwPBhrmpBpIrIHxtcq6/GIiAofAycLcQ07shd8rRIRFR0GTkREREQWYuBE+fbmm29K6dKlNbOxaNEih92Dbdq0kVGjRplOV65cWaZMmXJLt1kYt0FERNbDwMlBDRw4UAMb/Hh6ekr16tVlwoQJkpqaeku3e+jQIXnrrbdkxowZcvHiRbnvvvsKJRBr2LCh2Lrt27fL0KFDLbrurFmztC3ArdwGERHZHs6qc2CdO3eWmTNnSlJSkvz+++/yzDPPaIPEcePGFWh2IYKwEydO6Olu3brZRS1NcnKyBo6FoWTJkjZxG0REZD3MODkwLy8vbYRYqVIlGT58uLRv316WLFmilyGYeumll6R8+fLi5+cnLVq0kL/++uumjAmuX6dOHb2twYMHywMPPKCXu7q6Zgqcvv76a6ldu7Z4e3tLrVq1ZNq0aZm25dy5c9KvXz8pUaKE3l/Tpk1l69atej/IYO3du9eUIcN5OWXRHnroIb0+ApDAwEAZNmyYBkfmw2vPPvusDrGFhYVJp06d9Pz9+/drdszf31+HGR9//HG5du2a6f/FxcVJ//799XL0P/roo4/yHGZDm4qnnnpKbw+Pu27durJs2TLdj4MGDZKoqCjTY0JWLbvbOHPmjAahuF88nt69e8vly5dvysb98MMP+n+DgoKkb9++EhMTY+GrgIiIChMzTgWEA21O0DsHB1JLrosAxNh7J7frIti4Vbif69ev698ILg4ePCjz5s2TcuXKycKFCzVDtW/fPrntttv0Omim+N5772lQhB5WCCgQmCAowDCd0ezZs+WNN96Qzz77TBo1aiS7d++WJ598Urd5wIABEhsbK61bt9YgDYEYgrldu3ZJenq69OnTR4OaFStWyOrVq/X2EBzkZM2aNbpvEZycOnVKtwXb9u6775qu891332mguHHjRlOA07ZtW3niiSdk8uTJkpCQIGPGjNEgZe3atXqd0aNHy7p162Tx4sXa/+iVV17RbcxpCBHbjkAMAcyPP/4o1apV0/2J5/6OO+7Q4Aj75MiRI3p9BEbZ3YYxaMJ9YxgVWUHsE/MgFlk+1JIhKLtx44Zu96RJkzI9ZiIiKiYGMkRFRRmwK/A7q4SEBMPBgwf1tzlcP6efLl26ZLqur69vjtdt3bp1puuGhYVle738GjBggKFbt276d3p6umHVqlUGLy8vw0svvWQ4ffq0wc3NzXD+/PlM/6ddu3aGcePG6d8zZ87U+92zZ0+m6yxcuPCm7alWrZphzpw5mc57++23Da1atdK/Z8yYYQgICDBcv349220dP368oUGDBhY9phIlShji4uJM502fPt3g7+9vSEtL09PYn40aNbppWzp27JjpvLNnz+rjOHLkiCEmJsbg6elpmD9/vulybKuPj49h5MiRpvMqVapkmDx5sv79xx9/GFxdXfX/Zwf7Lygo6KbzzW9j5cqV+jycOXPGdPmBAwd0u7Zt22baN3j9REdHm64zevRoQ4sWLXLcTzm9ZomI7FFkZKQex6wVB2TFjJMDQ4YC2Qw0QkR245FHHtGhH2QzULNUo0aNTNfH8J15d3TUBtWvXz/X+0CGDBmRIUOGaJbJCNkTY+Zoz549monCMN2tatCgQaZmpK1atdKM1tmzZ3VIEpo0aZLp/2AY8M8//8w264NtRwYKw30YrjTCttasWTPH7cBjqlChwk37ML+F9uHh4fpjhGFRDJHismbNmul5GKILCAgwXQeZP+OyKkREjmzXrl3So0cPGTFihLz44otiCxg4FRAO1jnJusxFbgc5DNWZw/BTYbn33ntl+vTpGgBhOM7d3d207djGnTt33rSt5sEFhvbyKgA37oevvvoqU+ABxts2H4osDlmHNbGNqM3CsGNWCEKOHz+e7/sozseEgn5zeE4QCBMRObKZM2dq2QW+1KNkBCUmqLe1NgZOBZSfmqOiuq4lt4U2BFkh+4OMEwK6u++++5buA4XRCMr+/fdfefTRR7O9DrJWeNFHRERkm3VCYIftsQSyR8gQGQOXLVu2aLBnnrXJqnHjxvLLL79o5sYYPJpDfRKCExSrV6xYUc9DLdHRo0e1Niunx4SCd1wnu6yTJY8JxfTIlOHHuP2ok0JNFjJPRETO6oMPPpCXX35Z/+7atatOkLGFoAk4q84J4UCPIAezyH799Vc5efKkbNu2TSZOnCi//fZbvm8Ps9zwf6dOnaqBBArM8U3h448/1ssxmw4F4ZgRh4JtBFkIZDZv3qyXI6DBNmD4CzPd8O0iJxhSw7AgAgy0WBg/frx+C8mauTOHgmsEbdgO9FHC8Nwff/yhheUIbhB44TZRII5icRSrYwZfbreJgOqee+6Rnj17yqpVq3T7ly9frkXuxseETBeK2fGYUGifFWY51qtXT58LpKPxHOA5wW1j1iERkbPq1auXzoxG/0FM2smuL561MHByUghscJDGmDFqeRDUIKgwZlzyA7PVkFHCbSIQwIEfLQWqVKliyr6sXLlSZ6t16dJFr4NZYcahPAQfmNGHoUW0GZg7d26O99WuXTud9YegBbPPHnzwQdNU/5wgI4aADUFSx44d9f7RrgBvRGNwhG83yL5hSA8BzV133XVTrVRWCP5Qh4SADBkifDsyZpkwsw6tErCNeEzvv//+Tf8fQ274QAgJCdHHg/utWrWq/PTTTxbsdSIix3LmzBnT3zh+HDt2TF5//fVcv8RagwsqxMXJRUdHayEz+u6gl465xMREzSbgSTRvMUDFD1kgDGM58jIvhYGvWSKyJwaDQVvFjB07Vlvj3H///TYVB2RlW2EcEREROY3Y2FjN2mP0AzPAUfJg61gcTkRERMXu6NGj2mrgwIEDOnEHWSfUpNo6Bk5kN3JaioWIiOzL4sWLtc4WQ2RoC7NgwQK58847xR4wcCIiIqJis3v3bp2QBJiIM3/+fA2e7AUDJyIiIio2jRo10gXSMeEKM5qzNvm1dQycLMROzWQv+FolIlvMMoWHh2tvJpg2bZrNtRmwFAOnPKAHEZ7cCxcuaD8enM5rGRIia03pRYPQq1ev6msWr1UiImv77rvvtK8dhuXQJBg9/Ow1aAIGTnnAk4seThcvXtTgicjWYRFkNDK15w8mIrJ/ycnJ2mwYa6YCvsxhyazsFly3JwycLIAnGwei1NRUi9dUI7IGfJPDtF5mRYnIms6fP6/LpmA9UXweYXksW+wCXhAMnCyEJx4FbPZWxEZERFSc1q1bJ71799aF5LG01Y8//miVbuBFhYETERERFYrU1FStZ0LQVL9+fV1Ivlq1ag61d+0/Z0ZEREQ2wd3dXfsyDRkyRDZv3uxwQRMwcCIiIqICO3bsmPz000+m0/Xq1ZOvv/5aJ6o4Ig7VERERUYEsXbpUHnvsMZ0th0lUrVq1cvg9yYwTERER5UtaWprOknvwwQd1vbnmzZtL5cqVnWIvMuNEREREFouIiJBHH31Um1nCs88+Kx999JHTNN21qYzTpEmTdNo/GmYZJSYmyjPPPCOhoaHaNKtnz55y+fLlTP/vzJkzOtUR46mlSpWS0aNHa2U/ERERFZ49e/ZI06ZNNWjy8fGRH374QT799FOnCZpsKnDavn27zJgxQ6cvmnv++ed1DHXBggXaGwLdu3v06JEpXYigCR1KN23apK3dZ82aJW+88YYVHgUREZHjWrVqlZw8eVKqVq2qs+ZQ3+RsXAxY4MrKYmNjpXHjxrro3zvvvCMNGzaUKVOmSFRUlK4PN2fOHO1ACocPH5batWvrE9ayZUtZvny5dO3aVQOq0qVL63W++OILGTNmjK7ZZUkUjPHZoKAgvb/AwMAif7xERET2yGAwyPvvvy9Dhw6VkJAQcRT5iQNsIuOEoThkjdq3b5/p/J07d0pKSkqm82vVqqWV+wicAL8x9dEYNEGnTp10Jxw4cKAYHwUREZFjQVJi8ODBmuAAFxcXTUw4UtBkd8Xh8+bNk127dulQXVaXLl3SjBFatptDkITLjNcxD5qMlxsvy05SUpL+GCHIIiIiov/3999/69IpqCvGGnPozURWzjidPXtWRo4cKbNnzxZvb+9iu9+JEydqSs74Ex4eXmz3TUREZOvDcZ988om0bdtWg6a6devK2LFjrb1ZNsOqgROG4rCeDeqb0KYdPygAnzp1qv6NzBGKviMjIzP9PzyRZcqU0b/xO+ssO+Np43WyGjdunI5jGn8QwBERETm7uLg4LfjG7HZMvurXr59s2bJFqlevbu1NsxlWDZzatWsn+/bt0+mNxh9Mc0R/COPfHh4esmbNGtP/OXLkiLYfMHYnxW/cBgIw86p/FHfVqVMn2/v18vLSy81/iIiInBlmy+GYiglZSF5gkhZGhPz8/Ky9aTbFqjVOAQEBmgI0hycIPZuM52OhwBdeeEFKlCihAc6IESP0icWMOujYsaMGSI8//rhW+qOu6bXXXtOCcwRIRERElDccM5GEwGgPWgDdfffd3G22WByel8mTJ2tRGhpfoqAbM+bQtsDIzc1Nli1bJsOHD9eACoHXgAEDZMKECVbdbiIiInuoZ8JMOShXrpweT/EbP2TDfZysjX2ciIjIGZdOwWjNwIED5eGHHxZnFm1vfZyIiIio+Ozdu1friH///XctbUFROFmGgRMREZET+fHHH7W0BcXgVapUkZUrV7IAPB8YOBERETkBtPfBBCsMzyUkJEjnzp1lx44duswZOVBxOBEREd0aTK5CC6CNGzfq6ddff13Gjx+vE6wofxg4EREROUGrgRYtWsj+/fvlhx9+kAceeMDam2S3OKuOs+qIiMgBYdI8ir79/f31dGpqqpw7d04qV65s7U2zOZxVR0RE5MTi4+O1lgm9D1HbBOgGzqDp1nGojoiIyIGcOHFCevToIf/884/WMKGu6d5777X2ZjkMzqojIiJyEOjLhP5MCJpKlSqla70yaCpcDJyIiIjsXHp6urz11lvStWtXiYyM1PVcd+3aJa1bt7b2pjkcBk5ERER27oUXXpA333xTC8KffvppWbdunZQvX97am+WQGDgRERHZOSx0X7p0aZk1a5Z8/vnn4unpae1NclgsDiciIrJDhw8fllq1aunfNWvWlH///Vd8fX2tvVkOjxknIiIiO5KSkiIjR46UunXrytq1a03nM2gqHgyciIiI7MTFixelbdu2MnXqVElLS9O15qh4caiOiIjIDqAf08MPP6zBU2BgoHz//ffSrVs3a2+W02HGiYiIyIZhptxnn30mbdq00aCpTp06sn37dgZNVsLAiYiIyIatXr1aRowYoWvN9e7dW7Zu3So1atSw9mY5LQ7VERER2bD27dvLoEGDtBj8+eefFxcXF2tvklNj4ERERGRjVq1aJc2aNZPg4GANlL755hsGTDaCQ3VEREQ2tHTKhAkTpFOnTvL444/raWCWyXYw40RERGQDsMYcgqVly5bpaSyZgpYDrq7McdgSBk5ERERWtm/fPunRo4ccP35cvLy8ZPr06VrXRLaHgRMREZEVzZ07V5544gmJj4+XSpUqyS+//CJNmjThc2KjmP8jIiKyEgRLY8eO1d8dOnTQTuAMmmwbM05ERERWgvXlkGFatGiRvPXWW+Lm5sbnwsYxcCIiIipGmzZtkrNnz0qfPn30dNOmTfWH7AMDJyIiomJaOgVF36NGjdKZcjVr1pSGDRty39sZBk5ERERFLCEhQYYNG6YL80KvXr2kWrVq3O92iIETERFRETp58qS2GtizZ49mmt577z158cUX2dTSTjFwIiIiKiJ//PGH9OvXT27cuCElS5aUn376Se69917ubzvGwImIiKgIC8ERNDVv3lx+/vlnCQ8P5762cwyciIiIisj48eM10/Tkk09qR3Cyf2yASUREVEj279+vbQZQDK4HWVdXefbZZxk0ORBmnIiIiAoB6pcGDx6sXcArVqwoH3zwAferA2LGiYiI6BakpKTICy+8IH379tWgqX379jJmzBjuUwfFwImIiKiALl++rGvMTZ48WU9j3bkVK1ZIWFgY96mD4lAdERFRAezatUsefPBBOX/+vPj7+8t3332n/ZrIsTFwIiIiKoASJUpoEXitWrVk4cKF+pscHwMnIiIiC6WlpYmbm5v+XblyZVm5cqXUqFFDAgICuA+dBGuciIiILHDq1Clp0aKFLFu2zHRekyZNGDQ5GQZOREREeUBmCUHSzp07dQZdamoq95mTYuBERESUg/T0dHn33Xelc+fOEhERIc2aNZPVq1eLuzsrXZwVn3kiIqJsREVFyYABA2Tx4sV6GsumTJ06Vby9vbm/nBgDJyIiomyCJizMe/ToUfH09JTPP/9cnnjiCe4nYuBERESUVVBQkLRt21bbDfzyyy86REcELgaDweDsuyI6OlrfJPiGERgYaO3NISIiK0DBd1xcnB4PICkpSWJiYtgF3AlE5yMO4FAdERE5vStXrkifPn3E1dVV/vjjDy3+9vLy0h8ic5xVR0RETm3r1q3SuHFj+euvv2Tbtm1y4MABa28S2TAGTkRE5JRQqTJjxgy55557dL25mjVrauDUoEEDa28a2TAGTkRE5HRQ9D1kyBAZNmyYJCcn6+K8CJpq165t7U0jG8fAiYiInM7AgQNl5syZWtM0adIk+fnnnzk5iCzCwImIiJzOa6+9JpUqVdJC8DFjxoiLi4u1N4nsBGfVERGRU9Qz7dq1S9ebg3r16smxY8fEw8PD2ptGdoYZJyIicmjo0YMappYtW8qmTZtM5zNoooJg4ERERA7r4MGD2vV70aJFWs908uRJa28S2TkGTkRE5JAWLFhgWm+uQoUKsn79enn00UetvVlk5xg4ERGRwy2dMnr0aOndu7cuoXLvvffKzp07NYgiulUMnIiIyKHMnz9fPvzwQ/0bAdTKlSulVKlS1t4schCcVUdERA6lX79+snr1arnvvvvk4YcftvbmkINh4ERERHZv7ty58sADD4i/v7/2ZPr222+tvUnkoDhUR0REdisxMVGeeOIJeeSRR2Tw4MHar4moKDHjREREdunMmTPSs2dP2bFjh7YaaNy4sbU3iZwAAyciIrI7qGHq27evXL9+XUJDQ3WorkOHDtbeLHICHKojIiK7gaG49957Tzp16qRBE5ZQQasBBk1UXBg4ERGR3YiIiJApU6ZIenq61jRt2LBBF+slKi4cqiMiIruBYTl0BD9w4IAMHTpUZ9ARFScXA6cg6AKQQUFBEhUVJYGBgcX6BBARUe5++eUXHaLr1asXdxVZPQ5gxomIiGx26ZRXX31V3n//ffHz85P69etLjRo1rL1Z5OQYOBERkc25evWqdgBfs2aNnh42bJhUrVrV2ptFxMCJiIhsy/bt27U/09mzZzXThC7gWLCXyBZwVh0REdmMb775Ru666y4NmjAst3XrVgZNZFMYOBERkc04fPiwJCcnS7du3WTbtm1y++23W3uTiDJhjRMREdmMiRMnahH4o48+qsuoENkaviqJiMhq1q5dq9klZJnA3d1dHn/8cQZNZLMYOBERUbFDXya0GcBSKUuWLJHJkyfzWSC7wKE6IiIqVjExMTJo0CBtbAkDBw6U5557js8C2QUGTkREVKzF3z169JBDhw6Jh4eHTJ06VZ566ikunUJ2g4ETEREViz/++EMefvhhzTiVL19efv75Z2nZsiX3PtkVq9Y4TZ8+XWdPYF0Y/LRq1UqWL19uujwxMVGeeeYZXdTR399fG6Jdvnw5022cOXNG7r//fvH19ZVSpUrJ6NGjtU0/ERHZlipVqmhmqXXr1rJz504GTWSXrBo4VahQQSZNmqRvoB07dkjbtm11dgVWvYbnn39eli5dqithr1u3Ti5cuKApXqO0tDQNmjAbY9OmTfLdd9/JrFmz5I033rDioyIiIiPjbDlAQ8v169fLqlWrpHTp0txJZJdcDJjaYENKlCghH3zwga6CXbJkSZkzZ45pRWyMjdeuXVs2b96s31SQneratasGVMY34RdffCFjxozRdY48PT0LfVVkIiKyDL4QY6mUL7/8Utq3b8/dRjYrP3GAzbQjQPZo3rx5EhcXp0N2yEKlpKRkerPVqlVLKlasqIET4He9evUyfXPp1KmT7gBj1io7SUlJeh3zHyIiKjxYXw5Lp5w8eVJHAWzsOzpRgVk9cNq3b5/WL3l5eenq1wsXLpQ6derIpUuXNGMUHByc6foIknAZ4HfWdK/xtPE6OXWmRWRp/AkPDy+Sx0ZE5GzwxRSz5IYMGaJ/P/jggzo6gNomIkdg9cCpZs2asmfPHl3Icfjw4TJgwAA5ePBgkd7nuHHjNB1n/MFikkREdGvwWXrPPffo0BwCpXfeeUe/DOMLKpGjsHo7AmSVqlevrn83adJEtm/fLp988on06dNHiwojIyMzZZ0wq65MmTL6N35jEUhzxll3xutkB9kt/BARUeE4f/68foajvjQkJETmzp2rpRNEjsbqGaes0tPTNb2LNyCao61Zs8Z02ZEjR7T9AGqgAL8x1HflyhXTdTBbA4VdGO4jIqLiUa5cOenSpYs0bNhQa1QZNJGjsmrGCUNm9913nxZ8oyEaZtD99ddf2iQNqV2Mkb/wwgs60w7B0IgRIzRYMjZM69ixowZIWBASax6hrum1117T3k/MKBERFS18buPLLj6vMTSH3nzg4+PDXU8OK9+BE7JBqEc6ffq0xMfHa8uARo0aaWOz/EKmqH///nLx4kV946EZJoImLPoIWPTR1dVVG1/ifvENZtq0aab/7+bmJsuWLdPaKARUfn5+WiM1YcKEfG8LERFZDiMA3bt3l6pVq+oivfisZsBEzsDiPk4bN27U2iM0pESbAAQ6eJNERERoUIM3z9ChQ3VmXEBAgNgT9nEiIrIcCr7xJRUZJwzRoQFxpUqVuAvJbhV6HydMJ0WxduXKlWXlypX6Zrl+/bqcO3dOs07Hjh3TITLUI6EzLOqMiIjIsaDf3iuvvKIrOOA4gBl0qGdi0ETOxKKhOixr8ssvv2ixdnaQbcKPsZUAht6IiMhxXLt2TR555BHTF+NRo0ZpbWlOxwUiR2VzS65YA4fqiIhyh4V5//77b11Q/euvv5Z+/fpxl5HDsMslV4iIyHZ99NFHUrduXdmyZQuDJnJqFg3VoZmZpe3yUSxORET2DZN+sEjvnXfeqaebNm0qe/fu1dlzRM7MosBpypQppr9RFI42+mgNYGxEicV20Ubg9ddfL7otJSKiYoGJP7169dLlsDZs2KBBEzBoIipAjRN6Kt17773y7LPPZjr/s88+k9WrV8uiRYvsbr+yxomIKAOaEGMWNfrsYbmrBQsWSPv27bl7yKFFF2WNEzJLnTt3vul8nIfAiYiI7A++Q3/88ccaJCFoatCggbYaYNBEdIuBU2hoqCxevPim83EeLiMiIvsSGxurBd8vvvii9mrCMlZoaok2M0R0i0uuvPXWW/LEE09oOrdFixZ6HpZgWbFihXz11Vf5vTkiIrKyWbNmyU8//STu7u661BXW+7R0QhCRsylQHycESlOnTpVDhw7p6dq1a8tzzz1nCqTsDWuciMiZYaFeLJk1aNAg0yw6ImcSnY8aJzbAZOBERE4Gw3FYMB2jB1yYl0iKvgHmiRMndG06tN9HESEsX75cDhw4wP1PRGTD0FIGy2hhlGD48OHW3hwiu5PvwGndunVSr149Ha7D+nUoKgQ0Rhs/fnxRbCMRERWCXbt2aU8mzI5Gpqljx47cr0RFHTiNHTtWG2BioUdPT0/T+W3bttVW/EREZHu+++47rV86deqUVKtWTT+vMWpAREUcOO3bt0+6d+9+0/mlSpXS1bOJiMh2JCcny9NPPy0DBw6UxMREHabbvn271K9f39qbRuQcgRM6yV68ePGm83fv3i3ly5cvrO0iIqJCgDrU+fPna3sBtJNZsmSJrj9KRMXUx6lv374yZswYbcOPNyKmsW7cuFFeeukl6d+/fwE3g4iIikKFChW0RxMW7e3SpQt3MtEtync7AqR90RwNDdMwpRUN0/AbY+U4z83NTewN+zgRkaPARzoWZkcd04MPPmjtzSFy7j5OuOrZs2elZMmSWs+EeifMqmvUqJHcdtttYq8YOBGRI4iLi9PeTPPmzdMPfzQpLleunLU3i8ih4oB8DdUhcKpevbr2a0KgFB4efqvbSkREheDYsWPSo0cP2b9/v44EYPZz2bJluW+JrFkc7urqqgETGqgREZFtWLp0qTRr1kyDpjJlysiff/4pI0aM4HpzRLYwq27SpEkyevRofYMSEZH1YBTgjTfe0FomDDGgTxOaXN511118WohsZVYdZs7Fx8dLgwYNtAFm1nWOIiIiCnP7iIgoB5jZbBwBQIbpww8/zNSYmIhsIHDCbA0iIrJupglBE0yePFnuu+8+6dq1K58SIltsR+CIOKuOiOzF999/rw0tFy1apEXgRGTDs+qM0Ldp4cKFOtUV6tSpI926deObmIioiKCH3gsvvCCff/65ae25IUOGcH8TFbN8B05oRYBCxEuXLknNmjX1vPfee097O2FmR926dYtiO4mInNaFCxfk4Ycflk2bNunp8ePHy6BBg6y9WUROKd+BE5qr3X777bJjxw7Tekc3btzQBSSHDh1qemMTEdGt+/vvv6V3795y+fJlHUr48ccfWc9EZE+B0549ezIFTYC/3333Xe0jQkREhVfPNHjwYC2PqFevnvz666/ahJiI7KiPU40aNfSbT3YrcPMNTURUeBo3bixeXl66FujmzZv5GUtkjxmniRMnynPPPSdvvvmmtGzZUs/bsmWLTJgwQWudUJlulFdlOhER3bzenJ+fn/6NmtHdu3frig3G9gNEZGftCLDsiuk///dGNt6E+Wn8jfSyPWA7AiKyBcuWLdOhOQzJsfs3kYO0I8AaSEREVHjS09Plrbfe0sy9sdEwAyci25TvwKl169YWXe/pp5/W2XdhYWEF2S4iIqeAWcmPPfaY/P7773r62WeflY8++sjam0VEhVUcbilMmTWvdyIiosz27t0rTZs21aDJ29tbZ9F9+umnXG+OyIYVWb9+ruRCRJSzgwcPSqtWrSQhIUGqVKmidU0NGzbkLiOycVzoiIjICmrXrq2NLGNiYmT27NlSokQJPg9EdoCBExFRMbl48aL4+/tLQECAzjzGenOenp7i5ubG54DI2WuciIjo/61fv14bWqLdgLGUwcfHh0ETkZ1h4EREVIQQJKHgu23btro4+uHDhyUiIoL7nMhOFVnghOm17BxORM4sPj5eHn/8cV1tITU1Vfr27asrLYSGhlp704iouAKnypUra5O2M2fO5Hq96dOns4cTETmtEydO6Kw5FH6jhmny5MkyZ84c03IqROQkgdOoUaN02mzVqlWlQ4cOMm/ePElKSiqarSMistNO4A8++KD8888/UqpUKVmzZo1+dnK9OSInDZz27Nkj27Zt0+m0I0aMkLJly2q32127dhXNVhIR2RGs6fnll1/KPffco5+Llq64QEQOuMhvVikpKTJt2jQZM2aM/l2vXj0dzx80aJDdfLviIr9EVBhLp6ATeJs2bUznGRc8JyLHiQMKXByOIGn+/Pmajn7xxRd12YCvv/5aevbsKa+88oo8+uijBb1pIiK7giE5fAbef//9sm/fPtP5DJqIHE++G2Ai7Txz5kyZO3eupqP79++vRY+1atUyXad79+7SrFmzwt5WIiKbg+LvJ598UpdOweSZtLQ0a28SEdlS4ISACEXhmDX30EMPiYeHx03XwbpLmHZLROSokHV/6aWXZOrUqXq6U6dOGkSx1QCRY8t34PTvv/9KpUqVcr0OptsiK0VE5KhLp/Tu3Vs2bNigp1977TV588032QWcyAlYFDiZFzjmFTQRETk61HMiaEIR6