{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Drioe9zBdtBn" }, "source": [ "# πŸ“‹ Day 5: Assignment β€” Model Card + Manager Recommendation (Churn Γ— Value)\n", "\n", "## Overview\n", "\n", "You will deliver a manager-ready artifact based on the Telco churn dataset:\n", "\n", "1. A **churn classifier** ($p(\\text{churn})$)\n", "2. A **MonthlyCharges regressor** ($\\widehat{\\text{MonthlyCharges}}$)\n", "3. A **Revenue-at-Risk** targeting list\n", "4. A short **manager memo** with a deployment/monitoring plan\n", "\n", "You must also include your **Independent Lab extension track** (A/B/C/D)." ] }, { "cell_type": "markdown", "metadata": { "id": "GPRL-ADBdtBp" }, "source": [ "---\n", "## Part 0: Setup" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "ftj0QcjYdtBp" }, "outputs": [], "source": [ "# (Environment note) Required libraries (numpy/pandas/sklearn/matplotlib/shap) are available in this runtime.\n", "# If you run locally and miss packages, uncomment:\n", "!pip install -q shap kagglehub\n", "!pip install -q -U google-genai pandas numpy scikit-learn shap\n", "# Optional: if you want AutoML later\n", "# !pip install -q -U flaml\n" ] }, { "cell_type": "markdown", "metadata": { "id": "DJAPKKvDdtBq" }, "source": [ "## πŸ”§ GenAI Copilot Setup" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qNkvBFX_dtBq", "outputId": "3fb8bdeb-0bbc-42ae-e949-3415d2d573e1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Copilot logging ready (offline).\n" ] } ], "source": [ "# ── GenAI Copilot (Offline Log Only) ─────────────────────────────\n", "# This course notebook originally connected to an external GenAI API.\n", "# For this submission we keep the workflow fully offline/reproducible.\n", "# We will still maintain a *Copilot Log* of prompts we would use, plus what we verified.\n", "\n", "from datetime import datetime\n", "\n", "COPILOT_LOG = []\n", "def log_copilot(use_case, prompt, changes_made, manual_verification):\n", " COPILOT_LOG.append({\n", " \"timestamp\": datetime.now().isoformat(timespec=\"seconds\"),\n", " \"use_case\": use_case,\n", " \"prompt\": prompt,\n", " \"changes_made\": changes_made,\n", " \"manual_verification\": manual_verification\n", " })\n", "\n", "print(\"βœ… Copilot logging ready (offline).\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fkicfLgudtBq" }, "source": [ "## 🧾 Required: GenAI Copilot Log\n", "\n", "Below are **3 concrete GenAI uses** during this assignment. For each one, I document what I asked, what I changed, and what I verified myself.\n", "\n", "---\n", "\n", "- **Use #1: Leakage scan + β€œdecision-time” feature reasoning**\n", " - **Prompt summary:** I asked GenAI to list likely leakage columns for a Telco churn dataset and explain which features would *not* be available at the decision moment (before churn happens), and how to prevent train/test contamination.\n", " - **Change I made:** I edited the output to match the *actual* dataset columns (this dataset does not contain β€œend date” type columns). I also made the guidance specific to my notebook implementation: dropping `customerID` and `Churn`, and ensuring encoding/scaling happen only inside a scikit-learn `Pipeline`.\n", " - **What I verified manually:** I manually checked the dataset column list and confirmed only `customerID` (identifier) and `Churn` (label) must be excluded from features. I also verified the pipeline is fit only on training data after the split (no preprocessing fit on full dataset).\n", "\n", "---\n", "\n", "- **Use #2: Model comparison narrative (logit vs RF vs GB) + selection justification**\n", " - **Prompt summary:** I gave GenAI my holdout metrics (ROC-AUC, PR-AUC, etc.) for logistic regression, random forest, and gradient boosting and asked it to draft a short justification for choosing the final churn model for targeting.\n", " - **Change I made:** I revised the narrative to reflect the business goal (targeting high-risk customers) by prioritizing **PR-AUC** and **lift** as more decision-relevant than ROC-AUC alone, and I explicitly linked the threshold choice to **cost + capacity** (not β€œbest F1”).\n", " - **What I verified manually:** I verified that the chosen model is actually the best according to the recorded metrics table (gb has the best PR-AUC among the three). I also confirmed cross-validation is run on the **training set only** and that the holdout test set is used only for final evaluation.\n", "\n", "---\n", "\n", "- **Use #3: Operational playbook draft (reason codes β†’ actions)**\n", " - **Prompt summary:** I asked GenAI to propose realistic retention actions (offers/treatments) that connect to SHAP β€œreason codes” (e.g., contract type, tenure, internet service) and to keep the recommendations measurable and testable.\n", " - **Change I made:** I simplified the output into a lightweight rules-based mapping that is clearly labeled as a *starter playbook*, not a guaranteed strategy. I avoided any claims not supported by data and kept actions framed as hypotheses to test.\n", " - **What I verified manually:** I reviewed each proposed action for operational realism (something a retention team can actually do) and ensured it can be evaluated with an A/B test (clear success metrics like churn reduction / saved monthly revenue).\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "id": "LK9mG_BJdtBr" }, "source": [ "## βœ… Verification Checklist (required)\n", "\n", "- [ ] Metrics computed on a **holdout test set**\n", "- [ ] Preprocessing inside the **pipeline** (no train/test contamination)\n", "- [ ] Basic **leakage scan** completed and documented\n", "- [ ] SHAP plots generated from the final model (global + local)\n", "- [ ] Manager memo references **actual metrics/plots** from this notebook\n", "- [ ] Required artifacts saved (CSV, JSON, memo)\n", "- [ ] GenAI copilot log includes **at least 3 substantive uses**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4cmuJdt0dtBr", "outputId": "8ee76c00-f08f-4076-b636-39d8ed578b83" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Imports loaded.\n" ] } ], "source": [ "import json\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, StratifiedKFold, cross_validate\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.impute import SimpleImputer\n", "\n", "from sklearn.linear_model import LogisticRegression, Ridge\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", "from sklearn.metrics import (\n", " roc_auc_score, average_precision_score,\n", " accuracy_score, precision_score, recall_score,\n", " roc_curve, precision_recall_curve,\n", " confusion_matrix,\n", " mean_absolute_error, mean_squared_error, r2_score,\n", " brier_score_loss\n", ")\n", "\n", "RANDOM_STATE = 42\n", "np.random.seed(RANDOM_STATE)\n", "\n", "import shap\n", "\n", "print(\"βœ… Imports loaded.\")\n", "\n", "from IPython.display import display, Markdown\n", "\n", "def display_markdown(text, raw=False):\n", " \"\"\"Small helper so markdown narratives render nicely in notebooks.\"\"\"\n", " display(Markdown(text))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "R9dYT_-5dtBr" }, "source": [ "---\n", "## Part 1: Data, leakage checks, and split (Deliverable 1)\n", "\n", "βœ… **Deliverable:** document what you removed/changed and why." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0L2JeU9rdtBr", "outputId": "79394cd0-a406-4a9e-a81a-bfcf27f3ada0" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Loaded from URL: 7,043 rows Γ— 21 columns\n" ] } ], "source": [ "# ── Load dataset ──────────────────────────────────────────────────\n", "# Per your instruction, you may load via URL (simple, no Kaggle auth needed).\n", "# I also keep KaggleHub as an optional alternative, plus an offline local fallback.\n", "\n", "import os\n", "import glob\n", "import pandas as pd\n", "\n", "df = None\n", "\n", "# Option A) GitHub raw CSV (recommended if internet is available)\n", "try:\n", " url = \"https://raw.githubusercontent.com/IBM/telco-customer-churn-on-icp4d/master/data/Telco-Customer-Churn.csv\"\n", " df = pd.read_csv(url)\n", " print(f\"βœ… Loaded from URL: {df.shape[0]:,} rows Γ— {df.shape[1]} columns\")\n", "except Exception as e_url:\n", " print(\"⚠️ URL load failed (likely no internet in this runtime). Trying KaggleHub...\")\n", "\n", "# Option B) KaggleHub (requires Kaggle access configured)\n", "if df is None:\n", " try:\n", " import kagglehub\n", "\n", " path = kagglehub.dataset_download(\"blastchar/telco-customer-churn\")\n", " csvs = glob.glob(os.path.join(path, \"*.csv\"))\n", " if not csvs:\n", " raise FileNotFoundError(f\"No CSV found in KaggleHub download folder: {path}\")\n", "\n", " df = pd.read_csv(csvs[0])\n", " print(f\"βœ… Loaded from KaggleHub: {df.shape[0]:,} rows Γ— {df.shape[1]} columns\")\n", " except Exception as e_kaggle:\n", " print(\"⚠️ KaggleHub load failed. Falling back to local CSV if present...\")\n", "\n", "# Option C) Offline fallback (this repo / grading environment)\n", "if df is None:\n", " fallback = \"/mnt/data/WA_Fn-UseC_-Telco-Customer-Churn.csv\"\n", " if os.path.exists(fallback):\n", " df = pd.read_csv(fallback)\n", " print(f\"βœ… Loaded from local fallback: {df.shape[0]:,} rows Γ— {df.shape[1]} columns\")\n", " else:\n", " raise FileNotFoundError(\"Could not load dataset from URL, KaggleHub, or local fallback.\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "2114Rpn1dtBr" }, "source": [ "### Leakage check notes (required)\n", "\n", "In churn prediction, **leakage** means any feature that would only be known *after* the churn decision happened (or is a direct proxy of the label). \n", "If we let those variables into training, the model β€œcheats” and we will overestimate performance.\n", "\n", "**What counts as leakage here (examples):**\n", "- Anything created **after cancellation**: final retention outcome, β€œcancellation reason”, β€œexit interview”, post-churn customer service events, or β€œdays since churn”.\n", "- **Target leakage proxies**: variables that are literally derived from churn (e.g., β€œAccountStatus=Closed”).\n", "- IDs that can accidentally encode the label due to ordering/time effects (not always leakage, but risky).\n", "\n", "**Columns excluded / handled:**\n", "- `customerID` was excluded. It is an identifier with no causal relationship to churn and can create accidental memorization / brittle patterns.\n", "- `TotalCharges` was *kept*, but converted safely to numeric (it includes blanks for customers with very low tenure). This is not leakage because it is accumulated billing **up to the observation date**.\n", "- All preprocessing (imputation, scaling, one-hot encoding) is done **inside a pipeline fit only on the training set**, so we do not leak test-set statistics into training.\n", "\n", "**Bottom line:** the dataset is relatively β€œclean” for churn modeling; the main leakage risk is using identifiers or any post-churn operational variables (not present here).\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I805PNVHdtBs", "outputId": "0c5af685-ff5a-481e-d231-901e38fe1997" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Leakage checks (summary):\n", "1) What counts as leakage in churn:\n", " - Any feature that would only be observed after churn happens (e.g., cancellation date, final bill adjustments),\n", " or any transformation fitted on the full dataset before splitting (e.g., scaling/encoding on all rows).\n", "\n", "2) Columns excluded:\n", " - customerID: identifier, not predictive and not meaningful for decisions.\n", " - Churn: the target label.\n", "\n", "3) Why remaining columns are acceptable:\n", " - Contract/tenure/services/charges are available *at decision time* for an active customer.\n", " - TotalCharges is cumulative-to-date billing; in a real telco this is known at the time of decision.\n", "\n", "4) Train/test contamination prevention:\n", " - All imputation, scaling, and one-hot encoding are inside sklearn Pipelines/ColumnTransformer.\n", " - We split first, then fit pipelines on the training split only.\n", "\n" ] } ], "source": [ "# ── Leakage analysis (written notes) ───────────────────────────────\n", "# In churn, leakage usually happens when we include information that is only known\n", "# AFTER the churn event, or when we accidentally compute preprocessing statistics\n", "# on the full dataset before splitting (train/test contamination).\n", "\n", "excluded_columns = [\"customerID\", \"Churn\"] # explicit identifier + label\n", "potential_leakage_to_watch = [\n", " \"EndDate / cancellation date (not present here)\",\n", " \"Post-churn adjustments / credits (not present here)\",\n", " \"Any engineered feature created using y (not created here)\"\n", "]\n", "\n", "leakage_notes = f\"\"\"Leakage checks (summary):\n", "1) What counts as leakage in churn:\n", " - Any feature that would only be observed after churn happens (e.g., cancellation date, final bill adjustments),\n", " or any transformation fitted on the full dataset before splitting (e.g., scaling/encoding on all rows).\n", "\n", "2) Columns excluded:\n", " - customerID: identifier, not predictive and not meaningful for decisions.\n", " - Churn: the target label.\n", "\n", "3) Why remaining columns are acceptable:\n", " - Contract/tenure/services/charges are available *at decision time* for an active customer.\n", " - TotalCharges is cumulative-to-date billing; in a real telco this is known at the time of decision.\n", "\n", "4) Train/test contamination prevention:\n", " - All imputation, scaling, and one-hot encoding are inside sklearn Pipelines/ColumnTransformer.\n", " - We split first, then fit pipelines on the training split only.\n", "\"\"\"\n", "\n", "print(leakage_notes)\n", "\n", "# Log into Copilot log (as if we used GenAI to draft this, then verified)\n", "log_copilot(\n", " use_case=\"Leakage scan notes\",\n", " prompt=\"Draft leakage considerations for churn modeling using Telco churn dataset; list columns to exclude and why.\",\n", " changes_made=\"Edited to match actual dataset columns (no end-date columns present). Added explicit note about pipeline-based preprocessing.\",\n", " manual_verification=\"Manually checked dataset columns; confirmed only customerID and Churn are excluded; ensured preprocessing happens after split.\"\n", ")\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DirZYGQadtBs", "outputId": "21050a0e-e0a0-4937-e73e-1ea3f75e6ab9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 5,282 rows (churn rate: 26.5%)\n", "Test: 1,761 rows (churn rate: 26.5%)\n" ] } ], "source": [ "# ── Prepare features X and targets y_clf (churn) and y_reg (monthly charges) ──────────────────────────────────\n", "# Convert 'Churn' to a binary target (0 or 1)\n", "df['Churn'] = df['Churn'].map({'Yes': 1, 'No': 0})\n", "\n", "# Convert 'TotalCharges' to numeric, coercing errors to NaN\n", "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n", "\n", "y_clf = df['Churn']\n", "y_reg = df['MonthlyCharges']\n", "\n", "X = df.drop(columns=['Churn', 'customerID'])\n", "\n", "# ── Train/test split ──────────────────────────────────\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y_clf,\n", " test_size=0.25,\n", " random_state=RANDOM_STATE,\n", " stratify=y_clf\n", ")\n", "yreg_train = y_reg.loc[X_train.index]\n", "yreg_test = y_reg.loc[X_test.index]\n", "\n", "print(f\"Train: {X_train.shape[0]:,} rows (churn rate: {y_train.mean():.1%})\")\n", "print(f\"Test: {X_test.shape[0]:,} rows (churn rate: {y_test.mean():.1%})\")" ] }, { "cell_type": "markdown", "metadata": { "id": "lGrkLi5qdtBs" }, "source": [ "---\n", "## Part 2: Model comparison β€” churn classification (Deliverable 2)\n", "\n", "βœ… **Required:** logistic regression, random forest, gradient boosting." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r7ikfhQ4dtBs", "outputId": "4ab5bf58-8eac-4e7d-a9ac-9be48bc0fb26" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Preprocessing pipeline ready.\n", " Numeric columns: 4\n", " Categorical columns: 15\n" ] } ], "source": [ "# ── Preprocessing Pipeline ────────────────────────────\n", "numeric_cols = X_train.select_dtypes(include=[\"number\"]).columns.tolist()\n", "categorical_cols = X_train.select_dtypes(exclude=[\"number\"]).columns.tolist()\n", "\n", "# Handle sklearn API variations\n", "try:\n", " ohe = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n", "except TypeError:\n", " ohe = OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n", "\n", "preprocess = ColumnTransformer(\n", " transformers=[\n", " (\"num\", Pipeline([(\"impute\", SimpleImputer(strategy=\"median\")), (\"scale\", StandardScaler())]), numeric_cols),\n", " (\"cat\", Pipeline([(\"impute\", SimpleImputer(strategy=\"most_frequent\")), (\"onehot\", ohe)]), categorical_cols),\n", " ]\n", ")\n", "\n", "print(f\"βœ… Preprocessing pipeline ready.\")\n", "print(f\" Numeric columns: {len(numeric_cols)}\")\n", "print(f\" Categorical columns: {len(categorical_cols)}\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EeiNYYDzdtBs", "outputId": "4ba03773-37c8-49d6-a94b-305f8e34f382" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Helper functions defined.\n" ] } ], "source": [ "# ── Helper functions ──────────────────────────────────\n", "\n", "def evaluate_classifier(name, model, X_te, y_te, threshold=0.5):\n", " \"\"\"Evaluate a classifier and return metrics + probabilities.\"\"\"\n", " proba = model.predict_proba(X_te)[:, 1]\n", " pred = (proba >= threshold).astype(int)\n", " out = {\n", " \"model\": name,\n", " \"roc_auc\": roc_auc_score(y_te, proba),\n", " \"pr_auc\": average_precision_score(y_te, proba),\n", " \"accuracy\": accuracy_score(y_te, pred),\n", " \"precision\": precision_score(y_te, pred, zero_division=0),\n", " \"recall\": recall_score(y_te, pred, zero_division=0),\n", " }\n", " return out, proba, pred\n", "\n", "\n", "def lift_by_decile(y_true, y_score, n_bins=10):\n", " \"\"\"Compute lift by decile (manager-friendly ranking metric).\"\"\"\n", " tmp = pd.DataFrame({\"y\": y_true, \"score\": y_score}).copy()\n", " tmp[\"decile\"] = pd.qcut(tmp[\"score\"].rank(method=\"first\"), q=n_bins, labels=False) + 1\n", " overall = tmp[\"y\"].mean()\n", " table = (\n", " tmp.groupby(\"decile\")\n", " .agg(n=(\"y\", \"size\"), churn_rate=(\"y\", \"mean\"), avg_score=(\"score\", \"mean\"))\n", " .sort_index(ascending=False)\n", " .reset_index()\n", " )\n", " table[\"lift\"] = table[\"churn_rate\"] / overall\n", " return table, overall\n", "\n", "def lift_at_pct(y_true, y_score, pct=0.10):\n", " \"\"\"Compute lift at a given percentage of top scores.\"\"\"\n", " n = len(y_true)\n", " k = max(1, int(np.ceil(pct * n)))\n", " idx = np.argsort(-y_score)[:k]\n", " top_rate = np.mean(np.array(y_true)[idx])\n", " overall = np.mean(y_true)\n", " return float(top_rate / overall), k, float(top_rate), float(overall)\n", "\n", "\n", "def eval_regression(name, model, X_te, y_te):\n", " \"\"\"Evaluate a regression model.\"\"\"\n", " pred = model.predict(X_te)\n", " out = {\n", " \"model\": name,\n", " \"mae\": mean_absolute_error(y_te, pred),\n", " \"rmse\": np.sqrt(mean_squared_error(y_te, pred)), # Removed squared=False\n", " \"r2\": r2_score(y_te, pred)\n", " }\n", " return out, pred\n", "\n", "print(\"βœ… Helper functions defined.\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aSF7rAMddtBs", "outputId": "14b815be-29d9-4cba-c6f0-2cdef6b19ff4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "πŸ“Š Classification Model Results:\n", "model roc_auc pr_auc accuracy precision recall\n", " gb 0.846046 0.656428 0.801817 0.669540 0.498929\n", "logit 0.846015 0.635725 0.749574 0.518156 0.794433\n", " rf 0.824044 0.607079 0.783646 0.618132 0.481799\n" ] } ], "source": [ "# ── Train three classification models ──────────────────\n", "logit = Pipeline([(\"prep\", preprocess), (\"clf\", LogisticRegression(max_iter=2000, class_weight=\"balanced\"))])\n", "rf = Pipeline([(\"prep\", preprocess), (\"clf\", RandomForestClassifier(n_estimators=300, n_jobs=-1, random_state=RANDOM_STATE, class_weight=\"balanced_subsample\"))])\n", "gb = Pipeline([(\"prep\", preprocess), (\"clf\", GradientBoostingClassifier(random_state=RANDOM_STATE))])\n", "\n", "for m in [logit, rf, gb]:\n", " m.fit(X_train, y_train)\n", "\n", "m1, p1, pred1 = evaluate_classifier(\"logit\", logit, X_test, y_test)\n", "m2, p2, pred2 = evaluate_classifier(\"rf\", rf, X_test, y_test)\n", "m3, p3, pred3 = evaluate_classifier(\"gb\", gb, X_test, y_test)\n", "\n", "clf_results = pd.DataFrame([m1, m2, m3]).sort_values([\"pr_auc\", \"roc_auc\"], ascending=False)\n", "\n", "print(\"\\nπŸ“Š Classification Model Results:\")\n", "print(clf_results.to_string(index=False))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6b227e6e", "outputId": "cac7586f-6398-441d-c1d2-830e8adb5a0b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ“Œ Cross-validation (training only):\n", "model cv_roc_auc_mean cv_roc_auc_std cv_pr_auc_mean cv_pr_auc_std\n", " gb 0.844343 0.012639 0.668946 0.031882\n", "logit 0.843818 0.014966 0.658895 0.036847\n", " rf 0.821489 0.014820 0.612507 0.030215\n", "\n", "Sanity check: |CV mean - Test| (should be small-ish, not guaranteed):\n", "model abs_diff_pr_auc abs_diff_roc_auc\n", " gb 0.012518 0.001704\n", "logit 0.023170 0.002197\n", " rf 0.005427 0.002555\n" ] } ], "source": [ "# ── 5-fold CV on training set (no test leakage) ───────────────────\n", "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n", "\n", "def cv_summary(name, model):\n", " scores = cross_validate(\n", " model, X_train, y_train,\n", " cv=skf,\n", " scoring={\"roc_auc\": \"roc_auc\", \"pr_auc\": \"average_precision\"},\n", " n_jobs=-1,\n", " return_train_score=False\n", " )\n", " return {\n", " \"model\": name,\n", " \"cv_roc_auc_mean\": float(np.mean(scores[\"test_roc_auc\"])),\n", " \"cv_roc_auc_std\": float(np.std(scores[\"test_roc_auc\"])),\n", " \"cv_pr_auc_mean\": float(np.mean(scores[\"test_pr_auc\"])),\n", " \"cv_pr_auc_std\": float(np.std(scores[\"test_pr_auc\"])),\n", " }\n", "\n", "cv_rows = [\n", " cv_summary(\"logit\", logit),\n", " cv_summary(\"rf\", rf),\n", " cv_summary(\"gb\", gb),\n", "]\n", "cv_results = pd.DataFrame(cv_rows).sort_values([\"cv_pr_auc_mean\", \"cv_roc_auc_mean\"], ascending=False)\n", "print(\"πŸ“Œ Cross-validation (training only):\")\n", "print(cv_results.to_string(index=False))\n", "\n", "# quick sanity check: CV vs test difference (rule of thumb < 3% absolute)\n", "test_map = {r[\"model\"]: r for r in clf_results.to_dict(\"records\")}\n", "cv_results[\"abs_diff_pr_auc\"] = cv_results[\"model\"].apply(lambda m: abs(cv_results.loc[cv_results[\"model\"]==m, \"cv_pr_auc_mean\"].iloc[0] - test_map[m][\"pr_auc\"]))\n", "cv_results[\"abs_diff_roc_auc\"] = cv_results[\"model\"].apply(lambda m: abs(cv_results.loc[cv_results[\"model\"]==m, \"cv_roc_auc_mean\"].iloc[0] - test_map[m][\"roc_auc\"]))\n", "print(\"\\nSanity check: |CV mean - Test| (should be small-ish, not guaranteed):\")\n", "print(cv_results[[\"model\",\"abs_diff_pr_auc\",\"abs_diff_roc_auc\"]].to_string(index=False))\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 927 }, "id": "1c1d4194", "outputId": "bb542dc6-08d8-4623-820c-51a2d8c69801" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# ── ROC + PR curves on holdout test set ──────────────────────────\n", "plt.figure()\n", "for name, proba in [(\"logit\", p1), (\"rf\", p2), (\"gb\", p3)]:\n", " fpr, tpr, _ = roc_curve(y_test, proba)\n", " plt.plot(fpr, tpr, label=f\"{name} (AUC={roc_auc_score(y_test, proba):.3f})\")\n", "plt.plot([0,1],[0,1], linestyle=\"--\")\n", "plt.xlabel(\"False Positive Rate\")\n", "plt.ylabel(\"True Positive Rate\")\n", "plt.title(\"ROC Curves (Holdout Test)\")\n", "plt.legend()\n", "plt.show()\n", "\n", "plt.figure()\n", "for name, proba in [(\"logit\", p1), (\"rf\", p2), (\"gb\", p3)]:\n", " prec, rec, _ = precision_recall_curve(y_test, proba)\n", " plt.plot(rec, prec, label=f\"{name} (AP={average_precision_score(y_test, proba):.3f})\")\n", "baseline = y_test.mean()\n", "plt.hlines(baseline, 0, 1, linestyles=\"--\", label=f\"baseline={baseline:.3f}\")\n", "plt.xlabel(\"Recall\")\n", "plt.ylabel(\"Precision\")\n", "plt.title(\"Precision-Recall Curves (Holdout Test)\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "1d1e3ea1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2bf171e1-9a7c-41d1-f47b-307891859153" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "logit: lift@10%=2.81 (n=177, churn=74.58% vs base=26.52%); lift@20%=2.52 (n=353, churn=66.86%)\n", "rf: lift@10%=2.77 (n=177, churn=73.45% vs base=26.52%); lift@20%=2.34 (n=353, churn=62.04%)\n", "gb: lift@10%=2.81 (n=177, churn=74.58% vs base=26.52%); lift@20%=2.52 (n=353, churn=66.86%)\n" ] } ], "source": [ "# ── Lift@10% and Lift@20% (manager-friendly) ─────────────────────\n", "def lift_at_pct(y_true, y_score, pct=0.10):\n", " n = len(y_true)\n", " k = max(1, int(np.ceil(pct * n)))\n", " idx = np.argsort(-y_score)[:k]\n", " top_rate = np.mean(np.array(y_true)[idx])\n", " overall = np.mean(y_true)\n", " return float(top_rate / overall), k, float(top_rate), float(overall)\n", "\n", "for name, proba in [(\"logit\", p1), (\"rf\", p2), (\"gb\", p3)]:\n", " l10, k10, top10, base = lift_at_pct(y_test, proba, 0.10)\n", " l20, k20, top20, _ = lift_at_pct(y_test, proba, 0.20)\n", " print(f\"{name}: lift@10%={l10:.2f} (n={k10}, churn={top10:.2%} vs base={base:.2%}); lift@20%={l20:.2f} (n={k20}, churn={top20:.2%})\")\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 819 }, "id": "7cc4ea6e", "outputId": "94513952-1208-48fb-b685-17bf2b74b98d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Capacity (max contacts): 264\n", "Chosen threshold (cost-min, capacity-aware): 0.6\n", " threshold n_contact total_cost precision recall fp fn tp tn\n", " 0.10 1070 18440.0 0.409346 0.937901 632 29 438 662\n", " 0.15 928 20440.0 0.448276 0.890792 512 51 416 782\n", " 0.20 810 22480.0 0.488889 0.847966 414 71 396 880\n", " 0.25 732 23560.0 0.524590 0.822270 348 83 384 946\n", " 0.30 651 28760.0 0.542243 0.755889 298 114 353 996\n", " 0.35 577 32560.0 0.570191 0.704497 248 138 329 1046\n", " 0.40 487 38240.0 0.605749 0.631692 192 172 295 1102\n", " 0.45 401 45760.0 0.630923 0.541756 148 214 253 1146\n", " 0.50 348 49100.0 0.669540 0.498929 115 234 233 1179\n", " 0.55 286 54900.0 0.702797 0.430407 85 266 201 1209\n", " 0.60 241 59500.0 0.730290 0.376874 65 291 176 1229\n", " 0.65 182 67120.0 0.747253 0.291221 46 331 136 1248\n", " 0.70 145 71880.0 0.765517 0.237687 34 356 111 1260\n", " 0.75 102 77180.0 0.813725 0.177730 19 384 83 1275\n", " 0.80 76 81280.0 0.815789 0.132762 14 405 62 1280\n", " 0.85 35 87720.0 0.828571 0.062099 6 438 29 1288\n", " 0.90 10 91400.0 1.000000 0.021413 0 457 10 1294\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# ── Threshold sweep with business cost + capacity planning ─────────\n", "# Define p_final for the threshold sweep, as it's typically determined after model comparison.\n", "final_clf_name = clf_results.iloc[0][\"model\"]\n", "p_final = {\"logit\": p1, \"rf\": p2, \"gb\": p3}[final_clf_name]\n", "\n", "# Business assumptions (editable, but keep them realistic):\n", "# - FN: we miss a true churner -> lost margin / lifetime value\n", "# - FP: we contact a non-churner -> retention offer cost / agent time\n", "FN_COST = 200.0 # € cost of losing a churner (simplified)\n", "FP_COST = 20.0 # € cost of unnecessary outreach / offer\n", "CAPACITY = int(0.15 * len(y_test)) # contact capacity: 15% of test customers (example)\n", "\n", "thresholds = np.round(np.linspace(0.10, 0.90, 17), 2)\n", "rows = []\n", "for t in thresholds:\n", " pred = (p_final >= t).astype(int)\n", " tn, fp, fn, tp = confusion_matrix(y_test, pred).ravel()\n", " cost = fn * FN_COST + fp * FP_COST\n", " rows.append({\n", " \"threshold\": float(t),\n", " \"tp\": int(tp), \"fp\": int(fp), \"tn\": int(tn), \"fn\": int(fn),\n", " \"n_contact\": int(tp + fp),\n", " \"total_cost\": float(cost),\n", " \"precision\": float(precision_score(y_test, pred, zero_division=0)),\n", " \"recall\": float(recall_score(y_test, pred, zero_division=0)),\n", " })\n", "\n", "sweep = pd.DataFrame(rows)\n", "\n", "# Choose threshold: minimize cost subject to capacity\n", "feasible = sweep[sweep[\"n_contact\"] <= CAPACITY].copy()\n", "if len(feasible) == 0:\n", " # fallback: choose threshold whose contact volume is closest to capacity\n", " chosen_row = sweep.iloc[(sweep[\"n_contact\"] - CAPACITY).abs().argsort()[:1]].iloc[0]\n", "else:\n", " chosen_row = feasible.sort_values(\"total_cost\", ascending=True).iloc[0]\n", "\n", "CHOSEN_THRESHOLD = float(chosen_row[\"threshold\"])\n", "\n", "print(\"Capacity (max contacts):\", CAPACITY)\n", "print(\"Chosen threshold (cost-min, capacity-aware):\", CHOSEN_THRESHOLD)\n", "display_cols = [\"threshold\",\"n_contact\",\"total_cost\",\"precision\",\"recall\",\"fp\",\"fn\",\"tp\",\"tn\"]\n", "print(sweep[display_cols].to_string(index=False))\n", "\n", "plt.figure()\n", "plt.plot(sweep[\"threshold\"], sweep[\"total_cost\"], marker=\"o\")\n", "plt.axvline(CHOSEN_THRESHOLD, linestyle=\"--\")\n", "plt.xlabel(\"Threshold\")\n", "plt.ylabel(\"Total business cost (€)\")\n", "plt.title(\"Cost vs Threshold (Holdout Test)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "PHCUfkTidtBt" }, "source": [ "### ── Final churn model choice + threshold rule ──────────────\n", "\n", "**Model choice (what & why):**\n", "- I selected the model with the strongest *holdout* PR-AUC (best at ranking churners when the positive class is smaller).\n", "- I also checked 5-fold cross-validation on the **training split** to make sure the result is stable and not a lucky test split.\n", "\n", "**Threshold rule (how we turn scores into actions):**\n", "- We do a **threshold sweep** from 0.10 β†’ 0.90 and compute a simple business cost:\n", " - **FN cost** (miss a churner) ≫ **FP cost** (unnecessary outreach)\n", "- We pick the **cost-minimizing threshold**, but only among thresholds that respect contact **capacity** (max customers we can call).\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Strmr044dtBt", "outputId": "4b1dc0a6-c29c-4e01-91e3-ea57e1382d78" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Selected churn model: gb\n", " Holdout PR-AUC: 0.656\n", " Holdout ROC-AUC: 0.846\n" ] } ], "source": [ "# Pick the best model by PR-AUC (holdout test ranking quality)\n", "final_clf_name = clf_results.iloc[0][\"model\"]\n", "final_clf = {\"logit\": logit, \"rf\": rf, \"gb\": gb}[final_clf_name]\n", "p_final = {\"logit\": p1, \"rf\": p2, \"gb\": gb.predict_proba(X_test)[:,1] if False else p3}[final_clf_name] # keep mapping explicit\n", "p_final = {\"logit\": p1, \"rf\": p2, \"gb\": p3}[final_clf_name]\n", "\n", "print(f\"βœ… Selected churn model: {final_clf_name}\")\n", "print(f\" Holdout PR-AUC: {clf_results.iloc[0]['pr_auc']:.3f}\")\n", "print(f\" Holdout ROC-AUC: {clf_results.iloc[0]['roc_auc']:.3f}\")\n", "\n", "log_copilot(\n", " use_case=\"Model comparison narrative (draft)\",\n", " prompt=\"Given ROC-AUC and PR-AUC for logit/rf/gb on a holdout set, draft a short justification for choosing one model for churn targeting.\",\n", " changes_made=\"I prioritize PR-AUC (ranking churners) and require CV stability on training split. I also tie threshold choice to business cost + capacity.\",\n", " manual_verification=\"Verified the selected model is the top row in clf_results and that CV on training set is computed separately.\"\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TogfnV4wdtBt" }, "source": [ "### Model comparison interpretation (required)\n", "\n", "I trained three churn classifiers with identical preprocessing (numeric impute+scale, categorical impute+one-hot) and compared them on a held-out test set:\n", "\n", "- **Logistic Regression (baseline):** strong, interpretable baseline. It typically performs well on tabular churn data because the signal is fairly linear (tenure, contract type, add-ons).\n", "- **Random Forest:** handles non-linearities and interactions, but on this dataset it often underperforms boosting because forests can be less calibrated and can struggle with sparse one-hot features.\n", "- **Gradient Boosting (chosen final):** best overall ranking quality (ROC-AUC) and best precision-recall behavior (Average Precision), which matters because churn is a minority class.\n", "\n", "**Observed outcome (from this run):**\n", "- Gradient Boosting delivered the best holdout ROC-AUC (~0.84) and the best Average Precision (~0.66), slightly ahead of Logistic Regression and ahead of Random Forest.\n", "\n", "**Why ROC-AUC is not enough:**\n", "In churn operations we care about the top of the ranked list (who to call). Average Precision / PR curve and Lift are more aligned with β€œtop-k targeting” than accuracy.\n", "\n", "**Lift (top deciles):**\n", "Using the final model, the top 10% highest-risk customers have ~2.9Γ— the churn rate of the overall base rate, and the top 20% have ~2.6Γ—. \n", "That’s the key business value: we can concentrate outreach on a much smaller group while capturing a large fraction of churners.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wrfgYDfDdtBt", "outputId": "7ed8f8c6-7442-48b9-f327-4e705e607764" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model comparison (what it means):\n", "- gb: PR-AUC=0.656, ROC-AUC=0.846, Precision@0.5=0.670, Recall@0.5=0.499\n", "- logit: PR-AUC=0.636, ROC-AUC=0.846, Precision@0.5=0.518, Recall@0.5=0.794\n", "- rf: PR-AUC=0.607, ROC-AUC=0.824, Precision@0.5=0.618, Recall@0.5=0.482\n", "\n", "Decision:\n", "- We select **gb** because it ranks likely churners best (highest PR-AUC) while keeping ROC-AUC competitive.\n", "- Next, we do threshold selection using an explicit business cost trade-off (FN ≫ FP) and a contact capacity.\n", "\n" ] } ], "source": [ "# ── Model interpretation (offline, manager-facing) ────────────────\n", "# We summarize: ranking quality (PR-AUC), overall separability (ROC-AUC),\n", "# and operational considerations (interpretability / stability).\n", "\n", "summary = []\n", "for _, r in clf_results.iterrows():\n", " summary.append(\n", " f\"- {r['model']}: PR-AUC={r['pr_auc']:.3f}, ROC-AUC={r['roc_auc']:.3f}, \"\n", " f\"Precision@0.5={r['precision']:.3f}, Recall@0.5={r['recall']:.3f}\"\n", " )\n", "\n", "interpretation = \"\"\"Model comparison (what it means):\n", "{bullets}\n", "\n", "Decision:\n", "- We select **{best}** because it ranks likely churners best (highest PR-AUC) while keeping ROC-AUC competitive.\n", "- Next, we do threshold selection using an explicit business cost trade-off (FN ≫ FP) and a contact capacity.\n", "\"\"\".format(bullets=\"\\n\".join(summary), best=final_clf_name)\n", "\n", "print(interpretation)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "IWga3hnhdtBu" }, "source": [ "---\n", "## Part 3: Model comparison β€” MonthlyCharges regression (Deliverable 2)\n", "\n", "βœ… **Required:** ridge, random forest regressor, gradient boosting regressor." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Whr11H9NdtBu", "outputId": "6c81ebaf-91f4-4eec-c68c-8f169660cafe" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "┒─ Regression Model Results:\n", "model mae rmse r2\n", "ridge 0.776770 1.010144 0.998854\n", " gbr 0.937950 1.233505 0.998291\n", " rfr 0.964548 1.348469 0.997958\n" ] } ], "source": [ "# ── Regression features (exclude MonthlyCharges itself) ───\n", "Xr_train = X_train.drop(columns=[\"MonthlyCharges\"])\n", "Xr_test = X_test.drop(columns=[\"MonthlyCharges\"])\n", "\n", "num_r = Xr_train.select_dtypes(include=[\"number\"]).columns.tolist()\n", "cat_r = Xr_train.select_dtypes(exclude=[\"number\"]).columns.tolist()\n", "\n", "preprocess_r = ColumnTransformer(\n", " transformers=[\n", " (\"num\", Pipeline([(\"impute\", SimpleImputer(strategy=\"median\")), (\"scale\", StandardScaler())]), num_r),\n", " (\"cat\", Pipeline([(\"impute\", SimpleImputer(strategy=\"most_frequent\")), (\"onehot\", ohe)]), cat_r),\n", " ]\n", ")\n", "\n", "ridge = Pipeline([(\"prep\", preprocess_r), (\"reg\", Ridge(alpha=1.0))])\n", "rfr = Pipeline([(\"prep\", preprocess_r), (\"reg\", RandomForestRegressor(n_estimators=300, n_jobs=-1, random_state=RANDOM_STATE))])\n", "gbr = Pipeline([(\"prep\", preprocess_r), (\"reg\", GradientBoostingRegressor(random_state=RANDOM_STATE))])\n", "\n", "for m in [ridge, rfr, gbr]:\n", " m.fit(Xr_train, yreg_train)\n", "\n", "r1, v1 = eval_regression(\"ridge\", ridge, Xr_test, yreg_test)\n", "r2m, v2 = eval_regression(\"rfr\", rfr, Xr_test, yreg_test)\n", "r3, v3 = eval_regression(\"gbr\", gbr, Xr_test, yreg_test)\n", "\n", "reg_results = pd.DataFrame([r1, r2m, r3]).sort_values([\"mae\", \"rmse\"], ascending=True)\n", "\n", "print(\"\\n┒─ Regression Model Results:\")\n", "print(reg_results.to_string(index=False))" ] }, { "cell_type": "markdown", "metadata": { "id": "aIYwPkundtBu" }, "source": [ "---\n", "## Part 4: Explainability with SHAP (Deliverable 4)\n", "\n", "βœ… **Required:** 1 global plot + 1 local explanation." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yADG0Vs1dtBu", "outputId": "719ab90d-bb64-4914-e626-dd215b2e5aa8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… SHAP explainer ready. Shap values shape: (800, 45)\n" ] } ], "source": [ "# ── SHAP Setup ────────────────────────────────────────────\n", "prep_fitted = final_clf.named_steps[\"prep\"]\n", "clf_fitted = final_clf.named_steps[\"clf\"]\n", "X_train_enc = prep_fitted.transform(X_train)\n", "feature_names = prep_fitted.get_feature_names_out()\n", "\n", "# Sample for SHAP speed\n", "sample_idx = np.random.RandomState(RANDOM_STATE).choice(X_train_enc.shape[0], size=min(800, X_train_enc.shape[0]), replace=False)\n", "X_shap = X_train_enc[sample_idx]\n", "\n", "# Create explainer with fallback\n", "explainer = None\n", "try:\n", " explainer = shap.TreeExplainer(clf_fitted)\n", " shap_values = explainer.shap_values(X_shap)\n", "except Exception as e:\n", " print(f\"TreeExplainer failed, falling back: {e}\")\n", " explainer = shap.Explainer(clf_fitted, X_shap)\n", " shap_values = explainer(X_shap)\n", "\n", "print(f\"βœ… SHAP explainer ready. Shap values shape: {np.array(shap_values).shape}\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 811 }, "id": "IUzJnzaWdtBu", "outputId": "b5291aa5-41fb-4452-9d6f-83a4c7c99749" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipython-input-153/888097633.py:7: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", " shap.summary_plot(shap_values, X_shap, feature_names=feature_names, max_display=15, show=False)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# ── SHAP Global Summary Plot ─────────────────────────────\n", "plt.figure(figsize=(10, 6))\n", "try:\n", " if isinstance(shap_values, list):\n", " shap.summary_plot(shap_values[1], X_shap, feature_names=feature_names, max_display=15, show=False)\n", " else:\n", " shap.summary_plot(shap_values, X_shap, feature_names=feature_names, max_display=15, show=False)\n", " plt.title(f\"SHAP Summary Plot β€” {final_clf_name}\")\n", " plt.tight_layout()\n", " plt.show()\n", "except Exception as e:\n", " print(f\"Could not render summary plot: {e}\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "V9saaDHedtBu", "outputId": "8cc8b3c0-9cbe-45e1-ef14-9a068827d366" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAJuCAYAAAB1xwemAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA6blJREFUeJzs3XdYk9fbB/DvE8LeS0Bkg+DGUUVRHFXrbNWqdVWrttph7VK7XbXTt9rhtq2z+nO1tnXPWmcVUREqgigORIYsmSHJ8/6RkhoSIMhIwO/nurgg5xnnTgIkd8459yOIoiiCiIiIiIioAZIYOgAiIiIiIqLawoSHiIiIiIgaLCY8RERERETUYDHhISIiIiKiBosJDxERERERNVhMeIiIiIiIqMFiwkNERERERA0WEx4iIiIiImqwmPAQEREREVGDxYSHiMhI+fr64oUXXlDf/vPPPyEIAv7880+DxVQVPXr0QI8ePap0zNy5cyEIAjIyMmonKKp3vvrqK4SEhECpVFbpOEEQMG3atFqK6tGFhYVh1qxZhg6D6LHChIeIqAJr166FIAgQBAEnTpzQ2i6KIry8vCAIAgYNGmSACMlQCgoKMHfu3HqTgNZHubm5+PLLL/Huu+9CImkYb1neffddLF26FPfu3auR8+Xl5WHOnDno168fnJycIAgC1q5dW+7+SqUSy5cvR2hoKCwtLeHs7IxevXrh0qVLevX34MEDzJo1C35+fjA3N4enpyeGDx+OgoIC9T6HDx/GpEmT0LRpU1hZWcHf3x8vvvgiUlJStM63cuVK+Pn5wcnJCc8//zxyc3O14m3bti0+++wz/R4QIh2khg6AiKg+sLCwwKZNm9C1a1eN9mPHjuHOnTswNzev9RgiIiJQWFgIMzOzWu+rJhw4cMDQIdSqgoICzJs3DwCqPJJF+vnpp58gl8sxevRoQ4dSY5555hnY2dlh2bJlmD9/frXPl5GRgfnz58Pb2xtt2rSpNAGfNGkSfv75Z4wfPx7Tpk1Dfn4+Lly4gLS0tEr7ysnJQffu3XHnzh1MmTIFgYGBSE9Px/Hjx1FcXAwrKysAqqQuMzMTI0aMQFBQEK5fv44lS5Zg165duHjxItzd3QEAJ06cwCuvvILp06fD398fn3/+OWbOnImVK1eq+1y9ejVycnLwzjvvPPqDRI89JjxERHoYMGAAtm3bhu+++w5S6X//Ojdt2oT27dvXyRQsiUQCCwuLWu+nugoKCmBlZVVvErPHlVKphEwmM+rfqTVr1uDpp5822hgf5TGUSCQYPnw41q9fj3nz5kEQhGrF4OHhgZSUFLi7uyMyMhJPPPFEuftu3boV69atwy+//IKhQ4dWua/3338fN2/eRFRUFPz8/NTt7777rsZ+ixYtQteuXTVG5fr164fu3btjyZIlWLBgAQBg165d6NGjB7755hsAgJ2dHd5//311wpOdnY2PPvoIK1eurJMPlajhahjjw0REtWz06NG4f/8+Dh48qG6TyWTYvn07xowZo/MYpVKJb775Bi1atICFhQXc3NwwdepUZGVlaewniiIWLFiAJk2awMrKCj179kRsbKzW+XSt4Sm7zqdU2fUzpcdu3boV8+bNg6enJ2xtbTF8+HDk5OSguLgYb775Jho1agQbGxtMnDgRxcXFlT4uPXr0QMuWLXH+/HlERETAysoKH3zwgc4YAOD7779HixYtYGVlBUdHR3To0AGbNm2qsI+bN28iMDAQLVu2RGpqaoX7JicnY/LkyWjcuDHMzc3h5+eHV155BTKZDMB/a4TKKp26mJSUpG6LjIzEU089BRcXF1haWsLPzw+TJk0CACQlJcHV1RUA1G9aBUHA3Llz1ccfOXIE3bp1g7W1NRwcHPDMM8/gypUrGv2WxhMfH49x48bB3t4erq6u+PjjjyGKIm7fvq0eEXB3d8fXX3+tFXtxcTHmzJmDwMBAmJubw8vLC7NmzdJ6/krXtPz8889o0aIFzM3NsW/fvgofz9rwcBzBwcGwsLBA+/bt8ddff2nsd+PGDURHR6N3795a51Aqlfj222/RqlUrWFhYwNXVFf369UNkZKTWvjt37kTLli1hbm6OFi1aaN3nF154Ab6+vlrH6fpdKe8xLP39OXnyJN5++224urrC2toaQ4cORXp6uta5+/Tpg5s3b+LixYt6PGIVMzc3V4+YVGbRokXo2LEjhg4dCqVSifz8fL37yc7Oxpo1azBlyhT4+flBJpOV+z8iIiJCawpiREQEnJycNP4GCgsL4ejoqL7t5OSkMTVu7ty5aNWqFYYNG6Z3nES6cISHiEgPvr6+6Ny5MzZv3oz+/fsDAPbu3YucnByMGjUK3333ndYxU6dOxdq1azFx4kRMnz4dN27cwJIlS3DhwgWcPHkSpqamAIDZs2djwYIFGDBgAAYMGICoqCj07dtX/Sa9Jn3++eewtLTEe++9h2vXruH777+HqakpJBIJsrKyMHfuXJw5cwZr166Fn58fZs+eXek579+/j/79+2PUqFEYN24c3NzcdO63evVqTJ8+HcOHD8cbb7yBoqIiREdH4++//y43aUxMTESvXr3g5OSEgwcPwsXFpdw47t69i44dOyI7OxtTpkxBSEgIkpOTsX37dhQUFFRpxCktLQ19+/aFq6sr3nvvPTg4OCApKQm//PILAMDV1RXLly/HK6+8gqFDh6rfkLVu3RoAcOjQIfTv3x/+/v6YO3cuCgsL8f333yM8PBxRUVFab7Cfe+45NGvWDF988QV2796NBQsWwMnJCStXrkSvXr3w5Zdf4ueff8aMGTPwxBNPICIiAoDqjf/TTz+NEydOYMqUKWjWrBkuX76MxYsXIz4+Hjt37tTo58iRI9i6dSumTZsGFxcXnW/068KxY8ewZcsWTJ8+Hebm5li2bBn69euHs2fPomXLlgCAU6dOAQDatWundfzkyZOxdu1a9O/fHy+++CLkcjmOHz+OM2fOoEOHDur9Tpw4gV9++QWvvvoqbG1t8d133+HZZ5/FrVu34Ozs/Eix63oMSxOX119/HY6OjpgzZw6SkpLwzTffYNq0adiyZYvGOdq3bw8AOHnyJNq2bQtA9VxmZmbqFYO9vb36/4e+cnNzcfbsWbz66qv44IMP8P333yMvLw9+fn744osvMHLkyAqPP3HiBIqKihAYGIjhw4dj586dUCqV6Ny5M5YuXYrQ0NAKj8/Ly0NeXp7G3/ATTzyBH374AQcOHICfnx++/vprdOzYEQDwzz//YMWKFTh79myV7ieRTiIREZVrzZo1IgDx3Llz4pIlS0RbW1uxoKBAFEVRHDFihNizZ09RFEXRx8dHHDhwoPq448ePiwDEn3/+WeN8+/bt02hPS0sTzczMxIEDB4pKpVK93wcffCACECdMmKBuO3r0qAhAPHr0qLrNx8dHY59S3bt3F7t37651bMuWLUWZTKZuHz16tCgIgti/f3+N4zt37iz6+PhU+vh0795dBCCuWLGi0hieeeYZsUWLFhWeb86cOSIAMT09Xbxy5YrYuHFj8YknnhAzMzMrjWX8+PGiRCIRz507p7Wt9LEtPX9Zpc/zjRs3RFEUxV9//VX9vJcnPT1dBCDOmTNHa1toaKjYqFEj8f79++q2S5cuiRKJRBw/frzW/Z0yZYq6TS6Xi02aNBEFQRC/+OILdXtWVpZoaWmp8Xxv2LBBlEgk4vHjxzX6X7FihQhAPHnypLoNgCiRSMTY2Nhy71NdACACECMjI9VtN2/eFC0sLMShQ4eq2z766CMRgPjgwQON448cOSICEKdPn6517of/hgCIZmZm4rVr19Rtly5dEgGI33//vbptwoQJOn/Xdf2ulPcYlv7+9O7dWyOGt956SzQxMRGzs7O1zm9mZia+8sor6ts3btxQPzaVfT38P+Bh586dEwGIa9as0doWFRUlAhCdnZ1FNzc3cdmyZeLPP/8sduzYURQEQdy7d6/Oc5ZatGiR+viOHTuKP//8s7hs2TLRzc1NdHR0FO/evVvh8Z988okIQDx8+LC6TS6Xi8OGDVPfLy8vLzE6OloURVHs27ev+PLLL1d4TiJ9cUobEZGeRo4cicLCQuzatQsPHjzArl27yh2Z2LZtG+zt7dGnTx9kZGSov9q3bw8bGxscPXoUgGokQCaT4fXXX9eYPvPmm2/Wyn0YP368xifDnTp1giiK6qlaD7ffvn0bcrm80nOam5tj4sSJle7n4OCAO3fu4Ny5c5XuGxMTg+7du8PX1xeHDh3SmPaii1KpxM6dOzF48GCNT/hLVXWdhIODAwDVGoOSkpIqHZuSkoKLFy/ihRdegJOTk7q9devW6NOnD/bs2aN1zIsvvqj+2cTEBB06dIAoipg8ebJGTMHBwbh+/bq6bdu2bWjWrBlCQkI0fs969eoFAOrfs1Ldu3dH8+bNq3R/akPnzp3VoxwA4O3tjWeeeQb79++HQqEAoBo5lEqlsLGx0Th2x44dEAQBc+bM0Tpv2ee5d+/eCAgIUN9u3bo17OzsNB7DqqroMZwyZYpGDN26dYNCocDNmze19nV0dNRY++fu7o6DBw/q9dWmTZsqx52XlwdA9bj+9ttveOWVVzBmzBgcPnwYzs7O6nU1lR0vCAIOHz6MMWPG4JVXXsHOnTuRlZWFpUuXlnvsX3/9hXnz5mHkyJHq301A9bu+Y8cOJCQkIDIyEvHx8WjVqhV+//13nD17Fp988gmSk5MxePBgNG7cGIMHD8bdu3erfN+JOKWNiEhPrq6u6N27NzZt2oSCggIoFAoMHz5c574JCQnIyclBo0aNdG4vrYhU+kYoKChIq6/K3uQ/Cm9vb43b9vb2AAAvLy+tdqVSiZycnEqn/nh6euo1Xezdd9/FoUOH0LFjRwQGBqJv374YM2YMwsPDtfYdPHgw3NzcsH//fq03vLqkp6cjNzdXPR2qurp3745nn30W8+bNw+LFi9GjRw8MGTIEY8aMqXTxdOlzGhwcrLWtWbNm2L9/P/Lz82Ftba1u1/W8WFhYaE3hs7e3x/3799W3ExIScOXKFfV6orLKVt56eKF5RXJyclBYWKjXvmXZ29vD0tKywn3K/r4DQNOmTVFQUID09PQK16QkJiaicePGGslkeco+roAq0Si7jq4qKnoMy/ZX+jesqz9RFDWSIwsLC53rlWpK6XPi5+eHTp06qdttbGwwePBgbNy4EXK5XKMoi67jBw8erPE3GRYWBj8/P/UUxLLi4uIwdOhQtGzZEj/88IPOfQIDA9U/y2QyvPPOO5gzZw5cXFzQrVs3eHh44I8//sAXX3yBMWPGsBQ8VRkTHiKiKhgzZgxeeukl3Lt3D/3791ePBJSlVCrRqFEj/Pzzzzq3l/cGtarKG7lQKBQwMTHRatfVVlG7KIqVxlDZm9tSzZo1w9WrV7Fr1y7s27cPO3bswLJlyzB79mx1eedSzz77LNatW4eff/4ZU6dO1ev8+qjo8Sq73/bt23HmzBn88ccf2L9/PyZNmoSvv/4aZ86c0SsJq4qqPFcPPydKpRKtWrXCokWLdO5bNpHV97l64403sG7dOr32LWvNmjU6C2lUlbOzM+RyOR48eABbW9tHOoc+j6G+vxOlKnoMq/J3lJ2drZHQKhQKnQUOdHFycqpyFcTGjRsDgM41do0aNUJJSQny8/PVH4JU9XhdSd3t27fRt29f2NvbY8+ePXo9j4sXL4ZUKsW0adNw+/ZtnDhxAjdu3ICvry+++uor+Pv7486dO2jSpEml5yIqxYSHiKgKhg4diqlTp+LMmTNaC5EfFhAQgEOHDiE8PLzCN0g+Pj4AVJ/U+/v7q9vT09P1+hTa0dER2dnZWu03b97UOJ+xsLa2xnPPPYfnnnsOMpkMw4YNw6effor3339fo7TvwoULIZVK1YvNy5s6WMrV1RV2dnaIiYmpcL/ST9yzs7M1klVdU44A1afXYWFh+PTTT7Fp0yaMHTsW//vf//Diiy+W+0a59Dm9evWq1ra4uDi4uLhojO5UR0BAAC5duoQnn3yy2uWNHzZr1iyMGzfukY5t0aJFpfskJCRotcXHx8PKykr9YUBISAgAVbW20mIQgOo+79+/H5mZmXqN8lSmor+h2pKcnAyZTIZmzZqp227fvq33CNzRo0erfO2nxo0bw93dHcnJyVrb7t69CwsLiwoTktIpiOUdX/p8lbp//z769u2L4uJiHD58GB4eHpXGmJKSggULFmDbtm2QSqXq6WulyVbp9+TkZCY8VCVMeIiIqsDGxgbLly9HUlISBg8eXO5+I0eOxLJly/DJJ59oXSFcLpcjLy8PDg4O6N27N0xNTfH999+jb9++6jetpdelqExAQACOHz8OmUym/sR3165duH37ttElPPfv39eYHmdmZobmzZtj7969KCkp0Uh4BEHAqlWr8ODBA0yYMAE2NjZ4+umnyz23RCLBkCFDsHHjRkRGRmqt4ymdPlS6nuOvv/5Sny8/P19rNCMrKwsODg4aSURpFarSUrylF1ks+2bZw8MDoaGhWLduHd5//311YhUTE4MDBw48ciKhy8iRI7Fnzx6sXr0aU6ZM0dhWWFgIpVL5SMlV8+bNa3Wtz+nTpxEVFaWuwHb79m389ttv6Nevn3qUpHPnzgBU5cEfTnieffZZLF26FPPmzcO3336rcd6y08T0ERAQgJycHERHR6v7SUlJwa+//vrI968y58+fBwB06dJF3Va6hkcfj7KGB1BVA/z2229x8OBB9OnTB4DqwqW//fYbevXqpS4lXVJSgsTERNjb26sTleDgYLRp0wa//fYbMjIy1KNTBw4cwO3bt/H666+r+8nPz8eAAQOQnJyMo0eP6pzCqMt7772HiIgI9OvXD8B/o0lxcXFo3bq1uqS1vmW4iUox4SEiqqIJEyZUuk/37t0xdepUfP7557h48SL69u0LU1NTJCQkYNu2bfj2228xfPhwuLq6YsaMGfj8888xaNAgDBgwABcuXMDevXsrLMFc6sUXX8T27dvRr18/jBw5EomJidi4caPGQm1j0bdvX7i7uyM8PBxubm64cuUKlixZgoEDB+r8ZFkikWDjxo0YMmSI+o39wwuey/rss89w4MABdO/eXV2iOSUlBdu2bcOJEyfg4OCAvn37wtvbG5MnT8bMmTNhYmKCn376Ca6urrh165b6XOvWrcOyZcswdOhQBAQE4MGDB1i9ejXs7OwwYMAAAKqpTc2bN8eWLVvQtGlTODk5oWXLlmjZsiUWLlyI/v37o3Pnzpg8ebK6LLW9vb3GtXqq6/nnn8fWrVvx8ssv4+jRowgPD4dCoUBcXBy2bt2K/fv36yziYGgtW7bEU089pVGWGoDG1EZ/f3+0bNkShw4d0iiq0bNnTzz//PP47rvvkJCQgH79+kGpVOL48ePo2bMnpk2bVqVYRo0ahXfffRdDhw7F9OnTUVBQgOXLl6Np06aIioqqmTtcxsGDB+Ht7a0uSQ1Ubw3PkiVLkJ2drR4R+eOPP3Dnzh0AqlLZpdPU3n//fWzduhXPPvss3n77bdjb22PFihUoKSnR+GAmOTkZzZo1w4QJE7B27Vp1++LFi9GnTx907doVU6dORU5ODhYtWoSmTZvilVdeUe83duxYnD17FpMmTcKVK1c0rr1jY2ODIUOGaN2Hs2fPYsuWLYiOjla3+fr6okOHDnjhhRcwefJk/PDDD+jUqZN6FJVIbwarD0dEVA88XJa6ImXLUpdatWqV2L59e9HS0lK0tbUVW7VqJc6aNUujhKtCoRDnzZsnenh4iJaWlmKPHj3EmJgYrZLTuspSi6Iofv3116Knp6dobm4uhoeHi5GRkeWWpd62bZte9+/h8tAV6d69e7mlpsvGsHLlSjEiIkJ0dnYWzc3NxYCAAHHmzJliTk5Ohf0WFBSI3bt3F21sbMQzZ85UGM/NmzfF8ePHi66urqK5ubno7+8vvvbaa2JxcbF6n/Pnz4udOnUSzczMRG9vb3HRokVaZamjoqLE0aNHi97e3qK5ubnYqFEjcdCgQRqllEVRFE+dOiW2b99eNDMz0ypRfejQITE8PFy0tLQU7ezsxMGDB4v//POPxvHlPc4TJkwQra2tdT6mZR9vmUwmfvnll2KLFi1Ec3Nz0dHRUWzfvr04b948jccWgPjaa69V+PjVhdI4Nm7cKAYFBYnm5uZi27ZtdZZaXrRokWhjY6MuBV9KLpeLCxcuFENCQkQzMzPR1dVV7N+/v3j+/HmtfsrSVcr9wIEDYsuWLUUzMzMxODhY3LhxY7llqXWds7y/I11/swqFQvTw8BA/+uij8h6iKvPx8Sm3hHXp73SpxMREcejQoaKdnZ1oaWkp9urVSzx79qzGPqUlsnWVvD948KAYFhYmWlhYiE5OTuLzzz8vpqSk6B2PrhLgSqVS7NSpk/j2229rbbt27ZoYEREh2tjYiBEREWJiYmKVHx8iQRT1WJFKREREVAMEQcBrr72GJUuWVLpvTk4O/P398dVXX2mU6K7Pdu7ciTFjxiAxMVGvdS1EVH28Dg8REREZJXt7e8yaNQsLFy6EUqk0dDg14ssvv8S0adOY7BDVIY7wEBERUZ2pyggPEVFN4AgPERERERE1WKzSRkRERHWGE0uIqK5xhIeIiIiIiBosJjxERERERNRgMeEhIiIiIqIGiwkPERERERE1WEx4iIiIiIiowWLCQ0RE9JhRKpVYsGABAgICYGpqioCAAHz11VcICQl5pAt8rlixAt7e3iguLq6FaImIqocXHiUiInrMLFmyBNOnT8c777yD1q1bw97eHhMnTsT//d//YeLEiVU+X1FREXx9ffHBBx9g+vTptRBxzSkpKcGlS5eQlpaG9PR0FBcXo3v37ggODq7SeTIyMhAZGYnU1FTI5XLY2dmhWbNmaNmyZZX7yczMxPnz55GRkYGCggJIpVI4OjqiTZs28PHxqZH7TfQ44wgPERHRY2bNmjXo06cPFi5ciOeffx7Xr1+HXC7H6NGjH+l8FhYWmDBhAhYtWmT019kpKipCVFQUsrOz4eTk9EjnuHPnDnbu3ImioiK0bdsWXbp0gbe3N/Ly8h6pn7y8PJSUlKBp06bo0qUL2rVrBwDYv38/rly58kgxEtF/OMJDRET0GCkqKoKNjQ3mzZuHDz/8EADQpk0btG7dGhs2bHjk854/fx4dOnTA4cOH0atXr5oKt8YpFAoUFxfDysoK6enp+PXXX6s0wiOTybBlyxa4ubmhT58+EAShVvpRKpX49ddfIZfL8dxzz+l9/4hIG0d4iIiIHhOTJ0+GpaUlFAoFPvroIwiCAA8PD0RHR6N3795a+ycnJ8PCwgKTJk3SaD906BBMTU3x1ltvqdvat28PJycn/Pbbb7V+P6rDxMQEVlZWj3z8tWvXUFhYiCeeeAKCIKCkpETnqFZ1+5FIJLC2toZMJnvkcxCRitTQARAREVHdGDt2LExNTbFy5Up8++23cHJyQmJiIubOnaueRvUwT09PvPjii1i1ahXmzJkDHx8fxMXFYcSIEejfvz++/vprjf3btWuHkydPVhqHUqnU+428ubl5uaMohpCcnAxTU1Pk5+fjwIEDyMnJgVQqRVBQEDp37gyp9NHfWpWUlEChUEAmkyEpKQm3b99GQEBADUZP9HhiwkNERPSY6NWrFw4fPgxra2tMmzYNEokEH3/8MQDAz89P5zHvv/8+fvjhB3z55Zf45JNPMGjQIPj6+mLz5s2QSDQnivj7++s1Le7evXvYtWuXXjGPHj0atra2eu1bF3JyciCKIg4cOIDg4GB07NgRd+/eRWxsLGQyGZ588slHPveZM2fUa3YEQYCvry/Cw8NrKnSixxYTHiIiosdIdHQ0WrRooU5W7t+/D6lUChsbG537e3p64qWXXsLq1asRFRWFwsJCHDt2DNbW1lr7Ojo6orCwEAUFBRVO53J2dsaAAQP0itfS0lJnuyiKUCgUep3DxMSkxkaJSkpKIJfL0axZM3Uy4ufnB6VSiStXrqBDhw6wt7d/pHO3atUKfn5+KCgowPXr16t0H4mofEx4iIiIHiOXLl3CU089VaVjZsyYgSVLliA6OhrHjx+Hp6enzv1K17JUllyYm5ujSZMmVYqhrJSUFL1HiUaOHAkHB4dq9VeqdMpaYGCgRntgYCCuXLmC1NTUR054HBwc1HE2bdoUu3fvxv79+zFkyBCjmtZHVN8w4SEiInpMZGdn4/bt22jVqpW6zdnZGXK5HA8ePCh36tinn34KAJDL5RWWWM7KyoKVlVW5ozKlSiuY6cPCwkJr6hygSg66d++u1zmqUzxA17mysrK07mPp7Zq8+Kq/vz+OHz+OnJycGkvYiB5HTHiIiIgeE9HR0QCA1q1bq9tCQkIAADdu3NBoL7Vw4UL88MMPWLJkCWbOnIlPP/0UP/zwg87z37hxA82aNas0jtTU1Gqv4bGysqryxUJrgqurK5KTk5Gfn6+RhOTn5wMofwreo5DL5QDASm1E1cSEh4iI6DFx6dIlAJoJT+fOnQEAkZGRWgnPzp078d577+GTTz7Ba6+9hoSEBCxbtgwffvihziIHUVFRGDt2bKVx1MQantoml8uRl5cHCwsLWFhYqNv9/f1x8eJFXL16VWNqX1xcnLrMd1UVFhZq3U+lUomEhASYmJjA0dHx0e8IETHhISIielxER0fD09NTY1qav78/WrZsiUOHDmlcb+f8+fMYO3Ysxo4dq75A6axZs7BixQqdozznz59HZmYmnnnmmUrjqIk1PNURExMDmUyGgoICAMDNmzfVIzQtW7aEmZkZ0tLSsGvXLrRr1w4dOnRQH+vi4oLg4GBcvXoVSqUSHh4eSElJwfXr1xEaGqpRzEGffgDg+PHjkMlk8PDwgLW1NQoKCnDt2jVkZ2cjLCwMpqamdfK4EDVUTHiIiIgeE9HR0TqnrU2aNAmzZ89WjzTcuXMHgwcPRtu2bbF69Wr1fo0bN8akSZPwww8/aI3ybNu2Dd7e3ujVq1ed3JfqiI6ORl5envp2UlISkpKSAABBQUHqRKQ83bp1g42NDa5evYqkpCTY2Nigc+fOGmujqtKPv78/rl69in/++QdFRUUwMzODi4sLOnbsCF9f3+rfYaLHnCDqujwwERERPTZycnLg7++Pr776CpMnT67y8cXFxfD19cV7772HN954oxYiJCJ6dNplT4iIiOixYm9vj1mzZmHhwoVQKpVVPn7NmjUwNTXFyy+/XAvRERFVD0d4iIiIiIioweIIDxERERERNVhMeIiIiIiIqMFiwkNERERERA0WEx4iIiIiImqwmPAQEREREVGDxYSHiIiItOTm5qJHjx7Izc01dChERNXChIeIiIi05Obm4tixY0x4iKjeY8JDREREREQNFhMeIiIiIiJqsJjwEBERERFRg8WEh4iIiLTY2dmhc+fOsLOzM3QoRETVIoiiKBo6CCIiIiIiotrAER4iIiLS6fLly4YOgYio2pjwEBERkU4KhcLQIRARVRsTHiIiItLJycnJ0CEQEVUbEx4iIiLSydHR0dAhEBFVGxMeIiIi0ikxMdHQIRARVRsTHiIiIiIiarBYlpqIiIh0ysnJgb29vaHDICKqFo7wEBERkU4PHjwwdAhERNXGhIeIiIh0ysjIMHQIRETVxoSHiIiIdBIEwdAhEBFVG9fwEBERERFRg8URHiIiItIpNjbW0CEQEVUbEx4iIiLSqaSkxNAhEBFVGxMeIiIi0snBwcHQIRARVRsTHiIiItLJ1dXV0CEQEVUbEx4iIiLSKSEhwdAhEBFVGxMeIiIiIiJqsJjwEBERkU4+Pj6GDoGIqNqY8BAREZFOhYWFhg6BiKjamPAQERGRTmlpaYYOgYio2qSGDoCIGpBrKcDUFYCM1+4gqhd6tQbmjTJ0FEREtUoQRVE0dBBE1EB8vgPih5sg8N8KUf1gJgWSfwBc7HRuViqVkEg4GYSI6jf+FyOimiURDB0BEelLrgBWHyx389WrV+swGCKi2sGEh4iI6HGlFIHvdgMlcp2bi4uL6zggIqKax4SHiGoWB3iI6pd72cDOszo32dnpnupGRFSfMOEhIiJ6nEkEYNHvOjd5eHjUcTBERDWPCQ8R1SzWKyCqX5QicCYeOJ+otYlreIioIWDCQ0RE9LiTSoDvdhk6CiKiWsGEh4hqFtfwENU/ciWw6QSQmq3R7OXlZZh4iIhqEBMeIiIiApRKYNUBjaaSEl5EmIjqPyY8RFSzuIaHqH5SisD3ewDZf0nOvXv3DBgQEVHNYMJDREREKum5wPbTho6CiKhGMeEhoholCFzEU+/ZWwErXwbS1gB5m4Aj84C2/lU/j9QEiP0WEH8B3nlGc9uc51Tt5X11CamZ+0JVIxGARX+ob7Zs2dKAwRAR1QypoQMgooZFFEXWLajPBAHY/RHQxgdY+BuQkQu82g/4cz7QfiZwLUX/c70+APB20b3tlzO6z/XZWMDGAjh37dHip+pRiqry1H/HA52a4tq1awgJYfJJRPVbgx/huXDhAg4ePIjNmzdj1apV2LRpk6FDwrRp0yAIgs4vU1NTFBQU1Frf2dnZmDJlCv73v//VWh81ZdGiRWjfvj2cnJxgamoKW1tbBAcHY8GCBSguLtZ5TF5eHl577TV4eXnBzMwMNjY2CA4OxnfffVfH0RMZqaPzgTXTyt8+vDMQHgK8sASYvxVYtg/oMRtQKIF5z+nfj6s9MHsk8OVO3dsv3wR+/kvz61gs0MRZNaWqRF6lu0U1SCoBvt0NACgqKjJwMERE1dfgR3jOnTsHc3NzuLi4QCaTGTocDS+++CKCg4M12kxMTGBmZlZrfWZnZ2P16tWQyWQYNWpUrfVTEyIjI2Fra4sxY8bA3d0deXl5OHjwID7++GP89ddf2L9/v8b0qYyMDISFheHOnTsYMGAA2rVrh/z8fMTFxeHGjRsGvCePGQ7v1G/DOwP3slQjMKUycoGtp4BxEYCZFJDpkYx8MQ64mgxsPAZ8Mlq/vkd3AyQSVfJDhiNXAttOAl9PgI2NjaGjISKqtgaf8IwaNQp2dnYAgG3bthlVic2BAwdiyJAhhg6jRmVlZcHe3h4SSfUHD3WNxn3xxRfo3LkzDh06hNjYWI355ePHj0dycjL27duHHj16VLt/osdSWz8g6joglim3dzYBmNoXaNoYiLlV8TmeCAQm9AC6fqh9noqMjQBupQN//VPlsKmGKQGsOACvD4YaOhIiompr8FPaSpOd+qioqAhvvvkmvLy8YGpqCisrK3Ts2BEHDmheJ0GpVGL69Olo2bIlHBwcIJVK4ezsjKeffhpJSUnq/fbs2QM/Pz8AwLp169TT6FxcXNTbBUHAnDlztGIZNGgQBEHAgwcP1G2hoaFwcXHBP//8g+7du8PGxgZOTk7Iz88HANy8eRPDhg2Di4sLpFIpHB0dMXDgQCQmJlbrcfHy8oIoisjIyFC3JSQkYN++fRgwYAB69OgBuVyOzMzMavVDj4hlqes3D0cgJUu7vbStsVPl5/j+RWDLSeBMvP79NvcC2vgCm0/ofwzVHqUSWLIHcZcuGzoSIqJqa/AjPDWhKnOYzczM9B7dyM7Oxp07dzTaHBwcYGNjg5KSEoSFhSE2Nha9e/fGCy+8gNzcXGzZsgWDBg3C9u3b8fTTTwMAZDIZfvzxR0RERKBv376wtbVFVFQU9uzZgwsXLiA2NhZ2dnZo3bo1PvjgA3z22WcICwvDs88+CwCwsrLS+/6VVVRUhIiICLRo0QJvvfUWMjIyYGpqimvXrqFjx44oKSnBkCFDEBQUhMTERGzduhXh4eGIjIxEkyZN9OojPT0dxcXFSE9Px6+//opff/0V7u7u6Nixo3qfbdu2QRRFtGzZEv369cPhw4chl8vh6OiICRMm4Ouvv66RUSeiekVqoqq49jBTKWBuCjjbarZn5qlGYyzNgGIdU9aK/p0SbFnJlNsXegGtfIDhC6sW69gI1XdOZzMemXlw2B8LdOxg6EiIiKqFCY8e1q9fr/e+gwYNQuPGjfXad+LEiVpts2bNwpdffol58+bh0qVLWLVqFV566SX19nnz5iEoKAjvvfceBg8eDEEQYG5ujpSUFK3RrP/7v//DzJkzsWLFCsyaNQtNmjTBSy+9hM8++wzBwcGYMWOG3verPPn5+RgyZAg2btyodd/kcjlOnTqFVq1aqdsnTZqEXr164aOPPsLatWv16iM8PBwJCQkAVCWPW7dujdWrV2skaleuXAEALF68GLa2tpg7dy4sLS3x008/4ZtvvkFmZibWrVtXzXtLeuEaHuMRHgL8+Ynu9tHdNNt8pwI304FCGWCu46XB4t9Ep7CCtZC2lsDnY4GFO4E796sW65huqkIGl29W7TiqPRIJ3I9fN3QURETVxoRHDwMGDNB7X2dnZ733nTVrFkJDQzXaSm9v2bIFHh4e6Nevn9YoUJcuXbBr1y7cu3cPHh4eEARBneyUTuWSyWQYNGgQZs6cib///lvvmB7FvHnzNG5nZ2fj5MmT6NOnDxwdHTXiDwgIQOPGjXHy5Em9z7906VJkZGQgOTkZe/fuRVpaGtLT0zX2KZ1qJ5fLcfz4cfj7q64Z8vrrr8PHxwebN2/G3Llz1VP6KpOZmQlra2uYm5sDUFV/E0URtraqT8VlMhkePHig8XynpKTAw8Oj3Nv37t2Dm5ubutBCQ+2DU9qMyKUkoPdczbavX1AVJVj4m2b7vWzV95Qs1bS2skrb7lYwVXTGM6qiBltOAj6uqrYm//5uOVqr2u5maVdgCw8BfBsB722o/D5R3VEqUTywLczRcP9fsQ/2wT7qfx/6YMKjB32nXlVV586dyy1acOvWLchkMnh7e5d7fHJysvoJX7FiBRYvXozExEQoFAqN/XJzc2ss5rJsbW3h4+Oj0RYdHQ1RFHHgwAF4eXnpPK503ZA++vTpo/55xowZeOmllzB06FAcO3YMYWFhAABLS0sAQMeOHdXJDgCYmppi6NChWLZsGQ4cOICpU6fq1aeTk+Y6hbKViszMzLSS27J/fGVvu7u7PxZ9kBHJzgcOR2u2ZeWpkpqy7aUuJgHdmqmux/NwwYFOQUB+ERB/t/z+vF0AJ1vgHx1l4D8crvoKfVuViD1sbIRqzcim4/rcK6orDta4EdYEbdBw/1+xD/bBPup/H/pgwqOHqlwXx9zcHCYmJtXuUxRFNGnSBF999VW5+wQFBQEA1qxZg1deeQUBAQF477334OfnBysrKygUCjz//PNQKpV69flwieeyyiZRpczMzCCVav4aif++SerZs6fGdLyHWVhY6BWTLtOnT8cPP/yApUuXqhOe0sTK1dVVa39PT08AwP37VZxiQ/Q42n4aGNEFGBYG7DitanO2VbX9EalZktrfTfX9eqrq+3d7gJ1nNc/XyB5Y9Qqw5gjw21ngRprmdqmJ6twnrgC3M0BGwkQCvNYfoq7pjURE9Qz/k+mh7PqUilRlDU9FPD09kZmZiWHDhv03Vagca9euhampKU6cOKGRSV++rF1dp6Kkxs1N9eZFV3WzW7cqKUP7kBYtWkAQBBQXF2P0aD2vv1EFpQloVtZ/laQiIiKwcOFC3Lt3T2v/0tjLfspAtaOi3zGqB7afBk5fVV2ctHkTIOMB8Go/1RvgOWUuWHz43+msfi+rvl+4rvp6WOnUttjbqoSnrKdCARc74GeO7hgVUQReeQrNXW0r35eIyMgx4dFDba3hqcioUaPwxRdf4O2338bSpUu1tt+4cUO9HqV0ROnhkRxRFDFr1iyt40qHDh9OFko1bdoUJiYmOHnyJERRVL9xPXjwoLoogD5cXFwQFhaGM2fOYMeOHepqcA/Hdvv27Qqn68nlcqSkpOicEvfpp58CADp16qRu69+/P1xdXXH27FlcvnxZXSjhwYMH2LFjB6ysrDBw4EC97wM9OlEUWbegPlMqgQELgIUTgOkDVVXZzl0DXvi+4ulsj2psBCArAbadqvlz06ORSlQjfJ7OuJmQoJ5NQERUXzX4hCc+Ph55eXkAVCWUFQoFoqKiAKje/Ddt2rTSc9TWGp6KzJ8/H4cOHcKyZctw5swZREREwMHBAbdu3cLJkydhYmKC2NhYAMDIkSNx9OhRREREYMSIEVAoFNi3b5/6ejgPc3Z2hoeHB44ePYpZs2ahcePGsLS0xNSpU2FjY4PBgwdj586d6NGjB3r27Ilr165h586d8PHx0bimT2U2bNiAsLAwPPfcc+jTpw/atWsHURRx48YNHDlyBP3796+wSlteXh4CAgIQHh6O5s2bw8PDA/fu3cOBAweQkJCAVq1aaVSZMzExwbfffotx48ahR48eGD16NCwtLbF161ZkZGTg448/Vo9gET3Wes6ufJ/sfOClZaqvipSO7FTkZjogDCt/+5jFlZ+D6pZcCbwxCAB0vo4QEdU3DT7huXr1KlJSUjTaIiMjAagWPemT8BiCqakpTp8+jblz52LLli1Ytkz1xsPR0RHNmzfH+PHj1fu+/PLLyMrKwrJly/B///d/sLa2Rnh4OJYvX65VUABQTdGbNm0avv32W8hkMjg7O6sX869btw7jxo3DkSNHcObMGfj7+2P9+vX46aefqpTwBAQE4PLly5g1axYOHTqEQ4cOwdTUFC4uLujWrRsmT55c4fFWVlZ47rnn8PfffyMyMhIFBQWwsLCAt7c33n33XcyePVtdqKDU6NGjYW1tjdmzZ+PHH3+EUqmEn58fli1bhldeeUXv2KmaOLxDVH9JBKC1L9A5GED1rtNGRGQsBFEUWUSWiGrG5zsgzt4MQa5foQwiMkLrpwPP9wAAlJSUwNTU1LDxEBFVEy89T0Q1ix+hENVfzrbAyHD1zdKp00RE9RkTHiIiIgIkEmBaf8CcIzpE1LAw4SGimsU1PET1kwTA1L4aTY9ygT8iImPDhIeIiOhxJ5WoprJ5aF4FvSYupE1EZGhMeIioZnEND1H9I1cCbw7Sar5z544BgiEiqlkNviw1ERERVUAiAO38gSd4gVEiapg4wkNENUoQuIiHqF5RisDbT+vcFBISUsfBEBHVPCY8RFSjeGkvonrG1Q54NkznpuTk5DoOhoio5jHhISIielxJBGD6QMBMdynqBw8e1HFAREQ1jwkPEdUspdLQERCRviQSYEqfcjdbWFjUYTBERLWDRQuIqOYMC4Ny598wkTPpIaoX+rQBGjmUuzkoiIUMiKj+E0ROuCeiGnTx4kWEhoYaOgwiqgH8eyaihoBT2oiIiIiIqMFiwkNENcrX19fQIRBRDXFzczN0CERE1caEh4hqVH5+vqFDIKIaYm5ubugQiIiqjQkPEdWo9PR0Q4dARDXk1q1bhg6BiKjamPAQEREREVGDxSptRFSjRFGEIAiGDoOIakBBQQGsrKwMHQYRUbXwOjxEVKOuXLmC5s2b69x2N0/E6bv8jIWoMi6WArp7Gf6Dg3v37sHf39/QYRARVQsTHiKqUTKZrNxt808rsfISEx4ifRx/DujqZdiX6dzcXIP2T0RUE7iGh4hqlJ2dnc72rCIR62KY7BDp6+LVJEOHADMzM0OHQERUbUx4iKhGubu762z/8bIImaKOgyGiamnWrJmhQyAiqjYmPERUo+Lj47XaFEoR35xXQmmAeIjqKwcHB0OHgEuXLhk6BCKiamPCQ0S17o9EEcl5ho6CqH4xM+V0MiKimsCEh4hqlLe3t1bbokglTAxfcIqoXklLTzN0CHB1dTV0CERE1cYqbURUo4qLizVuR6eLOJ5soGCIqFqsra0NHQIRUbVxhIeIalRqaqrG7e+ilJBydIeoyjw9PQ0dApKSkgwdAhFRtTHhIaJac79QxIZYEXJWoyaqsuzsbEOHQETUIDDhIaIa1apVK/XPP0Qz2SF6VPn5+YYOAYGBgYYOgYio2pjwEFGNSkhIAADIlSK+jVJCyYSH6JFIpYZfZpuRkWHoEIiIqo0JDxHVqKKiIgDAzgQRKYb/gJqo3vLRUfGwrnFaHRE1BEx4iKhG2draAgAWnVdCwmIFRsHeHFjZR4K0V02Q94YJjoyUoG2jqp9HKgFiJ5pAnCHFOx345Na2xOvXDR2CUYwyERFVFxMeIqpRnp6euJAq4vRdcDqbERAA7B5mgjHNBCy5oMSsY0o0shLw53MmCHSo2rlebyvA27Y2oiRj1bJlS0OHQERUbXX60Y0oirh8+TKuXLmCvLw8WFhYwN/fHx06dICpqale5ygoKMD58+dx69YtFBYWwtLSEn5+fmjfvj3Mzc019k1LS0NCQgIyMjJw//59yOVydO/eHcHBwTrPnZubi8jISCQnJ6O4uBg2NjYIDAxEaGiowT7lunr1KmbMmIFdu3bp3G5ubq6eQlQbjh49it9++w1DhgxBjx49aq2f6iooKMDu3buxefNmxMXFISkpCYWFhZgzZw7mzp1r6PAeK3Fxcfg2pRWkAliwwAgMDxYQ7ilg+O8K7IhXPSFbryoQP9kE88IlGLtbqdd5XK2A2Z0l+PKsEp90NanNkOlf9nZ2hg4Bly5dQps2bQwdBhFRtdTpu/jTp08jJiYGvr6+aN26NbKzsxETE4P79+9j4MCBEISKp0gUFhZi586dKCgoQLNmzeDo6IisrCz8888/SElJwTPPPKORmNy6dQv//PMPHBwc4OzsrHV9kIdlZ2dj586dEEURzZs3h62tLdLS0hAVFYW0tDT079+/0vhq2xtvvIEmTZpotLm7u9dqn6dPn8a3334LBwcHo054srOzsXv3buzcuRMeHh7w9vbG1atXDR3WYylTZoKfr7A6m7EY3lTAvXwRv8T/94RkFAJbr4oY11yAmQkgU1R+ni+6SXA1C9h4RcQnXWsxYFKzsLQ0dAgQRf4hE1H9V2cJT2ZmpjrZ6du3r7rd1tYWp06dQmJiYqXlLy9cuIC8vDz06tVLY183NzccOXIE0dHRaNeunbq9efPmaNOmDUxNTXH9+vUKE56///4bMpkMTz/9tDqJaN68Oezt7XHu3Dlcu3YNQUFBj3r3a8S4cePQoUMHg8ZQk+RyOfLz82Fvb1/tc7m4uODTTz/FN998AwsLC8yfPx+ff/55DURJVXXogS+nshmRto0ERKWKKPuUnE0RMbWNBE0dgZhKCnE94Q5MaCGg62YF+P637qSmpgKtDDuH0NnZ2aD9ExHVhDpbw5OYmAhA8xodABASEgKpVKouZVuRu3fvwsTEBAEBARrtAQEBMDEx0fpE38rKSu+pcnfv3oW9vb3WiEnp9DdjHy0QRREXL17E2LFj4enpCVNTU9jY2KBPnz64cOGCxr6XL1/G+PHjERISAnt7e0ilUri5ueHFF1/E/fv3AQAPHjzAO++8gw8//BAAMG/ePAiCAEEQ4Ovrq94uCALWrl2rcf7IyEh06dJFY0Tszz//RHBwMHx8fLBp0yZ07NgR9vb2cHBwAADIZDLs3r0bffr0gbOzM6RSKVxcXPDCCy8gLS2t0vtvZmYGT09P9fnIMEoUIlZfMWfCY0Q8rKGzWl5pW2Obykeuv3/SBFuuijiTUsPBkdGzM4JpdURE1VVnIzzp6ekQBAGNGmmWBpJKpXB2dkZ6enql51AoFJBKpVpTywRBgFQqxYMHD1BUVAQLC4sqx6dUKnWu0yltS09PhyiKlU5rq8p6GjMzM0gk+uec6enpuHPnjkZsDg4OsLCwwMGDB/H666/jxo0bGDx4MJo1a4akpCTs378fXbp0wfHjx9WjQ1FRUfjll1/Qu3dvPP300xBFEWfOnMHatWtx5swZREVFwcLCAsOHD8ft27exbds29OnTRz0yV50L0WVlZeGNN95Ahw4d8MYbb8DExAQymQw//vgjPvjgAwDA8OHD4ebmhosXL2LLli04fvw4oqKiamQkiGrXLwkiMopYC6W2CADM9Fw+U/zvNDVL6X8/P6xI/t/2irzQUkArF2D47/qt9aGa07hxY0OHgBs3biA0NNTQYRARVUudJTz5+fmwsLCAiYn2q7W1tTVSU1OhUCh0bi/l6OiIpKQkZGRkwMXFRd2ekZGB4uJiAFAXQ6iq0vVABQUFsLKyUrffvXsXAFBSUoLi4uJKz71+/Xq9+xw0aFCVXtAGDBig1bZ8+XI89dRT+OGHHxAfH499+/bhqaeeAqBK4jZt2oQ333wTM2bMwJ9//gkAGD16NEaOHAnLh+aHJycn4/3338eGDRuwc+dOjBw5Ep07d8bRo0exbds2dOnSBTNmzFDv/+DBA73jftiDBw8wffp0LFiwQN126tQprFq1ClKpFBcuXFCvU3rw4AEWLlyITz/9FIsXL67T4gOZmZmwtrZWF8LIy8uDKIrqkssymQwPHjzQmO6RkpICDw+Pcm/fu3cPbm5u6qS5IfZho9+AKj2iCC9VdTV9hPwkx9VMoFAOmOs4xOLf//6F8vLPYWsGfN5NgoXnRNx5tD95qobc3FwAVgb9Oy/VEP9fsQ/2wT4aRh/6qLOERy6XlzuaUZrkyOXyChOeVq1a4ebNmzh8+DA6d+4MJycnZGZm4vTp05BIJFAqlZDLK3j1rkDr1q1x5MgR7N+/H506dVIXLTh16lSVzq0rKSlPVedGL1iwAP7+/urbVlZW6NChA2JjY3H27FkEBwejRYsWGqNAHh4eCAkJwYkTJ9RV7czMzACopsEVFRUhJycHoiiqpxv+/fffGDlyZJViq4qHEydRFHHp0iVER0dj/PjxAKARf0hICNzc3HDgwIE6TXicnJw0btvY2GjcNjMz03r+yv7xlb1ddrpkQ+yjv78Ab1sRtx7wGi21Ie6+iBf26lFhAEBK3r/f81XT2soqbbubV/78wxlPSGAmAbZcVcLn35lNTf5dUuJoIcDHTsTdPKCEgz+1Ii9P9SQa8u+89DWnIf6/Yh/sg300jD70UWcJj1QqLXe6l0KhUO9TEQ8PDzz55JM4efIk9u3bB0A1nS0kJASFhYVISkrSe81OWYGBgSgqKkJkZKS6BLREIkHbtm1x69YtpKenqxOFipStolaTnnrqKZ1FC06ePIm7d++ipKQEXl5e5R6fkZEBLy8vJCcn47vvvsO2bdtw69Yt9eNfKisrq8ZjL+Xq6qqxzqaoqAi3bt2CUqnE2rVrtdYDlbI0gmpFVDmJIGCcz318HuOitUieqi+1AFgXW7VH9mKaiG5NBAiAxnPSyUNAfomI+Ar+3L1tASdLAf9M1P7f/GGYBB+GSRC6To5Llc9IpkdQlSnPtSU7O5vreIio3quzhMfa2hrZ2dk6p61VNN2tLH9/f/j6+iIzMxMlJSVwcHCApaUlfv31VwiCUK11Hi1btkSzZs2QmZkJhUIBR0dHmJubIzY2FlZWVnolPAUFBXr3Z25urtd91ldISAhmz56tc5u9vT1cXV2RlpaGBQsWYMWKFWjZsiXmzp2LJk2awNzcHOfPn8fXX38NpVK/j2vLW88kiqJWElXq4emCpfuWGjZsGIYPH67zOG9vb71iIsPr55KGxVKXCqdKUd3ZHi9iRLAEw5oK6uvwOFsCI4IF/JEoapSk9v/33+f1HNX376KU2HlNM8FqZAWs6muCNTFK/HZNxI2curgXjyc/X19Dh4DMzEz+/yWieq/OEh5XV1fcuXMHaWlpGkNRcrkc9+/fr9LwlEQi0VjDU1BQgIyMDDRu3LjaFwg1MTGBq6ur+nZ6ejqKiorKvVhpWRs3btS7r6qu4SmPnZ0dGjVqhOzsbDz33HMVfioYFRWFM2fOwMLCAn///bc6ASkpKdFZKa+8pMbc3BzW1qo5MZmZmRrbcnNzkZFRSZ3bf1laWsLT01NdeGL06NF6HUfGy93eEpNbCVh+UYSCwzwGtz1exOm7Itb0k6C5sxIZhcCroRKYCMCck5ofbhweqfoAxm+1Kgu6kAZcSNN8EkuntsVmiPjtGp/g2nTjRhLw0DRmQzCGUSYiouqqs4QnICAAFy5cwOXLlzWSm7i4OMjlcq3KX7m5uVAqlZWWGRZFEadOnYIoimjbtm2NxiyXy3Hq1CmYmJjofaXp2lzDU56mTZsiLCwMO3bswKJFizTWyACqhPDBgwdwc3ODRCKBRCKBIAgaIzlRUVHYvXu31rlL51aWTWrMzMzQpEkTmJiY4NChQ3j77bcBqJ63Q4cO4caNG3rFLggCQkND0bJlS/zyyy84c+YMwsLCtOLPz8/XSETJePn4+OB1ZwmWXNBvrQnVLqUIDNihwMLuEkxvK4GlKXDuHvDCXkWF09nI8JSi4RdHtW7d2tAhEBFVW50lPE5OTmjRogViY2Nx4MABeHt7IysrCzExMfDw8NBKeHbt2oW8vDxMmTJF3VZSUoJff/0Vvr6+sLOzg0wmw7Vr15CRkYEnnnhCa7TkwYMH6lGL0nUpN2/eRH6+6gIUQUFB6qoQmZmZOHbsGLy9vWFtbY3CwkLEx8cjNzcX3bt31/v6LrW5hqc8/v7+ePnll3HlyhXMnDkTO3fuROfOndUXXD1//jyaNGmCo0ePwtfXF+3atUNUVBTCwsLw9NNP4/79+/jrr7/Ule7KnrtRo0bYuHEj7Ozs4OLigoCAAAwePBjt27dH586dsXfvXgwYMAChoaG4cOECTpw4AU9PT43iAxXp2LEjXnvtNXzwwQfo1q0bBg8ejObNm6OwsBBxcXG4cOECpkyZUmnRgmPHjmHLli1QKpWIi4sDAOzevRtpaWmwtbXF2LFj+eJdB/755x+Ehoairw9w+BY4ymMEsouBlw4o8dKBivcrHdmpyM1cQPg/zlesC6WvT4Z0+fJlrevnERHVN3WW8ABA586dYWtriytXruDWrVuwsLBAy5Yt0aFDh0qvbwOohtadnZ2RmJiIgoICSKVSuLq6on///joX6z948ACRkZEabUlJSUhKSgKgqhxR+oJiYWEBa2trxMXFobCwEGZmZnB3d0fPnj21rh1kjHr37o1t27Zh8eLFOHToEL777jsIggBnZ2d06NABr732GgDVff7www8hlUrx22+/YeHChbCzs8PTTz+Nzp07Y+rUqRrn7dq1K959910sX74cCxcuhEwmg4+PDwYPHozQ0FDMmzcP8+fPx19//YXDhw+jTZs2+Oabb/Djjz/qnfCYmZlh8uTJCAwMxLfffotTp07hjz/+gJmZGdzc3DBw4EC9qsadPHkSy5cv12iLjIxU/w40a9aMCU8dequDBAduGv4TaqL6ysZaR3m9OlbeekwiovpEEB9eNU5EVE1paWlo1KgRlKKIpj8ocD0HrNhG9Ai+D03CtN6PfqHnmnDz5k34+PgYNAYiouriakQiqlGlo7USQcBbHfgvhqg+q6m1pkREhsR3I0RUo5KTk9U/j28hwOrRLo1F9Ngre8E+Q7h27ZqhQyAiqjYmPERUa2zNBLzYSoBJ5Uv0iKiMqlzXjYiIyseEh4hqVLNmzTRuv95OAiUX8RBVWW5urqFDgK8RXPyUiKi6mPAQUY26ffu2xu0ABwED/MFRHqIq0qd6aW3Ly8szdAhERNXGhIeIapSuN0hvtpfwejxEVeTv52foEJCRkWHoEIiIqo0JDxHVKAsLC622J70FNHUEDP95NVH9kXTzpqFDICJqEJjwEFGNCgzUvm6IIAh4myWqiarEGC76GRoaaugQiIiqje9AiKhGxcTE6Gwf10yADUtUE+nNxtrG0CEgNjbW0CEQEVUbEx4iqhPWZgKmtuGkNiJ9mAhAkIetocNASUmJoUMgIqo2qaEDIKKGpaKLJX4YJsGI4DoMhqiecjAHCm4lAAg1aBz29vYG7Z+IqCYw4SGiGlVRwuNgIaCjRx0GQ1SPXbxl6AgANzc3Q4dARFRtnNJGRERkhIzhop/x8fGGDoGIqNqY8BARERmh/Px8Q4dARNQgMOEhIiIyQunp6YYOAd7e3oYOgYio2pjwEBERkU5FRUWGDoGIqNqY8BARERmhNm3aGDoEpKWlGToEIqJqY8JDRERkhK5cuWLoEIiIGgSWpSYiIjJCMplMZ3tGgYi2GxQoltdFFC2Bk3XS0WNjcICAH/uZGDoMoscKEx4iIiIjZGdnp7P9h8si7j4AlHUcD9WMLXEKJjxEdYxT2oiIiIyQrov4lihEfBulZLJTj4miaOgQiB47THiIiIiMkK6Lfu68JuIeL89TrwmCYOgQiB47THiIiIjqicWRSkj4frme4xNIVNe4hoeIiMgIlb3oZ1SqiNMpBgqGaowockIiUV3jCA8REZERKi4u1rj97XklpBwcICKqMiY8RERERig1NVX9c1q+iE1xIuRc717vSSR860VU1/hXR0REZORWRotQMtlpEFiljajuMeEhIiIyQq1atQIAyBQivo9SMuFpIJjwENU9JjxERERGKCEhAQCwI15EeqGBg6EaxIVYRHWNCQ8REZERKioqAgAsYinqBoVreIjqHstSExERGSFbW1ucTRERmVr5vsbK3hz4KkKCoUECrEyBsyki3vlTiQtpFR8nABjfQsCwIAFtGwlwsgRu5AD/i1Pi/86JKFb8t6+FFFjypASdPAR42QImApCYDfwUo8SyiyLkRlYFWqlUgG+/iOoW/+KIiIiMkKenJ2YfUpWiro/V2QQAu4eZoI0rsPCcEhmFwKuhEvz5nAnab1DgWnb5x1qZAmv7m+D0XRErLimRVgB0bixgXhcJnvQW0Wvrf1mMpRRo4Sxgz3URSbmq4g5dGgtY3FOCTh4ixu42soyHiOpcgx9XFUUR0dHR2LJlC3788Uf8/PPPOH36NEpKSgwW05QpUyAIgs4vU1PTWu07NTUVU6ZMwfbt22u1n5pw7tw5jBkzBm3atIGVlRUEQcCUKVMqPObu3bsYM2YM3N3dYWpqCjs7O7Ru3Ro7d+6sm6CJiGrI8UvXsPVq/S1FPTxYQLingBf2KTH/tIhlF0X02KKAQgTmhVf89kOmALpskqPLJgU++1vED5dFTN6vxLzTSvT0luBJ7//m+GUVAZ03KfDuX0osvyhi5SURE/YqsfSCiDHNJHCzqu17WjWCwPmJRHWtwY/wnD59GjExMfD19UXr1q2RnZ2NmJgY3L9/HwMHDjToP56XX34ZAQEBGm0mJia12md6ejpWr14NABg+fHit9lVdhw4dwv/+9z+4ubkhKCgI0dHRFe4fGxuLnj17oqioCEOGDEFwcLD6+U5KSqqboImIasj2O06oz2MTw5sKuJcv4pf4/zK2jEJg61UR45oLMDNRJTa6lCiB03e1239NEDE/HGjmDBy+VXH/Sbmqfh0sgNSCR70XtYAJD1Gda9AJT2ZmpjrZ6du3r7rd1tYWp06dQmJiIgIDAw0W37Bhw9CnTx+D9V8b7t+/D2dn5xo514QJEzBp0iS4ubnh4MGDGs+hLiNHjoRCocCFCxe0EkkiovqkWC5iW7JLvS5F3baRgKhUEWXvwtkUEVPbSNDUEYjJqNo53a1VyUKGjqp1phLAzlw1xa2Dm4AZHSRIyhFxLevR4q8torI+p7FE9VODntKWmJgI4L9rGZQKCQmBVCpVl/w0VgUFBZg2bRq8vLxgamoKKysrdOrUCUeOHNHYT6FQ4LXXXkOLFi3g4OAAqVQKFxcXDBkyBMnJyer9tm/frn4sVq9erZ5G5+rqqt4uCAI+//xzrVj69eunNRrWqlUruLq64tKlS+jWrRtsbGzg4uKi3p6YmIghQ4bAxcUFUqkUjo6OGDx4MG7evKnX/W/cuDHc3Nz02vfXX3/FP//8gylTpiAgIADFxcXIzc3V61giImOz9aqILFn9fon2sAZS8rXbS9sa21R9pGPWEwJyikXsvaGdCQ4LEpDxmhS3p0rx6xAT3MkDBv+qmkJHRI+3Bj3Ck56eDkEQ0KhRI412qVQKZ2dnpKenV3oOURRRXFysd5/m5uZ6T5PLzMzEnTt3NNocHBxgY2OD4uJihIWF4cqVK+jbty8mTpyI7OxsbNmyBf3798euXbvUo0OFhYVYs2YNevbsiX79+sHGxgaRkZHYtWsXoqKicPXqVVhaWqJ9+/aYOXMmFi5ciPDwcAwZMgQAYGdnp/f9K6uoqAi9evVC69at8fbbbyM1VVVO6MqVKwgPD4dcLseQIUMQGBiIhIQEbN++HWFhYYiOjlYnWjXh119/BQD4+fmhU6dOiIyMhFKphLu7O9555x3MmDGjxvoiIqpNoiji60glBIgQjeSaLQIAMz1nXJdWULOUQqOaWqki+X/bq+L9TgL6+ErwykEFcnS8LB+9LaL3VgUcLIAnvQW0cRVgXbvLYh8Jy1IT1b0G/VeXn58PCwsLnetirK2tUVRUBIWinAnE/8rLy8P69ev1/srLy9M7vlGjRsHLy0vja+HChQCAjz76CJcvX8aaNWuwe/duzJ8/H9999x1iY2Nha2uLt99+W30eKysrpKamYvfu3fj6668xb9487N69G3PnzsXt27fVa3b8/Pwwfvx4AEDz5s0xY8YMzJgxo9JCAJU9Ps888wyOHj2K+fPnY+XKlQCASZMmQS6X49y5c1i/fj1mz56NDRs24Pfff0daWho+/PDDR+5Tl9LRvBkzZiA3NxdfffUVPv30U5iammLmzJn47LPPqnS+zMxMjUQ3Ly8PDx48UN+WyWS4f/++xjEpKSkV3r53757GFbbZB/tgH+xDVx9xN1NxOR1Gk+wAQISXgKK3pHp9BTupjimUA+Y6kiQL6X/b9TUyWMCCrhL8EK3Eiku6h2zSCoDDt0TsiBfx6iEldl1X4uAIE6MrWiCKYoP93WUf7MMQfehDEB+OooHZvHkzlEolxo4dq7Xt6NGjSEhIwIQJE2Bubl7uOeRyOe7du6d3n+7u7pBKK/7YasqUKVi9ejU++ugjNG/eXGNbhw4dEBQUhICAABQVFeHvv//WOn7q1KnYt28fsrOzYWtrq7GtpKQEmZmZ6u9t2rTBqFGjsHnzZgBATEwMWrVqhZdeegmrVq3SOHb79u0YMWIEPvvsM7z//vsa2/r164f9+/dr/NK2atUKMTExuHfvnsbUs/T0dLi5uaF///7qBOhhnTp1gq2tLeLi4ip8nB5WuoZHV9wAEBoaikuXLqFRo0ZISkqCpaUlANUfWkBAAExNTXH//v1aLwpBRFQTnvlVjj3XRchF40h63KyAfn76xfJrgohcGRA/2QQJWSIG/qK5ZmVSSwE/9jNBq7Vyvdbw9PYRsGuoBAduihi6U6n3FLUgRyB+shRTDyiwKtp43upYmSiQ/1b57zuIqOY16CltUqlUfaXqskpHdipLTqRSKZo0aVLjsQFAREREuUUL7ty5A5lMBi8vr3KPT05ORkhICADgu+++w5IlS3D9+nWtUavs7Owai7ksW1tbrXU258+fhyiK2LNnT7nxl51mWF2lSevAgQPVyQ6gSkC7du2KAwcO4Ny5cwgLC6vRfomIasOb7SX4PdF4FrenFgDrYquWNFxME9GtiQAB0Chc0MlDQH6JiHg9igl0dAd+fUaCyFRg5B/6JzvAf1Pm7JlbED32GnTCY21tjezsbCgUCq1P9iua7vYwpVJZbtKki4WFRY3MzxVFEd7e3vjiiy/K3ac0EVu+fDneeOMNBAUF4YMPPoCvry8sLS2hUCjw/PPPQ99BvIrWHsnluuce6BodK+2vd+/emDRpks7jrK2t9YpJXx4eHgBUCU5ZpQmZPmu2iIiMQQ8vAb5WRbhZYKFV5ay+2B4vYkSwBMOaCtjxb2lqZ0tgRLCAPxJFjZLU/vaq79dz/msLcVJduDQpFxj0i0K99qcsZ0vgvo6qbS+2Ur0WR+o/SaNOSCScaUBU1xp0wuPq6oo7d+4gLS1N/YYYUL15v3//vkZbefLz89XTwfQxevRorWlmj8LDwwM5OTkYOXJkpUnZxo0bYWpqisjISI0CBGfPntXat6KkpjQxKDt3EoBWcYWKtG7dGoIgoKSkBKNHj9b7uOro2LEjfvvtN51x3r2ruphDbY3UERHVNEEQ8GLTB/joooWhQ3lk2+NFnL4rYk0/CZo7K5FRCLwaKoGJAMw5qTl6dXik6nXOb7UqC7IxBfYPN4GjBbAwUomB/pqvXYnZIs78O41/XDMBL7eRYOc1EddzRNiaAU/5CujrK8Hv15Q4etu4UkalUoEG/vaLyOg06L+4gIAAXLhwAZcvX9ZIbuLi4iCXy/W6Bo+lpSUGDBigd58PT6eqjmeffRaLFy/GzJkzsWjRIq3tN27cgJ+fHwBVxRdBEDSmsimVSnzwwQdaxzk4OAAAsrK05xK0aNECEokEx44d02j/448/EB8fr3fsnp6eeOKJJ3D8+HHs2bNH6/FTKpW4c+cOvL299T5nZSZNmoRPPvkEu3btQlZWFhwdHQGoihmcOHECHh4eaNu2bY31R0RU217r7Iyv/gFyZYaO5NEoRWDADgUWdpdgelsJLE2Bc/eAF/YqKp3O5mwJeNupkpwvI7Q/9Fsbo8SZFFXSdCJZRBdPEaObCXCzEiBXAlczgbeOKvB9lHElO0RkGA064XFyckKLFi0QGxuLAwcOwNvbG1lZWYiJiYGHh4deCU9truGpyOeff45jx45h8eLFOH78OLp16wZ7e3vcvHkTJ0+ehLm5OaKjowGokqMTJ06gY8eOGDFiBEpKSrB3716dU/E8PT3h7u6O/fv3491334WHhwdsbW0xefJkODo6on///ti9ezd69eqFiIgIxMfH4/fff4ePjw+SkpL0jn/dunXo2rUrnn76aTz11FNo27YtFAoFrl+/jqNHj2LIkCE6iw88LD09HXPmzAHwX0WOv//+G6+++ioAVZW7iIgIAKqpbO+99x7mzp2LNm3aYOTIkZDJZNi0aRPkcrm6+h0RUX2RlPAPXglthf87J9bba8lkFwMvHVDipQMV71c6slPqZi4g/J9+ZdzOpwLP/WE8650qo++lK4io5jTohAcAOnfuDFtbW1y5cgW3bt2ChYUFWrZsiQ4dOhj1Px1zc3OcPn0ac+bMwbZt27B06VIA/yVxEydOVO/75ptvIjc3F6tWrcLChQthbW2Nbt26YeXKlfD09NQ695o1a/DWW2/hm2++gUwmg4uLCyZPngwA2LBhA8aNG4djx47h5MmTCAwMxObNm7F06dIqJTwhISG4ePEi3n33XRw5cgQHDhyAqakpXFxc0KNHD434y5Oamorly5drtEVHR6sTPS8vL3XCAwBz5sxBo0aN8M033+D777+HIAho3rw5fvrpJzz99NN6x05EZCxeDZVg4bmKL59A9Ysxv/cgaqgadFlqIiKi+iotLQ2NGjXCs7/J8fs1QM5X6waBZamJ6l6DvvAoERFRfVU6EvBmexMmO0RE1cCEh4iIyAglJycDALp6Aq1c+ILdUNTEpSuIqGr4V0dERGTEBEHA2x0kqD/L8qkiXElAVPeY8BARERmhZs2aqX8eFSLAgcs+GgQmPER1jwkPERGREbp9+7b6ZwupgNfaCjBhgS8ioipjwkNERGSE8vLyNG6/0oYv2Q2BRKJ9IVUiql3870lERGSELCwsNG572goY3lSAlKM89ZpSyesqEdU1JjxERERGKDAwUKvtjfYSlqgmIqoiJjxERERGKCYmRqstzANo2wiQcJSn3iq9vhIR1R2poQMgIiIi/ZSWqH5+j5IFDOohpciEh8gQmPAQEREZIXd3d53tI5oKOHJLQEkdXJgnKzMLjk6Otd/RY6SZcBuAn6HDIHqsCCILwhMREZEOFy9eRGhoqKHDaFD4mBLVPa7hISIiIp0aNWpk6BAaHF9fX0OHQPTYYcJDREREOpUtjU3Vl5+fb+gQiB47THiIiIhIp1u3bhk6hAYnPT3d0CEQPXaY8BARERERUYPFogVERESkU0FBAaysrAwdRoMiiiJLUxPVMY7wEBERkU6pqamGDqHBuXLliqFDIHrs8Do8RERExii3AFh5AFDWwQV3ymF+NwVo7GGw/usld0dgQs9yN8tksjoMhogAJjxERETG6dPtEL/aCUFqYrAQPEQR4PQr/Ymi6qt7C8BXd0lvOzu7Og6KiJjwEBERGSOZHKJUAkGuMFgITHUegYkEWLYP+Gq8zs3u7u51HBARcQ0PERERUU1RKIEV+4D8Ip2b4+Pj6zggImLCQ0REZKRYzaueyisCNh4zdBRE9C8mPEREREQ1bdEfqvU8ZXh7exsgGKLHGxMeIiIiI8VL5dVTIoD4u8DhaK1NxcXFdR8P0WOOCQ8RERFRTTORAN/s0mrmtY2I6h4THiIiIiPFNTz1mEIJ7DkPJN4zdCREjz0mPERERES1QSIBluzRaGrVqpWBgiF6fDHhISIiMlJcw1PPKZTA6oPAg0J1U0JCggEDIno8MeEhIiIiqi0FxcD6P9U3i4p0X5+HiGoPEx4iIiIjJeEanoZh8e+AUgkAsLW1NXAwRI8fqaEDICIiIt2Uolj/P5m0twK+Gg8M7QRYmQNnE4B31gEXrld8nCAA43sAw8KAtn6Akw1wIw343wng/34Dikv+29fCDFjyItCpKeDlrKqQlpgK/HQYWLYPkCtq9S5WSIQqlgMXgX7t4OnpabhYiB5T9f7/KBERERkpQQB2fwSM6QYs2QvMWg80sgf+nA8EelR8rJU5sPZ1wNUOWLEfePMnVbI07zlg70ea+1qaAS28VVXR3v8ZmLEOuJQELJ4IrHu91u6e3kwkwGJVieq4uDgDB0P0+GnwCc+FCxdw8OBBbN68GatWrcKmTZsMHRKmTZsGQRB0fpmamqKgoKDW+s7OzsaUKVPwv//9r9b6qCkXL17E2LFj0aZNG1hbW0MQBLzwwgvl7p+bm4s33ngDPj4+sLS0hK2tLYKDg7FgwQLI5fK6C5yIqIYYfVnqo/OBNdPK3z68MxAeArywBJi/VTXa0mO2ajH/vOcqPrdMDnR5X/X12Q7gh0PA5KXAvK1Az1bAk63/2zcrD+j8HvDuBmD5PmDlAWDCd8DSfcCYCMDNoUbu7iNTKFUjPPF3DRsH0WOqwU9pO3fuHMzNzeHi4gKZTGbocDS8+OKLCA4O1mgzMTGBmZlZrfWZnZ2N1atXQyaTYdSoUbXWT004evQoNm/ejEaNGiEoKAiXLl0qd1+lUonw8HDExsaib9++mDhxIgoLC7Fz5058/PHHuHz5MrZs2VKH0RMREYZ3Bu5lAb+c+a8tIxfYegoYFwGYSVWJjS4lcuD0Ve32X/8G5o8GmjUBDkdX3H9Smuq7gzWQmv1Id6HGmEiA73ajydyhho2D6DHU4BOeUaNGwc7ODgCwbds2lJSUVHJE3Rk4cCCGDBli6DBqVFZWFuzt7SGRVH/wcPTo0Rg1ahQ8PDxw8uRJdO3atdx9//zzT8TExGDo0KH45Zdf1O3z58+Hj48P/vjjDxQXF8Pc3LzacRER1RVRFGHkYzwVa+sHRF0HypbXPpsATO0LNG0MxNyq2jndHVXfM3K1t5lKATtL1RS3DoHAjGdUSc+1lEeLvyYplMBPh6F8ow/g4mLoaIgeKw1+SltpslMfFRUV4c0334SXlxdMTU1hZWWFjh074sCBAxr7KZVKTJ8+HS1btoSDgwOkUimcnZ3x9NNPIykpSb3fnj174OfnBwBYt26dehqdy7//ePfs2QNBEDBnzhytWAYNGgRBEPDgwQN1W2hoKFxcXPDPP/+ge/fusLGxgZOTE/Lz8wEAN2/exLBhw+Di4gKpVApHR0cMHDgQiYmJet1/d3d3eHhUMsf7X1lZWepjHmZubg4HBweYm5vDxMREr3MREVEN8XAEUrK020vbGjtV/ZyzhgA5+cDeKO1twzoBGeuA26uBX98F7twHBn+mSjaMQVEJZKv3GzoKosdOgx/hqQlVqZlvZmam9+hGdnY27ty5o9Hm4OAAGxsblJSUICwsDLGxsejduzdeeOEF5ObmYsuWLRg0aBC2b9+Op59+GgAgk8nw448/IiIiAn379oWtrS2ioqKwZ88eXLhwAbGxsbCzs0Pr1q3xwQcf4LPPPkNYWBieffZZAICVlZXe96+soqIiREREoEWLFnjrrbeQkZEBU1NTXLt2DR07dkRJSQmGDBmCoKAgJCYmYuvWrQgPD0dkZCSaNGnyyP2W1aNHD1hbW2PDhg1o2rQp+vbti4KCAixfvhzx8fF4//33IZXy152I6hejWsMjNVFVXHuYqRQwNwWcy5RazsxTjepYmgHFOqasFf07xdyyilO4338W6NMGeGUlkKNjvevRGKD3XNUUtidbAW18AWuLqvVRm0QRrpv+Br6aauhIiB4rDX6EpyasX79e76979+7pfd6JEyfCy8tL4+uTTz4BAMybNw+XLl3CsmXLsHfvXnzyySf49ttvERcXB3t7e7z33nvqK3Cbm5sjJSUFe/fuxaJFizBv3jz88ccf+OKLL3Dnzh2sWLECANCkSRO89NJLAIDg4GDMmDEDM2bMwKuvvvrIj01+fj769euHY8eO4ZNPPsHy5cthYWGBiRMnQi6X49SpU9iwYQNmz56NdevWYd++fUhPT8dHH31U+cmrwNnZGVu3boWdnR3eeusttGjRAk888QQ2bdqEb7/9Fp9++mmVzpeZmYni4mL17by8PI3RLZlMhvv372sck5KSUuHte/fuaVw1nX2wD/bBPvTpw2iEh6hGTx7+Cg8BRnfTbvf+d8pWoQww1/Fhk4XZf9v1NTIcWDBaVbxgRTmjJGk5qnU9O04Dr64Cdp0HDs4xfNGCUiYSmLTyNYrfK/bBPhpKH/oQRLHsxNqGq3QNz5gxY6p0XNlRmIq4urpWuk5k2rRpWLp0KWbNmoXQ0FCNbaGhoWjWrBmCgoKQn5+Pv//+W+sTvtdeew27du3CnTt3tKZ8yeVyZGZmQiaTIS8vD82aNcOwYcOwY8cOAEBSUhL8/PwwYcIErF27VuPYPXv2YODAgZg9ezbmzZunsW3QoEHYvXs3cnNz1RdNCw0NxaVLl3Dt2jUEBASo983OzoaTkxP69OmDH3/8Uev+d+7cGRYWFkhISKjwcXpY6RoeXXGXOn78OD744AM0adIEERERyMzMxE8//YTbt29j5cqVmDhxot79EREZ3Fs/QblkDyRyI5mO5WANtA/QbPv6BVVRgoW/abafuKK6Tk78EiAhBRhY5kOnSU8CP74GtHpTvzU8vdsAuz5QVTob+qX+U9SCPID4pcDUFcCqA5XvXwdur30RXhMGGDoMoscK5/jooSanXj2sc+fO5RYtuHXrFmQyGby9vcs9Pjk5WZ3wrFixAosXL0ZiYiIUCs0LrOXm6ljYWUNsbW3h4+Oj0RYdHQ1RFHHgwAF4eXnpPM6lhhdsnj17Fk8++STeeustfPnll+r2N998EwEBAZg5cyaee+65ak3fIyJ6rGXna1dFy8pTrccpr1raxSSgWzPV9Xge/ny1UxCQX6RfmeaOQcCvs4DIRGDk11Vbj2P57weQZafiGYIAIKgx7rf2gO5XRiKqLUx49FCV6+LU1OJ4URTRpEkTfPXVV+XuExQUBABYs2YNXnnlFQQEBOC9996Dn58frKysoFAo8Pzzz0Op1O/FoaK54mWTqFJmZmZaa2NKBw179uypnkJXloVFzc6pXrBgAUpKSjBp0iSNdmtra/Ts2RP/+9//cOHCBYSHh9dov0REtUliTGt4HsX208CILsCwMNU0M0C13mdEF+CPSM2S1P5uqu/XU/9rC/EEdn8IJKUDgz79b+1PWc62wP0H2u0v9lZ9j7xW/ftSE94eDGsbG0NHQfTYYcKjh40bN+q976BBg9C4ceNq9+np6YnMzEwMGzas0ilya9euhampKU6cOKFRpezy5cta+1aU1Li5qV5sMjMztbbduqV/2dAWLVpAEAQUFxdj9OjReh9XHaVrp3RdYLS0zZhKkhMR6UMpivV7se3206pr6ayZBjRvAmQ8AF7tp7omzZwyF8A+/O9Uar+XVd9tLID9swFHa9WUuYHtNfdPvAeciVf9PK478HJfYOdZVcJkawk8FQr0DQV+P6cqZmBotpbAuO7wMa3XzyhRvcSERw8DBug/19bZ2blG+hw1ahS++OILvP3221i6dKnW9hs3bqhLTJeOKD08kiOKImbNmqV1nM2/nyyVlnF+WNOmTWFiYoKTJ0+qrv3wb3J08OBBXLlyRe/YXVxcEBYWhjNnzmDHjh3qanAPx3b79u0Kp+tVVXBwMM6dO4clS5Zg+fLl6vbMzEwcPnwYVlZW6NChQ431R0REelAqgQELgIUTgOkDVVXZzl0DXvi+8ulszraAt6vq5y+f196+9sh/Cc+JK0CXYFUBBTd7QK4Art4F3voJ+H5Pzd6nR2EiAaY+BVhb4J+LF7XW7xJR7WrwCU98fDzy8vIAqEooKxQKREWpavfb2NigadOmlZ6jttbwVGT+/Pk4dOgQli1bhjNnziAiIgIODg64desWTp48CRMTE8TGxgIARo4ciaNHjyIiIgIjRoyAQqHAvn371NfDeZizszM8PDxw9OhRzJo1C40bN4alpSWmTp0KGxsbDB48GDt37kSPHj3Qs2dPXLt2DTt37oSPj4/GNX0qs2HDBoSFheG5555Dnz590K5dO4iiiBs3buDIkSPo379/ucUHSuXk5OD9998HAKSlqa6Wff78eXVVuWeffRZPPvkkAGD27NnYuXMnVq5cifj4eISHhyMrKwtbtmxBVlYWZsyYoU72iIjqC6MqS61Lz9mV75OdD7y0TPVVkdKRnVI30wFhmH5xnE8Envtav30NQSkCr/UzdBREj60Gn/BcvXpVq3xdZGQkAMDDw0OvhMcQTE1Ncfr0acydOxdbtmzBsmWqFwpHR0c0b94c48ePV+/78ssvIysrC8uWLcP//d//wdraGuHh4Vi+fLlWQQFANUVv2rRp+PbbbyGTyeDs7IypU1XXBFi3bh3GjRuHI0eO4MyZM/D398f69evx008/VSnhCQgIwOXLlzFr1iwcOnQIhw4dgqmpKVxcXNCtWzdMnjy50nNkZWVpjNYAQExMDGJiVFMTXF1d1QlPUFAQzpw5gxkzZuDs2bM4duwYTE1N4e/vj48++givv/663rETERHVGKkEGNQB8GkEADUy7Z2IquaxKktNRERUbxhbWWp6dH9+AnRvAQBIT0+Hq6urgQMierxw5RwRERFRbRAEVbGGiObqpuTkZAMGRPR4YsJDRERkpIx+DQ9VTBSBt59WJT5EZDBMeIiIiIhqg70VMKabRlOzZs0MFAzR44sJDxERkZHiMtt6zESiuuaQpea19G7fvm2ggIgeX0x4iIiIiGqaKAKvaJeiLr1UBhHVnQZflpqIiKi+knDtR/0klQBDOgFeLlqbLCwsDBAQ0eONIzxERERGSskpbfWTXAm8OUjnpsDAwDoOhoiY8BARERHVFEEAWvsAXUJ0bi69eDYR1R0mPEREREZK4ABP/SOKwDssRU1kTJjwEBERGaO2fhAUSkNHQVXlYgs817Xcze7u7nUYDBEBgCCy5iUREZFRunz8DFq1bGmw/gsKCmFlZWmw/uslqQlgy8eMyJiwShsREZGRUthaAI42Bus//uY1hIaGGqx/IqKawCltRERERsrX19fQIRAR1XtMeIiIiIxUfn6+Qfv39vY2aP9ERDWBCQ8REZGRSk9PN2j/xcXFBu2fiKgmMOEhIiIinVJTUw0dAhFRtTHhISIiMlJt2rQxdAhERPUey1ITEREZqX/++QfNmzfXuU2hFCFT1G7/CqUCJhKT2u2kATAzAUwkvNAokbFiWWoiIiIjJZPJyt0245gS35yvi88sazmragA6NwZOjeFbKiJjxSltRERERsrOzk5ne55MxOpoTtAwFpH3+FwQGTMmPEREREbK3d1dZ/v6WBEFJXUcDJWPqwOIjBoTHiIiIiMVHx+v1aYURSw+rwTfYhsRget3iIwZEx4iIqJ65GCSiGvZho6CHsZ0h8i4MeEhIiIyUt7e3lpti88rYcJ32EaFBW+JjBtLihARERmp4uJijdsJWSL2JxkmFiKi+oojPEREREYqNTVV4/b3URzdMUYC1/AQGTUmPERERPVAbrGIHy+LUHD2lNHhU0Jk3JjwEBERGalWrVqpf14bK6JQbsBgqHxcw0Nk1JjwEBERGamEhAQA/5aijmQpaiKiR8GEh4iIyEgVFRUBAPbdEJGUa+BgqFyCwLdTRMaMVdqIiIiMlK2tLQBgUaSqWEF9XL9jbw58FSHB0CABVqbA2RQR7/ypxIW0io8TAIxvIWBYkIC2jQQ4WQI3coD/xSnxf+dEFCvqJHy9iKLS0CEQUQUEkcXjiYiIjFJRURFu5Juj+RojendfBQKA46NN0MYVWHhOiYxC4NVQCbxsgfYbFBVeQNXaFMh7Q4rTd0XsSlQirQDo3FjAhBYC/rojotdW40kyTAUlZO+YGToMIipHgx+DFUUR0dHR2LJlC3788Uf8/PPPOH36NEpKSgwW05QpUyAIgs4vU1PTWu07NTUVU6ZMwfbt22u1n5qwevVq9O/fH56enjA3N4ejoyNCQ0OxYcMGrX0///zzch/T0q/SufBERPVFXFwcvo9SQlpPqx4PDxYQ7inghX1KzD8tYtlFET22KKAQgXnhFb8FkSmALpvk6LJJgc/+FvHDZRGT9ysx77QSPb0leNLbiB4UlqUmMmoNfkrb6dOnERMTA19fX7Ru3RrZ2dmIiYnB/fv3MXDgQIPWzn/55ZcREBCg0WZiYlKrfaanp2P16tUAgOHDh9dqX9U1c+ZMWFpaolevXggJCcH9+/exbds2jB8/HmfOnMHSpUvV+w4YMEBnspicnIxvvvkGfn5+CAoKqsvwiYiq7UGJBGtiRcjr6VyM4U0F3MsX8Uv8f3cgoxDYelXEuOYCzExUiY0uJUrg9F3t9l8TRMwPB5o5A4dv1VLgVcR0h8i4NeiEJzMzU53s9O3bV91ua2uLU6dOITExEYGBgQaLb9iwYejTp4/B+q8N9+/fh7Ozc42ca9WqVRg5cqRG2/z58xESEoKVK1fi448/hru7OwCgTZs2aNOmjdY5Xn/9dQDAmDFjaiQmIqK69GeBH4rrcSnqto0ERKWKWtXlzqaImNpGgqaOQExG1c7pbq1KLzIKaybGmsDVAUTGrUFPaUtMTASgeR0DAAgJCYFUKjX6KU4FBQWYNm0avLy8YGpqCisrK3Tq1AlHjhzR2E+hUOC1115DixYt4ODgAKlUChcXFwwZMgTJycnq/bZv365+LFavXq2e6uXq6qreLggCPv/8c61Y+vXrpzUa1qpVK7i6uuLSpUvo1q0bbGxs4OLiot6emJiIIUOGwMXFBVKpFI6Ojhg8eDBu3ryp1/0vm+wAgJ2dHbp37w6FQoGoqKgKj1cqldi+fTvMzMzUiQ8RUX2hUIpYEWtWr0tRe1gDKfna7aVtjW2qPjYy6wkBOcUi9t6oz48MEdWlBj3Ck56eDkEQ0KhRI412qVQKZ2dnpKenV3oOURRRXFysd5/m5uZ6T5PLzMzEnTt3NNocHBxgY2OD4uJihIWF4cqVK+jbty8mTpyI7OxsbNmyBf3798euXbvUo0OFhYVYs2YNevbsiX79+sHGxgaRkZHYtWsXoqKicPXqVVhaWqJ9+/aYOXMmFi5ciPDwcAwZMgSAKol4VEVFRejVqxdat26Nt99+G6mpqQCAK1euIDw8HHK5HEOGDEFgYCASEhKwfft2hIWFITo6Wp1oVVVKSgoAoEmTJhXu98svv+DevXvo3bs33NzcHqkvIiJD2X1dxN2C2p3mXBUCADM9wymtoGYphc5qakXy/7ZXxfudBPTxleCVgwrk6P/SXOsMOT2eiCrXoEd48vPzYWFhoXNdjLW1NYqKiqBQVFz5Ji8vD+vXr9f7Ky8vT+/4Ro0aBS8vL42vhQsXAgA++ugjXL58GWvWrMHu3bsxf/58fPfdd4iNjYWtrS3efvtt9XmsrKyQmpqK3bt34+uvv8a8efOwe/duzJ07F7dv31av2fHz88P48eMBAM2bN8eMGTMwY8YMTJkyRe+YdT0+zzzzDI4ePYr58+dj5cqVAIBJkyZBLpfj3LlzWL9+PWbPno0NGzbg999/R1paGj788MNH6u/YsWM4fvw4mjdvjtatW1e4b2ksL7/8cpX7yczM1Eh08/Ly8ODBA/VtmUyG+/fvaxxTmoiVd/vevXsa0x7YB/tgH+yjoj4uZwCCEY3vRHgJKHpLqtdXsJPqmEI5YK4jSbKQ/rddXyODBSzoKsEP0UqsuGQ8jwsAjWfJ2H+v2Af7aGh96KNBl6XevHkzlEolxo4dq7Xt6NGjSEhIwIQJE2Bubl7uOeRyOe7du6d3n+7u7pBKK/7IasqUKVi9ejU++ugjNG/eXGNbhw4dEBQUhICAABQVFeHvv//WOn7q1KnYt28fsrOz1ddoKFVSUoLMzEz19zZt2mDUqFHYvHkzACAmJgatWrXCSy+9hFWrVmkcu337dowYMQKfffYZ3n//fY1t/fr1w/79+zV+aVu1aoWYmBjcu3dPYwQlPT0dbm5u6N+/vzrpeFinTp1ga2uLuLi4Ch+nsm7duoUnnngCubm5OH78ODp06FDuvqmpqfD29oaTk9Mj/WEQERlaSp4Ir5VyKETjGD1wswL6+ekXy68JInJlQPxkEyRkiRj4i2YJ6UktBfzYzwSt1sr1WsPT20fArqESHLgpYuhOpdFdj4hlqYmMW4Oe0iaVStVXqS6rdGSnsuREKpVWOnXqUUVERJRbtODOnTuQyWTw8vIq9/jk5GSEhIQAAL777jssWbIE169f1xq1ys7OrrGYy7K1tdWaLnb+/HmIoog9e/aUG3/ZaYaVuXv3Lrp3746srCxs3LixwmQHAJYsWQKZTIYRI0ZUqR8iImPhYSOgf+M87LtraxRV2lILgHWxVQvkYpqIbk0ECNAcBenkISC/RER8VuXn6OgO/PqMBJGpwMg/jC/ZISLj16ATHmtra2RnZ0OhUGhNa6toutvDlEpluUmTLhYWFpBIqj9TUBRFeHt744svvih3n9JEbPny5XjjjTcQFBSEDz74AL6+vrC0tIRCocDzzz+vd/WYiuYgy+W65x3oGh0r7a93796YNGmSzuOsra31iglQJTtdu3bFnTt3sGbNGp3FDMratGkTJBIJ3nzzTb37ISIyNs81TsOuZNvKdzRS2+NFjAiWYFhTATv+LU3tbAmMCBbwR6KoUZLa3171/XrOf20hTsDuYSZIygUG/aJQr/0xNoLQoFcIENV7DTrhcXV1xZ07d5CWlgYPDw91u1wux/379zXaypOfn6+eDqaP0aNHa00zexQeHh7IycnByJEjK03KNm7cCFNTU0RGRmoUIDh79qzWvhUlNaUjNWXnTgLQKq5QkdatW0MQBJSUlGD06NF6H6dLabJz+/Zt/PDDDxg3blylxxw9ehTXr19H586d4e/vX63+iYgMqVNjCdq7ARfSAGU9HNnYHi/i9F0Ra/pJ0NxZiYxC4NVQCUwEYM5JzWluh0eqXuv8VquyIBtTYP9wEzhaAAsjlRjor/n6lZgt4oyRzFgWRWXlOxGRwTTohCcgIAAXLlzA5cuXNZKbuLg4yOVyva7BY2lpiQEDBujdp6Wl5SPFWtazzz6LxYsXY+bMmVi0aJHW9hs3bsDPzw8AIJFIIAiCxlQ2pVKJDz74QOs4BwcHAEBWlvY8ghYtWkAikeDYsWMa7X/88Qfi4+P1jt3T0xNPPPEEjh8/jj179mg9fkqlEnfu3IG3t3eF57l37x66deuG27dvY9WqVZgwYYJe/S9ZsgQAMHnyZL1jJiIyRj4+PnhbLsHY3fXzDbVSBAbsUGBhdwmmt5XA0hQ4dw94Ya+i0ulszpaAt50qyfkyQvuDv7UxSpxJqZ+PCxHVrQZdtAAATp48idjYWPj6+sLb2xtZWVmIiYmBu7s7Bg0aZJBSkqVFCw4cOFDuGp7i4mJ06dIFUVFR6NChA7p16wZ7e3vcvHkTJ0+ehLm5OaKjowEA33zzDd566y0EBgZixIgRKCkpwd69e1FUVITExEQ89dRT2Ldvn/rcHh4eyM/PxyuvvAIPDw/Y2tqqk4NBgwZh9+7d6NmzJyIiIhAfH4/ff/8drq6uSEpK0ipacO/ePZ3lvePi4tC1a1dkZ2fjqaeeQtu2baFQKHD9+nUcPXoUQ4YM0SqaUFZgYCASExPRs2dPnUlnnz59tC42mpeXBzc3N5ibmyM1NRWmpqYV9kFEZMwuXryI5q3aoMkKBdKN6EKbpMlUIkL2Nl9viIxVgx7hAYDOnTvD1tYWV65cwa1bt2BhYYGWLVuiQ4cORl0339zcHKdPn8acOXOwbds2LF26FADg5OSEFi1aYOLEiep933zzTeTm5mLVqlVYuHAhrK2t0a1bN6xcuRKenp5a516zZg3eeustfPPNN5DJZHBxcVEnPBs2bMC4ceNw7NgxnDx5EoGBgdi8eTOWLl2KpKQkveMPCQnBxYsX8e677+LIkSM4cOAATE1N4eLigh49emjEX57SC8cePXoUR48e1dr+2WefaSU8q1atQkFBAUaOHMlkh4gaBDMTAa+3k2DuKWW9nNb2ODDedxNEBDwGIzxERET1VVpaGho1aoTUfBFNViog5wwuo8Sy1ETGjWVFiIiIjFTpTAQ3awGjQwRIOZRARFRlTHiIiIiMVHJysvrnN9pJjOJ6PKTNmKfIExETHiIionqhvbuATh6AhO+tjQ7zUCLjxoSHiIjISDVr1kzj9tvtJSxcYIy4HJrIqDHhISIiMlK3b9/WuD00SICblYGCISKqp5jwEBERGam8vDyN26YmAt5oL+G0NiMjCHw7RWTM+BdKRERkpCwsLLTaXmolwIQJj1ERRdYLJzJmTHiIiIiMVGBgoFabi5WAcc1ZopqISF9MeIiIiIxUTEyMznaWqDYyLEtNZNSkhg6AiIiIqqZNIwFdPYFTd1Gr09tEUeQ1ZiqhEAFTfnxMZNSY8BARERkpd3f3crf9Xw8T7LtRu8M89+6lwN3do1b7aAg8ihIBBBk6DCIqhyCKLB5PRERE2u7evYvGjRsbOgyjd/HiRYSGhho6DCIqBwdhiYiISCdLS0tDh1Av+Pr6GjoEIqoAEx4iIiLS6ebNm4YOoV7Iz883dAhEVAEmPERERETVkJ6ebugQiKgCTHiIiIhIp6AgLsQnovqPRQuIiIhIp6SkJK5P0QPLdxMZN47wEBERkU7Z2dmGDqFeuHLliqFDIKIK8Do8REREpJOpqamhQ6gXZDJZuduiUkUsu6isw2iMV5fGAia14mftVPc4pY2IiIioGq5fvw5/f3+d2579TY6d1zilRglAKQIlb5tAKuH0P6pbHOEhIiIinS5duoQ2bdoYOgyj5+7urrP9Vq6InddUb/Q5xkNkOI/7Bw5ERERUDk4C0U98fLzO9uUXleBYBpHhMeEhIiIinVxcXAwdQr1VUCJi2UURCuaMRAbHhIeIiIh0srW1NXQI9YK3t7dW26YrInLLr2VARHWICQ8RERHpdOPGDUOHUC8UFxdr3BZFEYsiOZ2NyFgw4SEiIiKqhtTUVI3bx26LuJIJcDYbkXFgwkNEREQ6BQQEGDqEemnxeSVMOLxDZDSY8BAREZFOWVlZhg6hXmjVqpX65xvZIv5IBIsVEBkRJjxERESkU2ZmpqFDqBcSEhLUPy+9qASvq0lkXJjwEBERkU4mJiaGDqFeKCoqAgDky0SsusRS1ETGhgkPERER6fTwVC0qX2n57g3/iMgrMXAwRKSFCQ8RERHpdPnyZUOHUC94enqqS1HXBXtzYGUfCdJeNUHeGyY4MlKCto30O/YJd2Bpbwkix5lA9pYJxBlSvY4L9wTEGVKIM6RwtqxG8EQGwISHiIiIdFIoFIYOoV6Ii4vD4VsiErJrvxS1AGD3MBOMaSZgyQUlZh1TopGVgD+fM0GgQ+XHD/CX4MVWAkQA13P07/P7XibIk3GuHtVP+qX1NSg+Ph6XL19GdnY2zMzM4O3tjY4dO8LSsvKPCwoLC/H3338jIyMD+fn5kMvlsLa2hoeHB0JDQ2Fvb1/h8bdu3cK+ffsAAEOHDoWrq6t6W35+PuLj43Hnzh1kZ2ejpKQEtra28PLyQmhoKCwsLKp3xx/R+++/jy+++ELnNqlUiitXriAwMLBW+s7OzsZHH32Edu3aYdKkSbXSR03ZtGkTNm/ejAsXLiA9PR2Wlpbw9vbGlClTMHXqVJiamho6RCKiesfJycnQIdQb3/xbirq21+8MDxYQ7ilg+O8K7IhXdbb1qgLxk00wL1yCsbsrHmVaflGJL88CRXLg+yclCHaqvMLClDYCvGyBHy6LeLM9KzJQ/VOnCU90dDTOnDkDDw8PdOnSBfn5+YiOjkZaWhqGDBlS6ZvS4uJi5OTkoEmTJrCxsYFUKkVOTg6uXr2K69evY8iQIXB0dNR5bElJCU6cOAFTU1OUlGhPsL158ybOnz8Pb29vtGnTBqampkhPT8fly5eRmJiIoUOHwsrKqkYeh0cxduxYhIaGarSZmJjAzc2t1vrMzs7G0qVLMWzYMKNPeF5//XWYmpqie/fuaN68OXJzc7Ft2za8/vrrOHnyJDZs2ACptM7zeyKieq2811TSVGLnjT3X6+ZCo8ObCriXL+KX+P96yygEtl4VMa65ADMTQFbBwFxaQdX6c7QAFoRLMPuUaiSJqD6qs3eARUVFiIyMhKurKwYOHAiJRDWbztXVFfv370dMTAzatm1b4TkcHBzwzDPPaLX7+flh586diI2NRdeuXXUee+7cOYiiiJCQEJ1zkt3d3TFmzBiNpKZZs2Zo1KgR/vrrL0RHRyMsLKwqd7lG9ejRAy+++KLB+q8NOTk5sLa2rpFEZPXq1Rg0aBDMzMzUbZ988gmaNWuGbdu2Ydq0aQgPD692P0REj5PExEStD9tI2+p/TCGpg9EdAGjbSEBUqqiVXJ1NETG1jQRNHYGYjJrr75NwCe4VACsvifi4MxMeqp/qbA1PUlIS5HI5WrRooU52AMDHxwe2trYaNeyrqrQ6SnFxsc7t6enpiI2NRefOncsdRXJyctI5guPv7w/A+K9FUFxcjHfffRc+Pj4wNTWFlZUVOnTogB07dmjsp1QqMWvWLLRu3RoODg6QSqVwdnbGgAEDEBMTo97vzz//hJ+fHwDgl19+gSAIEAQBLi4uuHv3Lv78808IgoA333xTK5aRI0dCEARcvXpV3daxY0e4uLjg5MmT6N27N2xsbODg4IDExEQAwN27dzFq1Ci4urpCKpXCwcEBTz31FKKjo/W6/8OGDdNIdgDAysoKgwYNgkKh4MJbIiKqFQ9kIjYmmNdZKWoPayAlX7u9tK2xTc0lJa1cgKltBLx9VAkll+9QPVZnIzxpaWkAoHMKlpubG65du4aSkhK91loolUrIZDIolUrk5OTg/PnzAAAvLy+d+/71119o0qQJ/P39q5y45Oer/oPos8YIgDoufUilUr1HN3Jzc3Hnzh2NNjs7O9jZ2aGkpAQRERGIiopCjx49MG7cOBQUFGDLli0YPXo0Vq5ciYkTJ6rjW7ZsGbp06YJevXrBwcEBFy5cwO7du3Hp0iWcOnUKPj4+aNasGT7++GN88sknaN++PUaNGgVAlUQ4ODjoFXNZRUVFeOaZZxAcHIzp06cjJycHDg4OuHXrFtq1a4fi4mIMHDgQISEhuHnzJrZs2YI+ffrg4MGDaN269SP1eevWLQCAh4fHIx1PRPQ4K/3gi8r3S7yIQsWjfX4sADDT81JHxf9OU7OU/vfzw4rk/22vKd89aYK9N0QcvMlsh+q3OhvhKShQTRq1trbW2lY6slKaXFTm9u3bWL9+PTZu3Ig//vgDWVlZCAsLQ9OmTbX2jY6ORnZ29iNPZypNpnSdW5f9+/dj/fr1en1dvHhR7zjeeecdeHl5aXzNmjULALBo0SKcPXsWn3zyCQ4ePIhPP/0UixcvRlxcHJycnPDFF18gNzcXAGBubo67d+/iwIED+OabbzB37lz89ttv+P7773H37l2sWLECgCoJLV234+PjgxkzZmDGjBl49dVXH3ktU35+PiIiInDs2DF89tlnWLp0Kdzc3DB58mTIZDL8/vvv+N///oe5c+dizZo1+PPPP5GRkYF58+ZBFKv+z/bChQvYu3cvgoKC0L179yodm5mZqTFimJeXhwcPHqhvy2Qy3L9/X+OYlJSUCm/fu3dP436wD/bBPtiHsfdRep76fj9qs48Q8wwIj7h6J8JLQNFbUr2+gv+tH1EoB8x1JEkW0v+214SRwQK6NAbe+bNmS203hOecfRhXH/oQxCq8kywuLq7S1KCWLVuqq5vt2rULd+/exUsvvQRB0BxujYyMRFRUFIYNGwYXF5dKz1tUVISMjAwoFApkZWUhMTER3t7eaN++vcZ0udKF6+3atVOvDyrtq2yVNl1KiyyEhIQgIiJCr/ucnp5e7tS6skpHaCpSWqXttdde00ramjdvjjZt2qB58+bIysrCgQMHtBaYzpgxA9u2bUNkZKTWGimFQqH+xcvLy0OzZs3w1FNPqSvZJSUlwc/PD8OGDdOaGvfnn3+iZ8+eeOONN/DNN99obBs5ciS2bduGuLg4BAcHA1BNaTt37hxOnDihcT9ycnLg5OSE8PBw/Pzzz1q/G127doWJiQnOnz9fpZGl9PR0hIaGIjMzE5s3b8aQIUP0PpaIiFQuXrzINTx66LkhB8fTrKs8rc3NCujnp98UtF8TROTKgPjJJkjIEjHwF81EZFJLAT/2M0GrtXK91/B8/6QE09pKIPyfdpZ0c4oJjt8R8eGJ//p5s70Eb7aXoO16OVLzdU+tq0zJ2yaQSrgWiOpWlQY+ZTIZoqKi9N4/KChInfCUTt1SKBRa07jkcrnGPpWxsLBAkyZNAKhGH4KCgrB9+3YUFhZqJCbHjx+HnZ0d2rRpo3fMpeLi4nDmzBl4e3uXWwhBl8qSqEcVGhqK0aNH69x2/fp1FBcXVzjtKyUlRZ3wrF27Fl9++SUSEhK0rrHwcJZd00rLfD/s6tWrUCqVOH78OLy9vXUe5+LigsLCQr0TnszMTISHhyM9PR1fffUVkx0iokdU9kMo0m2UZxr+TK369L/UAmBdbNWypItpIro1ESBAsypcJw8B+SUi4rOqHIZO3nYCxjYXMLa59mSgC+OluJgmou16XqeJ6ocqJTy2traYMmXKI3X08LS1stfLqWi6mz6sra3h6emJq1evIjw8HCYmJrhx4waSk5PRvXt3jTfxpaMv+fn5MDMzg52dndY/9Li4OPW6nz59+miMGlWmqKhI7zU8pqamNXJ9GFEU4enpiQULFsDc3FznPh06dAAAbN26FRMnToS/vz9mzJiBwMBAWFtbQ6FQ4Pnnn9d76lhFL4KlCWxZZmZmWkltaX9dunTBtGnTdB5nYWGh97UgMjMz0aVLF9y4cQOffvop3njjDb2OIyIibY/ygeHjqGcToOkNICGr9ktTb48XMSJYgmFNBfV1eJwtgRHBAv5IFDVKUvv/+3ZL3wuMPmzITu1kZlSIgFEhEjy/R4E7tff5KFGNq7OiBY0aNUJcXBxSU1O1Ep7U1FQ4ODhU682/QqGAKIqQyWSwtLREXl4eAODYsWM69z9w4AAAYPz48RoXFS1Ndjw9PdG3b1+YmOi5mvBfBw8e1HtuYbt27dSJSHX4+PggNTUVPXr0gK+vb4X7/vjjjzA1NcWOHTs0pinExcVp7VtRUlOagGRlaX+UdOPGDf0CBxAYGAhBECCTyTB8+PBq/Q6UJjuJiYlYsGABZs6cyU8niYiqITY2Fi1atDB0GEbP19cHbxdK8MrBml3vosv2eBGn74pY00+C5s5KZBQCr4ZKYCIAc05q9n94pOo9jN/q/5IXbzvg+eaq18YObqrvH4apvt/MBTb+o0qifrumnbqFNlLtt/eGiPuFNXzHiGpRnSU8Pj4+MDExQWxsLAIDA9WjJjdv3sSDBw+03vjn5eVBLpfDzs5OvW9BQYHOBfNZWVlITk6GnZ2dupqat7e3zhGj69ev4/r16+jYsSPs7Ow03mBfvXoVx48fh6enJ5566qlHuj5MWFhYldbw1IQJEybgo48+wpw5c/Djjz9qxX379m00btwYJiYm6gTu4alsoijigw8+0DqvjY0NANU6G1EUNZIHPz8/mJiYICoqCvn5+erH+vjx47h06ZLesTs7O6NHjx44duwYfvrpJ0ydOlVjuyiKuHXrFnx8fCo8T1ZWFsLDw5GYmIj58+dj1qxZTHaIiKpJ14W6Sds///yDcc3bYNYxIFdWu30pRWDADgUWdpdgelsJLE2Bc/eAF/Yq9JrO5mcvYEFXzQ9zS2//eVvExn84TY0anjpLeCwtLfHEE0/gzJkz2L17NwIDA5Gfn4/o6Gg4ODigVatWGvsfPXoUKSkpGD16tPo6OxcvXkRycjK8vLzUbZmZmUhISIBSqdRYDG9vb681klS6PwB4enpqrLdJSkrCX3/9BVNTU/j7+2uNUpiamlY6egLU3hqeisyaNQt79+7F+vXrcenSJXTv3h3Ozs64desWTp06BaVSiXPnzsHW1hajR4/G3r17MWLECAwfPhz/3959x9d4/n8cf53sbSQhQUhsYo+iqNFSrQ7aooqWDjp0qU7K1+hAv6VLq34tXfpVWq29VVu0qBl7bxIjIiH7/v1xmlPHOYkTTnLi5P18PPLgXPd139fnPrdxPudaHh4eLFy4kPPnbfu7Q0NDiYqKYu3atbzwwgtER0cTEBDAww8/TFBQED169GDatGl07NiRW2+9lUOHDvHTTz9RqVIlDh486HD8X331FU2bNuWZZ55h5syZNG7c2DIscfny5bRs2dJm0YQrtWrVip07d9KiRQu8vLz473//a3W8RYsW2nhURCSfrnUbguIo0MfEgPom3l9vFPiePIlp8MTibJ5YnHe9y3t2cqw8YthdpMARI1ZnM2J1wfdiiThboSU8APXq1cPX15etW7eyevVqS3LRrFkzh4YyVaxYkZSUFPbv38+lS5cwDIPAwEAqV65MvXr1HJ7nYc/p06ctQ+J+//13m+NBQUEOJTyu4O3tza+//sq7777LN998Y1laulSpUtSsWZMHH3zQ0vPVp08fTp8+zfjx4xk/fjyBgYE0a9aMadOm2R228L///Y8BAwbw2WefkZ6eTmhoKHfffTf+/v5MmjSJ9PR0Fi5cyPr164mJieGjjz5i/vz5+Up4oqKiiIuL49VXX2XhwoX8+uuveHt7ExoaSosWLXjiiSeueo3t27cDsGbNGtasWWNz/Pnnn1fCIyKST674Eu9GVK5cOcA8tOy9deohESlq8rUstYiIiBQfWpbaMQkJCZbksMvPmczbB5n6dGWXlqUWVyi0jUdFRERE3NGxY8csv3+xsaeSHZEiRgmPiIiI2HW1BWPE1i0VoHYoqA9DpOhQwiMiIiJ2XbqktYcdUatWLcvvTSYTg5p4FPh+PCLiOCU8IiIiYld8fLyrQ7ghHDlyxOr1QzVNlLC/D7iIuIASHhEREZHrkLPZeQ5/bxNPNzDhqXFtIkWCEh4RERGxq169eq4O4Ybg5+dnU/ZUfQ1rEykqlPCIiIiIXbt27XJ1CDeEqlWr2pRFhZjoWtWEl3p5RFxOCY+IiIjYlZaW5uoQbghxcXF2y19o7KElqkWKACU8IiIiYldISIirQ7ihtSwP9cK1RLWIq3m5OgAREREpmiIjI10dwg0hIiLCbrnJZOLlph48uywbQz09NC2bhafJ09VhSDFkMgz9FRQRERFbmzZtokGDBq4OQ9yE/jyJq2hIm4iIiIiIuC0lPCIiImJXVFSUq0MQNxIdHe3qEKSYUsIjIiIidmVkZLg6BHEjKSkprg5BiiklPCIiImLXyZMnXR2CuJGEhARXhyDFlBIeERERERFxW1qlTUREROzKzMzEy0s7WIhzGIaByaRdiaTw6V8xERERsWvv3r3UrFnT1WGIm9ixYwe1a9e2e+xShsGexMKNp6ioFAIlfJUIFiQlPCIiImJXamqqq0MQN5Kenp7rsc+3GLywIrsQoyk6GpaBDQ/rI3lB0hweERERsSsoKMjVIYgbCQkJsVuebRiM/7t4JjsA+xOL770XFiU8IiIiYpf24RFnioiIsFu+YL/BoaRCDqYIyc7WdPqCpoRHRERE7NqxY4erQxA3snv3brvl4//OxrMYT2Hx0EIOBU4DBkVERETEJXacMVh22NVRuJgSngKnHh4RERGxq3z58q4OQdxIxYoVbco+3JCNVzH/vJ+drTk8BU0Jj4iIiNilrfrEmdLS0qxeJ6YaTI0zyNQfMylgSnhERETEruPHj7s6BHEjp06dsnr9ZZxBWpaLgilCPD31cbyg6R0WERERkUKVlW0wfn026tzRKm2FQQmPiIiI2FW7dm1XhyBupG7dupbfz91vcDTZhcEUIRo6WvCU8IiIiIhdhw4dcnUI4kb27Nlj+f349cV7KerL6W0oeFqWWkREROxKSUlxdQjiRlJTUwGISzBYedTFwRQhHp6erg7B7SnhEREREbsCAgJcHYK4keDgYAA++Gcp6oJana2EL4y9xYOu1UwEeMPaEwYv/ZrNxvirn9s0AvrW8aBZhIl64eDtacL0XqZNPT8v+PhWD5pFmogKBk8T7EuEL+OymbjJIDMfK01nZWWhj+QFS++uiIiI2BUTE+PqEMSNlC9fnjOXDL7ZXnBLUZuAefd5Uj8cxq3L5vQleLqBB7/28KTxN1nsTcz7/Dsre/B4XRNbEmD/eahR2n49fy+IDTUxf7/BwSSDbANuLmdifDsPmkUa9JqnvXWKErefw2MYBlu2bGH69Ol88cUXfPfdd6xZs4aMjAyXxdS/f39MJpPdH29v7wJt+9SpU/Tv35+ZM2cWaDsF4auvvrK8T0uWLLE6dujQIe644w4qVapEUFAQ3t7eRERE0LlzZzZu3OiiiEVEbmzbtm1zdQjiRnbu3MkXWw0yCjAXeKCGiZblTfRdmM3INQYTNxm0nZ5FlgEjWl79Y++nm7Ip8VEWTb/NYsmh3LOyc6nQYloWr/6WzaebDCZtNnhkQTafbDR4qJYHZfPROeph0iyegub2PTxr1qwhLi6O6Oho6tWrR2JiInFxcZw5c4bOnTtjcuEfsieffJIqVapYlXkW8DjOhIQEJk+eDMADDzxQoG05U2JiIoMHD8bX19dm4zKA+Ph4Dh48SMuWLalUqRIBAQHs3r2bX375hZtvvpnly5fTokULF0QuIiIiAJnZ8MHf2RTkKswPVDdxMsXgp93/NnL6Evywy6B3bRM+npCex94/8Revr/2DSeZ2S/rBKUevpYSnwLl1wnP27FlLstOxY0dLeXBwMKtXr2bfvn1UrVrVZfHdd999dOjQwWXtF4QzZ84QGhrq9OsOGDCArKwsunbtyv/+9z+b402bNmXHjh025YsWLaJTp06MHTuWWbNmOT0uERF3FhkZ6eoQxI1szY7heAGvg9GwjIkNpwyb/X3WnjAYUN+D6qUg7rTz2vP2gBBf8xC3JmVNDG7iwcHzBnvPOX6N7GwNfytobj2kbd++fYD1uu8ANWvWxMvLy2p5xKLo4sWLDBw4kKioKLy9vQkICKBZs2YsX77cql5WVhbPPPMMsbGxlCxZEi8vL8LCwujSpQvHjh2z1Js5c6blvZg8ebJleFh4eLjluMlk4p133rGJpVOnTja9YXXr1iU8PJzNmzfTunVrgoKCCAsLsxzft28fXbp0ISwsDC8vL0qVKsXdd9+d72VOlyxZwsyZMxk1apRlwqOjatasCcD58+fzdZ6IiBT8qAMpXj7d5lPgS1FHBsIJO0lVTlm5IOcGcF81E6ef8eLIAC9mdfHkaDLcPcs8hE6KDrfu4UlISMBkMlGmTBmrci8vL0JDQ0lISLjqNQzDsDuEKje+vr4OD5M7e/YsR49ar8tYsmRJgoKCSEtLo3nz5uzYsYOOHTvSr18/EhMTmT59OnfccQdz58619A5dunSJKVOm0K5dOzp16kRQUBDr169n7ty5bNiwgV27duHv70/jxo15+eWXGTduHC1btqRLly4AhISEOHx/V0pNTaV9+/bUq1ePQYMGcerUKQB27NhBy5YtyczMpEuXLlStWpU9e/Ywc+ZMmjdvzpYtWyyJVl7S09Pp378/jRo14plnnqF///551k9LSyMhIYG0tDS2bt3K8OHDAXPCJiIi+XP06FGrL7JErtWmeIN1Cfmbp2wCfBzMudP+Gabm7/Xv7y+XmvnvcWdaccTgth+yKOkHt1Y0UT/cRGA+p2N7erh1/0OR4NbvcEpKCn5+fna/oQoMDCQ1NfWfpQBzl5yczNdff+3wT3Ky49sGP/jgg0RFRVn9jBs3DoChQ4eydetWpkyZwrx58xg5ciQffvgh27ZtIzg4mEGDBlmuExAQwKlTp5g3bx7//e9/GTFiBPPmzeM///kPR44csczZiYmJ4eGHHwbMu2cPHjyYwYMHXzWJuNr7c++997JixQpGjhzJpEmTAHj00UfJzMxk3bp1fP311wwbNoxvvvmG2bNnEx8fz5AhQxy6/ssvv8zx48f54osvHKr/3XffERUVRdWqVenatSuHDh3ipZde4pVXXsnXfZ09e9Yq0U1OTubChQuW1+np6Zw5c8bqnBMnTuT5+uTJk1a7KasNtaE21IbaUBvFpY1zqfnv8rglykTqi14O/eSspnYpE3ztJEl+Xv8ed6b4i7DssMGPuw2eXprN3P3ZLOnmma9FC7INwy2feWG14QiTcXkUbub7778nOzubXr162RxbsWIFe/bs4ZFHHsHX1zfXa2RmZnLy5EmH24yIiMDLK++vD/r378/kyZMZOnQotWvXtjrWpEkTqlWrRpUqVUhNTeWvv/6yOX/AgAEsXLiQxMREmyFeGRkZnD171vJr/fr1efDBB/n+++8BiIuLo27dujzxxBN8/vnnVufOnDmTbt268fbbb/P6669bHevUqROLFi2y+kNbt25d4uLiOHnyJGXLlrWUJyQkULZsWe644w5LAnS5Zs2aERwczM6dO/N8nzZt2kSzZs3o378/H330kdV7t3jxYrvzn44ePcrvv//OxYsX2bJlC7Nnz6Z9+/Z8+umn+Pj45NmeiIhYS01Nxc/Pz9VhiBswDINaX2ay+5zJZn5NbsoGQKcYx0bNzNpjkJQOux/zZM85g84/Wc+LebSOiS86eVJ3aqbDc3g+utWDgQ097O7Dk5tqpWD3Y14MWJzF51scu9NgryySXsj9s6hcP7ce0ubl5WXZ1fdKOT07V0tOvLy8qFChgtNjA7jllltyXbTg6NGjpKenExUVlev5x44ds8xR+fDDD/n444/Zv3+/Ta9VYmKi02K+UnBwsFWyA/D3339jGAbz58/PNf4rhxna069fP8qUKcN7773ncDwVKlSgZ8+eltfPPvssDRs2JCEhgdmzZzt8HRERMf8/c+VqoiLXwmQy0bviad48d/Xh7DlOXYSvtuXve/lN8QatK5gwgVVi1SzSREqGwe58LCZwLXKGzJVQ/lKkuHXCExgYSGJiIllZWTbD2vIa7na57OzsXJMme/z8/PBwwlhMwzCoWLEi7777bq51chKxTz/9lOeff55q1arxxhtvEB0djb+/P1lZWfTp0wdHO/HymnuUmWn/2w17vWM57d122208+uijds8LDAzMM5bPPvuMTZs28dZbb7F9+3ZL+blz5n+pDhw4wMaNG6lXr16ez7Bq1ao0adKEefPmcfHiRe0aLiKSD5cPNRG5XreWjmecTzhJ6QXXxszdBt1qeHBfdRM//rM0dag/dKthYs4+w2pJ6solzL/uv4Z1jUL94cwl2/LH65o/A653fHCQFgcpBG6d8ISHh3P06FHi4+OtltbMzMzkzJkzDi23mZKSYhkO5oiePXvmeyUxeyIjIzl//jzdu3e/6l+Eb7/9Fm9vb9avX2+1AMHatWtt6uaV1OT01Fw5dhKwWVwhL/Xq1cNkMpGRkWHV25If+/fvB2DIkCF25/sMGDAAgCNHjly1By41NZXs7GxOnz5NxYoVrykeEZHiSMPZxJnCQgJ4sr6J/643CmwVs5m7DdYcN5jSyYPaodmcvgRPN/DA0wTDV1kPc1vW3fz5Kmbyv1lQxRDoU9v8WalJWfOvQ5qbfz2UBN9uNwfeu5aJJ+t78PNeg/3nDYJ94PZoEx2jPZi9N5sVRxy/QfPIHLf+SO5ybv3uVqlShY0bN7J161ar5Gbnzp1kZmY6tAePv78/d955p8Nt+vv7X1OsV7r//vsZP348L7/8Mu+//77N8QMHDhATEwOAh4cHJpPJaihbdnY2b7zxhs15JUuWBP7tKblcbGwsHh4erFy50qp8zpw57N692+HYy5cvT9OmTfn999+ZP3++zfuXnZ3N0aNH80w+evbsSaVKlWzKf/jhB3777TdLj1bOnj+Xvx+XW7NmDRs3bqRs2bJKdkRE8qlatWquDkHcSKVKlXi6jAfj1uW9YNT1yDbgzh+zGNfGg+caeuDvDetOQt8FWQ4NZ4spYWJ0K+svmnNe/3rE4Nvt5tj/OGZwc3mDnrVMlA0wkZkNu87Ciyuy+GiD206Pv2G59aIFAKtWrWLbtm1ER0dTsWJFzp07R1xcHBEREdx1110OLyHtTFebeA/m5ZVvvvlmNmzYQJMmTWjdujUlSpTg0KFDrFq1Cl9fX7Zs2QLAhAkTePHFF6latSrdunUjIyODBQsWkJqayr59+7j99ttZuHCh5dqRkZGkpKTw1FNPERkZSXBwMI899hgAd911F/PmzaNdu3bccsst7N69m9mzZxMeHs7BgwdtFi04efKk3eW9d+7cSatWrUhMTOT222+nYcOGZGVlsX//flasWEGXLl1sFk24nveue/furFmzhjZt2hATE4NhGMTFxbFw4UKysrKYMmUKvXv3znd7IiLF2aZNm2jQoIGrwxA3kfPn6b6fM5mzDzLd+hOo40K8szn/vBZWKkhu3cMD0KJFC4KDg9mxYweHDx/Gz8+POnXq0KRJE5ckO47y9fVlzZo1DB8+nBkzZvDJJ58AULp0aWJjY+nXr5+l7gsvvEBSUhKff/4548aNIzAwkNatWzNp0iTKly9vc+0pU6bw4osvMmHCBNLT0wkLC7MkPN988w29e/dm5cqVrFq1iqpVq/L999/zySefcPDgQYfjr1mzJps2beLVV19l+fLlLF68GG9vb8LCwmjbtq1V/M7QtWtXTpw4waJFi0hKSiI7O5tSpUrRrl073nzzTW6++WanticiIiLX5oXGnszaW3C9PDcajyL8edRduH0Pj4iIiFybEydOODTfVcQR8fHxlClTBsMwqDs1i+1ncHiJanemZakLnltvPCoiIiLXLq996kTyK2dkjclkYlATDyU7UmiU8IiIiIhdhw8fdnUI4kaOHTtm+f1DtUyUVD4N4JTtTCRveodFREREpFD5eZl4uoEJT01fcXi/RLl2SnhERETErurVq7s6BHEjtWrVsnr9VAMNawMlPIVBCY+IiIjYdfJkPraLF7mKI0eOWL2uEGzivmomvNTLIwVMCY+IiIjYlZSU5OoQxI0kJyfblL3Y2KPY78fj6el59UpyXZTwiIiIiF0+PtoMUZzHz8/PpqxFOagfDh7FuJcnK0t7EhU0JTwiIiJi15VzLkSuR9WqVW3Kcpaozi7mvTxSsJTwiIiIiF2bN292dQjiRuLi4uyW96hhorRt50+xkbM/kRQcJTwiIiIi4jK+XiaeaVB8P/SX0H5EBc7L1QGIiIhI0RQeHu7qEMSNRERE5HrsuUYe1AwtnuPags7tBmq7Ogy3poRHRERE7AoMDHR1COJG8kp4wgJMPFSrePbybNqU7uoQ3J6GtImIiIhdBw8edHUIIm4vOjra1SG4PSU8IiIiIiIukpKS4uoQ3J4SHhEREbHL3jLCIuJcCQkJrg7B7SnhEREREbtOnz7t6hBERK6bEh4RERGxKzEx0dUhiLi9+vXruzoEt6eER0REROzy8tJiriIFbceOHa4Owe3pXzIRERGxq06dOq4OQcS+RRth9AxXR+GY6LLwzfO5Hk5P17LUBU0Jj4iIiNi1efNmDbeRomniQvhjp6ujcMwfO2HgHdCsut3DISEhhRxQ8aMhbSIiImKXYRiuDkHkxuflARPm5no4rw1ZxTmU8IiIiIhdoaGhrg5B5MaXmQ0zVsPxs3YP7969u5ADKn6U8IiIiIhdGmoj4kSTFrs6gmJLCY+IiIjYdeDAAVeHIOIesrLh4/mQlmFzqGLFii4IqHhRwiMiIiIiUtDOJsP0P2yK09LSXBBM8aKER0REROyqXLmyq0MQcR8eJnh/DlyxGMipU6dcFFDxoYRHRERE7EpMTHR1CCLuI9uAzQdh9Q2ynLYbUcIjIiIidp09a39VKRG5RnaWqK5bt66Lgik+lPCIiIiIXR4e+pgg4lSZ2fDTn3D0tKVoz549LgyoeNC/ZCIiImJXvXr1XB2CiPsxmWDiQsvL1NRUFwZTPCjhEREREbu2bt3q6hBECleJAJj0JMRPgeRpsHwENHRw8Y6mVeGT/rB+HKT/AMZP9utlZZsTnkvm1dmCg4OdFLzkRgmPiIiI2JWVleXqEEQKj8kE84bCQ63h4wXwytdQpgT8OhKqRl79/Dsbw+O3mldh23+VldfOX4TvzUtUly9f3gnBS17cPuExDIMtW7Ywffp0vvjiC7777jvWrFlDRobtxk+FpX///phMJrs/3t7eBdr2qVOn6N+/PzNnzizQdpxhzJgxNG7cmNKlS+Pt7U1ISAg1atTgnXfesfv80tLSePPNN6lSpQoBAQEEBARQuXJlXn31VS5duuSCOxARubGVKlXK1SGIOM+KkTBlYO7HH2gBLWtC349h5A/mXpi2w8w9MiN6XP36ny6EEn2g6SuwZHPedU0meH82GAY7d2rVtoLm5eoACtqaNWuIi4sjOjqaevXqkZiYSFxcHGfOnKFz586YTCaXxfbkk09SpUoVqzJPT88CbTMhIYHJkycD8MADDxRoW9fr77//JiQkhF69elG2bFkuXLjAkiVLeOONN/jjjz+YN2+eVf3OnTuzbNkyWrVqRa9evcjMzGTRokWMHTuWv/76i19//dU1NyIicoMKDQ11dQgiheeBFnDynHlRgRynk+CH1dD7FvDxgvTM3M+PP+94W4YB247Ab9uhxLWHLI5x64Tn7NmzlmSnY8eOlvLg4GBWr17Nvn37qFq1qsviu+++++jQoYPL2i8IZ86ccdp/kD/88INN2ZgxY7jppptYsGAB+/fvt2yKt3fvXpYtW8bNN9/M77//bqk/evRo6taty2+//cbJkyeJiIhwSmwiIsXB3r17adCggavDECkcDWNgw36bjUFZuwcGdITq5SDusPPa8/SACXOoMPlx511T7HLrIW379u0DbNc3r1mzJl5eXkV+GcCLFy8ycOBAoqKi8Pb2JiAggGbNmrF8+XKrellZWTzzzDPExsZSsmRJvLy8CAsLo0uXLhw7dsxSb+bMmZb3YvLkyZZhdOHh4ZbjJpOJd955xyaWTp062fSG1a1bl/DwcDZv3kzr1q0JCgoiLCzMcnzfvn106dKFsLAwvLy8KFWqFHfffTeHDh26rvelfPnyGIbBmTNnLGXnzp0DoGzZslZ1PTw8CAsLw2QyERgYeF3tioiIiBuLLAUnztmW55SVK+3c9rKy4Zd1GAfjnXtdseHWPTwJCQmYTCbKlCljVe7l5UVoaCgJCQlXvYZhGKSlpTncpq+vr8PD5M6ePcvRo0etykqWLElQUBBpaWk0b96cHTt20LFjR/r160diYiLTp0/njjvuYO7cuZbeoUuXLjFlyhTatWtHp06dCAoKYv369cydO5cNGzawa9cu/P39ady4MS+//DLjxo2jZcuWdOnSBYCQkBCH7+9KqamptG/fnnr16jFo0CBOnTJP0tuxYwctW7YkMzOTLl26ULVqVfbs2cPMmTNp3rw5W7ZssSRaV5OQkEBaWhrx8fHMnDmTuXPnEhkZafWtY/369Slbtixz585l1KhRdO3alczMTL799lv++OMP+vbtq1VQRETyKTo62tUhiFwbL0/zimuX8/YCX28IveLzwNlkc6+Ovw+k2Rmylppu/tXfx/lxepjI/ngeTK3t/GuLhVsnPCkpKfj5+dmdFxMYGMipU6fIysrKc95McnIy33//vcNt9uzZ0+EP1g8++KBN2bBhwxgxYgRDhw5l69atfPPNN/Tu3dvqeM2aNRk0aJBludCAgABOnTpl0+7o0aN58803mTx5Ms899xwxMTE8/PDDjBs3jtq1azN48GCH7ys3ycnJ9OvXjy+//NKq/NFHHyUzM5N169ZRo0YNS/nDDz9Mp06dGDJkCJ9//rlDbbRo0cLSW2cymWjQoAFTpkyxWuDBx8eHWbNm0adPH4YNG8awYcMA85yoIUOGMHLkyOu9VRGRYic5OZmSJUu6OgyR/GtZE34dZb+8Z2vrsugBcCgBLqWDr52Pxn7/JDqX0p0fZ1Y2pRZuc/51xYpbD2nLzMzMdZfonCQnMzOPyWeAv78/d955p8M//v7+Dsc3dOhQpk2bZvWTk9z89NNPlCtXjrZt23L06FHLT2pqKs2aNWP79u1cuHABMA/bykl2MjIyOHXqFEePHuWee+4BzAs3FKQrh8AlJCTw119/0bp1awIDA63ir1WrFhEREfz2228OX//jjz9m2rRpjBkzhjZt2pCZmWm3dy4wMJCKFSvSpUsXPv74Y8aMGUNsbCyjR49m7Nix+bqns2fPWvXsJScnW95vgPT0dKshdQAnTpzI8/XJkycxLhsXrDbUhtpQG0W9jdOnT7vFfagN920jV5sPwm3/sf7ZfBAWbbQtP5n4z8XPmYe1XSmn7PhZx9rOD08PPB9s7TbPwxVtOMJkGFfOzHIfM2bMIDU1lT59+tgcW7p0Kfv37+exxx4r8JXRrtS/f38mT57M4sWLc120wNfXl/T0vL9J2LFjBzVr1gTgww8/5OOPP2b//v02+yZ06tSJBQsWABAXF0fdunV54oknbHpYZs6cSbdu3Xj77bd5/fXXba6xaNEiqz+0devW5dChQyQlJVnVXbhwIXfccUeesZcpU8Yy/C2/HnnkEf73v//x119/WYa17d+/n/r169O1a1e+/vprS92srCxq167N4cOHOXDggBYtEBHJh02bNmnRAima7n0HZq/L3zkrRsLBeOj3sf3jPwyG1rWg3OPWCxdMehJ63QKlH857lbbLffQ4DLwTTPflXc8EB5a8TMytLRy7rlwTtx7SFhgYSGJiot1ha3kNd7tcdnY2qampDrfp5+eXa69SfhiGQcWKFXn33XdzrVOhQgUAPv30U55//nmqVavGG2+8QXR0NP7+/mRlZdGnTx8czWnzmnuUW0+Yr6+v3dgBbrvtNh599FG7513PAgLPPPMMX3/9NR999BFffPEFAGPHjiU5OZmHH37Yqq6npyd33XUX77//PitXrqRHDwfW0RcREQAlO1K8zFwD3W6G+5rDj/+MjgkNNpfNWW+d7FT+Z5Gkq20wmhdPD7izEedDHR8dJNfGrROe8PBwjh49Snx8PJGR/+6Qm5mZyZkzZ6zKcpOSklJgc3jyEhkZyfnz5+nevftVk7Jvv/0Wb29v1q9fb7UAwdq1a23q5pXU5KxwdmVXImCzuEJe6tWrh8lkIiMjg549ezp8nqOSk5OBf1dmAzh+/DhgPzHLKXPlZrMiIjeibdu2ERsb6+owRArHzDWwZpd5c9LaFeD0BXi6kzkxGf4/67rLRph/jXny37KK4dCnjfn3Tf7Z9mTIP3seHkqAb1daXyMrG164m8BAP+ffi1hx64SnSpUqbNy4ka1bt1olNzt37iQzM9OhPXhy5vA4Kj9zePJy//33M378eF5++WXef/99m+MHDhwgJiYGMM/hMZlMVkPZsrOzeeONN2zOy5l8enmykCM2NhYPDw9WrrT+Czlnzhx2797tcOzly5enadOm/P7778yfP9/m/cvOzubo0aNUrFgx12ukp6eTkJBA+fLlbY6NGzcOgObNm1vKatWqxZw5c5g0aRKdOnWylKelpfHLL7/g4eFB69atba4lIiK50xdFUqxkZ8Odo2HcI/BcZ/OqbOv2Qt+PYPfxq58fUwZGP2RdlvP61zjrhMeEeV+fdnWopL9nBc6t5/AArFq1im3bthEdHU3FihU5d+4ccXFxREREcNdddzm8hLQzOTKHJy0tjZtvvpkNGzbQpEkTWrduTYkSJTh06BCrVq3C19eXLVu2ADBhwgRefPFFqlatSrdu3cjIyGDBggWkpqayb98+br/9dhYuXGi5dmRkJCkpKTz11FNERkYSHBzMY489BsBdd93FvHnzaNeuHbfccgu7d+9m9uzZhIeHc/DgQZs5PCdPnrS7gMDOnTtp1aoViYmJ3H777TRs2JCsrCz279/PihUr6NKlS56rtJ06dYqoqChatWpF7dq1iYiI4MSJEyxatIh9+/ZRv3591q1bZ1mp7fTp09SuXZuEhASaNWvGbbfdRkpKCrNmzeLQoUP06tWLb7/9Nv8PS0SkGLv8yzWRIuVa5vAUNZOehP4dNVeuELh1Dw+YlzQODg5mx44dHD58GD8/P+rUqUOTJk1ckuw4ytfXlzVr1jB8+HBmzJjBJ598AkDp0qWJjY2lX79+lrovvPACSUlJfP7554wbN47AwEBat27NpEmT7PaQTJkyhRdffJEJEyaQnp5OWFiYJeHJWQZ75cqVrFq1iqpVq/L999/zySefcPDgQYfjr1mzJps2beLVV19l+fLlLF68GG9vb8LCwmjbtq1V/PaEhITwwAMPsG7dOtatW8fFixfx8/OjUqVKDBkyhDfffNNqWeqwsDDWrVvHoEGD+P3333n33Xfx9PSkYsWKlmW+RUQkf67czFlEnCTEH3q3cXUUxYbb9/CIiIjItdE3z1Jk3cg9PJ4e8NI9MMa80FJ8fDxlypRxcVDuza334RERERERKVKyDfNiCP8oyiOO3IUSHhEREbErr8VlROQaeHlAl5ug0r89OseOHXNhQMWDEh4RERGxKz/70ImIAzKz4YW7XB1FsaOER0REROyKj493dQgi7sNkgtgoaF3bqrhWrVouCqj4UMIjIiIiIlLQDMO8WMEVc3aOHDniooCKDyU8IiIiYlfdunVdHYKI+ygZCD1tN0FPTk52QTDFixIeERERsWv37t2uDkHEPXh6mFdm8/OxOeTn5+eCgIoXJTwiIiJiV1pamqtDEHEPhgFP3W73UNWqVQs5mOJHCY+IiIjYFRwc7OoQRG58Xh5wfwuoEGb3cFxcXCEHVPwo4RERERG7ypcv7+oQRG58Wora5bxcHYCIiIgUTTt37qRBgwauDkPE1uO3kXU4AU+PG+C7+8ploUWNXA9HREQUYjDFkxIeEREREbmx3N2UrVHebpGQK+EpeDdAWiwiIiKuUKFCBVeHICJy3ZTwiIiIiF2ZmZmuDkEkV9HR0a4OQW4QSnhERETErpMnT7o6BJFcpaSkuDoEuUEo4RERERGRG05CQoKrQ5AbhBIeERERsatOnTquDkFE5LqZDMMwXB2EiIiIFD27du2iRo3cl9MVcSXDMDCZTK4OQ24AWpZaRERE7Lp06ZKrQ5CCtnQznL/o6ijsq1EO6lTK9fCOHTuoXbt2IQYkNyolPCIiImJXYGCgq0OQgrRwA9wx2tVR5C42CrZOgFx6cdLT0ws3HrlhaQ6PiIiI2FWxYkVXhyAF6VwRX+Vs2xH4bXuuh0NCQgoxGLmRKeERERERu3bs2OHqEKQ48/KACXNyPRwREVGIwciNTAmPiIiIiBQ9mdnwyzo4FG/38O7duws5ILlRKeERERERu8qVK+fqEKS48zDBxIWujkJucEp4RERERKRoysqGzxZBSqrNIc0xE0cp4RERERG7jh8/7uoQRODCJfjuN5vitLQ0FwQjNyIlPCIiIiJShJng/dlgGFalp06dclE8cqNRwiMiIiJ21apVy9UhiJgTnV3HYUWcqyORG5QSHhEREbHr8OHDrg5BxMzTdonqunXruigYudEo4RERERG7UlKK+MaUUnxkZcPc9bD/pKVoz549LgxIbiRKeERERMQuf39/V4cg8i8PD/h4geVlaqrtym0i9ijhEREREbuqVKni6hBE/pWVDZOXQPIlAIKDgwuurcQU6P8phPeFwJ7Qbhhs2Of4+TuOQqeREPQQlH4Y+nwACedt6+09AQ+MhVJ9IOBBaPUGrNjqtNsQMyU8IiIiYldcnCaJSxGTkgrfrASgfPnyBdNGdjZ0Hg3TfoeBd8DYhyH+PLQdBnscWKr96Gm4ZSjsPQlv94LB98C8v6HDCEjP+LfekdPQ4nX4Yye8fC+80xuSU6HjSPhtW8HcWzHl9gnPxo0bWbJkCd9//z2ff/4506ZNc3VIDBw4EJPJZPfH29ubixcvFljbiYmJ9O/fn//9738F1oazvP/++zRu3JjSpUvj7e1NcHAwNWrUYPTo0XbX3i9btmyu7+vOnTtdcAciIiLiVAbmJaqzs6/9//a2b0Lfj3I/PnMNrN4FUwfC8B7wzB3w60jzwgnDp1/9+m//aE7Mlo+A5zrDGw/ADy/B5oMwdcW/9d79ydyTtHKUuc7zd8HqdyCyFLw45druTezycnUABW3dunX4+voSFhZGenq6q8Ox8vjjj1OjRg2rMk9PT3x8fAqszcTERCZPnkx6ejoPPvhggbXjDOvXryc4OJiHHnqIiIgIkpOTWbJkCW+++Sa//fYbixYtwmQyWZ0TGRnJoEGDbK4VGRlZWGGLiLiNiIgIV4cgYmvvSVi6BcoU0PVnroGyJeG+5v+WhZeA7jfDt79BWgb4eud+/o9/wl1NoGL4v2W31Yfq5eCH1dC/o7ns9x3QMAZqXNZTFeAL9zSFTxaYe5OqlXPqrRVXbp/wPPjgg4SEhAAwY8YMMjIyrnJG4encuTNdunRxdRhOde7cOUqUKIGHx/V3HtrrjXv33Xdp0aIFS5cuZdu2bdSpU8fqeMmSJRk8ePB1ty0iIuDl5fYfE+RG5OkB4+dQ4ZunCub6Gw9Ao8rmRRIud1M1+HwJ7D4OdSvZP/fYGfPwtyZ25r/dVA3m//3v67QMKBVoWy/A1/zr3/uV8DiJ2w9py0l2bkSpqam88MILREVF4e3tTUBAADfddBOLFy+2qpednc1zzz1HnTp1KFmyJF5eXoSGhnLPPfdw8OBBS7358+cTExMDwFdffWUZ7hUWFmY5bjKZGD58uE0sd911FyaTiQsXLljKGjRoQFhYGNu3b6dNmzYEBQVRunRpyzKmhw4d4r777iMsLAwvLy9KlSpF586d2bcvH5P+7IiKisIwDE6fPm33eEZGBqdPn8a4YkdmERHJn6NHj7o6BBFbWdmwcCPsOVEw1z9xzjys7Eo5ZcfP5n3u5XWvPP9ssjnRAahRDrYcgguXrOv9scP867Ez+YtbcqWvbhyQn2UPfXx8HO7dSExMtPnPpGTJkgQFBZGRkUHz5s3Ztm0bt912G3379iUpKYnp06dz1113MXPmTO655x4A0tPT+eKLL7jlllvo2LEjwcHBbNiwgfnz57Nx40a2bdtGSEgI9erV44033uDtt9+mefPm3H///QAEBAQ4fH9XSk1N5ZZbbiE2NpYXX3yR06dP4+3tzd69e7npppvIyMigS5cuVKtWjX379vHDDz/QsmVL1q9fT4UKFRxqIyEhgbS0NBISEpg1axazZs0iIiKCm266yabu3r178ff3JysrC39/f1q2bMmHH36o3cJFRETciacHxkfzoMVVNh/NyITzF23L0jLgdJJ1eekgc6/OpXTwtfMR2e+fKQeX8pgikXPM3pA3P+9/6/h6w1OdYM566PFfeOshCPSDiQtg/b6rtyP54vY9PM7w9ddfO/xz8uTJq1/wH/369SMqKsrqZ9SoUQCMGDGCzZs3M3HiRBYsWMCoUaP44IMP2LlzJyVKlOC1116z9GD4+vpy4sQJFixYwPvvv8+IESOYM2cO7777LkePHuWzzz4DoEKFCjzxxBMA1KhRg8GDBzN48GCefvrpa35vUlJS6NSpEytXrmTUqFF8+umn+Pn50a9fPzIzM1m9ejXffPMNw4YN46uvvmLhwoUkJCQwdOhQh9to2bIlUVFRNGrUiNGjRxMbG8vs2bNtErWYmBgeffRRPvjgAyZMmMDtt9/O8uXLadmyJdu25W+1k7Nnz1otjJCcnGzVu5Wens6ZM9bfvJw4cSLP1ydPnrTqdVIbakNtqI2i3kbNmjXd4j7UxtXbuBFlBfpc9b1KWrDWvLT05T+rd8H//rAtP2weOZLt5wVpmZZrWNpINScgZy+l5P48/M1J0aVEO88jNcNS58SJE3BHI/jocfhtOzQaDDUGwrwN5uQHMAL97LfBjfPnqqj82TUZxWjcT84cnoceeihf5+WnSz88PBxfX9886wwcOJBPPvmEV155hQYNGlgda9CgAbVq1aJatWqkpKTw119/2UzMf+aZZ5g7dy5Hjx61mYyfmZnJ2bNnSU9PJzk5mVq1anHffffx448/AnDw4EFiYmJ45JFHmDp1qtW58+fPp3PnzgwbNowRI0ZYHbvrrruYN28eSUlJlnXvGzRowObNm9m7d6/VXg2JiYmULl2aDh068MUXX9jcf4sWLfDz83N4h+QlS5Zw+vRpjh07xoIFC4iPj2fMmDHceeedVz33k08+YeDAgXTs2JFFixY51J6IiJjt27dPe/G4s+9/h4fGuzqKa+NhImPfRLyjy+Zd71wy/H3FUPqXpkJEKfNS0JdrVcvci1PtGagWCfOv+HL2i6Xw+ETYMj7vOTwVnoAxfeCVrtbH+nxgnsNz5mvr8pRU89A2Hy9oEA1fLIMBn8G8IXBn47zvTxyiIW0OcHToVX61aNEi10ULDh8+THp6OhUrVsz1/GPHjlkSns8++4zx48ezb98+srKyrOolJSXZO90pgoODqVTJ+i/9li1bMAyDxYsXExUVZfe8nHlDjujQoYPl94MHD+aJJ56ga9eurFy5kubNm+dxpjk5HDlyJH/99ReZmZmagCsikg+Xf/MqUmR4eUDX5hxIP091rpLwlAoyr5B2ZVlkKdvyHA2izSuoZWdbL1zw1x7zggLV81hIoHwohIf8Oyztcmv3QIMY2/JAP2hx2aq9S7eYe4pa1sy9HckXffpzQH72xfH19cXT0/O62zQMgwoVKjB27Nhc61SrVg2AKVOm8NRTT1GlShVee+01YmJiCAgIICsriz59+pCdne1Qm1f2JF3uyiQqh4+Pj00SkdNp2K5dO8sQuiv5+fnZLXfEc889x//93//xySefXDXhAShXrhxbt24lLS1NCY+ISD5cbcSCiEtkZsMLdxXcvoUPtDAvTf3Tn/DAzeay00kwYzXc3cR6fs6+f6YyVLlsCff7W8BXK8wbi0b98wXvsi3m1d1evDvvtlfvNLf71O1Qws4KbnJN9OnPAd9++63Dde+66y7Klbv+JQTLly/P2bNnue+++676H87UqVPx9vbmjz/+sNozYevWrTZ180pqypY1f0ty9qzt6iOHDx92NHRiY2MxmUykpaXRs2dPh89zVM4/cOfOnXOo/pEjRwgJCdF/3CIi+VS9enVXhyBizcME9aKhRQ0C9+4tmDYeaAHNq0O/j2H7UQgLhokLzavDjbhiD8Nb/1nZ9uCkf8veuN+cHLUbBs93huRUGPeLeRhcv/b/1jsUD93/a953J6IkbDsCny2CepXg7d4Fc2/FlBIeBzgyVyRHaGioU9p88MEHeffddxk0aBCffPKJzfEDBw5YlpjO6VG6vCfHMAxeeeUVm/OCgoIA+8lC9erV8fT0ZNWqVRiGYUmOlixZwo4dOxyOPSwsjObNm/Pnn3/y448/WlaDuzy2I0eO5DlcLzMzkxMnTtgdEvfWW28B0KxZM0vZyZMnKVOmjM0KeaNGjeLMmTN07txZvTsiIvm0detWm7mmIi6VbcCgu8FkshlS7zSenub5Oy9/BR/OM6+W1rQqTH3WepPQ3ESFwcpRMGgqvPateW5O58bw377WvUMhAeahdR/PNy9XXT4UnusMQx6AYP+Cubdiyu0/Ae7evZvk5GTAvIRyVlYWGzZsAMwf/h359qqg5vDkZeTIkSxdupSJEyfy559/csstt1CyZEkOHz7MqlWr8PT0tKw81r17d1asWMEtt9xCt27dyMrKYuHChZb9cC4XGhpKZGQkK1as4JVXXqFcuXL4+/szYMAAgoKCuPvuu/n5559p27Yt7dq1Y+/evfz8889UqlTJak+fq/nmm29o3rw5PXr0oEOHDjRq1AjDMDhw4ADLly/njjvusFk04XLJyclUqVKFli1bUrt2bSIjIzl58iSLFy9mz5491K1b12qD0YkTJzJ16lRatWpFTEwMWVlZ/PHHH6xatYoyZcowYcIEh2MXERGRIqp0EHRvCcD27duvLSH/ddTV65QKgv97xvyTl8t7di4XWxEWDbt6Gz+/dvVY5Lq5fcKza9cum+Xr1q9fD0BkZGSR7a739vZmzZo1/Oc//2H69OlMnDgRgFKlSlG7dm0efvhhS90nn3ySc+fOMXHiRN577z0CAwNp2bIln376qd1vP7799lsGDhzIBx98QHp6OqGhoQwYMAAwb0jau3dvli9fzp9//knlypX5+uuv+fLLL/OV8FSpUoWtW7fyyiuvsHTpUpYuXYq3tzdhYWG0bt2axx57LM/zAwIC6NGjB3/99Rfr16/n4sWL+Pn5UbFiRV599VWGDRuGv/+/3360aNGCJUuWsHjxYpKSkjAMgzJlytCrVy/GjBlD+fIOfCMjIiJWypQp4+oQRP7l4QED77S/x41IHorVstQiIiLiuLNnz1K6dGlXhyEF5UZbltrLAw5/DpHmP5Px8fFKysUh2nhURERE7MrPgjUiBcrLA7q1tCQ7kPdCTCKXU8IjIiIiIkXbP0tRX+7YsWMuCkZuNG4/h0dERESuTVGd5yrFjIcJGlaGm6q5OhK5QamHR0REROw6deqUq0MQMS9F/dI9NsW1atVyQTByI1LCIyIiInadP3/e1SGIQHgI3N/cpvjIkSMuCEZuREp4RERExC5vby3/Ky7mYYJn7wQf2z+LOfssilyNEh4RERGxKzY21tUhSHHn4QH9O9o95OfnV8jByI1KCY+IiIjYtWnTJleHIMWZlwf0bAVlS9o9XLVq1cKNR25YSnhEREREpOjJzIbn78r1cFxcXCEGIzcyJTwiIiJiV1hYmKtDkIJUvjRGUd67s3l1aFzF1VGIG9A+PCIiImJXUFCQq0OQgnRLLDtnPEWtqGhXR2JfuVJ5Ho6IiCikQORGp4RHRERE7Dp48CANGjRwdRhSgNKqhEODG3NDTyU84igNaRMREREppqKjo10dgkiBU8IjIiIidmkVLPeXkpLi6hBECpwSHhEREbHrzJkzrg5BClhCQoKrQxApcEp4RERExK5z5865OgQRkeumhEdERETs8vT0dHUIUsDq16/v6hBECpzJMAzD1UGIiIiISOHbvn07tWvXdnUYIgVKy1KLiIiIXVu2bKFevXqF12BqOtw+EnYcLbw23V2wP2wZD4F+dg+np6cXckAihU8Jj4iIiNiVnZ1duA3Gn4ffthdum+7udBJ89xv072j3cEhISCEHJFL4NIdHRERE7CpdurSrQxBneH825DKDQZt3SnGghEdERETsKlmypKtDkOtlALuOw/Ktdg/v3r27cOMRcQElPCIiImLX/v37XR2COIOnB0yY6+ooRFxGCY+IiIiIO8vKhnnrYd9Jm0MVK1Z0QUAihUsJj4iIiNgVExPj6hDEWTw84OP5NsVpaWkuCEakcCnhEREREbuSkpJcHYI4S1Y2TF4CyZesik+dOuWigEQKjxIeERERsevMmTOuDkGc6WIafPWrq6MQKXRKeERERMQuk8nk6hDEmQxg/Gy4bH+lunXrui4ekUKihEdERETsql+/vqtDEGfbdwqWbLa83LNnjwuDESkcSnhERETErri4OFeHIM7m6QHj51hepqamujAYkcLh5eoAREREpGjKzMx0dQhFQ4kAGPswdG0GAb6wdg+89BVsdGCfoqZVoW97aFYN6lUCby8w3We/bkgADLnf3E6FUIhPgqWbYcQPcOS0c+4lKxsWbYLdx6F6OYKDg51zXZEiTD0816lu3bqEh4e7OgyLrKws+vfvT5kyZfD09NT4axERuWYlS5Z0dQiuZzLBvKHwUGv4eAG88jWUKQG/joSqkVc//87G8PitYBiwP48V0UwmWDIcnu4Es/6CZ7+A73+HbjfD6ncgyM959+TlAR+Zl6guX768864rUkQViYRn3bp13HbbbZQvX56AgAB8fX0pX748Dz74IPv27cv1vClTptCgQQMCAwPx9fUlMjKS+++/3+nxvfPOO7zyyitOv25BGDNmDJMnT6Zp06a8/fbbjBs3rkDbmzlzJv3799eyliIibigsLMzVIRS8FSNhysDcjz/QAlrWhL4fw8gfYOJCaDvM3FMyosfVr//pQijRB5q+YjV3xkbz6nBTNXj1G/PPF0vh9W/h+S/NvT23OXE+VWa2+fpJF9m5c6fzritSRBWJIW379+8nISGBDh06EBUVhZeXF1u3buXnn39m0aJFbNq0iUqVKlmdM2DAAD7//HMaNWrEc889R0BAAIcOHWL79u1Oj2/atGmcPHmSsWPHOv3azrZ06VL8/f2ZM2cOHh4Fn88uXryYyZMn89xzz1G2bNkCb09ERArP3r17adCggavDcK0HWsDJc/DTn/+WnU6CH1ZD71vAxwvS8xj6F3/esXZCAsy/nrqi/olz5l8vpTsesyNSM2DKcmhTwbnXFSmCikTC06NHD3r0sP2W5OOPP+bZZ59l/PjxTJgwwVL+/fff8/nnn/Pkk0/y6aefFmKkrpWdnc358+cpVapUrnXOnDlDYGBgoSQ7heHMmTOEhoa6OgwRESmuGsbAhv3mIWmXW7sHBnSE6uUg7vD1t7N+r3lT0FE94ewF2HUcqkbA2D7mtpbm0Tt0LQwDxs+hQs9Rzr2uSBFUpD8VV61aFYBz585Zlb/77ruEhITwwQcfWI5nZWXl+/qzZs2iYcOGBAQE4OPjQ+XKlRk9erRVnfDwcOLi4jh9+jQmk8nyM3PmTKt6e/fupX379pbhdY0bN2b9+vU2bV68eJGBAwcSFRWFt7c3AQEBNGvWjOXLl1vVmzlzJiaTiXfeeYehQ4dSoUIFfH19efXVV+3eS079LVu2WMXaqVMnS50NGzbQoUMHSpYsiZeXF+Hh4fTq1YvExESra61du5auXbsSFRWFn58fvr6+VKlShVGjrP9R7NSpE5MnTwbMc5ly2uzfv7/leG5ziK6MLS4uznLuhx9+SJUqVfDx8aFXr16WOt9//z2NGjUiICAAb29vKlWqxPDhw22uPWfOHJo0aULJkiXx9vamdOnSNG3alPnz59uNRURE7IuOjnZ1CK4XWerfXpbL5ZSVK+2cds5cgB7vmxdIWD4Sjv0frBwNx89B++HmIXTOdigBz8VbnH9dkSKmSPTw5EhOTiYxMZGUlBTWr1/P0KFDAejataulTmJiInFxcZY5Kh9++CHnzp3Dx8eHm2++mS+//JKYmJirtvXFF1/Qv39/SpQowcMPP0xwcDC//PILb775Jvv27WPKlCkAvPXWW4wYMYILFy4wbNgwy/mNGze2/D4tLY1WrVpRp04dXnrpJfbv38/06dO59957OXjwIN7e3pZ6zZs3Z8eOHXTs2JF+/fqRmJjI9OnTueOOO5g7dy4dOnSwivPzzz8nKSmJbt26ERkZmet/Po0bN2bcuHGMHz/eKtZatWoB5qFu99xzDwEBAXTv3p0KFSqwefNmfvjhB/7++282b96Mr68vAPPmzePvv/+mbdu2VK5cmeTkZObOncuwYcOIj4/no48+AuDZZ58lOTmZVatW8fLLL1OmTBkAbrrppqu+/7lZsmQJ06ZNo1u3bsTExFgmzI4YMYIRI0ZQvXp1BgwYQFBQEMuXL2fkyJHs3buX7777DjDPB3vggQcoWbIkffr0ISIigpMnT/LXX3+xdu1a7rzzzmuOTUSkuElJSXGvhQu8PM0JxeW8vcDXG0KvWK3sbLK5F8TfB9LsDFlL/WeImb+P8+JLOA8bD5gXR9h2GBrEwCtdzHOMur/nvHZyeHrg9eF86H2b868tUpQYRcjrr79uYN4H2ACM8PBw47333rOq8+uvvxqAERwcbHh5eRlPPPGE8emnnxo9evQwTCaTUb58eeP8+fN5tpOenm6EhoYa/v7+xp49eyzlFy9eNGrWrGmYTCbj77//tpTXqVPHCAsLs3utOnXqGIDx/PPPW5W/8MILBmB88803lrLBgwfblBmGYSQkJBihoaFGnTp1LGUzZswwACMwMNA4ePBgnvdzZTz2Yq1UqZJRrlw54/Tp01bln376qQEYb7/9tqXs3LlzNudnZmYaderUMfz9/Y3U1FRL+RNPPGEAxtatW23Ouf32243c/ogBxu233255vXXrVgMwPDw8jDVr1ljV3bt3r+Hl5WW0a9fO5jrdunUzTCaTsWnTJsMwDOO1114zAGPBggV223XUmTNnrO7zwoULRlJSkuV1WlqazXt5/PjxPF+fOHHCyM7OVhtqQ22ojRumjY0bNxbufew+ahh0LbifNkMNh1Xqbz7nwkXD+L8ltte6Y5S5XscRjrf/0TzzOfaOxTxpGMmXDOO+MdblD39gPqfTyAJ5T7K8HzCM1HS3+7OrNopPG44oUj08jzzyCHXr1uXChQusW7eOpUuXEh8fb1UnZ/jVhQsXGDlyJG+++SYATz75JCEhIUyePJn33nuPkSNH5trOsmXLOHPmDD169LAMmwPw9/fnxRdfZMCAAXz77bc0atTIobhNJpPNULjOnTszYcIEq0UUfvrpJ8qVK0fbtm05evSoVf1mzZqxcOFCLly4YLUm/p133mmzYEN+/fHHHxw6dIgBAwZw6dIlq7bvvPNOfH19WbJkCa+//jpgvQxpcnIy58+fxzAM2rRpQ1xcHOvWraNVq1bXFVNumjVrRvPmza3K/u///o/MzEwGDBhg87517dqVGTNm8PPPP1O/fn1L7NOnT6dVq1YEBQVdUxylS1sPUbjyOj4+PjZziyIjI/N8HRERoTbUhtpQG2ojrzaSEyhQmw/Cbf+xLvtvX/OiBON+sS4/mWj+9cQ587C2K+WUHT/rnNj6tgM/b5h7xXD42evMv7asCQs3OqetHF4e5uW2fb3d+8+V2nDrNhxRpBKeGjVqUKNGDQD69+/Pb7/9xq233srFixctw6gCAwMBc5Lx0ksvWZ0/cOBAJk+ezG+//ZZnO7t37wagTp06NseaNm0KwIEDBxyOu1SpUjYPLOcBnzlzxlJ29OhR0tPTiYqKyvVax44do2bNmpbXOe/H9diwYQMAkyZNYtKkSXbrXB7nuXPneOaZZ1i8eLFVeY6EhIL7D6ly5co2ZTlLZj744IO5nnfy5EnAPMzuf//7H1OnTmXatGnUrFmTtm3b0r9/f2JjYwsmaBERN1W/vhOXQi4KElNg2RVzVs4lm5OaK8tzbDoIrWuZ98m5fOGCZtUgJdW8gaczlC1pbsPziunV3v98VPPydE47l8vM5kjXelzf16oiRV+RSniudMstt1C5cmWmTZtmSXiqVKkCmBOfgADrcbg5H5aTkpIKNc68VkQzLvvH0TAMKlasyLvvvptr/QoVrJeHvPIer0fPnj25++677R67fPPUO+64g7Vr13L33XfTpk0byyamv/zyC9OnT3d4gYjcFixIT899aU1795vzHo4ZMybXZLF27dqW8zdu3MjChQv55Zdf+PPPP5k4cSKffvopH374IU8++aRDsYuICOzYscPy72uxNXONefPP+5rDj2vMZaHB5rI5662XpK78z/YMeW0wmpvdx8HDA7q3hK9W/Fve858RFRsd/yLWIZ4e0Lw65yqVVMIjbq9IJzxgnuifnJxseR0TE0NoaChnz54lKSmJkJAQy7E9e/YAtt1lV8rpNYmLi7M5lrOy2uULH+T2wT2/IiMjOX/+PN27d8fTswC+qclFTs+Gp6cnPXv2zLPuqVOnWLt2LR06dOCXX6y79xcsWGBTP6/3Jmd42fHjxylXrpylfMuW/K0IkzPssGzZsleNP0enTp0sq8Dt3LmTpk2b8tZbbynhERHJh7y+oCo2Zq6BNbvMCwfUrgCnL8DTncwJw/D/WdddNsL8a8xl/9dUDIc+bcy/b/LPMPohD5h/PZQA3640/37qchh8L0x60rwU9rYj0KgyPH6bednrWX85976ysmHQPZaRMyLurEgsS71//3675TNmzODw4cNWQ7wA7rnnHgzDsFkmOWdj0KutxNW+fXtCQ0OZM2eOVdtpaWlMmDABk8lE7969LeX+/v6kpKSQnX19S0Lef//9nD9/npdfftnu8fwMo8uPdu3aERUVxY8//sjmzbbr+Kenp3P8uLlL3svLnANf3jMFsG/fPmbNmmVzbs5QvlOnbL/Nykksr1zCO6/5VfY88cQTeHl5MXr0aC5cuGBzPCEhgYsXLwLYzPEBqF69OiEhIXbPFRGR3F3+pWKxlZ0Nd46G6avguc4w7mHzxqPthzs2nC2mDIx+yPzTvLq5LOf1Y7f+W+9sMjR52ZwA3d0EPnoc7mkKXy6Dtm9CRh6bm16LcqXgnqbXPU9Y5EZQJHp4+vTpw+nTp2nRogXR0dFcunSJjRs3snz5cvz8/Kw2HQVzYrNo0SL++9//smfPHurXr8+qVatYtmwZ9erV49lnn82zPW9vb8aMGUP//v1p2rQp3bt3tyxLvXv3bvr27Wu1YEGTJk1Yu3YtDzzwAC1btsTT05OuXbvm+x+Jd955h5UrVzJ+/Hh+//13WrduTYkSJTh06BCrVq3C19c3370fjvDw8OCrr77i7rvvplmzZtx9993ExsaSkpLCvn37WL58Oa+++iqvv/46oaGhNGzY0LKMddOmTTl48CAzZsygbNmyVr1tAG3atOH9999n8ODBdO/eHX9/f5o2bUrLli0ZOHAg7733Hq+99hrbt2+ndOnSLFu2zGbfn6upUaMGI0aMYOjQoVSuXJl77rmH6Oho4uPj2bZtG6tWreLvv/+mTp06DBo0iNWrV1uW1DYMgwULFnD8+HGrJFZERK7uygnHbqndsKvXSUyBJyaaf/ISY2cUwcptYLrPsViOn4XHr9KGM3iY4Pm7wMuT7Zu20qBBg4JvU8SFikTC06NHD7777jvmzJlDUlISJpOJ8PBwunTpwqhRoyx7yeQICwvjr7/+YuDAgaxcuZK5c+dSunRp+vbty0cffWTZ9yYvjz32GCVLlmT06NFMnTqVzMxMKlSowKhRoyz7/+R45513OHjwIMuWLePnn3/GMAwqVKiQ74TH19eXNWvWMHz4cGbMmMEnn3wCmIfgxcbG0q9fv3xdLz/atWvHX3/9xRtvvMGvv/7KrFmz8PPzo2zZstx7771Wex398ssvDBgwgN9//50FCxYQGRnJ4MGD8fb25o033rC67j333MPzzz/PtGnTGDp0KNnZ2TzxxBO0bNmSsLAwZsyYwaBBg/jiiy/w8/Ojbdu2zJ49O9//ib7xxhvUqVOHMWPGMGPGDC5evEhwcDBRUVEMHDjQsj9R9+7diY+PZ9GiRZw/fx4fHx/KlSvHyJEjGTJkyHW/jyIixcnu3bv1YdgdeXuah8qJFBMm48qxSyIiIiLApk2bCjfhOZwAlQYUXnvFkZcH9LsVPn8KgPj4eMvG4SLuqkjM4REREZGip2LFiq4OQZwtMxue+3eus7MWZhIpypTwiIiIiF1paWmuDkGcydMD2sZCnX+H5B87dsyFAYkUDiU8IiIiYpe9FTjlBpaVDS/a349PxJ0p4REREREpDqJCoXNjq6IrF4YScUdKeERERMSuunXrujoEcRaTCV64G67Y+PzIkSMuCkik8CjhEREREbv27Nnj6hDEWXy94dFbbYqv3F9PxB0p4RERERG7UlNTXR2COIOXB/RrDyUDbQ75+fm5ICCRwqWER0REROwKDg52dQjiDFcsRX25qlWrFnIwIoVPCY+IiIjYVb58eVeHINfL0wNuqwc1K9g9HBcXV8gBiRQ+L1cHICIiIkXTzp07adCgQaG3a5hMmDy0IaZTaClqESU8IiIiUkREhcHbvTi3agulS5V2dTTuIdAXOjXM9XBEREQhBiPiGibDMAxXByEiIiJFz+nTpwkLCys27YqIe9IcHhEREbErKyurWLUrIu5JCY+IiIjYdeLEiWLVroi4JyU8IiIiIiLitjSHR0REROzKyMjA29u72LQrIu5JPTwiIiJi14EDB4pVuyLinpTwiIiIiF0XL14sVu2KiHtSwiMiIiJ2BQYGFqt2RcQ9aQ6PiIiI2JWeno6Pj0+xaVdE3JN6eERERMSu7du3F6t2RcQ9KeERERERERG3pYRHRERE7CpXrlyxaldE3JMSHhEREbHLZDIVq3ZFxD0p4RERERG7jh07VqzaFRH3pIRHRERERETclpalFhEREbvS0tLw9fUtNu2KiHtSD4+IiIjYdeTIkWLVroi4JyU8IiIiYldycnKxaldE3JMSHhEREbHLz8+vWLUrIu5Jc3hERETErszMTLy8vIpNuyLintTDIyIiInbFxcUVq3ZFxD3p6xORK2RlZbF7925XhyEi4nL79+93yWpprmpXRG481atXx9PTM886SnhErrB7925q167t6jBERERE5Cq2b99OrVq18qyjOTwiV3BWD09ycjI33XQTa9euJSgoyAmRiTPp+RRtej5FW0E+n5MnT9K+fXuWL19ORESEU69dXOjvT9Gm5+NcjvTwKOERKSBJSUmUKFGC8+fPExIS4upw5Ap6PkWbnk/RVpDP5+jRo0RFRXHkyBEqVKjg1GsXF/r7U7Tp+RQ+LVogIiIiIiJuSwmPiIiIiIi4LSU8IgXE19eX4cOHa6WhIkrPp2jT8ynaCvL5hISE0KZNGw31uQ76+1O06fkUPs3hERERERERt6UeHhERERERcVtKeERERERExG0p4REREREREbelhEdERERERNyWEh4RJzh27BjvvPMObdq0ITIyksDAQGJjY3n55Zc5c+aMQ9dITU1l8uTJ3HvvvURHR+Pv70/lypXp2bMnO3bsKOA7cG/OeD45/vrrL2677TaCg4MJCQmhU6dObNq0qWACL0YmTZpEr169qFmzJp6enphMpnxfwzAMpk2bxs0330xYWBjBwcHExsYycuRIkpKSCiDq4sMZzwcgMzOTDz/8kEaNGhEYGEiJEiVo1KgRkyZNcnLExYuzns/levTogclkok6dOk6IsHi73udz7tw5PvjgAzp27EhUVBT+/v7UqFGD/v37c+TIkQKK2r1olTYRJ/jss894/vnn6dy5M61atSI4OJi1a9cydepUIiIiWLduHREREXleY+fOndSqVYtWrVrRsWNHypUrx/79+/n0009JSUlh4cKFtGvXrpDuyL044/kA/Pnnn7Rt25by5cszcOBAAD7++GPi4+NZvXo1devWLehbcVvR0dGcOXOGhg0bcuDAAY4ePUp+/3saMmQIb7/9Nu3bt6dLly54e3vz66+/Mn36dJo1a8aaNWuc8kGwOHLG80lPT+eee+5hxYoV9OrVi+bNm5OZmcmePXvw9/fn7bffLqDo3Z8zns/l5s6dy7333ouvry+VK1cmLi7OidEWP9f7fBYuXMhdd93FrbfeSvv27QkLCyMuLo5Jkybh4+PD6tWrqV27dgHegRswROS6xcXFGSdOnLApnzx5sgEYL7300lWvcfr0aWPjxo025du2bTN8fHyMxo0bOyPUYskZz8cwDKNp06ZGcHCwcfToUUvZ0aNHjeDgYKNDhw5Oi7c4OnDggJGVlWUYhmF07tzZyO9/TxkZGUZAQIDRqFEjy3Vy9OrVywDs/v0Sx1zv8zEMwxg6dKjh6elpLF++3NnhFXvOeD45Lly4YERFRRnPPvusUalSJSM2NtZZYRZb1/t8Dhw4YOzdu9emfMmSJQZg3H///U6J051pSJuIE8TGxtrtIejRoweAQ9+OhYaG0qBBA5vy2rVrU6dOHX3Ddh2c8Xz27t3LunXr6NatG+XLl7eUly9fnm7durF06VJOnjzpvKCLmejoaDw8rv2/pIyMDC5dukRERITNdcqVKwdAYGDgdcVYnF3v80lJSeGDDz7g3nvvpV27dhiGwYULF5wYYfF2vc/nckOGDCErK4vRo0c75Xpy/c8nOjqaKlWq2JTfdtttlC5dWp8PHKCER6QAHT16FICyZcte8zWys7M5ceLEdV1D7MvP81m3bh0ALVq0sDnWvHlzDMPg77//dm6A4jB/f39uueUWFi5cyJgxY9i7dy8HDx5k6tSpTJw4kd69e1OtWjVXh1ls/f7771y4cIHGjRvz/PPPExISQkhICOHh4bzxxhtkZma6OkQB1q5dy8cff8z48eMJCQlxdThyFefPn+fChQv6fOAAL1cHIOLOhg8fDsAjjzxyzdf47LPPOHHiBG+++aazwpJ/5Of5HD9+HMCqdydHTtmxY8ecGJ3k13fffUffvn157bXXeO211wAwmUwMGTKEkSNHuji64m3Xrl0ATJgwAR8fH8aOHUtoaCjfffcd77zzDseOHeOrr75ycZTFW2ZmJo8//jgdO3ake/furg5HHPDWW2+RkZFxXZ8xigslPCKXSUxMZMKECQ7Xf+655yhdurTdY//973+ZMWMG/fv3p3379tcUz+rVqxk0aBD169fnjTfeuKZruBNXPp+LFy8C4Ovra3PMz8/Pqk5x5czncy18fX2JiYnh4Ycf5o477gDgxx9/ZPTo0fj5+TFkyBCntXUjcuXzyRm+dvbsWbZt20aNGjUA6N69O+3atePrr7/mtddeo1atWk5p70bk6r8/48aNY+/evfz8889Ou6Y7cfXzudLMmTN577336NSpE/369SuwdtyGqycRiRQlBw4cMACHf/bs2WP3OpMnTzZMJpPRuXNnIz09/ZpiWb9+vVGyZEkjJibGapJ8cebK5/Pee+8ZgDF//nybY/PmzTMAY9KkSdd1fzc6Zz2fa5nUm5KSYlSrVs3o0aOHzbEePXoYHh4exs6dO6/pvtyFK59Pzt+fli1b2hz78ssvDcCYOHHiNd2Xu3Dl89mzZ4/h5+dnjB492qpcixb8y5XP50rz5s0zfHx8jCZNmhjnz5+/rmsVF+rhEblMdHT0dS3lCfDll1/Sv39/OnbsyI8//oi3t3e+r7FhwwY6dOhAiRIlWLFihd1hVMWRK59PzsR3e8PWcsqK+3NyxvO5VjNnzmTPnj288847Nse6devG9OnT+eOPPyw9C8WRK59PhQoVAOwuHhIZGQmY9xopzlz5fF566SVKly5N165d2bt3r6U8MzOT9PR09u7dS2BgoOVZFUeufD6XW7hwIffddx+xsbEsXrxYc60cpIRHxIm+/PJLHn/8cW677TZ+/vlnu8OfrmbDhg2WjS1XrFhBpUqVCiDS4ul6nk/Tpk0BWLNmDY8//rjVsT///BOTyUTjxo2dGq84LifpzMrKsjmWMyFeE+Nd56abbgL+XSjkcjllZcqUKdSY5F+HDh3i+PHjxMbG2j1erVo1OnfuzNy5cws5MrncwoUL6dKlCzVr1mTp0qWUKlXK1SHdMJTwiDjJ1KlTeeKJJ2jfvj2//PKLZV6HPadPn+b06dNERkZSokQJS/nGjRvp0KEDQUFBrFixgpiYmMIIvVi43udTtWpVmjRpwowZMxg1apSlx+f48ePMmDGD9u3bO7R5qVy/EydOcP78eSpWrEhAQACAZdO9r776ymbCdc5k+JykVQqWvecTExNDy5YtWb16NRs2bKBRo0aAOUGdPHkyXl5edOzY0ZVhFxv2ns97771HYmKiTd2nn34aPz8/3n///WLdu1OY7D0fgMWLF9O1a1dq1KjBsmXLCnR+kDsyGUWhf07kBjd79my6du1KSEgIY8eOxd/f3+p4UFAQXbp0sbz+z3/+w4gRI5gyZQp9+/YFzN+wNW7cmLNnzzJ8+HC7a+537dpVe4lcA2c8HzAvItGuXTsqVKjAs88+C8BHH33EqVOnWLVqFfXr1y+M23FLc+bMYfPmzQB8++237Nq1i1GjRgFQsmRJBg4caKnbt29fvvrqK1asWEHbtm0B8wfnm2++mbVr19K6dWvuu+8+AH766Sd+//13unXrxg8//FC4N+VGrvf5gPkLndatW+Pj48Nzzz1HaGgo06dPZ9WqVQwbNowRI0YU6j25E2c8H3uio6MJCgrSPi/X6Xqfz/r162ndujWGYfDuu+8SFhZm00bv3r0L/kZuZC6bPSTiRoYPH57n5MVKlSrZrT9lyhRL2YoVK646CfLAgQOFel/uwhnPJ8fq1auN9u3bG4GBgUZQUJDRsWNH4++//y6cG3FjjzzyiMPPJ6fuihUrrMqTkpKM119/3ahRo4bh4+Nj+Pr6GnXq1DHGjBljZGRkFN7NuCFnPB/DMIzNmzcbd999t1GiRAnD19fXaNCggd2/Z5I/zno+V9KiBc5xvc9nypQpV/18IHlTD4+IiIiIiLgtD1cHICIiIiIiUlCU8IiIiIiIiNtSwiMiIiIiIm5LCY+IiIiIiLgtJTwiIiIiIuK2lPCIiIiIiIjbUsIjIiIiIiJuSwmPiIiIiIi4LS9XByAiIiLupW/fvnz11VcAxMbGEhcXZ3U8Ozubt99+mylTpnD48GEqVqzIvn37GDt2LF9++SXbt2/HwyP/38l+9tlnvP322+zZswdfX1+rYxMmTODFF1+0vE5ISCAsLOwa7q7wZGRksHnzZuLj40lISCAtLY02bdpQo0YNh84/fvw4c+fOtXvs3nvvpWzZsvmqd7nTp0+zfv16Tp06RWZmJiEhIdSqVYs6deo4eHcihUcJj4iIiDhs0aJFdOrUKdfjOYlOWFgY48ePp2TJkjZ1Jk6cyLBhw3jppZeoV68ekZGRJCUlMWbMGN577z2bZGfEiBGMGDGCbdu2UatWLatjjz76KF999RWzZ8+mb9++/Oc//2HSpEk899xzVvU6depEWFgYP/30E7NmzbrGuy9cqampbNiwgaCgIEqXLs2JEyeu6Tp16tQhPDzcqqxEiRLXXO/o0aMsXLiQsLAwGjZsiLe3N0lJSSQnJ19TfCIFTQmPiIiIOGzz5s0AfPjhh5QqVcrm+O23387y5csJDAykd+/edq8xZcoUOnTowLhx4yxlEyZMIDMzk549e9rUf+qpp3j33XeZMGECkyZNspR/9NFHTJkyhdGjR9O5c2cAHnnkEd5//32effZZTCaTpW7NmjWpWbMme/fuvWESnoCAAHr37k1AQAAJCQnXHHdERASVK1d2Sr309HRWrFhBxYoV6dChg9V7LFJUKeERERERh23ZsoUSJUowcODAa/qwm5qayubNmxkxYoRV+ZQpU7jnnnvw8/OzOadMmTL06tWLb775hrfeeouwsDBWrlzJoEGDuP/++xkyZIilbvfu3Rk7diwrVqygffv2+b/BIsTT05OAgACnXCs9PR0vL6+rDhW8Wr29e/dy6dIlmjZtislkIiMjAy8vLyU+UqQp4RERERGHbd68mYYNG17TB9zHHnuML7/8EoChQ4cydOhQmjdvzrRp09iyZQuDBg3K9dwXX3yRL774gs8++4xHHnmE7t27U7NmTaZOnWpVr3HjxpQuXZpffvnFKQlPdnY26enpDtX19fUtkh/8V65cSUZGBiaTiYiICJo3b24zdM3ReseOHcPb25uUlBQWL17M+fPn8fLyolq1arRo0QIvL320lKJHfypFRETEIenp6ezatYtWrVpx+vRpm+MlSpTA29s71/N79eqFt7c3kyZN4oMPPqB06dJUqlSJ1atXA9CoUaNcz42NjaVjx4588skn/Pzzz2RkZPDzzz8TFBRkU7dRo0asWrXqGu7Q1smTJ3Od0H+lnj17Ehwc7JR2ncHDw4OYmBiioqLw8/MjMTGRzZs3M3v2bO69917Log2O1gM4f/48hmGwePFiatSowU033cTx48fZtm0b6enp3Hrrra66XZFcKeERERERh2zfvp2MjAw+++wzPvvsM5vju3btonr16rme3759e5YtW0ZgYCADBw60DJt68803AYiJicmz/UGDBtGpUyfi4+OZP38+VapUsVuvcuXKfPPNN47eVp5CQ0O58847Harr7+9vt9wwDLKyshy6hqenp9N6iSIiIoiIiLAqi4mJYebMmaxdu9ZyX47WA/PKcZmZmdSqVYuWLVta6mZnZ7Njxw6aNGlid6EDEVdSwiMiIiIO2bJlCwBTp06lfPnyNserVavm0DViY2Ot5oicOXMGLy8vu701l9uxYwdgTmhuv/32XOuVKlWKS5cucfHixeueA+Pr60uFChWu6xonTpxwuJeoe/fudle2c5YSJUoQHR3NgQMHyM7OznWuTm71coasVa1a1ap+1apV2bFjB6dOnVLCI0WOEh4RERFxyObNm/Hy8qJnz574+Phc8zXySlZys3TpUgYPHky1atXYs2cPixcvpmPHjnbrGoYB4JSekqysLNLS0hyq6+fnZzeBKFmyJG3atHHoGs5apCAvgYGBZGdnk5mZmedztFcvICCAc+fO2fRm5bx29L0SKUxKeERERMQhW7ZsISYm5pqTncTERI4cOULdunWtykNDQ8nMzOTChQt258Ds37+fHj160LBhQ5YuXUr16tUZP358rgnPuXPnCAgIyHWIWX6cOnXquufwBAQEOLxZaGG4cOECnp6eec63yq1eeHg4x44dIyUlxaonKiUlBch9WJ+IKynhEREREYds2bKF5s2bX9f5APXq1bMqr1mzJgAHDhywOZacnMy9996Lt7c3s2bNokSJEjz99NOMGDGCHTt22GxEmnMde+XXwhlzeApaZmYmycnJ+Pn5WS3rfenSJZuYzpw5w6FDh4iKirL0gDlaD8zDCTdt2sSuXbushjXu3LkTk8lEZGRkQdyiyHVRwiMiIiJXdfLkSeLj4y3JybXI2bT0yqSmRYsWAKxfv97qmGEY9OnTh127drFixQrLXJqnn37a7kakOTZs2ECvXr2uOc7LOWMOz/WIi4sjPT2dixcvAnDo0CFLb0qdOnXw8fEhPj6euXPn0qhRI5o0aWI5d9myZXh6elK2bFn8/f05d+4cO3fuxMvLi5tuuinf9QDCwsKoUaMGu3btIjs7m8jISE6cOMH+/ftp0KABgYGBhfCuiOSPEh4RERG5qpxkJSEhgW+//dbmeP369W2Gql1py5YtlC9fntKlS1uVV65cmTp16rB06VIeffRRS/l//vMffv75ZyZNmmRZEQzMw6p69+7NN998w9tvv01oaKjl2N9//83Zs2e59957r+k+i5otW7aQnJxseX3w4EEOHjwImBeJyGt4YXR0NHv27GHr1q2kp6fj7+9PdHQ0jRs3tlpYwNF6OVq3bk1QUBC7du3i4MGDBAUF0aJFi6s+fxFXUcIjIiIiV5UzHG3KlClMmTLF5vjXX3/tUMJzZe9OjkcffZRhw4ZZhlfNmjWLUaNG8eSTT9K/f3+b+pdvRDpkyBBL+YwZM6hYsaJTNh0tCh566KGr1ilXrpzd96hOnTrUqVPnquc7Wi+Hh4cHjRs3pnHjxg6fI+JKJiNnKRMRERERJ+jbty/Lly9nw4YNeHl5ObTM8vnz56lcuTJjx47lscceu6Z209LSiI6O5rXXXuP555+3OpaamkpycjJjx45l3LhxJCQkWG2oKSLuy/7i6yIiIiLX4ciRI4SHh9OqVSuH6pcoUYJXXnmFcePGkZ2dfU1tTpkyBW9vb5588kmbY5999hnh4eGMGzfumq4tIjcu9fCIiIiIU23fvp3jx48DEBQUdF0ruznLkSNH2LVrl+V1mzZtrross4i4ByU8IiIiIiLitjSkTURERERE3JYSHhERERERcVtKeERERERExG0p4REREREREbelhEdERERERNyWEh4REREREXFbSnhERERERMRtKeERERERERG3pYRHRERERETc1v8DQIiK/Jb4tQMAAAAASUVORK5CYII=\n" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# ── SHAP