Q8//KC1nkTkHCwqDr/99tu1X1NycnKu1zt27JgMHz5cJk2aVFjbR0Rkc8aNG6efddu3b2fQRORkLGpHgOZtaDeAYTrUN2H2SLly5cTb21un4B48eFC/fR04cED7OWFWHab12Qu2IyCivJZO+eijj+Tll18WLy8v7iwiB5OfOMCiobp27drJjh07NDj66aeftADy9OnTOoskLCxMGjVqpLPr0IIgJCSksB4HEZHV4Qtjjx49tEfT5cuX5bPPPrP2JhGRvRSH33XXXfpDROQMli9frl8IkVnH0im9evWy9iYRkZUVuAEmEZEjrzX39ttva0NLBE0tWrSQnTt3ZuoKTkTOKd/tCIiIHFlkZKSWHixdulRPDxs2TKZMmcLaJiJSDJyIiMxcu3ZN/v77bw2UvvjiCxk4cCD3DxGZMHAiIjJTvXp1XYcTHcCbNGnCfUNEmbDGiYjE2ZdOeeGFF2TVqlWm8zp27MigiYgKnnFCfwNL5dX/gIjIVly6dEn69OmjQ3PoAH7ixAl+hhHRrQdOwcHBpiVX8sKVwYnIHmzevFnbC1y4cEECAgJ0GRV+8SOiQgmc/vzzT9Pfp06dkrFjx2rBZKtWrUwfQN99951MnDjRkpsjIrIaLJYwffp0GTVqlA7T1alTR3799VepWbMmnxUiKpwlV7J2EX/iiSekX79+mc6fM2eOfPnll/LXX3+JveGSK0TOITU1VT+/8EUPHn74Yfn222/F39/f2ptGRHYSB+S7OBzZJaxVlxXO27ZtW35vjoio2Li5uWnZAX5/+OGHuoQUgyYiyo98B07h4eHy1Vdf3XQ+6gNwGRGRLXYCBwRN06ZN02LwF1980eLaTSKiAvdxmjx5svTs2VPXcMIyBIBM07Fjx+SXX37J780RERVpwPS///1Ptm7dKosXLxZXV1fx8fGRO+64g3udiIon49SlSxc5evSoPPDAAxIREaE/+Bvn4TIiIluAWoWHHnpIXn/9dVm2bJn89ttv1t4kInLG4nBHxOJwIseyf/9+6dGjh2bCsXQKZtENGjTI2ptFRM5YHA7r16+Xxx57TNPd58+f1/PQPG7Dhg0F22IiokKCgm+UESBoqlixomzcuJFBExEVmnwHTqhj6tSpk9YJ7Nq1S5KSkvR8RGmoJSAispZJkyZJ3759JT4+Xtq3by87d+7k0ilEZN3A6Z133tEVwzGzzsPDw3T+nXfeqYEUEZG1YI05b29vGTdunKxYsULCwsL4ZBCRdWfVHTlyRO65556bzsfYYGRkZGFtFxGRRTBBpUSJEvp348aNdYiuQoUK3HtEZBsZpzJlysjx48dvOh/1TVWrVi2s7SIismjplMqVK8uOHTtM5zNoIiKbCpyefPJJGTlypPZFQfM4LJA5e/Zseemll2T48OFFs5VERGYSEhK04Pvpp5+WmJgY+f7777l/iMg2h+qwwC+aymHNOhRgYtgO030ROI0YMaJotpKI6D8nT57UJry7d+/WhpYoCMfnDxGRTfdxSk5O1iG72NhYXV3cntd7Yh8nIvuwcuVKXWAcdU0o/EbrgbZt21p7s4jIzhVpH6fBgwdratzT01MDpubNm2vQFBcXp5cRERUF1FF27txZg6ZmzZppqwEGTURk8xknrCp+8eJFKVWqVKbzr127poXjqampYm+YcSKyfSgR6Natm5QtW1amTp2qbQeIiGw244QbxQ0izkLGCaeNPzdu3JDff//9pmAqLxMnTtRvjgEBAfp/sa4U2h2YS0xMlGeeeUZCQ0M1s4XahsuXL2e6zpkzZ+T+++8XX19fvZ3Ro0fbZQBHRJkdPnxYaykB9UxowPvll18yaCIiq7E4cAoODtZeKZhJV6NGDQkJCTH9oNYAw3QIcPJj3bp1+n+2bNkiq1atkpSUFG1gh2E/o+eff16WLl0qCxYs0OtjFh/WoDJKS0vToAk1V5s2bZLvvvtOZs2aJW+88Ua+toWIbMv8+fOladOmMmzYMP3CBigRICKyi6E6BC24KmoK8K3P2HDO+GFWqVIlKVeu3C1tzNWrVzVjhPvCbD1kuEqWLClz5syRXr16mb6B1q5dWzZv3iwtW7aU5cuXS9euXTWgKl26tF4Hnc3HjBmjt2fJBy2H6ohsB7LFmL370Ucf6WnM4F2yZIlmlImIikJ+4gCL2xG0bt3aNBUYC2ci81TYsMFgDMpQ/IksFNacMqpVq5bevzFwwu969eqZgibAWnroKXXgwAFp1KjRTfeD9fWMa+wZdxgRWd+VK1ekT58+8tdff+lpfAHCMk/u7vnunEJEVCTyPatu7dq18vPPP990PobSMEx2K4Wfo0aN0jXv6tatq+ddunRJM0YYJjSHIAmXGa9jHjQZLzdellNtFSJL4094eHiBt5uICgea6mLJFARNqGfE5wx6NDFoIiK7DpwQdGS3cCaG2P73v/8VeENQ67R//36ZN2+eFDUsAIrslvHn7NmzRX6f5DhuxCXLyWtx+psKBzLAmPhx/vx5qVmzpmzbtk1PExHZmnznvzGDrUqVKjedjxonXFYQzz77rCxbtkz+/vvvTOtMob0Bir6xeLB51gmz6nCZ8Tr4kDVnnHVnvE5W6HSOH6L8SExJk2X/XJAdp25IfHKq+Hq6S9PKIdK1fjnx9nDjzrwFeD8iYz1jxgz5+uuv86wxICKym4wTMkv//PPPTefv3btXWwbkB4rNETQtXLhQhwCzBmRNmjQRDw8PWbNmjek8tCtAgNaqVSs9jd/79u3T2ggjzNDDBy8adBIVFgRNqw5eFlcXFykX7KO/cRrnU/6dOnUq03sbReCYScegiYgcKuOE5Q6ee+457b2EmW+AWXBY+Ldv3775Hp7DjLnFixfr7RlrklB35OPjo7+HDBkiL7zwghaM4wMV6+EhWEJhOKB9AQKkxx9/XN5//329jddee01vm1klKiwYlkOmKdTPS0oGZGQrSwZkZJl2nroh7WqVlhA/TpW3FL7c4PMCM+i2b9+uLU6IiBwycHr77bf1myK+HRqLNlHY3b9//3zXOE2fPl1/t2nTJtP5M2fOlIEDB+rfkydP1sZ3qHdAHQRmzE2bNi1TJ3MM82EWHQIqPz8/GTBggEyYMCG/D40oR5EJKTo8h0yTuUAfd7kQmaCXM3CyLMv83nvvyauvvqqfG+jTxA7gROQUi/wePXpUh+eQGUI7ANQ42Sv2cSJLMk7vrTisw3PGjBNcjUnSYODlzrUYOFnwPsMXIgzNwxNPPCGffvopAycicsw+Tlkhtc70OjkLZJNQCI6aJmOmKTohVa7HJUmHOhymy8vBgwe14z9qFNFi5LPPPpMnn3yyGJ45IqLCZVHghBojDNFhGAx/5+bjjz8urG0jsimYPWesacLwnJ+nuwZNxvMpZ998840GTZg1i5UHmjdvzt1FRI4bOO3evVs7eBv/zklRdBMnshVoOdCrSbgWgqOmKdjHg8Nz+ej/ZuwEnt/FwImIHKLGyZGwxomocKE9CNaae/fdd9n5m4hsXrHUOBERZcfY9fvcuXM6I9aYbSIicgQWBU4o6rTUr7/+eivbQ0R27KuvvtKmtuj4j8kj6K9GROR0ncPNF8RFCgvdfnfs2GG6fOfOnXoeLici55OYmKjtBYYOHapBU/fu3bWxJbv3E5FTZpzQkNIIxZ29e/eWL774QptPQlpamjz99NNcKoHICWEJJAzN4csUhuZQ14TPCU4WISJHlO/i8JIlS8qGDRt0BXNzmGp8xx13yPXr18XesDic6NZ6NKG9ADqAz507Vzp06MDdSUR2pUiLw7G21OHDh28KnHAellAgIueC4Tj0ZqpVq5ZdryBARGSJfAdOgwYN0oV3T5w4YWpit3XrVpk0aZJeRkSO/80MtUxYSPvuu+/W87CGJBGRM8h34PThhx9KmTJltEfLxYsX9byyZcvK6NGj5cUXXyyKbSQiG3Ho0CGdZYsM8+bNm+XYsWO6hAoRkbO4pQaY+OYJeY0H2jrWOBHlDcNxWKQ3NjZWypcvr6dbtGjBXUdEdi8/cYBF7Qiyq3NavXq1FoIaZ85cuHBBP1CJyLHg/Y5Zcr169dL3eJs2bWTXrl0MmojIKeV7qO706dPSuXNnnYKclJSkM2gCAgLkvffe09NoU0BEjiEuLk66deumfdrgpZde0k7g7u5cdICInFO+M04jR46Upk2byo0bN8THx8d0PhreGT9cicgx+Pr6SmhoqPj5+clPP/0kH3zwAYMmInJq+f7auH79etm0adNNBaGVK1eW8+fPF+a2EZGVpKSkiIeHhw7Ff/PNN3L27FmpXbs2nw8icnr5zjihVxM6hWeFBT0xZEdE9gvD7Wg10K9fPzHOG/H392fQRERU0MCpY8eOMmXKFNNpfCNFwej48eOlS5cuYs9uxCVbexOIrAZZJfRlwkK9WKwb/dmIiOgWAyf0cdq4caN2C8bCno888ohpmA4F4vZsyuqj8vPOs5KYcnNGjciRrV27Vho3bqwL85YoUUJWrFghLVu2tPZmERHZf41TeHi47N27VwtF8RvZJnQSf/TRRzMVi9sjVxcXWXXwsv7dq0m4tTeHqMhhOA5fhsaOHavD8I0aNdJsE74MERHRLQZOKBjFelTLli3TQAk/jiTM30u80j1k56kb0q5WaQnxY0dkcmwjRoyQzz//XP9Gc8tp06bZ/RcgIiKbGarDLBsMzzmyQB93iUtOlciEFGtvClGRw1A7Wg1Mnz5dvv32WwZNRESFXeOEhT1Ry4Ruwo4oOiFV/DzdJdjHw9qbQlQk0OXf6I477pBTp07JsGHDTKsAEBFRIdY4oXgUjS5Xrlwp9erV02+r5lAfYa+uxSZJrCFdOtThMB05HrQRee211+TTTz/VBXrx/oWwsDBrbxoRkeMGTsHBwdKzZ09xRIkpqdKhfgXpWr+ctTeFqFBdu3ZNezNhjUnArDlj4EREREUYOM2cOVMcVUa7PyLHsmPHDv2yg/UlkSFGJ/A+ffpYe7OIiBy7xglTlVHbdOedd0qzZs10+nJCQoI4Eh8Pd21HsOyf/68BIbJnKPi+6667NGi67bbbZMuWLQyaiIiKI3B699135ZVXXtHlF8qXLy+ffPKJFoo7WjuCUD8vbUfALuJk7xYuXKg91rCMyoMPPqj1iXXr1rX2ZhEROUfg9P3332uPlz/++EMWLVokS5culdmzZ2smypGwHQE5igceeECXSHrnnXc0iAoKCrL2JhEROU+NE1L95mvRtW/fXqcvY2pzhQoVxFGwHQHZMwzFofu3l5eXuLu7y++//y5ubm7W3iwiIufLOKFvk7e3900NMdFN3FGgHcH1uCRpUjmEXcPJLpdOQQ3iqFGjTOczaCIislLGCR/MWJIB32SN0EUcjfPMeznZcx8nPEb0cGI7ArInMTExWsu0YMECPR0fH689mxg0ERFZMXAaMGDATec99thj4khGtq8hlcqyGSDZjyNHjkj37t3l0KFDmgGeMmWKDB8+nF3AiYisHTg5cv8mIy7qS/YEkzT69++vGaeyZcvKzz//rEuoEBGRDa1VR0TWFxkZKYMHD9ag6e6775Zdu3YxaCIissXO4URkfVj66IcfftAlVN5//30dpiMioqLnYkBFtJOLjo7WHjdRUVESGBho7c0hytbOnTv1Ndq2bVvuISIiK8UBHKojsgOoMUSrgYcfflhOnjxp7c0hInJaDJyIbBiWS0HLD9Qz4W8Uf4eEhFh7s4iInBYDJyIbde7cOWndurXMmDFD2wtMmDBBFi9erPVNRERkHSwOJ7JBf/31l/Tu3VuuXr2qgdKcOXPkvvvus/ZmERE5PQZORDYIgRKCpgYNGmg3/qpVq1p7k4iIiBknIts0depUKVeunLz88svi6+tr7c0hIqL/sMaJyAYcPXpUnnvuOV1jDrCg9ptvvsmgiYjIxnCojsjKUPCNpVPQRwRZprFjx1p7k4iIKAfMOBEVohtxyXLyWpz+zguyS6+99po89NBDGjRh6ZSBAwfy+SAismHMONkpHJgjE1Ik2MeDixPbgMSUNFn2zwXZceqGxCeniq+nuzStHCJd65cTbw+3m65//fp1efTRR+WPP/7Q0yNHjpQPPviAS6cQEdk4Bk52FgDl9wBNxQPPyaqDlyXUz0vKBftIdEKqnoZeTcIzXXfPnj3SvXt3OXXqlPj4+MjXX38tjzzyCJ8qIiI7wMDJSgoaAOXnAE3FF/ziecRzUjLAS88rGZDxHO48dUPa1SqdKSjG8pCXLl2SatWqaauB+vXr86kiIrITrHGyEmMA5OriogEQfuM0zrf0AO3l7qa/cRoHaEvqapyxlqioIWOI4DfQJ/P3EJyOS07Vy83X0m7UqJEsWbJEtm/fzqCJiMjOMHCygoIGQJYcoJ0hU/fzzrPy3orDMnnVEf2N0zjfWjDMiowhsn/mcNrP013ib1yRtm3byrZt20yXdejQgWvOERHZIQZOVlDQACivAzQud3QFydQVNQzDYZj1elySXI1JkqTUNP2N097Xj0jbu1rqEipPPvmkpKenW207iYjo1jFwsoKCBkC5HaCbVA5x+Nl1tjxUidq0DnVK65DchcgEDZCSdi+Vt5/uJ1euXNEhOdQzubryLUdEZM/4KW4FtxIAZT1A4zdO43xHZ8tDlSjoR3H+y51rydBW5eXovHflu8kTtFcT2g5s3rxZi8GJiMi+cVadlRgDHWRKEAD5ebpbFAAZD9CYqeVsfZzMM3XGWWu2NlSZGh8lfbu2kwMHDoi7u7t8/PHH8uyzz4qLi4u1N42IiAoBAycrudUACNd1loApa6bO2H4BmSYETcjUIei0hf0RGhoqVatW1QaXCxYskLvuusvam0RERIWIgZMZ1MgEBkqxcsYAyBqZuqKE4biUlBRdmBc1TD/88IPExcXpunNERORYXAzmDWacFNYJCwoKklHfb5Q764SzC7cdsJUlZyIiIrSGKSwsTL7//nsOyRER2eFnvjEOiIqKksA8MijMOJkxTm0HZ+/CbSuBiS1n6rB0So8ePeTkyZO6dAoW7K1Zs6ZVt4mIyJEk2uAyYwyczIT5e4lXuke2y2Q4C1t8kdoiZJeeeuopSUxM1JomtBpg0EREVLhscZkxtiMwk5iaZhNT263J1hpM2tLSKpCcnCzPPPOMDBgwQIOmLl26yI4dO6RBgwbW3jQiIodyw0Z79zHjZGbzv9elRHCqhIf42sTUdltfrNYZM1+9e/eWxYsX69/jx4+XN954g00tiYiKsHcfvsSbQ4IDk4NwuTVGhphxMpOckib/Xo0VN9eMGhpnY0sNJm0t82X03HPPacuBpUuXyptvvsmgiYioiNjqMmMMnMx4erhJ1TB/SUs32MzQkDO+SG0pPYtJpydOnDCdxmK9KAbv2rVrsW0DEZEzCrHRZcYYOJlpWTVUGlYMlqTUdKescbKVF6mtZL7QiwmtBho1aiRHjhwxnR8QEFAs909E5Oy62uAyY6xxMuPj7mb1FKC12UKDSVtYWuX48ePSvXt32b9/vy6dsn37ds6aIyIqZra4zBgDJzPXYpMk1pBuM8t3OOuL1NpLqyxbtkwee+wxbYRWunRpXTrl7rvvLtL7JCIi2+7dZ8ShOjO2kAK0FXiBVgnzs9oL1RrpWSydgllyDzzwgAZNrVq1kl27djFoIiIiEy65YtZqff3+U3LX7ZX48nDSDuYzZsyQYcOG6d/o1fTxxx+Lp6dtfMMhIqKik58lVxg4me2wu99eJnfUriBj76stwb48YDobLNSL2XIoCO/fv7+1N4eIiIoJ16orIFcXkbWHr+jfk3qyE7QzWL58ubRv3148PDz0Z8WKFVyol4iIcsQaJzMl/Lwk0NtDtp+8ISevxua818juYemUESNG6JIpY8eONZ3v4uJi1e0iIiLbxsApC39vd12z7lxkgnWeESpyFy5ckHvvvVc+++wzPe3n56fF50RERHlhO4IsYhNTxdvdTSpkWRuHHMP69et1vblLly5pXdsPP/ygs+iIiIgswYyTmYi4JIlOTJFmVUKkSkl/i3Yg2QdklKZOnapLpiBoqlu3rja1ZNBERET5wYyTGYzWtK1dSmfVOduU/9PX41DgI5VK+NpMk7HCdP78eXn11VclNTVV+vbtK19//bUO0REREeUHAycznz7SSOpXdZ7ml4kpabJw93lZtPucXIxK1PPKBvrIQ43LSfdGFbSLuKOoUKGCzJw5U86dOycjR45kETgREdnfUN3ff/+tQyXlypXTA9miRYtuGl5BJ+eyZcuKj4+PThs/duxYputERERo3x00rAoODpYhQ4ZIbGzBZsRVDnOu4bll/1yQ2VtOy/nIRF0Dzs/LXc5HJcjsrWf0Mnv3+++/62vMqFevXjJq1CgGTUREZJ+BE1afb9CggXz++efZXv7+++9rXcoXX3whW7du1aGVTp06SWJiRnYEEDQdOHBAVq1apWuM4UA5dOjQYnwU9js8t/H4NUlISdOu3IH48faQEF8PSUhOk03Hr+l17FF6erq89dZb2szy4Ycf1ll0RERk227EJcvJa3E2f+yx6lDdfffdpz/ZQbZpypQp8tprr0m3bt30vO+//14XXUVmCnUqhw4d0oaFKPJt2rSpXufTTz/V3jwffvihZrIoe1jGJCohRf/28vj/+NnT3VUDJ1yOH3urd7px44Y8/vjj8ttvv5myTGFhYdbeLCIiyqVsBKMcO07dkPjkVPH1dNeF3rE2qS2WjNjsrLqTJ0/q7CcMzxlh+niLFi1k8+bNehq/MTxnDJoA13d1ddUMFeUMWaYgHw/9Oykl3XR+cmq6GP67HD/25J9//tHXAoImb29vmTVrlmYzud4cEZHtWvbPBVl18LK4urhIuWAf/Y3TtloyYrPF4QiaABkmczhtvAy/S5Uqlelyd3d3KVGihOk62UlKStIf8zVqnA0ySXdWD5Njl2M1s5SWbhBxEYlJTJVgXw+5o3qYXWWbZs+eLU8++aQkJCRI5cqV5ddff5VGjRqJoyjOxY6JiIrzs23HqRsS6uclJQO89LySARlZpp2nbki7WqVt7jPPZgOnojRx4kStgcnuCcxjUWSHgjRoSpoh06y68kEZs+pwmb3AsC6yTAiaUAOHICo0NFQcgb2lsImI8gNfCPHZhkyTuUAfd7kQmWCTJSM2GziVKVNGf1++fFln1RnhdMOGDU3XuXIlY1FeI/TpwUw74//Pzrhx4+SFF17IlHEKDw+XKauPyp11EmzmoFTUWQY8xn7NK0rn28vYdR8nzMj86quvdBj32WefFTc36z93hZ3CxrcxfLBEJ6TqaejVJNzam0dEdEtwfMMXQny2GTNNgNN+nu42WTJiszVOVapU0eBnzZo1mQIc1C61atVKT+N3ZGSk7Ny503SdtWvX6qwqHERz4uXlpe0LzH/AVsZVkWX4eedZeW/FYZm86oj+xmmcXxQQKDWsGCINw4PtJmjauHGjDB8+3LTGHGZcoj+TIwVNWVPYXu5u+hunkcK29ZknRER5wTEHWfTrcUlyNSZJklLT9DdON6kcYpPHJKsGTui3tGfPHv0xFoTj7zNnzmgWAT133nnnHVmyZIns27dP+vfvrzPlHnroIb1+7dq1pXPnzlrbsm3bNj2YIuOAGXcFmVEX5m8bByV7K5QrTgiUMHOyTZs22qbiyy+/FEdPYSNlbQ6n45JT9XIiInvXtX456VCntH6+Y3gOv3HaVktGrDpUt2PHDl2l3sg4fDZgwACdEfXyyy9rryf0ZUJm6a677tL2A5gxZYR6FgRL7dq109l0PXv21N5PBWXtcVV7LJQrLuevRsqzzwyXRQvm6ek+ffpoHy9HZY8pbCKigpSNoPQAxzd7mARj1cAJWQPjUEt2kHWaMGGC/uQEM+jmzJlTaNtk7YOSPRbKFTUMUX79+yaZMPIJuXr6qLi4ukn/ka/K9Emvi4+nzZbpFVoK21jThNcAXp9IYePbmLO9DojIsYX4edrF55rN1jhZw7VY64+rmmcZbiWgs5cOrJZ498t58uIj92vQ5B8cKv3GfylejR6Q3/ZdFEdnbylsW+RI7wUisj7H/bpeALZwULrVLIOjTV/Hwe5UVLqkJidIpdoNZcBrn0hwyTJaPOgMQ5f2lsK2JY72XiAi28DAyczI9jWkUtkwq7cVMAZuCAyQZfDzdLc4oLPW9HU8xv3nIyUmKVVqlwmUKiVvbcFkzIxEzZoOTVa5XYa8+43cdntjcff0dMqhS3tJYdsStnIgoqLAwMlMYR+YCvqNt6BZBmsUluMxzt9xVmZtPCmXo5Mk3WAQfy93aV2zpLx2fx0J9s3//WEG5WOPPaZrE1asXlv3m3/1RqagyRZq0ci2cZIFERUV1jjZcFsBBDlVwvwsDnasMX0dj2XGuhOa/fFwcxEfTzeJT06TP/ZfkknLD+X79ubOnSstW7bUdecwy9Iee3yQ9bGVAxEVFQZODtS8sLAKyy2Fx/Dn4St6+5jd5uflLj4ebuLnlZHl2nIiQk5ejbXotlJSUrRv1yOPPCLx8fHSoUMHmT9/vl7GAmmy9fcCETkPDtU5UFuB4p6+jsdwPS5Z0g3p4un+/wciN1cX/UlISZVzkQl51jthQebevXvL+vXr9fQrr7yiLSiMXcBvZeiSBdXOia0ciKioMHBysOaFt1JYnl94DKF+nuLq4irJqeni7pnxONPSDfrj4+EuFbIEjln9+++/2tj04sWLEhAQoHVNxs7wBS2Q5mwqKu73AhE5DwZODvaNtzinr+N2761VSvacjZTrsUnazsHV1UWSUtLF1UWkZbUSeWabKlWqJHXr1pWQkBD59ddfpWbNmre8XZxNRcBWDkRUFBg4Oeg33uKavo7HgmJw46w6Q2q61jphVt3Y+2pn+38SEhK01QAWW8Zw3Lx588TDw0MzTreKs6koK7ZyIKLCxMApy0E3MLDwdq4zfOPFY+zfqrI8UL+cRX2csJBzjx49pEWLFrpIr3HZnMLCJWuIiKgocVadmSmrj8rPO89qjUxhym9bAXuEx3Z3jVLSpV65HIMmLNDcpEkT2bNnjw7LoSi8sHE2FRERFSUGTmZSUtPz1WeJLO8C/vbbb0uXLl3kxo0b0rx5c9m1a5eUKVOm0Hch+z4REVFRYuBk5ujVWImKT5Gt/163uwVBbXUh08jISJ0l98Ybb2jx+FNPPSV///23VKhQocjuk32fHJutvNZtZTuIqHixxsmMId0gl6MTJTE1zW7WQLPlqfcIlDp16iTbtm3TQvBp06bJ4MGDi/x+naG2zBnZymvdVraDiKyDGSczmFKPpUmQdRKDQZxhWZei/Ibt4uIi48ePl8qVK8uGDRuKJWhyttoyZ1Kcr3V72A4isg5mnMykpKVLQmJKxrdGFxexdcU19T4/37CxdMrRo0fl9ttv19Ooazp8+LBmnIjsvc2ErWwHEVkPM05m4lPSxd3VVZs32kPGqbgWMrX0G/bly5elffv2cvfdd2vbASMGTUXP0ettbGXRXlvZDiKyHmacMjFIYmq6pKYZ7CLjVBzLulj6DXvLli3Ss2dPuXDhgjayPH78uFSpUuWW759y5yz1NtZawshWt4OIrIcZJ/Od4eIiCJe0j5OVMk75yRzkNPX+YlSCVAr1LZZv2Dfik2X69Olyzz33aNBUq1YtLQbv0KFDodw/5c5Z6m1spc2ErWwHEVkPM05mfD3cJM3dVdJFJCox1S4yB+bLupyNiJdrsUkiBhfZdz5KTl0/fMvZh9y+YXsaUuW155+WubN/0POQcZo5c2ahLJ1CeXO2ehtbWbTXVraDiKyDgZOZuJR08fdCAOVe7Bmngi5Maz71ft72M7LtZISUDfIxLSpsyW0UdLHi6E0LZN7sH3TduTcmvCNvvDJWZ9JR