Local Explanations (3 customers: high / medium / low risk) ──\n", "# We pick customers from the *holdout test set* to tell three different stories.\n", "\n", "X_test_enc = prep_fitted.transform(X_test)\n", "p_series = pd.Series(p_final, index=X_test.index)\n", "\n", "hi_idx = p_series.sort_values(ascending=False).index[0]\n", "lo_idx = p_series.sort_values(ascending=True).index[0]\n", "mid_idx = p_series.iloc[(p_series.rank(method=\"first\") - (len(p_series)/2)).abs().argsort()[:1]].index[0]\n", "\n", "selected = [(\"High risk\", hi_idx), (\"Medium risk\", mid_idx), (\"Low risk\", lo_idx)]\n", "\n", "def waterfall_for_index(idx, title):\n", " x_one = X_test_enc[[X_test.index.get_loc(idx)]]\n", " try:\n", " exp = explainer(x_one) # works for shap.Explainer\n", " shap.plots.waterfall(exp[0], show=False)\n", " plt.title(title)\n", " plt.show()\n", " except Exception:\n", " # legacy TreeExplainer path\n", " sv_all = explainer.shap_values(X_test_enc)\n", " if isinstance(sv_all, list):\n", " sv = sv_all[1][X_test.index.get_loc(idx)]\n", " base = explainer.expected_value[1]\n", " else:\n", " sv = sv_all[X_test.index.get_loc(idx)]\n", " base = explainer.expected_value\n", " shap.plots._waterfall.waterfall_legacy(base, sv, feature_names=feature_names)\n", " plt.title(title)\n", " plt.show()\n", "\n", "for label, idx in selected:\n", " prob = float(p_series.loc[idx])\n", " waterfall_for_index(idx, f\"{label} customer β€” p(churn)={prob:.2%}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-sHUu8i4dtBu" }, "source": [ "### SHAP narrative (required)\n", "\n", "Below I generate **three customer-level explanations** (high / medium / low churn risk) using SHAP. \n", "How to read it:\n", "\n", "- A **positive SHAP contribution** pushes the prediction toward **higher churn risk**.\n", "- A **negative SHAP contribution** pushes the prediction toward **lower churn risk**.\n", "- We only need the **top drivers** (usually 4–6) to write a human explanation.\n", "\n", "Typical churn drivers in Telco:\n", "- Month-to-month contracts, low tenure, no tech support / online security, higher monthly charges, fiber optic internet β†’ higher churn\n", "- Long contracts (1–2 year), longer tenure, bundled services, auto-pay / paperless billing patterns β†’ lower churn\n", "\n", "I use SHAP to convert the model into a β€œwhy” story that a retention agent can act on.\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 442 }, "id": "-ZQge3yddtBu", "outputId": "58dab33b-d94c-4975-c8af-92428ab7e268" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "**High risk** β€” predicted churn probability β‰ˆ **0.92**\n\nTop drivers:\n- tenure (↑ churn risk)\n- Contract=Month-to-month (↑ churn risk)\n- TotalCharges (↑ churn risk)\n- MonthlyCharges (↑ churn risk)\n- OnlineSecurity=No (↑ churn risk)" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "**Medium risk** β€” predicted churn probability β‰ˆ **0.17**\n\nTop drivers:\n- Contract=Month-to-month (↑ churn risk)\n- tenure (↓ churn risk)\n- InternetService=Fiber optic (↓ churn risk)\n- OnlineSecurity=No (↑ churn risk)\n- TechSupport=No (↓ churn risk)" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "**Low risk** β€” predicted churn probability β‰ˆ **0.01**\n\nTop drivers:\n- tenure (↓ churn risk)\n- Contract=Month-to-month (↓ churn risk)\n- MonthlyCharges (↓ churn risk)\n- TechSupport=No (↓ churn risk)\n- OnlineSecurity=No (↓ churn risk)" }, "metadata": {} } ], "source": [ "# ── Customer narratives from SHAP (offline, fast) ───────\n", "# We explain 3 customers (high/medium/low churn risk) with top SHAP drivers.\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import shap\n", "from IPython.display import display, Markdown\n", "\n", "# Ensure prep_fitted and clf_fitted are available from the pipeline, as they are needed.\n", "# These should be in the kernel's scope from previous cells, but we explicitly access them.\n", "prep_fitted = final_clf.named_steps[\"prep\"]\n", "clf_fitted = final_clf.named_steps[\"clf\"]\n", "\n", "# Regenerate X_train_enc and X_shap for explainer robustness in this cell\n", "# (copied from cell yADG0Vs1dtBu to make this cell more self-contained)\n", "X_train_enc = prep_fitted.transform(X_train)\n", "sample_idx = np.random.RandomState(RANDOM_STATE).choice(X_train_enc.shape[0], size=min(800, X_train_enc.shape[0]), replace=False)\n", "X_shap = X_train_enc[sample_idx]\n", "\n", "\n", "# Helper: robustly get transformed feature names\n", "def get_feature_names_from_column_transformer(ct):\n", " names = []\n", " for name, trans, cols in ct.transformers_:\n", " if name == \"remainder\":\n", " continue\n", " if hasattr(trans, \"get_feature_names_out\"):\n", " try:\n", " n = list(trans.get_feature_names_out(cols))\n", " except Exception:\n", " n = list(trans.get_feature_names_out())\n", " names.extend([f\"{name}__{x}\" for x in n])\n", " else:\n", " # simple passthrough\n", " names.extend([f\"{name}__{c}\" for c in cols])\n", " return np.array(names)\n", "\n", "feature_names = get_feature_names_from_column_transformer(prep_fitted)\n", "\n", "# Transform test set once\n", "X_test_enc = prep_fitted.transform(X_test)\n", "p_series = pd.Series(p_final, index=X_test.index)\n", "\n", "# Pick 3 customers by predicted risk\n", "hi_idx = p_series.sort_values(ascending=False).index[0]\n", "lo_idx = p_series.sort_values(ascending=True).index[0]\n", "mid_idx = p_series.sort_values().index[len(p_series)//2]\n", "\n", "selected = [(\"High risk\", hi_idx), (\"Medium risk\", mid_idx), (\"Low risk\", lo_idx)]\n", "\n", "# Build a SHAP explainer for the final classifier model (tree or linear supported)\n", "# For pipelines, we explain the *model* on already-transformed features.\n", "# GradientBoosting is tree-based, so TreeExplainer is appropriate.\n", "try:\n", " explainer = shap.TreeExplainer(clf_fitted) # Use the extracted classifier\n", "except Exception as e_tree:\n", " print(f\"TreeExplainer failed, falling back to general Explainer: {e_tree}\")\n", " # Fallback for non-tree models or if TreeExplainer fails.\n", " # Requires a background dataset X_shap\n", " explainer = shap.Explainer(clf_fitted, X_shap) # Use the extracted classifier and background data\n", "\n", "def shap_values_for(X_rows):\n", " # Works across SHAP API versions\n", " try:\n", " exp = explainer(X_rows)\n", " # For shap.Explanation objects, .values is the SHAP values array.\n", " # For TreeExplainer's older API, shap_values is already the array or list of arrays.\n", " if isinstance(exp, shap.Explanation):\n", " return exp.values\n", " else: # Older TreeExplainer API might return directly a numpy array or list of arrays\n", " return exp\n", " except Exception as e_sv_for:\n", " # Fallback to explainer.shap_values(X_rows) for older TreeExplainer API\n", " # if explainer(X_rows) fails (e.g., if explainer was created with TreeExplainer directly)\n", " print(f\"explainer(X_rows) failed, trying explainer.shap_values(X_rows): {e_sv_for}\")\n", " sv = explainer.shap_values(X_rows)\n", " if isinstance(sv, list):\n", " # For multi-output models or classifiers, shap_values might return a list.\n", " # We want the SHAP values for the positive class (index 1).\n", " return sv[1]\n", " return sv\n", "\n", "def simplify_feature_name(fn: str) -> str:\n", " # Examples: \"cat__Contract_Month-to-month\" -> \"Contract=Month-to-month\"\n", " fn = fn.replace(\"num__\", \"\").replace(\"cat__\", \"\")\n", " if fn.startswith(\"cat__\"):\n", " fn = fn[len(\"cat__\"):]\n", " if \"__\" in fn:\n", " fn = fn.split(\"__\", 1)[1]\n", " # one-hot: \"Contract_Month-to-month\"\n", " if \"_\" in fn and fn.count(\"_\") >= 1:\n", " parts = fn.split(\"_\", 1)\n", " return f\"{parts[0]}={parts[1]}\"\n", " return fn\n", "\n", "def narrative_for_customer(row_pos: int, label: str, top_k: int = 5) -> str:\n", " row = X_test_enc[row_pos]\n", " sv = shap_values_for(row.reshape(1, -1))[0] # Get SHAP values for the single row\n", " s = pd.Series(sv, index=feature_names)\n", " top = s.reindex(s.abs().sort_values(ascending=False).index).head(top_k)\n", "\n", " # Build short story\n", " reasons = []\n", " for feat, val in top.items():\n", " direction = \"↑ churn risk\" if val > 0 else \"↓ churn risk\"\n", " reasons.append(f\"- {simplify_feature_name(feat)} ({direction})\")\n", "\n", " p = float(p_final[row_pos])\n", " story = f\"**{label}** β€” predicted churn probability β‰ˆ **{p:.2f}**\\n\\nTop drivers:\\n\" + \"\\n\".join(reasons)\n", " return story\n", "\n", "# Print narratives\n", "def display_markdown(text, raw=False):\n", " \"\"\"Small helper so markdown narratives render nicely in notebooks.\"\"\"\n", " display(Markdown(text))\n", "\n", "for label, idx in selected:\n", " # Need to get the positional index in X_test, not the original DataFrame index 'idx'\n", " pos = np.where(X_test.index == idx)[0][0]\n", " display_markdown(narrative_for_customer(pos, label), raw=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "KtzVrUj4dtBv" }, "source": [ "---\n", "## Part 5: Revenue-at-Risk call list (Deliverable 5)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PrsbRS05dtBv", "outputId": "fa33ba43-6b33-42f2-bd3a-5623c9ed11f3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "│─ Top 10 Customers by Revenue-at-Risk:\n", "customerID p_churn pred_monthly_charges revenue_at_risk\n", "7181-BQYBV 0.903608 104.798214 94.696466\n", "6861-XWTWQ 0.913295 99.823395 91.168185\n", "6023-YEBUP 0.900723 99.852276 89.939205\n", "1400-MMYXY 0.858654 104.739068 89.934636\n", "8884-ADFVN 0.897033 99.826246 89.547424\n", "0655-RBDUG 0.887637 99.868291 88.646801\n", "2012-NWRPA 0.885628 99.799567 88.385253\n", "0295-PPHDO 0.924460 94.835927 87.672020\n", "4587-VVTOX 0.833204 104.726810 87.258747\n", "2754-SDJRD 0.862542 99.844815 86.120302\n", "\n", "Total Revenue-at-Risk (test set): $34,362\n" ] } ], "source": [ "# ── Pick final regression model ───────────\n", "final_reg_name = reg_results.iloc[0][\"model\"]\n", "final_reg = {\"ridge\": ridge, \"rfr\": rfr, \"gbr\": gbr}[final_reg_name]\n", "v_final = final_reg.predict(Xr_test)\n", "v_final = np.clip(v_final, 0, None)\n", "\n", "# Get customer IDs for the test set\n", "customer_ids = df['customerID']\n", "\n", "# ── Compute Revenue-at-Risk ────────────\n", "call_list = pd.DataFrame({\n", " \"customerID\": customer_ids.loc[X_test.index].values,\n", " \"p_churn\": p_final,\n", " \"pred_monthly_charges\": v_final,\n", "})\n", "call_list[\"revenue_at_risk\"] = call_list[\"p_churn\"] * call_list[\"pred_monthly_charges\"]\n", "\n", "call_list = call_list.sort_values(\"revenue_at_risk\", ascending=False)\n", "\n", "print(f\"\\n│─ Top 10 Customers by Revenue-at-Risk:\")\n", "print(call_list.head(10).to_string(index=False))\n", "print(f\"\\nTotal Revenue-at-Risk (test set): ${call_list['revenue_at_risk'].sum():,.0f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "MESNs53NdtBv" }, "source": [ "### Reason codes (required)\n", "\n", "For the exported call list, each customer gets simple **reason codes** based on the **top absolute SHAP features**.\n", "\n", "Format:\n", "- 2–3 drivers per customer\n", "- concise β€œfeature=value” style\n", "- designed for action by an agent (e.g., *month-to-month contract*, *low tenure*, *no tech support*)\n", "\n", "These reason codes are not a legal/causal statement β€” they are **model explanations** used to guide outreach conversations.\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "rzRCRr8MdtBv", "outputId": "cade7a61-e2b7-4047-f5ba-3ecd16e1c562" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " customerID p_churn pred_monthly_charges revenue_at_risk \\\n", "471 7181-BQYBV 0.903608 104.798214 94.696466 \n", "644 6861-XWTWQ 0.913295 99.823395 91.168185 \n", "286 6023-YEBUP 0.900723 99.852276 89.939205 \n", "875 1400-MMYXY 0.858654 104.739068 89.934636 \n", "146 8884-ADFVN 0.897033 99.826246 89.547424 \n", "48 0655-RBDUG 0.887637 99.868291 88.646801 \n", "967 2012-NWRPA 0.885628 99.799567 88.385253 \n", "605 0295-PPHDO 0.924460 94.835927 87.672020 \n", "1341 4587-VVTOX 0.833204 104.726810 87.258747 \n", "901 2754-SDJRD 0.862542 99.844815 86.120302 \n", "\n", " reason_codes \n", "471 Contract=Month-to-month; tenure; MonthlyCharges \n", "644 Contract=Month-to-month; MonthlyCharges; tenure \n", "286 Contract=Month-to-month; tenure; MonthlyCharges \n", "875 Contract=Month-to-month; tenure; MonthlyCharges \n", "146 Contract=Month-to-month; MonthlyCharges; tenure \n", "48 Contract=Month-to-month; tenure; MonthlyCharges \n", "967 Contract=Month-to-month; MonthlyCharges; tenure \n", "605 tenure; Contract=Month-to-month; TotalCharges \n", "1341 Contract=Month-to-month; tenure; MonthlyCharges \n", "901 MonthlyCharges; Contract=Month-to-month; tenure " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
customerIDp_churnpred_monthly_chargesrevenue_at_riskreason_codes
4717181-BQYBV0.903608104.79821494.696466Contract=Month-to-month; tenure; MonthlyCharges
6446861-XWTWQ0.91329599.82339591.168185Contract=Month-to-month; MonthlyCharges; tenure
2866023-YEBUP0.90072399.85227689.939205Contract=Month-to-month; tenure; MonthlyCharges
8751400-MMYXY0.858654104.73906889.934636Contract=Month-to-month; tenure; MonthlyCharges
1468884-ADFVN0.89703399.82624689.547424Contract=Month-to-month; MonthlyCharges; tenure
480655-RBDUG0.88763799.86829188.646801Contract=Month-to-month; tenure; MonthlyCharges
9672012-NWRPA0.88562899.79956788.385253Contract=Month-to-month; MonthlyCharges; tenure
6050295-PPHDO0.92446094.83592787.672020tenure; Contract=Month-to-month; TotalCharges
13414587-VVTOX0.833204104.72681087.258747Contract=Month-to-month; tenure; MonthlyCharges
9012754-SDJRD0.86254299.84481586.120302MonthlyCharges; Contract=Month-to-month; tenure
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "call_list", "summary": "{\n \"name\": \"call_list\",\n \"rows\": 1761,\n \"fields\": [\n {\n \"column\": \"customerID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1761,\n \"samples\": [\n \"6286-SUUWT\",\n \"7340-KEFQE\",\n \"3239-TPHPZ\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p_churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.24909016511796173,\n \"min\": 0.006430692081376636,\n \"max\": 0.9244599897800823,\n \"num_unique_values\": 1626,\n \"samples\": [\n 0.13032069392355156,\n 0.05260211597488042,\n 0.05134806859383393\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pred_monthly_charges\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 29.808419028114823,\n \"min\": 19.781004964948323,\n \"max\": 115.12639538140334,\n \"num_unique_values\": 1758,\n \"samples\": [\n 60.103418543011145,\n 90.06221220191898,\n 65.04057561208624\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"revenue_at_risk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 21.51376713748318,\n \"min\": 0.12765364795189077,\n \"max\": 94.69646571472525,\n \"num_unique_values\": 1758,\n \"samples\": [\n 4.822367204176954,\n 6.413546107230532,\n 1.4071870789771952\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"reason_codes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"Contract=Month-to-month; MonthlyCharges; InternetService=Fiber optic\",\n \"Contract=Month-to-month; tenure; MonthlyCharges\",\n \"Contract=Month-to-month; tenure; OnlineSecurity=No\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 26 } ], "source": [ "# ── SHAP-based reason codes for the call list ───────────────\n", "# We compute concise reason codes for the top N customers in the call list.\n", "# This is designed for *operational* use: short, readable, action-oriented.\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import shap\n", "\n", "TOP_N = 50 # required: top 20–50 is fine\n", "\n", "top_call = call_list.head(TOP_N).copy()\n", "\n", "# Ensure we have transformed features aligned to X_test rows\n", "X_test_enc = prep_fitted.transform(X_test)\n", "\n", "# Feature names from the preprocessor\n", "def get_feature_names_from_column_transformer(ct):\n", " names = []\n", " for name, trans, cols in ct.transformers_:\n", " if name == \"remainder\":\n", " continue\n", " if hasattr(trans, \"get_feature_names_out\"):\n", " try:\n", " n = list(trans.get_feature_names_out(cols))\n", " except Exception:\n", " n = list(trans.get_feature_names_out())\n", " names.extend([f\"{name}__{x}\" for x in n])\n", " else:\n", " names.extend([f\"{name}__{c}\" for c in cols])\n", " return np.array(names)\n", "\n", "feature_names = get_feature_names_from_column_transformer(prep_fitted)\n", "\n", "# Choose explainer (tree preferred for GradientBoosting)\n", "try:\n", " explainer = shap.TreeExplainer(clf_fitted)\n", "except Exception:\n", " explainer = shap.Explainer(clf_fitted)\n", "\n", "def shap_values_for(X_rows):\n", " try:\n", " exp = explainer(X_rows)\n", " vals = exp.values\n", " return vals\n", " except Exception:\n", " sv = explainer.shap_values(X_rows)\n", " if isinstance(sv, list):\n", " return sv[1]\n", " return sv\n", "\n", "def simplify_feature_name(fn: str) -> str:\n", " fn = fn.replace(\"num__\", \"\").replace(\"cat__\", \"\")\n", " if fn.startswith(\"cat__\"):\n", " fn = fn[len(\"cat__\"):]\n", " if \"__\" in fn:\n", " fn = fn.split(\"__\", 1)[1]\n", " if \"_\" in fn and fn.count(\"_\") >= 1:\n", " parts = fn.split(\"_\", 1)\n", " return f\"{parts[0]}={parts[1]}\"\n", " return fn\n", "\n", "def make_reason_codes(row_pos: int, top_k: int = 3) -> str:\n", " sv = shap_values_for(X_test_enc[row_pos].reshape(1, -1))[0]\n", " s = pd.Series(sv, index=feature_names)\n", " top_feats = s.reindex(s.abs().sort_values(ascending=False).index).head(top_k)\n", " return \"; \".join([simplify_feature_name(f) for f in top_feats.index])\n", "\n", "# Map call_list rows to X_test positions.\n", "# call_list was built from X_test in the same order, then sorted by risk.\n", "# We stored customerID, so we re-join on customerID to locate row positions.\n", "cust_to_pos = {cid: pos for pos, cid in enumerate(customer_ids.loc[X_test.index].values)}\n", "\n", "top_call[\"reason_codes\"] = top_call[\"customerID\"].map(lambda cid: make_reason_codes(cust_to_pos[cid], top_k=3))\n", "\n", "# Write back into main call_list (so later cells can use it)\n", "call_list.loc[top_call.index, \"reason_codes\"] = top_call[\"reason_codes\"]\n", "\n", "call_list.head(10)" ] }, { "cell_type": "markdown", "metadata": { "id": "kGsyZmWDdtBv" }, "source": [ "### Action recommendations (required)\n", "\n", "I turn the reason codes into a lightweight retention playbook:\n", "\n", "- **Contract risk (Month-to-month):** offer a 12-month renewal incentive / discounted bundle (anchor on value, not price).\n", "- **Low tenure / onboarding:** proactive onboarding call + simplify first 30 days (setup help, usage tips).\n", "- **No TechSupport / OnlineSecurity:** add-on trial (30 days) or bundle upgrade; highlight β€œpeace of mind”.\n", "- **High MonthlyCharges:** review plan fit, remove unused add-ons, or targeted discount to retain margin-positive customers.\n", "- **Fiber optic churn pattern:** check service quality tickets / speed issues; offer troubleshooting + escalation.\n", "\n", "For the top-at-risk list I output a recommended next step per customer so the deliverable is directly usable.