8XC25WVspc2ErWwHEVkHAyczPu4ukpiSLiX8XKRSqJ/dZQ5OX4/XoKmwsw/ZfcNuU7OkJFd+UrZt2yr1Oz8iCbXukF92nXO42hpbZiv1Nnj9FmcAYSuL9trKdhBR8WLgZAYr1Pl4ukuov6fVMwcJKWmSbjBIZHzGQSmvD+iizD4Yv2HfW6Ok/DBnrjzWr6/8deya/HUqWvq8+nmhZbeo8LKBGDoq6oO6sxSmExGZY+BkJjE5TYICXCXYx7NYhznMMwchvi5y9HKsXIhKkNjEVHFzdZH1R69K2SDvXA9GRZ19iIqKkiEDBsjixYvl3L/HxL15X6eprbFl1qy3KejwMhGRPWPglMXV2CQ5djmmWKcVm2cO/r0aq8u+IGBCuVCpQC/ZeOKa+HplZH0suY3Czj4cOHBAunfvLseOHRNPT08JK1NeLjhRbY0ts1a9jbMVphMRGbEdgZn0dJG0dJFrqNmIL95GgsgQ3FEtVK7EJOkQHQ6It5X2lyaVQvTghINRXi0KimJx2/nz50uLFi00aAoPD9elU54a+qQpu2WOvWysp7iXl2EjSCJyVsw4mUn/L5JMSkmTQ5eipUpJ/2J7IhAo3VOjlGw+cV1CfD0l0MfDNDRnaSbnVrMP5kW+AV6uMmbMGPn444/1snbt2sncuXOlZMmSetqatTXWVtzF0LbIVgrTiYiKGwOnbKQggrJC/0scbHw83SQ2CQ0nPQp8MMrvbJ/sinzLynWZNm2aXo4A6p133hF3d/dsa2tOXYsTN1dXubNamEP3simOYmh7CcqsXZhORGQtDJxy4F/M35hxUF5z+LJcuJEopyPixM/LXcJL+EjpQG+JjE8p0oNRdkW+B+MCZOhr78s9tcppY8usECggYIhLStXeUWnp6XLwYrTelqPOqirKYmh7nKHGRpBE5IwYOOUgtpgX6zQelCuF+Woh+JmIeDl8MUYSktPk4abhRZbJMRb5lvD1lGPrFkr5arWlUu0GepmhVmtp27lWrtu86cR1DSQcvSVBURdD2+MMNTaCJCJnxMApB7HJ6OpknYMymljeVjpA65o83Vz0oJxX1qGgQzz4P9GxsbJt9oeyc9VCCQorLS99sUQCfQNyratytllVRdkny973JRtBEpEzYeCUDSwa4ufpZtWDso+Hm1QI8cnzoHyrQzzRV87LrxOekMv/HhIXV1e5q9tj4hsQJNdik3Otq3K25T6Kshja2falI7KX2jQiunUMnLLh7eEi5bMcxGz1oGwc4vH3dNfbSElNt3iIZ9WqVdK3b1+JiIgQL/8g6f7CB9Kg5V0aNOVV5Jt1m9HpHEFcXKJjzqoqymLorPsy0bgvkxxzXzoSe6xNI6Jbw8ApG74ebsW6Vl1BD8r4lrvl3+sSFZ8i524kSEpauni4uUqAl7ts/fd6jkM86O80adIkee211yQ9PV0aN2kqT7z1qZxJ9LW4+7Rxm1fsv6RNO/FtG/VYqenpcvdtYTo70NEUVTF0pn15LVYnAyQkp0pqusjdt4U65L50FPZYm0ZEt4aBUxZuGKdzcdEGmMWZci/IQRnByvErsRo4+Xu7S4C3u8QlpcnZGwkSl5ya4xAPAqetW7dq0DRkyBD57LPPxNvbO8/hhqyXY9t2nIrQInY3V9Fv20G+3pqxwgHF1g8c+R1eKcpiaOO+PHQxWtxdXTP2pY+H3exLZ2TvtWlEVDAMnLJIN4gOd52LTCj2Bpj5PigbDHIjPhm/xN3VRW7EpUhMYkbmJzohWf7Yf1EG3lnFNGRgHih89913smzZMnn00UfzLPLNaTgCfZtQEdayagltn4D7wc/VmCSbPnDg8czffla2nrwuaekGCfb1zNfwSlEUQ+M5031ZJVT8vDP2pY8d7Etnxto0IufEwCkL9L2MS07T4S5rsPSgjIP/igOXJCYxVRcnvhaTpNvu5e4qrq6iQ3Ybjl+TsAAvDQhe/fhLWbNmjdw9cJz4eXlooNCzd99bGo7AfSKQKuHnpVksY22Op7urRMQl2WRRM7bvzSX7Zf2x6+LuKuKDGqKEFLkWm6SXWyuzY34Q9nL//+CNBeK2i93TiZwTA6dspBlEopMyr8NmS5A5+mn7GfnryBXN/ri74MCbqmvcIfOEruMY5ikd4C0bjlyWzye9KX/9/K3+39ub3ikBTdtbXIeR23AEhpXO3YiX/eejNNhMSkkXLw8MM7lJqQAv8fZwtbkhN+y39ceuaTYnyNdDtxlZHbBmZocHYfvD7umFizMTyV4wcMrBicsxcs9tGeuy2coHh3HIbN3RK7Lx+HVJSTWIi4tBi4hdXTJaGKA+C9mmMkHecu7SRflj6isS9e8e/f/1ujwm9e7qIF4enhYHCrkNRxy8iIApVSLikiU5FYXpLhKflK5/Y+hu4/FrRZrBye+MJuxfdDlHDRGCJvx298oI7lAndsMnY/9bI3DiQdg+sXv6rePMRLI3DJxyEPHf0E1hfSPK7f9a+sFhHDK7FJWomRIEKmnpGQXtySjOSk0XT3c3baVw9d8DsnLqGEmOuipunj5Sr99YKVHvHjlxLUHqlPXM1CMIctq27DIh2N5T12I1YAr2dpek1HRJTUMAl5Hx8vd2leph/jkGZpbsR0uuk98ZTbg91DRhlhqCO3fPjKAJWTIMO2K9PWtO/edB2P6we/qt48xEsjcMnHIQjzSOBYENinrND/BZD/jm/xcz9dxcXaRFlVDp3SzcFBRZ8sFhHDJDv6bE1LT/giaDpKQZTEN0OI2AKGrfGvl92ltiSEuRwNIVpcHgd8SvVEUNbLAgL5ZXwXVRS/P30Suy52ykRCWk6PDendUzFurFthkfS52ygbLxxDVJM6TL5ehEORuRoNfHvohJSBV3Nxfx93LTIc7UtHQtsPf2dLtpZp8lAaKlQWRBZjThOUEheHB8iml4DvVYyDYhAG1RpUSuAXFRDyXwIGy/2D29YDgzkewRA6ccJCb/f41TdoENeu5g+jhmQuEAjwNwRvAikpSaZjrgI7BZc+iyxCalmvrz7DkbpcNcbz5YVwMvSwIA45AZbhez6NxcXCQuJU0LwdFBAcNz6KGEv9N8SojBkCYVG7eRgWPfkx0XknTh4IysUEZND26nfJC3HLgQpdkrFxedpCfHrsSa7scYvOCxhfh6yImrcXI2Il6H4aqF+Wf831Rsg/ZwEB8PV0kwGPT2YhNTJMDbI1MGx3w/lvDz1Kn2v/1zMVOAaOm3z4LMaDIOhxkLwRH8YfahsfcUgtlsXwt5BHOFHVDxIEzOwplnJrKmy34xcMrBxRuJuX4jQqNCFEdj+jje9HvOROp5VUv6S8PwYD3gIyjAh4KLi4sOryHYQRCSlpwmfx25qoXK99QoleMHB7JD+y9ESd1yQaYhM7RKcHVBgJZRiK0ZHoQthnQNbm4vFySPdu8pwYH+UrlWA7kQmyzxKani4eqiQRpCHARznu4ucuxqrG5bmUAvDX7S0w0a3M3acEoqhfnpmnnG4OViVILeZ9PKJfS8JAzXRcRpd2v8P2TBkNHC38hA4QOvTa1Spg89435EVguB24WojIadaWkGWbD9nLY2QBBizKrhNpC5Mu7zrFkkS4ups+s9Zbw9tHJAsNusSgnp06xijq0IcgrmjA1H2TX61vAA4ryccVIEa7rsHwOnHJyLSsjxGxFe+AgwUFyMnjs4wMckpUqgt4fEJqZq5gYNKQ1RIqevx0uawSAxCSmSnGbQy5CgwZDdzzvP6bBd1g8OZECQzbocnSQzN5yUUoHemuFoEB6sM+m83V0lOV10AWBXcZXkM//IsSVTpPOLU5Cv0ALx++69W/48ckXOXI8XD6SlkBHydJNgXw/x9/KQ5FTMgkvTdNWVmIw2DAjsEJRFJKboIsPmgWJ0YoqcuBor9SuEaBE6ArBQP099LGi6iaHDdNRbubnqfkDzTgz7nbwWpx9+2I8YqkSvqcsxiRqkIOgQQ7pmwxbvOS9tapaSw5eiJTEFw30GvbxckI9UCvWRKzGZ2xvkVUyNx/rzzrPZBjV59csyP5BDThnBRbsuiK+XW6YAk12jLccDCDnjpAjWdNk/Bk45MCAayqU4GkNuOB8BAE4j+4BgCUNymJ6PAy+aUWJYyKxcSqEWKD3NoEuVLN93UT84kJ1CcBLs6y6bT1yX8zcSdaHhC1GJEo8+TbFJ0q52Kf0wWX/0mpyJiNfgJ2rLL3Ju9UwNQHYt+lqq9X5FZm85rdvi6+kqyWnp4pKG+zRIqL+nlArw1qAEmSLdjvSMYA7ZK9Q9oS8UHguG48yF+Wd8gF2PTdKsER53+RAfiU5MlVA/N2lUMVgzWTfiU+TemiX1/3+y+pgGLailQmbswIVo7e+kwaOri3i6uerfCLqQvYNLUUkaEIb4eWhAin2E/VglzO+mb5+5FVPn9eGU3XBYdgfyyqG+ev/hJXwzXRcZu4vRCdKgQjC7RheQpbV9XDzXsTnTpAjWdDkGBk45KB3oneM3IgQdCIYQQGgLgP9qjJBtwmK3WDcOw1YxWj+T/e0jLENm5Zdd56RxxRCtCUJGB4ELbifYx10qlPDV2zEWMv9zNkpe7lxLsyUz/zogU15/Xs7tXqeXVWx1v4R2GKb9kyqH+WV8a0tO1oM6fk5fj9PrYTYZAi4ESsgaZWTA8FfG3wYxaFCD65hLTjVI2UAfDQyxPdgPqFNCMOPn6a7BHYa9OtQppTP7zA+IKD5HAITmnFrMnm4QF62LQnLMRVy1tipGTl6P0wL063Fpuh+CfN11287eiJf76pW5KdDJqZi6oB9O2R3It56MkPikNAny8cw0lID6LAj1z7h9Z6rNKAx5PUfIVqKdBYdBHZ8zTYpw5pouR8LAKQcNK4Xk+o0Ii6/i4GkMItBp/HJ0ggYChv+G7pDBwSBZDrGTru+GQGTDiWtSo1SA1vlgKC7B1UWHABGAGN9CKGRGTQ7eWIlXz8iMF/rJ2aNHxNXNXao8+Kx43t5R3DyQZXLTITfjwQgzxtBaAdkUPdgjYnMRCfJGHZGruLkaNBuVlpKRYcP/R6YF92N8bMbU+UONy2mAaL4fHm1ZUQOLXWduaJCHIOnCjUSpFOar24D7RRCE4Tt9zAjSXA0atKFYvXSgl6BRNjJNCNiQ2fLzSNPHezUmWQK93aV0kLe0qBqaad9lzUSYf9gU5MMptwP5iasxWuNlvA3sDwTPZYO8NRA158i1GYUpr+do0e7z+lri4rnOwxkmRThjTZcjYuCUAywhkts3ItTQIENhDCLCS/hIqL+H7D4bpfVMCEoCvDBzLEmzLP9/axkyZsK56flB3h4aaGEIzdfLXeJT0iQqHkuZZBQfo6Acs7+Q0Tlz9IB07dROYmNjpUSpMtLqyXelVPW68u/VON0m1FShH1GN0v56sL+KocK0dFO/JvR8MhhctAcTsj4YUsPtIkgz1kChrqhBeJAu3ps1dY59Yb4f1hy+LNtOXdUDHLbzTEScnLwea6r9MR/GRF0Yzk9Jc83Yjv+WiMF1sL+1C7qrq5QO8tRMjnHYsGqYn3ZBt7QupiAfTrkdyMP8vaRu+SCtFzPuj/vrl9XnFYGuvdRm2NKwV27PEV4DRy/HcPFccjjOWNPliBg45eDsjYwMQ27fiLIGU/Dyz3u1lgdZCwQk0QkpkpKWedgLkHhBjY+nR0YXawx1IVuFgz+yPngzobYGy6cga4SCccz+uqNZBWnUqBEG1KThwPESEBwmAd7IdiVpMIbABDPWkAk5cS1WAxTUCqEYXTNg6JaNZVm8PbVxJe4TMLMOdTwI4O6oHqqPLacDrXE/GLM0CLauxibKhcjE/+q/0nU2YNWSfqYicAw/6t/uLpKQlKYF5QiWkGXCbSG0rBjiKxejM2YzYigR2xydmKaF6sb7t6QupiAfTrkdyJEt69usop7O2qML9Vm2Xpthi0XYuT1HjcKD5eDFaD3PHIczyBE4U02Xo2LglIOcFvnNaYjIeN7dt5WUgxdjJCo+WVLSDTqFPyuEKhiCqlshSIutEVR4ebhpkFQu2FtnnyEAwjXR0ToxJkruqRuuU+Y9PNxk8eLFci3JVT7984QeTFB8jf+HrBOCMRwo//0vw+Th7qpDdD6erjrrDUODyAwBhgORybkUnagBS/kQ30xv4LxS58YsDYra0RQT9V4IonAeMlo7Tt+QVlVDNbC7GpMoVcP8NauFXlFokIksXXiwr0TEJ+u6duVL+IiPl5tcjEzUbBNqoSqF+spDjcqb9r2ltUv5/XCyNNgy3x/2Upthq7N4cnqOMGR96no8hzPIIdnL5wbljIFTDnCAz+tbO4azENzsPRtpOq9OuUApG+ylhdyo4UEdk7eHi87SwxBagI/7f/83WAvQjYXTVf29dGo/Cq5DfD31IIKZZddOHpI/vxorLfo+LN59muq2hISEiMQlZ8qQ1CgdoJedvBqnPZVQVF05zFfrhBAo4bQLZv8jmHJ11Rqi0kE+cnv5ILmttL/O0ut/R2XtQWUpvOGRFTIGTcaZeOhMjsceFZeivajCQ3x1dhoyXsislQ/yQeMpHQLDtpkPe5X095bywb4ZAWVSql6GIb/81i4V5MOpoN8Ebbk2w5Zn8eT2HHE4gxydLX9uUO4YOOXEkPe39tlbz+j16lcINp03f/tZbQGALAtmzRkM2p5Sypfw1eAGw1ZNKoXo9HutjTILKnAa9R1d65eVNjVKypwfZsq3746RlORk+WP5col5+20JCAjIMUOCoAMBS73yQToTDcEIAqcgLMOSkFFDhVyTFoOnG7QPEwIeZLyQeaqUZcp9XrANNUsHaPsEBEAYTsQQIR43aoJQv/Roy0rawDPrcjS5DXuhZYG/l7u0rlkyU9BSkNql/Hw4OeI3QXuYxZPdc8ThDCKyVQycclAuxCfXb+3ubijgTtGMEg74yKTgN9oRYBmSjKn+6ZKSnvEbBym0CsAB4u4aJeWBBuUyHZxRyI2ZRCiK3Xrskkx85Xk5s3mZ3leVJq1l1P8+kdg0d7n2X0PJrF2wzYuWjX2M0BsKnbkxDOibZtB6K/yN2W/+nm7aGwkz526lMLFbw/Ly5+Grci0uSYMxBIbono5sGWbQGYOm7A6Q+R32KqrCyuyGX60dTORXTvVo9jqLxxGDWCJyDAyccpBRMn3zt3bMUDt6OVZnjmGaP4KQ5fsvaVYHEJwgs4OZdZh0j6VDMEEPmZj9F6KldpnAbKfQo2fNztM35PKFc/Ln52Ml7vxR3YoybQdI2faPyJebL8uCvRFSu1ygrgGXVxdsY1CF5UzQmRsZHAzd+Xl5yJnrcdqDCrVJfrdYmFg22EceblZBgzQM1aGdAHo+FTSYyStoKcxMhC0WTRf2Y7D3WTz2GMQSkWNj4JQDtATI7ls7MjSoSULn6HQxSKpBMg5YHq7i45nRHBMF4Vh6BENXmPoPmD2GqfU4LytjRis6LlFWf/iMJEZcFDefACn14GjxrdpY4lPSJSnNIJdikqRcQoo2Y8yrC7bxGzsKbbGcCYI93Lefp7t0/G85FAypFcY3efNgRtfkc3XV+y2KWSKFmYnIOvx6NTpJs354DgfcUUXsgSWF3xz2IiIqPAyccuBnlnEwfmtHVgW9fNBCAFPpMc3fQ4fqXCU2KU0DJ/RvwjR/N4RV6SLJBoRXGUt0+Hm5abYoa12JcR236GSD3PbAMDm+8gcp2/NV8QwurcNfmLqPjFGqwSCXoxOlTrkgi4t7kREa1qZ6kfbwQTCDgzMCjm0nIzRwxHRyHNSLKntzq5kI8+FXLI6MwBJtHFDv9d2m0+IiLtK7WbhNZ54sLfzmsBcRUeFh4JSDaqX8M51GAIBsE5ZFkdSMhXp1zTZ3N61vQl0Thu3qlguU3WcjJSklXVzdRDxdXHTmGYbdsMiJcZkSozOXrsu2PQclLd1bEpLTpErTtuJauZkkIJVlMGi/JySpMmbDuWiwhuEZBGJnIhLkdES8RQFEUQ95IEjadOK6HsSNw0HmmQ9bar6YdfgVQROyiGgBgdosbOvKg5f1tDWn6xd24TeHvYiIbh0DpxwgM2QO39rRBBHF25i5hgAIfYoQE6G2B8NgjSsGZ3TH9kTTRzSczOiMjeAnIQWF4gad7m+cRTZjyd/y9qgnJD46Ulq//LVEpfpqsXmAj5fERiVKmhi0R1MKiszT0nWIDrd3+lq81i1hPbnP1x7TWXoo0kZ2ydYyH1v/va5LsiADVRx1RJYGaMbhVwzPYQYiglvP/7KFeO7RENTa0/XzYq+F30RE9oyBUw42Hr8qDSv+/3p1gAMoaoOQScFUf8ySQzNHDNthSREED1GJKA4XXV8tDgv2JmXMsEPxOIKK5lVK6G29PuUb+eSNFyQlMU4CQ0tJac9kiXYLlBtxKdrryc/TVYMtNIH08XDVGinknTAM9s/5SA280AF8y78RsvVUhKw+fFkDO2sUNueW+djyb5RcjEqSiiV8i7T5Yn4LvfFcopfWtxv+1Y7nCHSvxrroPkV7ibAAL5uZrp8Tey/8JiKyRwyccrD5xDV5pm2Nm843L7RFZgnNHLHcSCQCHm93qVU6QM5HJeiyIzVKB+oitljWJCU1XXsdhfq6y6gXR8snH3+ot1OtfnPp/+pkCQgJk9LRCXL8cqz4e3lISkiaBl5IV4X6e2r9EDJd/16LlaQUg3baxhAeAirM9EMWCjVYUNzDSzllPpDNwXnVS/oXefPFgnXHRlNSF80aphlE3DNGVPV8e8nasPCbiKh4MXDKAeqNspO10BZ1SFPXHtOgBgdsXO5zMUoOXYzRISC0AMD0/NjkVGkR5iH9ej4oa9as0du6q/sA6fbkaHFz9zAt84LmlKhfcjO4aWfxRuEhmqXC3/vPR8rLv/wjJXzdtZUAulkiGEtKFR1iQkdwawwv5ZT5uByTmLFI7n9BU1E1XyxId2z8n71no6RxxRJSJjBB1/Xz83TXuAkLJSNriJ5Ytp61YeE3EVHxyli0jG7SsGLGkFpOcEBFA8moxFRdvgRZIeOQEDqEVy/lp3VJWMoEQ3kYOtm95FsNmvz8/OTB59+Tux970RQ0wf7z0XJNO317SMUSfhqM/Xnkii6Yi/sL8PHUGXyomUKAhR5R5vy83SQuOTUjoLNC5gOPEY8VQZHxMd9WKkCDKHOFnc0xDhVmtyhsTvvD/P/cXi5QapUO1OwTnjN0fm9RpYRdLbppfD3aeqBHRGTvmHHKwWMtK1lUU4PGlcevxOpQGTpm1yjtry0JygX5SglfL13/DUuZ4IB2X63/yfmzZ2XChAlyOCkoU4bGWKSMddpQJ3XoUowexNGOYMGOs1pbhdspG+ij18Pad2iqiYN9UqpBFw3GrD0snmuN4aWcMh8/7zxb5DU4BSmSzvx/vHSNwSol/TTo83Rz0QWVbbkVARERWQczTtlArUuwr6dFNTU+Hu5StaSfJKWly6FL0Tp7zLiMSbOK/rLlt3mmruLINP36669St27dmzI0aGeAA7iPh4scvhijNU2Y5YUfDB2hMSMCjYcal5MS/p7i7p5RPI4hRS83F621wnBgk8ohVs06ZM185JSJKsxsjnGoEPsc+x770vgcmO8PDM+dvBanv7P7P+jhhGD1juphzNwQEVG2mHHKBloooTi8Sw4H96w1Nej9gyG0k1fjtM8TGio2KpEmH498RHZs3y4RERHyyiuv5Fkr9eHKI/L30atar4S+Ta4uLtqRHEHcscsxer/dG1XQ+8L1cB6K0IP9PKRSmK+0qBpqc8NLxVWDk1uRdE4z7trXLp3j/yEiIsoOA6ccHLoYlWPglHX6vburq9QpG6TDbKhpqm04K68OekKuXr0qISEh0rhx45zuJlNTQgzBxSWnZSwc7IolWwxaQ4XgA38bi6mzBlwoerKVxpI5Kermi7kFaMbhwpxm3HEhWSIishQDpxycuh6f75qapJQ0ObV2nnw3e4qkp6dLw4YNdWiuSpW81z07eTVWezihOziKv5FxQk8h/MQkYv07Q6ZaHXaBzl7W/WLpjDtbDjqJiMh2sMYpB1gfLifZ1cecuxIh899/Uf784WMNmvr37y+bNm2yKGiCc5EJOlMOjTO90NLAE+vauWuxN7JN5UN8eHAvphl3REREOWHglIMmlYIlN1mLnq+fOymnd60TDw8P+fzzz2XWrFni42P5EigV0APK3U0zTKF+nuKGppnpBm2ciQCqR8PyFt8WZZ8dNGcvDS6JiMi2cKguh2jykRaV81lTU1falUuVatWqyR133JHvJ6JKSX9pXiVE1h6+IoHeHtpxHAf3eJdUaVe7lNQqF5Tv2yQuS0JERIWLgVM2QdOz7arlOSyWlpYmb731lvTo0UNrmeDxxx+/pSdj7H219ff2kzfkRnyKZqDaVy9tOp8KhsuSEBFRYXExYKzJyUVHR0tQUJB0++gPeax17TwXyr127Zo88sgjsmrVKqlatars378/X8NylhSKo+YJw3fIRFHhQKF4UbZEICIi+44DoqKiJDAwMNfrMuNk5pN+jaVS2bBcd9jOnTulZ8+ecvr0afH19ZV33323UIMmQLDEgKnwcfYcERHdKhaHm8krC/Htt9/KnXfeqUFT9erVZcuWLdK3b99bfhKIiIjIPjBwskBycrIMGzZMhgwZIklJSdK1a1fZvn271KtXr+ifISIiIrIZDJws4ObmJidPnhQXFxddoHfx4sUSHJx7uwIiIiJyPKxxsjBwmjNnjtY3dezYseifFSIiIrJJzDhlAxMNP/74Yxk+fLjpvNDQUAZNRERETs5hAid0665cubJ4e3tLixYtZNu2bQW6ndjYWOnXr5+8+OKL8sUXX8iff/5Z6NtKRERE9skhAqeffvpJXnjhBRk/frzs2rVLGjRoIJ06dZIrV67k63aOHz8uLVu21Ntzd3eXzz77TNq0aVNk201ERET2xSEaYCLD1KxZMw10AIvshoeHy4gRI2Ts2LEWN74KCAiQmJgYKVu2rCxYsEBbDxAREZFji85HA0xXR2gVgKLt9u3bm85zdXXV05s3b87XbSFouvvuuzVrxaCJiIiIHG5WHZY/wbpxpUuXznQ+Th8+fDjb/4NeTPgxQoQJgwcPlvfff188PDw0+iQiIiLHF/3fMd+SQTi7D5wKYuLEibpAb3adwfFDREREzicmJkaH7Bw6cAoLC9M+S5cvX850Pk6XKVMm2/8zbtw4LSY3ioyMlEqVKsmZM2fy3GFk298YUNt29uzZPMeoyXbxeXQMfB4dg7M8jwaDQYOmcuXK5Xlduw+cPD09pUmTJrJmzRp56KGHTMXhOP3ss89m+3+8vLz0JysETY78wnAWeA75PNo/Po+Ogc+jY3CG5zHIwsSJ3QdOgOzRgAEDpGnTptK8eXOZMmWKxMXFyaBBg6y9aURERORAHCJw6tOnj1y9elXeeOMNuXTpkjRs2FBWrFhxU8E4ERERkTh74AQYlstpaC4vGLZD88zshu/IfvB5dAx8Hh0Dn0fHwOfRQRtgEhERERUHu2+ASURERFRcGDgRERERWYiBExEREZGFnD5w+vzzz6Vy5cri7e2tiwVv27bN0n1HxeDvv/+WBx54QJuSubi4yKJFizJdjhI9zKbEwsw+Pj66RuGxY8cyXSciIkIeffRR7UESHBwsQ4YMkdjYWD5/xdytHwtxYyHtUqVKac+1I0eOZLpOYmKiPPPMMxIaGir+/v7Ss2fPmxrboknt/fffL76+vno7o0ePltTUVD6XxWT69OlSv359U0+fVq1ayfLly/kc2rlJkybp5+uoUaNM5/H9mDOnDpx++ukn7QGFGXVY2LdBgwbSqVMnuXLlirU3jf6Dflx4XhDgZgdrC06dOlW++OIL2bp1q/j5+elziDe9EYKmAwcOyKpVq2TZsmUajA0dOpT7uBitW7dOg6ItW7bo85CSkiIdO3bU59fo+eefl6VLl8qCBQv0+hcuXJAePXqYLsealAiasLD3pk2b5LvvvpNZs2Zp4EzFo0KFCnqQxcLqO3bskLZt20q3bt30/cXn0D5t375dZsyYoQGxOb4fc2FwYs2bNzc888wzptNpaWmGcuXKGSZOnGjV7aLs4eW6cOFC0+n09HRDmTJlDB988IHpvMjISIOXl5dh7ty5evrgwYP6/7Zv3266zvLlyw0uLi6G8+fPc1dbyZUrV/R5Wbdunel58/DwMCxYsMB0nUOHDul1Nm/erKd///13g6urq+HSpUum60yfPt0QGBhoSEpKssKjIAgJCTF8/fXXfA7tUExMjOG2224zrFq1ytC6dWvDyJEj9Xy+H3PntBknfGvFtyYM7Ri5urrq6c2bN1t128gyJ0+e1Ian5s8hWuZjyNX4HOI3hufQVd4I18dzjQwVWUdUVJT+LlGihP7GexFZKPPnslatWlKxYsVMz2W9evUyNbZFdhFraRkzHlR8kAGcN2+eZg0xZMfn0P4gC4wsrvn7DvhcOkkDzPy6du2avvGzdhfH6cOHD1ttu8hyCJogu+fQeBl+oxbGnLu7ux6wjdeh4oW1JFFLceedd0rdunVNzxPWnUSQm9tzmd1zbbyMise+ffs0UMJwOGrRFi5cKHXq1JE9e/bwObQjCHpRooKhuqz4fsyd0wZORGS9b7n79++XDRs28CmwQzVr1tQgCVnDn3/+WdcJRU0a2Y+zZ8/KyJEjtd4QE6Mof5x2qC4sLEzc3NxumrWD02XKlLHadpHljM9Tbs8hfmct9scsLMy04/Nc/LAsEgr0//zzTy00NsJzgeHzyMjIXJ/L7J5r42VUPJAZrF69ujRp0kRnS2LyxieffMLn0I5gKA6fi40bN9YMPH4Q/GKiDf5GJpfvx5y5OvObH2/8NWvWZBpCwGmkocn2ValSRT+szZ9D1Lugdsn4HOI3Dsb4oDBau3atPteohaLigdp+BE0Y1sH+x3NnDu9FDw+PTM8l2hWg/YD5c4lhIvNAGN+YMS0eQ0VkHXgvJSUl8Tm0I+3atdP3EjKHxh/UgWIGsvFvvh9zYXBi8+bN0xlYs2bN0tlXQ4cONQQHB2eatUPWn/Wxe/du/cHL9eOPP9a/T58+rZdPmjRJn7PFixcb/vnnH0O3bt0MVapUMSQkJJhuo3PnzoZGjRoZtm7datiwYYPOIunXr58VH5XzGT58uCEoKMjw119/GS5evGj6iY+PN11n2LBhhooVKxrWrl1r2LFjh6FVq1b6Y5SammqoW7euoWPHjoY9e/YYVqxYYShZsqRh3LhxVnpUzmfs2LE6E/LkyZP6fsNpzFBduXKlXs7n0H6Zz6oDPpc5c+rACT799FP9sPb09NT2BFu2bLH2JpGZP//8UwOmrD8DBgwwtSR4/fXXDaVLl9YguF27doYjR45k2ofXr1/XQMnf31+nrg8aNEgDMio+2T2H+Jk5c6bpOgh2n376aZ3e7uvra+jevbsGV+ZOnTpluO+++ww+Pj6GsLAww4svvmhISUnhU1lMBg8ebKhUqZJ+XiJoxfvNGDTxOXSswInvx5y54J/cMlJERERE5OQ1TkRERET5xcCJiIiIyEIMnIiIiIgsxMCJiIiIyEIMnIiIiIgsxMCJiIiIyEIMnIiIiIgsxMCJiIiIyEIMnIjI4bm4uMiiRYtyvc7169elVKlScurUKbEnK1askIYNG+qacURU9Bg4EVGh2bx5s7i5ucn999+f7/9buXJlmTJlitWejXfffVe6deum21FcwVph7KPOnTvrgqyzZ88u1PsiouwxcCKiQvPNN9/IiBEj5O+//5YLFy7YzZ6Nj4/XbR8yZIjYo4EDB8rUqVOtvRlEToGBExEVitjYWPnpp59k+PDhmnGaNWvWTddZunSpNGvWTLy9vSUsLEy6d++u57dp00ZOnz4tzz//vGZq8ANvvvmmDkOZQ8bFPCu0fft26dChg95eUFCQtG7dWnbt2pWvbf/999/Fy8tLWrZsqaexhGf16tXlww8/zHS9PXv26LYdP34819szbh8eH65vvr2LFy+Wxo0b6z6oWrWqvPXWW5Kammq6XzzmihUr6vaUK1dOnnvuuVz3ETzwwAOyY8cOOXHiRL4eNxHlHwMnIioU8+fPl1q1aknNmjXlsccek2+//VYDAaPffvtNA4kuXbrI7t27Zc2aNdK8eXO97Ndff5UKFSrIhAkT5OLFi/pjqZiYGBkwYIBs2LBBtmzZIrfddpveB8631Pr166VJkyam0whKBg8eLDNnzsx0PZy+5557NKjKDYI54/XxWIyncT/9+/eXkSNHysGDB2XGjBkaYGKYEH755ReZPHmynn/s2DEd6qtXr16e+wiBVunSpfX2iahouRfx7RORk8BQFwImY91NVFSUrFu3TjMlgOCgb9++mmExatCggf4uUaKE1kYFBARImTJl8nW/bdu2zXT6yy+/lODgYL3vrl27WnQbyOQgu5N1+OuNN96Qbdu2aYCXkpIic+bMuSkLlZ2SJUvqb2yH+ePBYx87dqwGeoCM09tvvy0vv/yyjB8/Xs6cOaPXb9++vdYtISAyBpd57SNsPx4HERUtZpyI6JYdOXJEA4x+/frpaXd3d+nTp48GU+bDXO3atSv0vX358mV58sknNdOEobrAwEAdNkQQYqmEhAQdOssaiGDIEZkz4zBjUlKSPPzwwwXe1r1792rGyN/f3/SDbUf2CHVWuG1sCwIqnL9w4ULTMF5efHx89DaIqGgx40REtwwBEg7w5lkbDNOhTuezzz7TgAYH9vxydXXNNNwHyPyYQ/YGrQQ++eQTqVSpkt5nq1atJDk52eL7QX3UjRs3bjr/iSeekMcff1yHzzDshmDQ19dXCgoBHbJOPXr0uOkyBG7h4eEahK5evVpWrVolTz/9tHzwwQeaPUMGKjcRERGmTBcRFR1mnIjoliBg+v777+Wjjz7SrJLxB9kVBFJz587V69WvX1/rmnLi6ekpaWlpmc5DIHDp0qVMwRNu29zGjRu1gBp1TbfffrsGTteuXcvXY2jUqJHWHGWF2/Tz85Pp06drvyTUPVkKgU7Wx4OicARGqJHK+oMgERBgotgbs+T++usvbfGwb9++HPcRJCYmamE4HgcRFS1mnIjolixbtkyzNZjKj8ySuZ49e2o2atiwYVrDg6G6atWqaa0TAi7MZhszZoxeFzPP0MYAlyH4QRYI9VFXr16V999/X3r16qXBy/Lly3U4zghDdD/88IM0bdpUoqOjZfTo0fnObnXq1EnGjRunjyMkJMR0PmqKUOuEy3A/yGRZCo8HgeKdd96pjwe3i5op1F2hdgmPB8ESAsz9+/fLO++8o4XiCIxatGihma0ff/xRHwsyaTntI0BRvDHTRkRFzEBEdAu6du1q6NKlS7aXbd26Fakiw969e/X0L7/8YmjYsKHB09PTEBYWZujRo4fpups3bzbUr1/f4OXlpf/HaPr06Ybw8HCDn5+foX///oZ3333XUKlSJdPlu3btMjRt2tTg7e1tuO222wwLFizQyydPnmy6Dm5v4cKFuT6O5s2bG7744oubzj9x4oT+//fffz9f+2XJkiWG6tWrG9zd3TNt74oVKwx33HGHwcfHxxAYGKj3++WXX+pl2MYWLVro+Xi8LVu2NKxevTrPfTR06FDDU089la/tI6KCccE/RR2cERHZOrRLQLYK2R/jsBlgij8yZWfPntUp/7YGw5JoAYE+TlWqVLH25hA5PA7VERGJ6Aw69E46f/68FmljBh2GCdGQErPdbDFoAqytN23aNAZNRMWEGSciomyg3gh1W+hcvmTJEilfvrzpMqwL99RTT2W731CPdODAAe5TIgfFwImIKJ/QlRz9o3KaTWcs5iYix8PAiYiIiMhC7ONEREREZCEGTkREREQWYuBEREREZCEGTkREREQWYuBEREREZCEGTkREREQWYuBEREREZCEGTkRERERimf8DkW9D5Vu7MmQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted vs actual: points on the diagonal are perfect predictions; scatter indicates error magnitude\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "ax.scatter(y_test, y_pred, alpha=0.5, s=20)\n",
    "max_val = max(y_test.max(), y_pred.max())\n",
    "ax.plot([0, max_val], [0, max_val], \"k--\", label=\"Perfect prediction\")\n",
    "ax.set_xlabel(\"Actual (y_test)\")\n",
    "ax.set_ylabel(\"Predicted (y_pred)\")\n",
    "ax.set_title(\"Predicted vs Actual — Final Test Set\")\n",
    "ax.legend()\n",
    "ax.set_xlim(left=0)\n",
    "ax.set_ylim(bottom=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6aa603",
   "metadata": {},
   "source": [
    "A good plot has points clustered around the diagonal line. Large scatter or a systematic curve (e.g. under-predicting high values) suggests room for improvement (e.g. different model, features, or target transformation). If points are mostly below the diagonal at high values, the model tends to under-predict revenue; if above, it over-predicts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c751f9a0",
   "metadata": {},
   "source": [
    "## Section 9 — Feature Importance\n",
    "\n",
    "Random forests provide **feature importances** (e.g. mean decrease in impurity). These are a *global* importance measure and can be biased (e.g. toward high-cardinality or scale-dependent features). In a later session we will cover more robust methods such as **permutation importance** and **SHAP** for interpretability.\n",
    "\n",
    "**Interpreting the bar chart:** The top 10 features are those that most often reduced impurity when split on. High importance suggests the feature is useful for predictions; it does not tell you the direction of the effect. RFM-style or spend-related features often rank high in revenue prediction; one-hot columns may appear if they separate high/low revenue segments well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80be3a21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:01.411901Z",
     "iopub.status.busy": "2026-03-24T15:31:01.411812Z",
     "iopub.status.idle": "2026-03-24T15:31:01.469174Z",
     "shell.execute_reply": "2026-03-24T15:31:01.468700Z",
     "shell.execute_reply.started": "2026-03-24T15:31:01.411893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# best_model was fitted on X_train_sel in Section 8; use its feature_importances_ and selected feature names\n",
    "imp = pd.Series(best_model.feature_importances_, index=X_train_sel.columns).sort_values(ascending=True)\n",
    "top10 = imp.tail(10)\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "top10.plot.barh(ax=ax)\n",
    "ax.set_xlabel(\"Feature importance\")\n",
    "ax.set_title(\"Top 10 features (Random Forest)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638846c6",
   "metadata": {},
   "source": [
    "### SHAP: model-agnostic feature importance (global + local)\n",
    "\n",
    "So far we used the **native feature importances** from the random forest (mean decrease in impurity). These are useful but can be:\n",
    "\n",
    "- **Model-specific** (not comparable across different algorithms),\n",
    "- **Biased** towards features with many possible split points (e.g. high-cardinality or continuous features),\n",
    "- Mostly **global**: they tell you which features matter overall, not how they behave for a *single* prediction.\n",
    "\n",
    "**SHAP (SHapley Additive exPlanations)** provides:\n",
    "\n",
    "- **Local explanations**: for a single row, how much each feature pushed the prediction **up or down** from a base value.\n",
    "- **Global summaries**: by aggregating local SHAP values across many rows.\n",
    "\n",
    "Reading SHAP plots:\n",
    "\n",
    "- In **summary plots** (beeswarm): each dot is one row; x-axis is SHAP value (impact on prediction), color encodes feature value (e.g. blue = low, red = high). Features are ordered by overall importance.\n",
    "- For a **single row** (force/waterfall plot): positive SHAP values push the prediction *up* (towards higher revenue), negative values push it *down*.\n",
    "\n",
    "Below we compute SHAP values for a sample of the training data using the tuned random forest, and show a global summary plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba600721",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:31:01.469632Z",
     "iopub.status.busy": "2026-03-24T15:31:01.469552Z",
     "iopub.status.idle": "2026-03-24T15:31:07.565482Z",
     "shell.execute_reply": "2026-03-24T15:31:07.564958Z",
     "shell.execute_reply.started": "2026-03-24T15:31:01.469624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x510 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------------------------------------------------------------\n",
    "# SHAP explanations for the tuned Random Forest (showcase)\n",
    "# ---------------------------------------------------------------------------\n",
    "\n",
    "# SHAP can be a heavy dependency; we import it lazily and give a helpful message if missing.\n",
    "try:\n",
    "    import shap\n",
    "except ImportError as e:\n",
    "    raise ImportError(\n",
    "        \"shap is not installed. Install it with `pip install shap` in your environment \"\n",
    "        \"(ideally in the same venv as this notebook).\"\n",
    "    ) from e\n",
    "\n",
    "# For tree-based models, TreeExplainer is efficient\n",
    "explainer = shap.TreeExplainer(best_model)\n",
    "\n",
    "# Use a subset of the training data for speed (SHAP can be expensive on full data)\n",
    "X_shap = X_train_sel.copy()\n",
    "if len(X_shap) > 500:\n",
    "    X_shap = X_shap.sample(500, random_state=SEED)\n",
    "\n",
    "# Compute SHAP values: same shape as X_shap (rows × features)\n",
    "shap_values = explainer(X_shap)\n",
    "\n",
    "# Global summary (beeswarm) — which features matter most, and how\n",
    "shap.plots.beeswarm(shap_values, max_display=15)\n",
    "\n",
    "# Optional: inspect a single example (local explanation)\n",
    "# Pick one row and show how features push the prediction up or down\n",
    "idx_example = X_shap.index[0]\n",
    "shap.plots.waterfall(shap_values[X_shap.index.get_loc(idx_example)], max_display=15)"
   ]
  },
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    "## Section 10 — Summary\n",
    "\n",
    "**Takeaways:**\n",
    "\n",
    "- **Baseline first** — Always compare against a simple baseline (e.g. mean, linear model) to ensure your pipeline adds value.\n",
    "- **Use CV for comparison** — Cross-validation gives more stable performance estimates than a single split when comparing models.\n",
    "- **Learning curves** — Plot train vs validation error (e.g. vs training set size) to diagnose underfitting, overfitting, and whether more data would help.\n",
    "- **Train/val development & early stopping** — For iterative models, monitor training vs validation error over iterations and stop when validation is best (or use patience) instead of overfitting.\n",
    "- **Tune carefully** — Hyperparameter tuning is done on the training/validation data only; avoid peeking at the test set.\n",
    "- **Feature selection after tuning** — After we have a chosen, tuned model, we select features on the training set only (e.g. via the model's importance); the final model and test evaluation use only the selected features.\n",
    "- **Test set once** — Evaluate the final chosen model on the held-out test set only once, and report those metrics as your generalization estimate.\n",
    "- **Interpretability next** — Feature importance from trees is a starting point; more robust methods (permutation importance, SHAP) will be covered in a follow-up session."
   ]
  }
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