\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HXQ9bIWCdtBv", "outputId": "9c50eab9-9abf-422c-e62e-03f905f267f6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ“Œ Example actions for top 10 Revenue-at-Risk customers:\n", "customerID p_churn pred_monthly_charges revenue_at_risk reason_codes recommended_action\n", "7181-BQYBV 0.903608 104.798214 94.696466 Contract=Month-to-month; tenure; MonthlyCharges Offer 12-month contract discount / upgrade\n", "6861-XWTWQ 0.913295 99.823395 91.168185 Contract=Month-to-month; MonthlyCharges; tenure Offer 12-month contract discount / upgrade\n", "6023-YEBUP 0.900723 99.852276 89.939205 Contract=Month-to-month; tenure; MonthlyCharges Offer 12-month contract discount / upgrade\n", "1400-MMYXY 0.858654 104.739068 89.934636 Contract=Month-to-month; tenure; MonthlyCharges Offer 12-month contract discount / upgrade\n", "8884-ADFVN 0.897033 99.826246 89.547424 Contract=Month-to-month; MonthlyCharges; tenure Offer 12-month contract discount / upgrade\n", "0655-RBDUG 0.887637 99.868291 88.646801 Contract=Month-to-month; tenure; MonthlyCharges Offer 12-month contract discount / upgrade\n", "2012-NWRPA 0.885628 99.799567 88.385253 Contract=Month-to-month; MonthlyCharges; tenure Offer 12-month contract discount / upgrade\n", "0295-PPHDO 0.924460 94.835927 87.672020 tenure; Contract=Month-to-month; TotalCharges Offer 12-month contract discount / upgrade\n", "4587-VVTOX 0.833204 104.726810 87.258747 Contract=Month-to-month; tenure; MonthlyCharges Offer 12-month contract discount / upgrade\n", "2754-SDJRD 0.862542 99.844815 86.120302 MonthlyCharges; Contract=Month-to-month; tenure Offer 12-month contract discount / upgrade\n" ] } ], "source": [ "# ── Action recommendations for top-at-risk (offline) ──────────────\n", "top_10 = call_list.head(10).copy()\n", "\n", "recommendations = []\n", "for _, r in top_10.iterrows():\n", " # Simple playbook based on reason codes (example)\n", " rc = (r.get(\"reason_codes\") or \"\").lower()\n", " offer = \"Standard retention outreach\"\n", " if \"contract\" in rc and \"month-to-month\" in rc:\n", " offer = \"Offer 12-month contract discount / upgrade\"\n", " elif \"internetservice_fiber optic\" in rc:\n", " offer = \"Proactive tech support + service credit\"\n", " elif \"paymentmethod_electronic check\" in rc:\n", " offer = \"Switch to autopay + small bill credit\"\n", " recommendations.append(offer)\n", "\n", "top_10[\"recommended_action\"] = recommendations\n", "\n", "print(\"πŸ“Œ Example actions for top 10 Revenue-at-Risk customers:\")\n", "print(top_10[[\"customerID\",\"p_churn\",\"pred_monthly_charges\",\"revenue_at_risk\",\"reason_codes\",\"recommended_action\"]].to_string(index=False))\n", "\n", "log_copilot(\n", " use_case=\"Operational playbook draft\",\n", " prompt=\"Propose contact/offer actions for top churn-risk customers using SHAP reason codes; keep recommendations testable.\",\n", " changes_made=\"Implemented a lightweight rules-based mapping from reason codes to suggested offers; kept it as a starting playbook.\",\n", " manual_verification=\"Ensured actions are operationally plausible and can be A/B tested; avoided making claims not supported by data.\"\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "eb2WCgSRdtB1" }, "source": [ "---\n", "## Part 6: Save Artifacts" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "spcyzR_fdtB1", "outputId": "db551398-a912-4e3e-a0cf-d31de1c1a906" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Saved call list to day5_assignment_call_list.csv\n", "βœ… Saved metrics to day5_assignment_metrics.json\n" ] } ], "source": [ "# ── Save call list ─────────────────\n", "call_list_out = \"day5_assignment_call_list.csv\"\n", "call_list.to_csv(call_list_out, index=False)\n", "print(f\"βœ… Saved call list to {call_list_out}\")\n", "\n", "# ── Save metrics (JSON) ───────────────────────\n", "# Include: model names + key metrics (ROC-AUC, PR-AUC, lift@10%, MAE/RMSE),\n", "# chosen threshold + rationale, extension track label.\n", "\n", "lift10, k10, top10, base = lift_at_pct(y_test, p_final, 0.10)\n", "lift20, k20, top20, _ = lift_at_pct(y_test, p_final, 0.20)\n", "\n", "metrics = {\n", " \"extension_track\": \"A\",\n", " \"final_churn_model\": final_clf_name,\n", " \"final_value_model\": final_reg_name,\n", " \"holdout_test_metrics\": {\n", " \"classification\": {\n", " \"models\": clf_results.to_dict(orient=\"records\"),\n", " \"lift_at_10pct\": lift10,\n", " \"lift_at_20pct\": lift20,\n", " },\n", " \"regression\": {\n", " \"models\": reg_results.to_dict(orient=\"records\"),\n", " },\n", " },\n", " \"threshold_selection\": {\n", " \"chosen_threshold\": CHOSEN_THRESHOLD,\n", " \"capacity_max_contacts\": CAPACITY,\n", " \"fn_cost\": FN_COST,\n", " \"fp_cost\": FP_COST,\n", " \"rationale\": \"Minimize total business cost (FN≫FP) subject to contact capacity.\",\n", " },\n", " \"verification\": {\n", " \"holdout_test\": True,\n", " \"pipeline_no_leakage\": True,\n", " \"leakage_scan_done\": True,\n", " \"shap_on_preprocessed_data\": True,\n", " \"cv_on_training_only\": True,\n", " \"memo_references_real_outputs\": True,\n", " },\n", "}\n", "\n", "metrics_out = \"day5_assignment_metrics.json\"\n", "with open(metrics_out, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(metrics, f, indent=2)\n", "print(f\"βœ… Saved metrics to {metrics_out}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "vejXh57ldtB1" }, "source": [ "---\n", "## Part 7: Manager Memo (Deliverable 3)\n", "\n", "βœ… Create a 1-page memo as a separate markdown file." ] }, { "cell_type": "markdown", "metadata": { "id": "hH-SInQCdtB2" }, "source": [ "### Manager memo (required)\n", "\n", "The notebook writes a manager-ready memo to `day5_assignment_manager_memo.md`.\n", "\n", "It includes:\n", "- what we built (churn model + value model),\n", "- how we evaluated it (holdout + lift + threshold decision),\n", "- the **operational rule** (contact top risk customers under capacity),\n", "- expected benefit framing and risks (calibration, fairness, drift),\n", "- next steps (A/B test retention offers, monitor drift, improve labels).\n", "\n", "(Scroll to the artifacts section to open the generated memo.)\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FEYb4A0NdtB2", "outputId": "9b1531b9-6b42-401c-982a-c63a4583009b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "# Day 5 β€” Manager Memo (Churn Γ— Value)\n", "\n", "## 1) Executive Summary\n", "- We built a churn targeting model and ranked customers by **Revenue-at-Risk = p(churn) Γ— predicted MonthlyCharges**.\n", "- Best churn model: **gb** with holdout **PR-AUC=0.656** and **ROC-AUC=0.846** (better ranking of likely churners than alternatives).\n", "- Targeting impact: top 10% of customers contains **2.81Γ—** the average churn rate (lift@10%). Top 20% contains **2.52Γ—** (lift@20%).\n", "- We chose an action threshold of **0.60** using a business cost tradeoff (missing churners is far more expensive than contacting safe customers) and a contact capacity of **264**.\n", "- Portfolio view: total Revenue-at-Risk in the holdout test set is **34,362** (units β‰ˆ monthly €). The top 50 customers contribute **3,973**.\n", "\n", "## 2) Model Performance (Churn + Value)\n", "- Classification (gb): PR-AUC 0.656, ROC-AUC 0.846.\n", "- Regression (predict MonthlyCharg\n", "...\n", "βœ… Saved manager memo to day5_assignment_manager_memo.md\n" ] } ], "source": [ "# ── Draft Manager Memo (offline) ──────────────────────────────────\n", "# Max 1 page, VP-friendly, references actual notebook outputs.\n", "\n", "# Pull key numbers\n", "best_row = clf_results.iloc[0]\n", "roc = float(best_row[\"roc_auc\"]); pr = float(best_row[\"pr_auc\"])\n", "lift10, k10, top10, base = lift_at_pct(y_test, p_final, 0.10)\n", "lift20, k20, top20, _ = lift_at_pct(y_test, p_final, 0.20)\n", "\n", "total_rar = float(call_list[\"revenue_at_risk\"].sum())\n", "top_rar = float(call_list.head(50)[\"revenue_at_risk\"].sum())\n", "\n", "reg_best = reg_results.iloc[0].to_dict()\n", "\n", "memo = f\"\"\"# Day 5 β€” Manager Memo (Churn Γ— Value)\n", "\n", "## 1) Executive Summary\n", "- We built a churn targeting model and ranked customers by **Revenue-at-Risk = p(churn) Γ— predicted MonthlyCharges**.\n", "- Best churn model: **{final_clf_name}** with holdout **PR-AUC={pr:.3f}** and **ROC-AUC={roc:.3f}** (better ranking of likely churners than alternatives).\n", "- Targeting impact: top 10% of customers contains **{lift10:.2f}Γ—** the average churn rate (lift@10%). Top 20% contains **{lift20:.2f}Γ—** (lift@20%).\n", "- We chose an action threshold of **{CHOSEN_THRESHOLD:.2f}** using a business cost tradeoff (missing churners is far more expensive than contacting safe customers) and a contact capacity of **{CAPACITY}**.\n", "- Portfolio view: total Revenue-at-Risk in the holdout test set is **{total_rar:,.0f}** (units β‰ˆ monthly €). The top 50 customers contribute **{top_rar:,.0f}**.\n", "\n", "## 2) Model Performance (Churn + Value)\n", "- Classification ({final_clf_name}): PR-AUC {pr:.3f}, ROC-AUC {roc:.3f}.\n", "- Regression (predict MonthlyCharges): best model **{reg_best['model']}** with MAE={reg_best['mae']:.2f}, RMSE={reg_best['rmse']:.2f}, RΒ²={reg_best['r2']:.3f}.\n", "\n", "## 3) Targeting Plan (Revenue-at-Risk + Capacity)\n", "- Each week, contact up to **{CAPACITY}** customers (or the top N by Revenue-at-Risk if capacity is fixed).\n", "- Use the exported call list (**day5_assignment_call_list.csv**) ranked by Revenue-at-Risk.\n", "- Suggested success metric: **reduced churn vs control** (A/B test) and **saved monthly revenue**.\n", "\n", "## 4) Top Drivers (SHAP)\n", "- Global SHAP shows the most influential features overall (see SHAP summary plot).\n", "- For each targeted customer, the call list includes short SHAP-based **reason codes** to guide the conversation and the offer.\n", "\n", "## 5) Risks & Monitoring\n", "- Leakage risk: avoided by removing identifiers/labels and keeping preprocessing inside pipelines after split.\n", "- Drift: monitor weekly churn rate, score distribution, and major feature distributions (tenure, contract type, charges).\n", "- Segment performance: verify the model doesn’t underperform for specific contract types or tenure bands before full rollout.\n", "\n", "## 6) Next Steps\n", "- Run a retention **A/B test**: compare offers (contract upgrade vs bill credit vs support outreach) for top Revenue-at-Risk customers.\n", "- Add calibration (Track B) or segment reporting (Track D) if this becomes a production roll-out.\n", "\n", "**Extension Track:** A (Decision rule / threshold + capacity planning)\n", "\"\"\"\n", "\n", "print(memo[:900] + \"\\n...\")\n", "\n", "# Save memo\n", "memo_out = \"day5_assignment_manager_memo.md\"\n", "with open(memo_out, \"w\", encoding=\"utf-8\") as f:\n", " f.write(memo)\n", "print(f\"βœ… Saved manager memo to {memo_out}\")\n" ] }, { "cell_type": "code", "source": [ "# ── Save manager memo ─────────────────────────────────────────────\n", "# This saves the memo drafted in the previous cell (variable: `memo`)\n", "# If `memo` is not defined (e.g., you re-ran out of order), it falls back to the existing file.\n", "\n", "memo_out = \"day5_assignment_manager_memo.md\"\n", "\n", "try:\n", " _ = memo # check existence\n", "except NameError:\n", " # Fallback: if you already have a memo file, keep it; otherwise raise a clear error\n", " import os\n", " if os.path.exists(memo_out):\n", " with open(memo_out, \"r\", encoding=\"utf-8\") as f:\n", " memo = f.read()\n", " else:\n", " raise NameError(\"`memo` is not defined. Run the previous 'Draft Manager Memo' cell first.\")\n", "\n", "# Write memo\n", "with open(memo_out, \"w\", encoding=\"utf-8\") as f:\n", " f.write(memo.strip() + \"\\n\")\n", "\n", "print(f\"βœ… Saved manager memo to: {memo_out}\")\n", "\n", "# Quick preview (first ~25 lines)\n", "preview_lines = memo.splitlines()[:25]\n", "print(\"\\n--- Memo preview (top) ---\")\n", "print(\"\\n\".join(preview_lines))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M6FYd7YEjk4L", "outputId": "55596ed5-4054-4982-cfbb-ba65af14d08f" }, "execution_count": 30, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Saved manager memo to: day5_assignment_manager_memo.md\n", "\n", "--- Memo preview (top) ---\n", "# Day 5 β€” Manager Memo (Churn Γ— Value)\n", "\n", "## 1) Executive Summary\n", "- We built a churn targeting model and ranked customers by **Revenue-at-Risk = p(churn) Γ— predicted MonthlyCharges**.\n", "- Best churn model: **gb** with holdout **PR-AUC=0.656** and **ROC-AUC=0.846** (better ranking of likely churners than alternatives).\n", "- Targeting impact: top 10% of customers contains **2.81Γ—** the average churn rate (lift@10%). Top 20% contains **2.52Γ—** (lift@20%).\n", "- We chose an action threshold of **0.60** using a business cost tradeoff (missing churners is far more expensive than contacting safe customers) and a contact capacity of **264**.\n", "- Portfolio view: total Revenue-at-Risk in the holdout test set is **34,362** (units β‰ˆ monthly €). The top 50 customers contribute **3,973**.\n", "\n", "## 2) Model Performance (Churn + Value)\n", "- Classification (gb): PR-AUC 0.656, ROC-AUC 0.846.\n", "- Regression (predict MonthlyCharges): best model **ridge** with MAE=0.78, RMSE=1.01, RΒ²=0.999.\n", "\n", "## 3) Targeting Plan (Revenue-at-Risk + Capacity)\n", "- Each week, contact up to **264** customers (or the top N by Revenue-at-Risk if capacity is fixed).\n", "- Use the exported call list (**day5_assignment_call_list.csv**) ranked by Revenue-at-Risk.\n", "- Suggested success metric: **reduced churn vs control** (A/B test) and **saved monthly revenue**.\n", "\n", "## 4) Top Drivers (SHAP)\n", "- Global SHAP shows the most influential features overall (see SHAP summary plot).\n", "- For each targeted customer, the call list includes short SHAP-based **reason codes** to guide the conversation and the offer.\n", "\n", "## 5) Risks & Monitoring\n", "- Leakage risk: avoided by removing identifiers/labels and keeping preprocessing inside pipelines after split.\n", "- Drift: monitor weekly churn rate, score distribution, and major feature distributions (tenure, contract type, charges).\n" ] } ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uz5jASgQdtB2", "outputId": "600ed532-3d49-4c11-e4f2-22f9b249d884" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "βœ… Saved manager memo to day5_assignment_manager_memo_human.md\n" ] } ], "source": [ "# ── Save manager memo (with human edits) ────────────────────────────────────\n", "\n", "memo = \"\"\"# Day 5 β€” Manager Memo (Churn Γ— Value Targeting)\n", "\n", "## Executive Summary\n", "\n", "We built a churn targeting system that ranks customers by **Revenue-at-Risk = p(churn) Γ— predicted MonthlyCharges** to prioritize retention outreach.\n", "\n", "The best-performing churn model is **Gradient Boosting (gb)** with:\n", "- Holdout **PR-AUC = 0.656**\n", "- Holdout **ROC-AUC = 0.846**\n", "\n", "This means the model ranks likely churners well and performs strongly in the high-risk region where targeting matters most.\n", "\n", "Targeting performance:\n", "- **Lift@10% = 2.81Γ—**\n", "- **Lift@20% = 2.52Γ—**\n", "\n", "This indicates the top 10% highest-risk customers are nearly 3 times more likely to churn than average.\n", "\n", "Using a business cost tradeoff (missing churners is much more expensive than contacting safe customers) and a weekly contact capacity of **264 customers**, we selected an operating threshold of **0.60**.\n", "\n", "On the holdout test set:\n", "- Total Revenue-at-Risk β‰ˆ **34,362 (monthly € equivalent)**\n", "- Top 50 customers account for β‰ˆ **3,973**\n", "\n", "This confirms strong revenue concentration among the highest-risk segment.\n", "\n", "---\n", "\n", "## What Decision Are We Supporting?\n", "\n", "We are deciding **which customers to contact each week for retention efforts** under limited call-center capacity.\n", "\n", "The system provides:\n", "1. A ranked call list (by Revenue-at-Risk)\n", "2. A probability threshold aligned with cost and capacity\n", "3. SHAP-based reason codes to guide agent conversations\n", "\n", "This enables targeted retention instead of blanket outreach.\n", "\n", "---\n", "\n", "## Model Performance\n", "\n", "### 1) Churn Model (Classification)\n", "\n", "Best model: **Gradient Boosting**\n", "- PR-AUC: 0.656\n", "- ROC-AUC: 0.846\n", "\n", "Interpretation:\n", "- ROC-AUC shows strong ranking performance overall.\n", "- PR-AUC confirms good precision among high-risk customers (critical for targeting efficiency).\n", "\n", "Lift metrics confirm business usefulness.\n", "\n", "---\n", "\n", "### 2) Value Model (Predicting MonthlyCharges)\n", "\n", "Best model: **Ridge Regression**\n", "- MAE β‰ˆ 0.78\n", "- RMSE β‰ˆ 1.01\n", "- RΒ² β‰ˆ 0.999\n", "\n", "The extremely high RΒ² occurs because MonthlyCharges is strongly related to contract structure and tenure (and TotalCharges is approximately MonthlyCharges Γ— tenure). This relationship makes MonthlyCharges highly predictable in this dataset.\n", "\n", "Business meaning:\n", "The model provides reliable estimates of customer value for Revenue-at-Risk calculation.\n", "\n", "---\n", "\n", "## Targeting Plan\n", "\n", "Capacity: **264 customers per week**\n", "\n", "Rule:\n", "- Rank customers by Revenue-at-Risk\n", "- Apply probability threshold of 0.60\n", "- If capacity binds, select the top-N customers by Revenue-at-Risk\n", "\n", "Expected impact:\n", "- Concentrated retention effort on highest-value churn risks\n", "- Higher ROI per contact\n", "- Measurable reduction in churn vs control group\n", "\n", "---\n", "\n", "## Top Drivers (SHAP Insights)\n", "\n", "Global SHAP indicates the most influential churn drivers include:\n", "\n", "- **Contract type (month-to-month increases churn risk)**\n", "- **Tenure (short tenure increases churn probability)**\n", "- **Internet service type (fiber customers show higher churn risk)**\n", "- **MonthlyCharges (higher charges increase churn likelihood)**\n", "\n", "The call list includes customer-specific SHAP-based reason codes so agents understand *why* a customer is high risk.\n", "\n", "---\n", "\n", "## Risks & Monitoring\n", "\n", "1) Data Drift\n", "Monitor weekly changes in:\n", "- Tenure distribution\n", "- Contract mix\n", "- Charge levels\n", "- Overall churn rate\n", "\n", "2) Segment Risk\n", "Certain segments (e.g., fiber + month-to-month) may dominate targeting. Monitor fairness and offer suitability.\n", "\n", "3) Calibration Drift\n", "Monitor predicted vs actual churn monthly. Recalibrate if probability alignment deteriorates.\n", "\n", "4) Near-Deterministic Value Relationship\n", "Because value prediction is structurally strong, ensure no future product changes alter billing logic.\n", "\n", "---\n", "\n", "## Next Steps\n", "\n", "1. Launch a controlled A/B retention experiment:\n", " - Treatment: Top-ranked customers contacted\n", " - Control: Similar high-risk customers not contacted\n", "\n", "2. Measure:\n", " - Retention lift\n", " - Saved monthly revenue\n", " - Cost per saved customer\n", "\n", "3. Review after 4–6 weeks and refine:\n", " - Threshold\n", " - Offer strategy\n", " - Capacity allocation\n", "\n", "This system enables financially prioritized, explainable, and measurable churn prevention.\n", "\"\"\"\n", "\n", "memo_path = \"day5_assignment_manager_memo_human.md\"\n", "with open(memo_path, \"w\", encoding=\"utf-8\") as f:\n", " f.write(memo)\n", "\n", "print(f\"βœ… Saved manager memo to {memo_path}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Gp2vWROidtB2" }, "source": [ "---\n", "## Part 8: GenAI Copilot Log Summary" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DTKhTEcxdtB2", "outputId": "19386371-bddf-4b9d-cba2-154a834063ac" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ“‹ Copilot interactions logged: 3\n", "βœ… Saved copilot log to day5_assignment_copilot_log.json\n", "--- Log Entry 1 ---\n", "Use case: Leakage scan notes\n", "Prompt: Draft leakage considerations for churn modeling using Telco churn dataset; list columns to exclude and why.\n", "Changes: Edited to match actual dataset columns (no end-date columns present). Added explicit note about pipeline-based preprocessing.\n", "Verified: Manually checked dataset columns; confirmed only customerID and Churn are excluded; ensured preprocessing happens after split.\n", "\n", "--- Log Entry 2 ---\n", "Use case: Model comparison narrative (draft)\n", "Prompt: Given ROC-AUC and PR-AUC for logit/rf/gb on a holdout set, draft a short justification for choosing one model for churn targeting.\n", "Changes: I prioritize PR-AUC (ranking churners) and require CV stability on training split. I also tie threshold choice to business cost + capacity.\n", "Verified: Verified the selected model is the top row in clf_results and that CV on training set is computed separately.\n", "\n", "--- Log Entry 3 ---\n", "Use case: Operational playbook draft\n", "Prompt: Propose contact/offer actions for top churn-risk customers using SHAP reason codes; keep recommendations testable.\n", "Changes: Implemented a lightweight rules-based mapping from reason codes to suggested offers; kept it as a starting playbook.\n", "Verified: Ensured actions are operationally plausible and can be A/B tested; avoided making claims not supported by data.\n", "\n" ] } ], "source": [ "# ── Export Copilot Log (required) ─────────────────────────────────\n", "copilot_out = \"day5_assignment_copilot_log.json\"\n", "with open(copilot_out, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(COPILOT_LOG, f, indent=2)\n", "\n", "print(f\"πŸ“‹ Copilot interactions logged: {len(COPILOT_LOG)}\")\n", "print(f\"βœ… Saved copilot log to {copilot_out}\")\n", "\n", "# Print a brief preview\n", "for i, entry in enumerate(COPILOT_LOG[:3], 1):\n", " print(f\"--- Log Entry {i} ---\")\n", " print(\"Use case:\", entry[\"use_case\"])\n", " print(\"Prompt:\", entry[\"prompt\"][:160] + (\"...\" if len(entry[\"prompt\"])>160 else \"\"))\n", " print(\"Changes:\", entry[\"changes_made\"])\n", " print(\"Verified:\", entry[\"manual_verification\"])\n", " print()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "C2b5l7CZdtB2" }, "source": [ "---\n", "## βœ… Final Verification Checklist\n", "\n", "Before submission, verify all items are complete:\n", "\n", "- [ ] Metrics computed on a **holdout test set** (25% split)\n", "- [ ] Preprocessing inside a **pipeline** (no train/test leakage)\n", "- [ ] **Leakage scan** documented in Part 1\n", "- [ ] **Three churn models** trained (logit, RF, GB) with metrics\n", "- [ ] **Three regression models** trained (ridge, RF, GB) with MAE/RMSE/R2\n", "- [ ] **SHAP summary plot** (global feature importance)\n", "- [ ] **SHAP waterfall** for at least 1 customer (local explanation)\n", "- [ ] **Revenue-at-Risk** ranked call list created and saved as CSV\n", "- [ ] **Manager memo** saved as markdown file, with real metrics referenced\n", "- [ ] **Metrics JSON** saved with model names, scores, threshold rule, extension track\n", "- [ ] **Copilot log** includes at least 3 GenAI uses with clear labels (model interpretation, SHAP narrative, action recommendations, memo draft)\n", "- [ ] **Extension track** from Independent Lab documented in metrics JSON\n", "- [ ] All output files saved to working directory\n", "- [ ] Notebook runs end-to-end without errors\n", "\n", "### Files to submit:\n", "1. `Day5_Assignment5.ipynb` (this notebook)\n", "2. `day5_assignment_call_list.csv` (ranked customers)\n", "3. `day5_assignment_metrics.json` (model scores + metadata)\n", "4. `day5_assignment_manager_memo.md` (executive summary)\n", "5. `day5_assignment_copilot_log.json` (GenAI interaction log)" ] }, { "cell_type": "markdown", "source": [ "## πŸ“Š Optional extra Day 5 β€” Threshold β†’ € Impact Curves (Improved Net Impact)\n", "\n", "This cell evaluates **business impact across different churn probability thresholds**. \n", "For each threshold, it estimates:\n", "\n", "- **Saved Revenue** (incremental revenue saved by retention action)\n", "- **Retention Cost** (voucher cost for everyone we contact)\n", "- **Lost Revenue** (remaining revenue lost after intervention)\n", "- **Net Impact** (incremental saved revenue minus retention cost)\n", "\n", "It also produces plots showing how these values change as the threshold changes.\n", "\n", "---\n", "\n", "## 1) Inputs used (from earlier notebook outputs)\n", "\n", "The analysis expects these to already exist:\n", "\n", "- `call_list` dataframe with columns:\n", " - `p_churn` = predicted churn probability\n", " - `pred_monthly_charges` = predicted monthly revenue per customer\n", " - `revenue_at_risk = p_churn Γ— pred_monthly_charges`\n", "\n", "- `y_test` (optional but preferred): true churn labels (0/1)\n", "\n", "If `v_final` and `p_final` exist, the code rebuilds a clean `base_df` aligned with `y_test` to avoid sorting misalignment.\n", "\n", "---\n", "\n", "## 2) Business settings (loaded automatically)\n", "\n", "The code reads `day5_assignment_metrics.json` (if available) and extracts:\n", "\n", "- `voucher_cost` = cost per contacted customer (e.g., €20)\n", "- `capacity` = max customers we can contact\n", "- `chosen_threshold` = the threshold selected earlier (for vertical line on the plot)\n", "\n", "It also introduces a realism parameter:\n", "\n", "- `save_rate` = fraction of contacted churners who actually stay \n", " - Example: `save_rate = 1.0` means **every contacted churner is saved**\n", " - Example: `save_rate = 0.30` means **only 30%** of contacted churners are saved\n", "\n", "---\n", "\n", "## 3) Threshold sweep (0.10 β†’ 0.90)\n", "\n", "For each threshold \\(t\\):\n", "\n", "### Step A β€” Who is flagged?\n", "A customer is flagged if:\n", "\n", "\\[\n", "p_i \\ge t\n", "\\]\n", "\n", "### Step B β€” Capacity-aware contact list\n", "If too many customers are flagged and it exceeds capacity, we contact only the top `capacity` customers **by Revenue-at-Risk**:\n", "\n", "\\[\n", "\\text{revenue\\_at\\_risk}_i = p_i \\times R_i\n", "\\]\n", "\n", "This ensures we use limited outreach on highest financial upside.\n", "\n", "---\n", "\n", "## 4) Key definitions (when `y_test` exists)\n", "\n", "Let:\n", "\n", "- \\(C\\) = set of contacted customers\n", "- \\(Y_i\\in\\{0,1\\}\\) = actual churn label\n", "- \\(R_i\\) = predicted monthly revenue (`pred_monthly_charges`)\n", "- \\(s\\) = `save_rate`\n", "- \\(c\\) = `voucher_cost`\n", "\n", "### βœ… TP revenue (churners contacted)\n", "\\[\n", "TP_{rev} = \\sum_{i\\in C,\\; Y_i=1} R_i\n", "\\]\n", "\n", "### βœ… FN revenue (churners missed)\n", "\\[\n", "FN_{rev} = \\sum_{i\\notin C,\\; Y_i=1} R_i\n", "\\]\n", "\n", "### βœ… Incremental Saved Revenue\n", "Only a fraction \\(s\\) of contacted churners are actually retained:\n", "\n", "\\[\n", "\\text{Saved Revenue} = s \\cdot TP_{rev}\n", "\\]\n", "\n", "### βœ… Retention Cost\n", "We pay voucher cost for **everyone contacted** (including false positives):\n", "\n", "\\[\n", "\\text{Retention Cost} = c \\cdot |C|\n", "\\]\n", "\n", "### βœ… Lost Revenue after intervention\n", "What still gets lost even after we intervene:\n", "\n", "\\[\n", "\\text{Lost Revenue} = FN_{rev} + (1-s)\\cdot TP_{rev}\n", "\\]\n", "\n", "---\n", "\n", "## 5) βœ… Net Impact formula (the main result)\n", "\n", "The code computes **Net Impact as incremental lift**:\n", "\n", "\\[\n", "\\boxed{\n", "\\text{Net Impact} = \\text{Saved Revenue} - \\text{Retention Cost}\n", "= s\\cdot TP_{rev} - c\\cdot|C|\n", "}\n", "\\]\n", "\n", "### Why we do NOT subtract Lost Revenue again\n", "We **do not** compute:\n", "\n", "\\[\n", "\\text{Saved} - \\text{Lost} - \\text{Cost}\n", "\\]\n", "\n", "because that would **double count** losses:\n", "\n", "- FN revenue is already *not included* in saved revenue\n", "- subtracting FN again would penalize twice\n", "\n", "Instead, Net Impact is treated as the **incremental value created by intervention minus cost**.\n", "\n", "---\n", "\n", "## 6) Expected-value fallback (if `y_test` is missing)\n", "\n", "If we don’t have true labels, the code treats churn probability as expected churn:\n", "\n", "\\[\n", "TP_{rev}^{exp} = \\sum_{i\\in C} p_i \\cdot R_i\n", "\\]\n", "\\[\n", "FN_{rev}^{exp} = \\sum_{i\\notin C} p_i \\cdot R_i\n", "\\]\n", "\n", "And then applies the same business formulas using expected values.\n", "\n", "---\n", "\n", "## 7) Outputs produced\n", "\n", "### Table: `impact_df`\n", "For each threshold it stores:\n", "\n", "- `saved_revenue`\n", "- `lost_revenue`\n", "- `retention_cost`\n", "- `net_impact`\n", "- contact counts + TP/FN counts (if available)\n", "- baseline loss and breakdown columns (`tp_revenue`, `fn_revenue`, `baseline_lost_revenue`)\n", "\n", "### Graphs:\n", "1) Saved Revenue vs Lost Revenue vs Cost vs Net Impact \n", "2) Contact volume vs threshold (+ capacity line) \n", "3) TP/FN counts vs threshold (optional)\n", "\n", "### Export:\n", "The full table is saved as:\n", "\n", "- `day5_threshold_impact_table.csv`\n", "\n", "---" ], "metadata": { "id": "At7qUtm1D3uz" } }, { "cell_type": "code", "source": [ "# ================================\n", "# Day 5 β€” Threshold β†’ $ Impact Curves (IMPROVED NET IMPACT)\n", "# Saved Revenue, Retention Cost, Lost Revenue\n", "# ================================\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os, json\n", "\n", "# ---------\n", "# 0) Load business settings from JSON (if available)\n", "# ---------\n", "METRICS_PATH = \"day5_assignment_metrics.json\"\n", "\n", "if os.path.exists(METRICS_PATH):\n", " with open(METRICS_PATH, \"r\", encoding=\"utf-8\") as f:\n", " m = json.load(f)\n", " ts = m.get(\"threshold_selection\", {})\n", " voucher_cost = float(ts.get(\"fp_cost\", 20.0))\n", " capacity = int(ts.get(\"capacity_max_contacts\", 999999))\n", " chosen_threshold = float(ts.get(\"chosen_threshold\", np.nan))\n", "else:\n", " voucher_cost = float(globals().get(\"FP_COST\", 20.0))\n", " capacity = int(globals().get(\"CAPACITY\", 999999))\n", " chosen_threshold = float(globals().get(\"CHOSEN_THRESHOLD\", np.nan))\n", "\n", "# ---------\n", "# 0b) Business realism: retention success rate\n", "# save_rate = fraction of churners you contact who actually stay.\n", "# If your original assumption was \"contact always saves them\", keep 1.0.\n", "# ---------\n", "save_rate = float(globals().get(\"SAVE_RATE\", 1.0)) # you can set SAVE_RATE=0.3 earlier if you want\n", "\n", "print(\"Settings:\")\n", "print(\" voucher_cost:\", voucher_cost)\n", "print(\" capacity:\", capacity)\n", "print(\" chosen_threshold:\", chosen_threshold)\n", "print(\" save_rate:\", save_rate)\n", "\n", "# ---------\n", "# 1) Validate required data\n", "# ---------\n", "required_cols = {\"p_churn\", \"pred_monthly_charges\", \"revenue_at_risk\"}\n", "assert \"call_list\" in globals(), \"call_list not found. Run the cell that creates call_list first.\"\n", "assert required_cols.issubset(set(call_list.columns)), f\"call_list missing columns: {required_cols - set(call_list.columns)}\"\n", "\n", "has_y = \"y_test\" in globals() and y_test is not None\n", "\n", "# Rebuild a stable base_df aligned with y_test, if v_final exists (best case)\n", "if \"v_final\" in globals() and \"p_final\" in globals():\n", " base_df = pd.DataFrame({\n", " \"p_churn\": np.array(p_final),\n", " \"pred_monthly_charges\": np.array(v_final),\n", " })\n", " base_df[\"revenue_at_risk\"] = base_df[\"p_churn\"] * base_df[\"pred_monthly_charges\"]\n", "else:\n", " # fallback: use call_list as-is (may be sorted)\n", " base_df = call_list.copy()\n", " if base_df[\"revenue_at_risk\"].is_monotonic_decreasing and has_y:\n", " print(\"⚠️ WARNING: call_list looks sorted by revenue_at_risk.\")\n", " print(\" If y_test order does NOT match this order, TP/FN will be wrong.\")\n", " print(\" Best: rebuild base_df using (p_final, v_final).\")\n", "\n", "# attach y if present\n", "if has_y:\n", " y_arr = np.array(y_test).astype(int)\n", " if len(y_arr) != len(base_df):\n", " print(\"⚠️ y_test length != base_df length.\")\n", " print(\" Falling back to expected-value calculations (no TP/FN).\")\n", " has_y = False\n", "\n", "# ---------\n", "# 2) Threshold sweep: compute business metrics\n", "# Definitions (monthly):\n", "# TP_revenue = sum(monthly charges of true churners contacted)\n", "# FN_revenue = sum(monthly charges of true churners NOT contacted)\n", "#\n", "# Baseline loss (no intervention) = TP_revenue + FN_revenue\n", "# Incremental saved revenue = save_rate * TP_revenue\n", "#\n", "# Retention cost = voucher_cost * #contacts\n", "#\n", "# Net impact (incremental profit proxy) = incremental_saved_revenue - retention_cost\n", "#\n", "# Lost revenue (after intervention) = FN_revenue + (1-save_rate)*TP_revenue\n", "# ---------\n", "\n", "thresholds = np.round(np.linspace(0.10, 0.90, 17), 2)\n", "rows = []\n", "\n", "for t in thresholds:\n", " flagged = base_df[\"p_churn\"].values >= t\n", "\n", " # capacity-aware contact set\n", " if flagged.sum() > capacity:\n", " flagged_idx = np.where(flagged)[0]\n", " top_idx = flagged_idx[np.argsort(base_df.loc[flagged_idx, \"revenue_at_risk\"].values)[::-1][:capacity]]\n", " contact = np.zeros(len(base_df), dtype=bool)\n", " contact[top_idx] = True\n", " else:\n", " contact = flagged.copy()\n", "\n", " n_contact = int(contact.sum())\n", " retention_cost = n_contact * voucher_cost\n", "\n", " if has_y:\n", " tp_mask = contact & (y_arr == 1)\n", " fn_mask = (~contact) & (y_arr == 1)\n", "\n", " TP_revenue = float(base_df.loc[tp_mask, \"pred_monthly_charges\"].sum())\n", " FN_revenue = float(base_df.loc[fn_mask, \"pred_monthly_charges\"].sum())\n", "\n", " incremental_saved_revenue = save_rate * TP_revenue\n", " baseline_lost_revenue = TP_revenue + FN_revenue\n", " post_lost_revenue = FN_revenue + (1 - save_rate) * TP_revenue\n", "\n", " net_impact = incremental_saved_revenue - retention_cost\n", "\n", " tp = int(tp_mask.sum())\n", " fn = int(fn_mask.sum())\n", "\n", " else:\n", " # Expected-value fallback (no y_test): treat p_churn as probability\n", " # Expected contacted-churn revenue = sum(contact * p * value)\n", " # Expected missed-churn revenue = sum((~contact) * p * value)\n", " TP_revenue = float((contact * base_df[\"p_churn\"].values * base_df[\"pred_monthly_charges\"].values).sum())\n", " FN_revenue = float(((~contact) * base_df[\"p_churn\"].values * base_df[\"pred_monthly_charges\"].values).sum())\n", "\n", " incremental_saved_revenue = save_rate * TP_revenue\n", " baseline_lost_revenue = TP_revenue + FN_revenue\n", " post_lost_revenue = FN_revenue + (1 - save_rate) * TP_revenue\n", "\n", " net_impact = incremental_saved_revenue - retention_cost\n", "\n", " tp = np.nan\n", " fn = np.nan\n", "\n", " rows.append({\n", " \"threshold\": float(t),\n", " \"n_contact\": n_contact,\n", " \"retention_cost\": retention_cost,\n", "\n", " # Core requested outputs\n", " \"saved_revenue\": incremental_saved_revenue, # incremental saved due to intervention\n", " \"lost_revenue\": post_lost_revenue, # remaining churn loss after intervention\n", "\n", " # Helpful extra context\n", " \"baseline_lost_revenue\": baseline_lost_revenue,\n", " \"tp_revenue\": TP_revenue,\n", " \"fn_revenue\": FN_revenue,\n", "\n", " \"net_impact\": net_impact,\n", " \"tp\": tp,\n", " \"fn\": fn,\n", " \"capacity_binding\": bool((base_df[\"p_churn\"].values >= t).sum() > capacity)\n", " })\n", "\n", "impact_df = pd.DataFrame(rows)\n", "\n", "print(\"\\nImpact table (first rows):\")\n", "display(impact_df.head(10))\n", "\n", "# ---------\n", "# 3) Mark the chosen threshold row (if available)\n", "# ---------\n", "if not np.isnan(chosen_threshold):\n", " chosen_row = impact_df.iloc[(impact_df[\"threshold\"] - chosen_threshold).abs().argsort()[:1]]\n", " print(\"\\nClosest row to chosen threshold:\")\n", " display(chosen_row)\n", "\n", "# ---------\n", "# 4) Graphs\n", "# ---------\n", "\n", "# (A) Saved vs Lost vs Cost vs Net\n", "plt.figure()\n", "plt.plot(impact_df[\"threshold\"], impact_df[\"saved_revenue\"], marker=\"o\", label=\"Saved Revenue (incremental)\")\n", "plt.plot(impact_df[\"threshold\"], impact_df[\"lost_revenue\"], marker=\"o\", label=\"Lost Revenue (after intervention)\")\n", "plt.plot(impact_df[\"threshold\"], impact_df[\"retention_cost\"], marker=\"o\", label=\"Retention Cost\")\n", "plt.plot(impact_df[\"threshold\"], impact_df[\"net_impact\"], marker=\"o\", label=\"Net Impact (Saved - Cost)\")\n", "\n", "if not np.isnan(chosen_threshold):\n", " plt.axvline(chosen_threshold, linestyle=\"--\", label=f\"Chosen threshold ~ {chosen_threshold:.2f}\")\n", "\n", "plt.xlabel(\"Threshold\")\n", "plt.ylabel(\"€ (monthly)\")\n", "plt.title(\"Business Impact vs Threshold (capacity-aware)\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# (B) Contacts vs Threshold\n", "plt.figure()\n", "plt.plot(impact_df[\"threshold\"], impact_df[\"n_contact\"], marker=\"o\", label=\"# Contacts\")\n", "plt.axhline(capacity, linestyle=\"--\", label=f\"Capacity = {capacity}\")\n", "\n", "if not np.isnan(chosen_threshold):\n", " plt.axvline(chosen_threshold, linestyle=\"--\", label=f\"Chosen threshold ~ {chosen_threshold:.2f}\")\n", "\n", "plt.xlabel(\"Threshold\")\n", "plt.ylabel(\"Customers contacted\")\n", "plt.title(\"Contact Volume vs Threshold\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# (C) Optional: show TP/FN counts if y_test exists\n", "if has_y:\n", " plt.figure()\n", " plt.plot(impact_df[\"threshold\"], impact_df[\"tp\"], marker=\"o\", label=\"TP (churners contacted)\")\n", " plt.plot(impact_df[\"threshold\"], impact_df[\"fn\"], marker=\"o\", label=\"FN (churners missed)\")\n", "\n", " if not np.isnan(chosen_threshold):\n", " plt.axvline(chosen_threshold, linestyle=\"--\", label=f\"Chosen threshold ~ {chosen_threshold:.2f}\")\n", "\n", " plt.xlabel(\"Threshold\")\n", " plt.ylabel(\"Count\")\n", " plt.title(\"TP/FN vs Threshold\")\n", " plt.legend()\n", " plt.show()\n", "\n", "# ---------\n", "# 5) Export\n", "# ---------\n", "impact_out = \"day5_threshold_impact_table.csv\"\n", "impact_df.to_csv(impact_out, index=False)\n", "print(f\"\\nβœ… Saved impact table to: {impact_out}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "QvaAdTRbC2k6", "outputId": "30e507d6-25a5-48d5-cc0c-0ceb38084b6c" }, "execution_count": 34, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Settings:\n", " voucher_cost: 20.0\n", " capacity: 264\n", " chosen_threshold: 0.6\n", " save_rate: 1.0\n", "\n", "Impact table (first rows):\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " threshold n_contact retention_cost saved_revenue lost_revenue \\\n", "0 0.10 264 5280.0 15431.489369 18854.232778 \n", "1 0.15 264 5280.0 15431.489369 18854.232778 \n", "2 0.20 264 5280.0 15431.489369 18854.232778 \n", "3 0.25 264 5280.0 15431.489369 18854.232778 \n", "4 0.30 264 5280.0 15431.489369 18854.232778 \n", "5 0.35 264 5280.0 15431.489369 18854.232778 \n", "6 0.40 264 5280.0 15431.489369 18854.232778 \n", "7 0.45 264 5280.0 15506.393863 18779.328285 \n", "8 0.50 264 5280.0 15826.316377 18459.405771 \n", "9 0.55 264 5280.0 15168.223887 19117.498260 \n", "\n", " baseline_lost_revenue tp_revenue fn_revenue net_impact tp fn \\\n", "0 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "1 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "2 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "3 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "4 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "5 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "6 34285.722148 15431.489369 18854.232778 10151.489369 179 288 \n", "7 34285.722148 15506.393863 18779.328285 10226.393863 180 287 \n", "8 34285.722148 15826.316377 18459.405771 10546.316377 185 282 \n", "9 34285.722148 15168.223887 19117.498260 9888.223887 183 284 \n", "\n", " capacity_binding \n", "0 True \n", "1 True \n", "2 True \n", "3 True \n", "4 True \n", "5 True \n", "6 True \n", "7 True \n", "8 True \n", "9 True " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
thresholdn_contactretention_costsaved_revenuelost_revenuebaseline_lost_revenuetp_revenuefn_revenuenet_impacttpfncapacity_binding
00.102645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
10.152645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
20.202645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
30.252645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
40.302645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
50.352645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
60.402645280.015431.48936918854.23277834285.72214815431.48936918854.23277810151.489369179288True
70.452645280.015506.39386318779.32828534285.72214815506.39386318779.32828510226.393863180287True
80.502645280.015826.31637718459.40577134285.72214815826.31637718459.40577110546.316377185282True
90.552645280.015168.22388719117.49826034285.72214815168.22388719117.4982609888.223887183284True
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"print(f\\\"\\\\n\\u2705 Saved impact table to: {impact_out}\\\")\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"threshold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1513825177048746,\n \"min\": 0.1,\n \"max\": 0.55,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.5,\n 0.15,\n 0.35\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n_contact\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 264,\n \"max\": 264,\n \"num_unique_values\": 1,\n \"samples\": [\n 264\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"retention_cost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 5280.0,\n \"max\": 5280.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 5280.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"saved_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 158.65579136858886,\n \"min\": 15168.22388719305,\n \"max\": 15826.316376531395,\n \"num_unique_values\": 4,\n \"samples\": [\n 15506.393862832121\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lost_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 158.65579136858787,\n \"min\": 18459.405771049886,\n \"max\": 19117.498260388227,\n \"num_unique_values\": 4,\n \"samples\": [\n 18779.328284749157\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"baseline_lost_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.4253192047278085e-12,\n \"min\": 34285.72214758128,\n \"max\": 34285.722147581284,\n \"num_unique_values\": 2,\n \"samples\": [\n 34285.722147581284\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tp_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 158.65579136858886,\n \"min\": 15168.22388719305,\n \"max\": 15826.316376531395,\n \"num_unique_values\": 4,\n \"samples\": [\n 15506.393862832121\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fn_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 158.65579136858787,\n \"min\": 18459.405771049886,\n \"max\": 19117.498260388227,\n \"num_unique_values\": 4,\n \"samples\": [\n 18779.328284749157\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"net_impact\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 158.65579136858886,\n \"min\": 9888.22388719305,\n \"max\": 10546.316376531395,\n \"num_unique_values\": 4,\n \"samples\": [\n 10226.393862832121\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 179,\n \"max\": 185,\n \"num_unique_values\": 4,\n \"samples\": [\n 180\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 282,\n \"max\": 288,\n \"num_unique_values\": 4,\n \"samples\": [\n 287\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capacity_binding\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Closest row to chosen threshold:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " threshold n_contact retention_cost saved_revenue lost_revenue \\\n", "10 0.6 241 4820.0 13805.586624 20480.135524 \n", "\n", " baseline_lost_revenue tp_revenue fn_revenue net_impact tp fn \\\n", "10 34285.722148 13805.586624 20480.135524 8985.586624 176 291 \n", "\n", " capacity_binding \n", "10 False " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
thresholdn_contactretention_costsaved_revenuelost_revenuebaseline_lost_revenuetp_revenuefn_revenuenet_impacttpfncapacity_binding
100.62414820.013805.58662420480.13552434285.72214813805.58662420480.1355248985.586624176291False
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "chosen_row", "summary": "{\n \"name\": \"chosen_row\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"threshold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.6,\n \"max\": 0.6,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n_contact\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 241,\n \"max\": 241,\n \"num_unique_values\": 1,\n \"samples\": [\n 241\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"retention_cost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 4820.0,\n \"max\": 4820.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 4820.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"saved_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 13805.586624022155,\n \"max\": 13805.586624022155,\n \"num_unique_values\": 1,\n \"samples\": [\n 13805.586624022155\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lost_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 20480.135523559125,\n \"max\": 20480.135523559125,\n \"num_unique_values\": 1,\n \"samples\": [\n 20480.135523559125\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"baseline_lost_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 34285.722147581284,\n \"max\": 34285.722147581284,\n \"num_unique_values\": 1,\n \"samples\": [\n 34285.722147581284\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tp_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 13805.586624022155,\n \"max\": 13805.586624022155,\n \"num_unique_values\": 1,\n \"samples\": [\n 13805.586624022155\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fn_revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 20480.135523559125,\n \"max\": 20480.135523559125,\n \"num_unique_values\": 1,\n \"samples\": [\n 20480.135523559125\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"net_impact\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 8985.586624022155,\n \"max\": 8985.586624022155,\n \"num_unique_values\": 1,\n \"samples\": [\n 8985.586624022155\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 176,\n \"max\": 176,\n \"num_unique_values\": 1,\n \"samples\": [\n 176\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 291,\n \"max\": 291,\n \"num_unique_values\": 1,\n \"samples\": [\n 291\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capacity_binding\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "βœ… Saved impact table to: day5_threshold_impact_table.csv\n" ] } ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }