diff --git a/model/npm/data/metrics.json b/model/npm/data/metrics.json index d332ec6..b83e992 100644 --- a/model/npm/data/metrics.json +++ b/model/npm/data/metrics.json @@ -56,11 +56,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -99,11 +99,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -228,11 +228,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -357,11 +357,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -443,11 +443,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -529,11 +529,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -658,11 +658,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -701,11 +701,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - 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"coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -916,11 +916,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -1002,11 +1002,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -1045,11 +1045,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - 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"coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -1346,11 +1346,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -1475,11 +1475,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -1518,11 +1518,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - 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"days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7323,11 +7323,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7452,11 +7452,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7495,11 +7495,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7538,11 +7538,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7624,11 +7624,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - 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"coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7882,11 +7882,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -7925,11 +7925,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -8054,11 +8054,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - 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"coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -8398,11 +8398,11 @@ "contributor_growth": 0, "contributor_growth_rate": 0, "active_branches": 0, - "days_since_last_commit": null, + "days_since_last_commit": 534, "casual_regular_contributors_rate": 0.0, "returning_contributors": 0, "commits_over_periods_rate": 0.0, - "coefficient_of_variation": NaN, + "coefficient_of_variation": 4.123105625617661, "file_types_code": 0, "file_types_binary": 0, "file_types_other": 0, @@ -8603,8 +8603,8 @@ }, "metadata": { "version": "0.1.0", - "started_at": "2026-07-16T17:16:58.543788+00:00", - "finished_at": "2026-07-16T17:19:36.417410+00:00", + "started_at": "2026-09-22T18:03:50.202702+00:00", + "finished_at": "2026-09-22T18:06:28.868475+00:00", "configuration": { "from_date": "2025-01-01T00:00:00", "to_date": "2026-06-19T00:00:00", diff --git a/model/npm/notebooks/npm_health_model.ipynb b/model/npm/notebooks/npm_health_model.ipynb new file mode 100644 index 0000000..9e72706 --- /dev/null +++ b/model/npm/notebooks/npm_health_model.ipynb @@ -0,0 +1,1329 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 96, + "id": "60e7fe1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (3.0.5)\n", + "Requirement already satisfied: seaborn in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (0.13.2)\n", + "Requirement already satisfied: matplotlib in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (3.11.1)\n", + "Requirement already satisfied: numpy in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (2.5.1)\n", + "Requirement already satisfied: statsmodels in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (0.14.6)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (1.3.3)\n", + "Requirement already satisfied: cycler>=0.10 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.28.2 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (4.63.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (1.5.0)\n", + "Requirement already satisfied: packaging>=20.0 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (26.2)\n", + "Requirement already satisfied: pillow>=9 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (12.3.0)\n", + "Requirement already satisfied: pyparsing>=3 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (3.3.2)\n", + "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from statsmodels) (1.18.0)\n", + "Requirement already satisfied: patsy>=0.5.6 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from statsmodels) (1.0.2)\n", + "Requirement already satisfied: six>=1.5 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n" + ] + } + ], + "source": [ + "!pip install pandas seaborn matplotlib numpy statsmodels" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "e35933aa", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.metrics import (ConfusionMatrixDisplay, classification_report,\n", + " confusion_matrix, roc_auc_score)\n", + "from sklearn.model_selection import StratifiedGroupKFold, cross_val_predict\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "HEALTHY, UNHEALTHY = \"#4477AA\", \"#EE6677\" # colorblind-safe pair, fixed per class\n", + "RANDOM_STATE = 42 # seed for a random number generator" + ] + }, + { + "cell_type": "markdown", + "id": "57f9e4be", + "metadata": {}, + "source": [ + "## 1. Load data\n", + "\n", + "Load the data from GrimoireLab and build a dataframe with the git reposites that were classified" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "c84bdea9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Packages with metrics: 200\n", + "Packages classified as healthy/unhealthy by the expert: 166\n", + "\n", + "Result\n", + "Healthy 89\n", + "Unhealthy 77\n", + "Name: count, dtype: int64\n", + "\n", + "Metrics collected from 2025-01-01T00:00:00 to 2026-06-19T00:00:00\n" + ] + } + ], + "source": [ + "\n", + "with open('../data/metrics.json', 'r') as f:\n", + " data = json.load(f)\n", + "\n", + "records = []\n", + "for spdx_id, info in data['packages'].items():\n", + " row = dict(info[\"metrics\"])\n", + " row[\"SPDXID\"] = spdx_id\n", + " row[\"repository\"] = info[\"repository\"]\n", + " records.append(row)\n", + "df_metrics = pd.DataFrame(records)\n", + "\n", + "df_expert = pd.read_csv(\"../data/expert-classification.csv\")\n", + "df_expert = df_expert[df_expert[\"Result\"].isin([\"Healthy\", \"Unhealthy\"])]\n", + "\n", + "df = df_expert[[\"SPDXID\", \"name\", \"Result\"]].merge(df_metrics, on=\"SPDXID\", how=\"inner\")\n", + "\n", + "print(f\"Packages with metrics: {len(df_metrics)}\")\n", + "print(f\"Packages classified as healthy/unhealthy by the expert: {len(df_expert)}\\n\")\n", + "print(df[\"Result\"].value_counts())\n", + "print(\"\\nMetrics collected from\", data[\"metadata\"][\"configuration\"][\"from_date\"],\n", + " \"to\", data[\"metadata\"][\"configuration\"][\"to_date\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "be46cb90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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found_file_adopters166.00.0000000.0000000.00.0000000.0000000.0000000.000000
\n", + "
" + ], + "text/plain": [ + " count mean std min \\\n", + "total_commits 166.0 26.885542 84.851843 0.0 \n", + "total_contributors 166.0 3.421687 7.451045 0.0 \n", + "total_organizations 166.0 1.849398 3.038376 0.0 \n", + "pony_factor 166.0 0.759036 0.928868 0.0 \n", + "elephant_factor 166.0 0.632530 0.605952 0.0 \n", + "recent_organizations 166.0 1.572289 2.663596 0.0 \n", + "recent_contributors 166.0 2.596386 5.328004 0.0 \n", + "recent_commits 166.0 21.198795 68.920791 0.0 \n", + "contributor_growth 166.0 0.343373 2.562465 -9.0 \n", + "contributor_growth_rate 166.0 0.300513 1.122160 -1.0 \n", + "active_branches 166.0 1.945783 3.668467 0.0 \n", + "days_since_last_commit 166.0 287.777108 232.727110 0.0 \n", + "casual_regular_contributors_rate 166.0 0.309790 0.563463 0.0 \n", + "returning_contributors 166.0 0.614458 1.296441 0.0 \n", + "commits_over_periods_rate 166.0 0.421376 0.431449 0.0 \n", + "coefficient_of_variation 166.0 3.162953 1.162320 0.4 \n", + "file_types_code 166.0 36.102410 155.757567 0.0 \n", + "file_types_binary 166.0 0.006024 0.077615 0.0 \n", + "file_types_other 166.0 105.783133 617.275471 0.0 \n", + "commit_size_added_lines 166.0 9605.180723 47047.061064 0.0 \n", + "commit_size_removed_lines 166.0 5522.825301 25567.265920 0.0 \n", + "message_size_total 166.0 8787.036145 33963.075632 0.0 \n", + "message_size_mean 166.0 180.374189 650.807182 0.0 \n", + "message_size_median 166.0 115.765060 487.988735 0.0 \n", + "developer_categories_core 166.0 1.024096 2.077154 0.0 \n", + "developer_categories_regular 166.0 1.253012 3.763761 0.0 \n", + "developer_categories_casual 166.0 1.144578 3.334421 0.0 \n", + "commits_per_week 166.0 0.352432 1.112290 0.0 \n", + "commits_per_month 166.0 1.510424 4.766957 0.0 \n", + "commits_per_year 166.0 18.376822 57.997983 0.0 \n", + "found_file_license 166.0 0.771084 0.421406 0.0 \n", + "found_file_adopters 166.0 0.000000 0.000000 0.0 \n", + "\n", + " 25% 50% 75% \\\n", + "total_commits 0.000000 2.000000 20.750000 \n", + "total_contributors 0.000000 1.000000 3.750000 \n", + "total_organizations 0.000000 1.000000 2.750000 \n", + "pony_factor 0.000000 1.000000 1.000000 \n", + "elephant_factor 0.000000 1.000000 1.000000 \n", + "recent_organizations 0.000000 1.000000 2.000000 \n", + "recent_contributors 0.000000 1.000000 3.000000 \n", + "recent_commits 0.000000 1.000000 14.500000 \n", + "contributor_growth 0.000000 0.000000 1.000000 \n", + "contributor_growth_rate 0.000000 0.000000 0.333333 \n", + "active_branches 0.000000 1.000000 2.000000 \n", + "days_since_last_commit 37.750000 264.500000 534.000000 \n", + "casual_regular_contributors_rate 0.000000 0.000000 0.500000 \n", + "returning_contributors 0.000000 0.000000 1.000000 \n", + "commits_over_periods_rate 0.000000 0.354167 0.880714 \n", + "coefficient_of_variation 2.131605 4.123106 4.123106 \n", + "file_types_code 0.000000 0.000000 5.750000 \n", + "file_types_binary 0.000000 0.000000 0.000000 \n", + "file_types_other 0.000000 2.500000 49.000000 \n", + "commit_size_added_lines 0.000000 16.500000 1045.000000 \n", + "commit_size_removed_lines 0.000000 6.500000 578.000000 \n", + "message_size_total 0.000000 65.000000 2239.000000 \n", + "message_size_mean 0.000000 30.090278 78.088750 \n", + "message_size_median 0.000000 23.000000 50.250000 \n", + "developer_categories_core 0.000000 1.000000 1.000000 \n", + "developer_categories_regular 0.000000 0.000000 1.000000 \n", + "developer_categories_casual 0.000000 0.000000 1.000000 \n", + "commits_per_week 0.000000 0.026217 0.272004 \n", + "commits_per_month 0.000000 0.112360 1.165730 \n", + "commits_per_year 0.000000 1.367041 14.183052 \n", + "found_file_license 1.000000 1.000000 1.000000 \n", + "found_file_adopters 0.000000 0.000000 0.000000 \n", + "\n", + " max \n", + "total_commits 930.000000 \n", + "total_contributors 57.000000 \n", + "total_organizations 24.000000 \n", + "pony_factor 8.000000 \n", + "elephant_factor 2.000000 \n", + "recent_organizations 23.000000 \n", + "recent_contributors 46.000000 \n", + "recent_commits 725.000000 \n", + "contributor_growth 16.000000 \n", + "contributor_growth_rate 8.000000 \n", + "active_branches 25.000000 \n", + "days_since_last_commit 534.000000 \n", + "casual_regular_contributors_rate 4.000000 \n", + "returning_contributors 11.000000 \n", + "commits_over_periods_rate 1.000000 \n", + "coefficient_of_variation 4.123106 \n", + "file_types_code 1242.000000 \n", + "file_types_binary 1.000000 \n", + "file_types_other 7399.000000 \n", + "commit_size_added_lines 407132.000000 \n", + "commit_size_removed_lines 208650.000000 \n", + "message_size_total 273758.000000 \n", + "message_size_mean 4546.360000 \n", + "message_size_median 4323.000000 \n", + "developer_categories_core 19.000000 \n", + "developer_categories_regular 38.000000 \n", + "developer_categories_casual 34.000000 \n", + "commits_per_week 12.191011 \n", + "commits_per_month 52.247191 \n", + "commits_per_year 635.674157 \n", + "found_file_license 1.000000 \n", + "found_file_adopters 0.000000 " + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "META_COLS = [\"SPDXID\", \"name\", \"Result\", \"repository\"]\n", + "metric_cols = [c for c in df.columns if c not in META_COLS]\n", + "\n", + "\n", + "# converts True/False to 1.0, we have to do this before the cols are removed\n", + "# and we use it to analyze the correlations\n", + "y = (df[\"Result\"] == \"Unhealthy\").astype(int)\n", + "\n", + "df[metric_cols].describe().T" + ] + }, + { + "cell_type": "markdown", + "id": "028ae2c7", + "metadata": {}, + "source": [ + "## 2. Redundant metrics\n", + "\n", + "We remove metrics in three passes:\n", + "\n", + "1. No information: constant (or almost constant) across packages.\n", + "2. Derived metrics: values that are a fixed transformation of another metric.\n", + "3. Highly correlated metrics (|Pearson| > 0.8 on the log scale). Within each correlated group we\n", + " keep the metric that separates Healthy from Unhealthy best on its own (univariate ROC AUC), so\n", + " the choice of the \"winner\" does not depend on column order." + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "a03e4bb9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constant or almost constant:\n", + " unique_values non_zero_rows\n", + "file_types_binary 2 1\n", + "found_file_adopters 1 0\n" + ] + } + ], + "source": [ + "# 2.1 Constant / near-constant metrics\n", + "# These indicators are artifacts, not metrics\n", + "\n", + "n_unique = df[metric_cols].nunique()\n", + "n_nonzero = (df[metric_cols] != 0).sum()\n", + "uninformative = sorted(set(n_unique[n_unique <= 1].index) | set(n_nonzero[n_nonzero <= 1].index))\n", + "print(\"Constant or almost constant:\")\n", + "print(pd.DataFrame({\"unique_values\": n_unique[uninformative], \"non_zero_rows\": n_nonzero[uninformative]}))" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "77bdbe04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "corr(total_commits, commits_per_year) = 1.0\n", + "corr(commits_per_week, commits_per_month) = 1.0\n", + "corr(message_size_mean, message_size_median) = 0.8362\n", + "corr(commit_size_added_lines, commit_size_removed_lines) = 0.905\n" + ] + } + ], + "source": [ + "# 2.2 Derived metrics\n", + "# values that are a fixed transformation of another metric (redundant), this makes the heatmap below simpler\n", + "\n", + "print(\"corr(total_commits, commits_per_year) =\", round(df[\"total_commits\"].corr(df[\"commits_per_year\"]), 4))\n", + "print(\"corr(commits_per_week, commits_per_month) =\", round(df[\"commits_per_week\"].corr(df[\"commits_per_month\"]), 4))\n", + "print(\"corr(message_size_mean, message_size_median) =\", round(df[\"message_size_mean\"].corr(df[\"message_size_median\"]), 4))\n", + "print(\"corr(commit_size_added_lines, commit_size_removed_lines) =\",\n", + " round(df[\"commit_size_added_lines\"].corr(df[\"commit_size_removed_lines\"]), 4))\n", + "\n", + "derived = [\n", + " \"commits_per_week\", \"commits_per_month\", \"commits_per_year\", # total_commits divided by the window\n", + " \"message_size_median\", # ~ message_size_mean\n", + " \"commit_size_removed_lines\", # ~ commit_size_added_lines\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "110ea508", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2.3 Correlated metrics\n", + "\n", + "def signed_log1p(x):\n", + " \"\"\"A log transformation to prepare data for the pearson correlation.\n", + " \"\"\" \n", + " return np.sign(x) * np.log1p(np.abs(x))\n", + "\n", + "candidates = [c for c in metric_cols if c not in uninformative + derived]\n", + "X_log = signed_log1p(df[candidates])\n", + "\n", + "corr = X_log.corr(method=\"pearson\")\n", + "plt.figure(figsize=(14, 12))\n", + "sns.heatmap(corr, cmap=\"RdBu_r\", center=0, vmin=-1, vmax=1, square=True, linewidths=0.5,\n", + " cbar_kws={\"shrink\": 0.75})\n", + "plt.title(\"Pearson correlation (signed log scale)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "87c57f9d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped (metric, kept twin, correlation):\n", + " ('file_types_other', 'days_since_last_commit', np.float64(-0.82))\n", + " ('total_commits', 'days_since_last_commit', np.float64(-0.83))\n", + " ('message_size_total', 'days_since_last_commit', np.float64(-0.86))\n", + " ('total_contributors', 'days_since_last_commit', np.float64(-0.82))\n", + " ('total_organizations', 'days_since_last_commit', np.float64(-0.81))\n", + " ('pony_factor', 'developer_categories_core', np.float64(0.91))\n", + " ('message_size_mean', 'days_since_last_commit', np.float64(-0.8))\n", + " ('recent_contributors', 'days_since_last_commit', np.float64(-0.84))\n", + " ('recent_commits', 'days_since_last_commit', np.float64(-0.85))\n", + " ('recent_organizations', 'days_since_last_commit', np.float64(-0.82))\n", + " ('elephant_factor', 'commit_size_added_lines', np.float64(0.8))\n", + " ('coefficient_of_variation', 'commit_size_added_lines', np.float64(-0.82))\n", + " ('casual_regular_contributors_rate', 'developer_categories_casual', np.float64(0.84))\n", + " ('contributor_growth', 'contributor_growth_rate', np.float64(0.86))\n", + "\n", + "Selected 11 out of 32 metrics:\n" + ] + }, + { + "data": { + "text/html": [ + "
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univariate_auc
days_since_last_commit0.98
commit_size_added_lines0.03
developer_categories_core0.04
commits_over_periods_rate0.07
active_branches0.08
file_types_code0.13
developer_categories_casual0.21
returning_contributors0.21
developer_categories_regular0.23
contributor_growth_rate0.43
found_file_license0.51
\n", + "
" + ], + "text/plain": [ + " univariate_auc\n", + "days_since_last_commit 0.98\n", + "commit_size_added_lines 0.03\n", + "developer_categories_core 0.04\n", + "commits_over_periods_rate 0.07\n", + "active_branches 0.08\n", + "file_types_code 0.13\n", + "developer_categories_casual 0.21\n", + "returning_contributors 0.21\n", + "developer_categories_regular 0.23\n", + "contributor_growth_rate 0.43\n", + "found_file_license 0.51" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "THRESHOLD = 0.8\n", + "\n", + "# How to read the value: AUC is the probability that a randomly chosen Unhealthy \n", + "# package has a higher value of the metric than a randomly chosen Healthy one.\n", + "# Good when it is close to 1 (likely Unhealthy) or 0 (likely Healthy)\n", + "univariate_auc = pd.Series({c: roc_auc_score(y, df[c]) for c in candidates})\n", + "strength = (univariate_auc - 0.5).abs().sort_values(ascending=False)\n", + "\n", + "selected, dropped = [], []\n", + "for metric in strength.index:\n", + " twin = next((k for k in selected if abs(corr.loc[metric, k]) >= THRESHOLD), None)\n", + " if twin is None:\n", + " selected.append(metric)\n", + " else:\n", + " dropped.append((metric, twin, round(corr.loc[metric, twin], 2)))\n", + "\n", + "print(\"Dropped (metric, kept twin, correlation):\")\n", + "for row in dropped:\n", + " print(\" \", row)\n", + "print(f\"\\nSelected {len(selected)} out of {len(metric_cols)} metrics:\")\n", + "pd.DataFrame({\"univariate_auc\": univariate_auc[selected].round(2)})" + ] + }, + { + "cell_type": "markdown", + "id": "98ca9148", + "metadata": {}, + "source": [ + "## 3. Let's build the model and see how it goes" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "4f6d7692", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "166 packages, 11 features, 164 distinct repositories\n" + ] + } + ], + "source": [ + "X = X_log[selected]\n", + "groups = df[\"repository\"] # we don't have the values in X_log\n", + "\n", + "def make_model(C=1.0):\n", + " return make_pipeline(\n", + " StandardScaler(),\n", + " LogisticRegression(C=C, max_iter=1000),\n", + " )\n", + "\n", + "\n", + "# Sometimes we have the same repository twice, this is why we need grouping.\n", + "# The 10 k-fold create sets of 10 items, train with 9 and test with 1,\n", + "# it does it a way (stratified) that the sets are balanced.\n", + "cv = StratifiedGroupKFold(n_splits=10, shuffle=True, random_state=RANDOM_STATE)\n", + "print(f\"{X.shape[0]} packages, {X.shape[1]} features, {groups.nunique()} distinct repositories\")" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "84fc6661", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 95.2% ROC AUC: 0.971\n", + "\n", + " precision recall f1-score support\n", + "\n", + " Healthy 0.95 0.97 0.96 89\n", + " Unhealthy 0.96 0.94 0.95 77\n", + "\n", + " accuracy 0.95 166\n", + " macro avg 0.95 0.95 0.95 166\n", + "weighted avg 0.95 0.95 0.95 166\n", + "\n" + ] + } + ], + "source": [ + "score_cv = cross_val_predict(make_model(), X, y, cv=cv, groups=groups, method=\"predict_proba\")[:, 1]\n", + "pred_cv = (score_cv >= 0.5).astype(int)\n", + "\n", + "cm = confusion_matrix(y, pred_cv)\n", + "ConfusionMatrixDisplay(cm, display_labels=[\"Healthy\", \"Unhealthy\"]).plot(cmap=\"Blues\", colorbar=False)\n", + "plt.title(\"Cross-validated confusion matrix (threshold 0.5)\")\n", + "plt.show()\n", + "\n", + "print(f\"Accuracy: {(pred_cv == y).mean():.1%} ROC AUC: {roc_auc_score(y, score_cv):.3f}\\n\")\n", + "print(classification_report(y, pred_cv, target_names=[\"Healthy\", \"Unhealthy\"]))" + ] + }, + { + "cell_type": "markdown", + "id": "8be0aebe", + "metadata": {}, + "source": [ + "### When did the model failed?" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "d4ea3aa5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameResultrepositoryscorepredictedtotal_commitsrecent_contributorsdays_since_last_commit
10tiny-relative-dateHealthyhttps://github.com/wildlyinaccurate/relative-d...0.503Unhealthy60366
15call-boundHealthyhttps://github.com/ljharb/call-bound.git0.609Unhealthy40472
26cross-envUnhealthyhttps://github.com/kentcdodds/cross-env.git0.012Healthy163214
42estree-walkerUnhealthyhttps://github.com/Rich-Harris/estree-walker.git0.074Healthy11284
50xmlbuilderUnhealthyhttps://github.com/oozcitak/xmlbuilder-js.git0.237Healthy32199
91@jridgewell/sourcemap-codecUnhealthyhttps://github.com/jridgewell/sourcemaps.git0.007Healthy116325
133@fastify/busboyHealthyhttps://github.com/fastify/busboy.git0.973Unhealthy00534
150http-parser-jsUnhealthyhttps://github.com/creationix/http-parser-js.git0.146Healthy40436
\n", + "
" + ], + "text/plain": [ + " name Result \\\n", + "10 tiny-relative-date Healthy \n", + "15 call-bound Healthy \n", + "26 cross-env Unhealthy \n", + "42 estree-walker Unhealthy \n", + "50 xmlbuilder Unhealthy \n", + "91 @jridgewell/sourcemap-codec Unhealthy \n", + "133 @fastify/busboy Healthy \n", + "150 http-parser-js Unhealthy \n", + "\n", + " repository score predicted \\\n", + "10 https://github.com/wildlyinaccurate/relative-d... 0.503 Unhealthy \n", + "15 https://github.com/ljharb/call-bound.git 0.609 Unhealthy \n", + "26 https://github.com/kentcdodds/cross-env.git 0.012 Healthy \n", + "42 https://github.com/Rich-Harris/estree-walker.git 0.074 Healthy \n", + "50 https://github.com/oozcitak/xmlbuilder-js.git 0.237 Healthy \n", + "91 https://github.com/jridgewell/sourcemaps.git 0.007 Healthy \n", + "133 https://github.com/fastify/busboy.git 0.973 Unhealthy \n", + "150 https://github.com/creationix/http-parser-js.git 0.146 Healthy \n", + "\n", + " total_commits recent_contributors days_since_last_commit \n", + "10 6 0 366 \n", + "15 4 0 472 \n", + "26 16 3 214 \n", + "42 1 1 284 \n", + "50 3 2 199 \n", + "91 116 3 25 \n", + "133 0 0 534 \n", + "150 4 0 436 " + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scores = df[[\"name\", \"Result\", \"repository\"]].copy()\n", + "df_scores[\"score\"] = score_cv.round(3)\n", + "df_scores[\"predicted\"] = np.where(pred_cv == 1, \"Unhealthy\", \"Healthy\")\n", + "\n", + "misclassified = df_scores[df_scores[\"Result\"] != df_scores[\"predicted\"]]\n", + "misclassified.join(df[[\"total_commits\", \"recent_contributors\", \"days_since_last_commit\"]])" + ] + }, + { + "cell_type": "markdown", + "id": "9ae9fff2", + "metadata": {}, + "source": [ + "### The final model\n", + "\n", + "The model used to score new packages is fitted on all labelled packages. The weights are on\n", + "standardised log features, so they are comparable: negative pushes towards Healthy, positive\n", + "towards Unhealthy." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "9e77137a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8ePAAc+fOLenwPrkDBw7g119/RUhICBITE3HhwgX06dMH1tbWGDp0aEmHR/9vwoQJ8Pb2RmxsLKKiorBq1SqsXr0aI0eOzPVqWSICOBWKiIg+if3792PlypXw8/ODXC5HvXr1MG3atFwfFfsSJCYmYtGiRTh06BDCwsIglUrRvn17/Pzzz7C2ti7p8Oj/PXz4EHPmzIGvry9iY2NhZ2eHQYMGYdKkSbk+9khETCyIiIiIiOgDYLpNREREREQqY2JBREREREQq0yjpAIgob1lZWYiMjISBgQEkEklJh0NERERfIEEQkJCQAAsLi0LXFjGxICqlIiMjuYiTiIiISoXw8HBYWVkVWIaJBVEpZWBgAODdf8h8xz8RlRbR0dHYunUrBg8eDDMzs5IOh4g+MplMBmtra/G+pCBMLIhKqezpT4aGhkwsiKjUSEpKgo6ODgwMDPi/TURfEGWmZXPxNhERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqUyjpAMgIiKiske2aDV0MoSSDoPoi2e8cUlJhyDiEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlLZF5VYREZGIjU1taTDKFRERATS0tJKOgwFpTGm0o5jRkRERF+SLyqxcHJygo+PT0mHUShra2tcvny5pMNQ8KFj+hJuunOO2ZfQZyIiIvpyfVGJRVlhaWkJHR2dkg7jo5HL5bC2tsb169dLOpSP6v3r+KX0mYiIiL5cn3ViIQgCYmNjIQh5fxk0KioKERERiIyMRGZmZq7j0dHRSExMzLX/1atXiI+PF7czMzPx9u3bIseX33m+vr5o3Lix2IeIiAikp6cDAOLj45GVlZVvnSkpKZDJZPkeT05OLvC4sgobu2x59TEqKgoA8Pr1a0RERODly5dKt1tY/+Li4vI8nnMc4+LikJGRIR7PysrK83eluOcBitdRlT4TERERlQWfbWJx/fp12Nvbw9raGhUqVMDMmTNz3fx5eHigWbNmaNiwIfT19dG7d2+Fm+CffvoJgwcPVjhHJpPB1tYWZ8+eRWZmJkaMGAF9fX04OjrC2toaO3bsKDS2ws57fwpNUlISrK2tMX/+fNja2qJKlSowMDDA6tWrFeoMCwuDu7s7DA0NYW1tjZYtWyIoKEg8/uzZM7Rv3x4VKlSAtbU1HBwccOrUKeUHNIfCxq6gPnbo0AEAMGrUKDRr1gx9+vQptL3C+nfnzh00aNAAZmZmMDExQfPmzfHkyRPxePY4Tpw4EZUrV0aVKlVQvnx5bNiwAdu3b4eFhQXs7OxgamqKM2fOqHweoHgdi9NnIiIiorLks0wsUlNT0bNnT3h6ekImkyEsLAy+vr5ITk5WKHfnzh1EREQgKioK4eHheP36NaZNmyYeHzZsGI4ePYrY2Fhx3+7du6Grq4suXbpg165d8Pb2xtOnTxETE4ObN2/i0aNHhcZXnPO8vb1x+fJlvH37Flu3bsX48eMRGhoq9rddu3bQ1tZGdHQ04uPj8csvv4jTbtLS0uDm5oZ69eohLi4O8fHx+Pnnn9GzZ0+EhIQoPa7vK2zsCurjgwcPAAD79u1DREQELl68WGBbhfUvNTUV3bt3R4MGDSCTyRAXFwdLS0v07Nkz19OUy5cv48aNG4iLi8OUKVPwww8/YN26dXjw4AHi4+MxdOhQDBs2LFcMxT0vmzJ9TktLg0wmU/ghIiIiKis+y8Ti8OHDSEhIwJIlS6ChoQFDQ0MsX7483/IymQypqano168fTp48Ke5v27YtrK2tsX37dnHfli1b4OXlBS0tLcTGxsLY2BiVKlUCAJiZmeHnn38uNL7inDdz5kxYWVkBAHr37g09PT3cvXsXAHDw4EG8ePECW7ZsQYUKFQAALVq0wIABAwAAR44cwevXr/Hjjz/izZs3iIqKQsuWLeHo6Ahvb+9C4y1IfmNX3LHJizL9i4mJwW+//QYtLS2UK1cOa9euhb+/f67F+tOnT4e5uTkAwMvLC3K5HNOnT4epqam4Lzw8HK9evfog5xXF4sWLIZVKxR9ra+ti10VERET0qX2WiUVgYCDs7e2hq6sr7qtVqxbU1dUVyv3222+oVKkSzMzM0KRJE0ybNg0RERHicYlEgiFDhmDLli0AgCdPnuDGjRsYMmQIAOCbb74BANjb22P06NE4dOiQwvz7/BTnvJw3mQYGBuJftB8/fgx7e3vxpjunhw8fIjk5GU2aNEHjxo3RuHFjNGnSBK9fv1ZYK1IUhY1dcccmL4X1LyAgAHZ2djAwMBD3mZmZwdzcHIGBgQpl3x9HfX39fPflfFpQ3POKYtq0aYiPjxd/wsPDi10XERER0af2WSYW2tra4mLbbHK5XGHR87///ovp06djz549SE5ORmRkJDZv3pxrYfTgwYPx6NEj3L59G3/++ScaNWqE2rVrAwBMTU3x8OFD7Ny5E+bm5pg+fToaNWqUa8pVTsU9Lz8aGhoFvsZUXV0d5ubmiIiIyPUze/bsIrenzNh9yD4W1r+8rjfwbmqRtrZ2kdsrKdra2jA0NFT4ISIiIiorPsvEok6dOnj69KnCm3euXLmisHj7wYMHsLe3h4uLi7jv0qVLueoyNzeHh4cHNm3ahO3bt2Po0KHisaysLKipqcHZ2RkzZ87ElStX8ODBA9y6davA+Ip7Xn6aNWuGsLAwhcXM2e1kH3/27Jk4dSqvMkWhzNgV1Ed1dXWoq6tDLpcr1V5h/atbty5CQkIUnpg8fvwYMTExqFu3bpH79zEUtc9EREREZY1GSQfwMbi7u6NGjRro168fli5dCplMhu+//x4SiUQs07BhQ/j7+2Pbtm1o3rw5Tp8+jfXr1+dZ37Bhw9C1a1fo6OiIU3wAYP78+UhOTkbXrl1hamqK3bt3Q09PD1WrVi0wvuKel58OHTqgVatW6N69O5YtWwZra2ucPn0acrkckydPRocOHeDm5oavv/4ay5cvR40aNRASEoJNmzZh3LhxCgmCMpQZu4L6KJFI4ODggOPHj8PBwQHlypUT12IUt3+NGjXCt99+i2XLlkEul2PMmDHo0KEDmjZtWpwh/eCK2mciIiKisuazfGIhkUhw7NgxVKxYEX369MHChQuxfPlyODo6oly5cgCA5s2bY9OmTVi7di08PDxw6dIlrFq1CpaWlrnq69SpE0xMTNCjRw9IpVJx/9SpU1GpUiVMnjwZnp6euH37Nnx8fAq9YSzsvPc/rKampgZLS8tcU3rMzc2hp6cn9vf48ePo1q0bpkyZgr59+yIqKgqjR48Wj3t7e2PYsGFYvHgxPD09sW7dOgwePFjppOL9mJQZu8L6uHHjRty5cwdt2rQp9NWryvavZs2aGDBgAIYNG4avvvoKe/fuFevIaxzV1dVhaWkJLS0tcZ+GhgYsLS2hoaGh0nk5x6yofSYiIiIqayRCfl+PI1FkZCRsbGxw5swZtGnTpqTDoS+ETCaDVCpFfHw811sQUakRFRWFTZs2oe/rVJhm8BaCqKQZb1zyUesvyv3IZzkV6kPJyMjAixcvMGfOHNSpU0fppCIxMRFxcXF5HtPU1Cx1U2BKOt6Sbp+IiIiIVMfEogBPnjyBh4cHbG1t8c8//yh93o4dO7BgwYI8j1WrVi3XtxVKWknHW9LtExEREZHqOBWKqJTiVCgiKo04FYqodClNU6E+y8XbRERERET0aTGxICIiIiIilTGxICIiIiIilTGxICIiIiIilfGtUERERFRkhjPGwtjcvKTDIKJShE8siIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZXzdLBERERWZbNFq6GQIJR0GUallvHFJSYfwyfGJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqeyjJRb6+vo4duzYx6o+XxKJBD4+Pp+8XcqN1yI3jgkRERF9rvjEooyTy+WQSCS4ePFiSYdCRERERF8wfnmbPhpB4BdZiYiIiL4UH+SJhb+/P1q1aoVy5cqhWrVq2LBhQ64yb968wZAhQ1CxYkUYGhrCxcUFt2/fFo937NgRQ4cOVTgnPT0dxsbG2Lp1q1J15CU0NBRdunSBvr4+DA0N0atXL0RGRorHExMTIZFIsGLFCjRr1gz6+vpwdHTEkSNHihR/dj0rV65Ew4YNoa2tjZUrVxY6dm/evMGwYcNQqVIlSKVS9O3bFy9fvhSPm5mZQSKRQE1NDTY2Nhg/fjxSUlLE4xUrVgQAtGnTBhKJBNWqVVN6rPz8/ODs7Kxw3XJOYVN2/PLqd85pP8rEtGDBAtjY2EBPTw/NmjXD+fPnCx3DDzGOBbUdFhYGiUSCp0+fKpTX0dFRGCtl2iAiIiL6XKmcWMjlcnTp0gWWlpYICwuDt7c3Nm/ejKSkJLFMZmYmOnToALlcjrt37+LFixfo0qULXF1dER0dDQDw8vLCgQMHkJaWJp53/PhxpKam4uuvv1aqjpwyMzPh6ekJiUSCgIAAPHz4EDExMejRo0euv6b//PPPWLRoEV68eIHvv/8ePXv2RGBgoNLxZ1u+fDlWr16NpKQk/Pjjj4WOnaurK548eQIfHx88f/4cPXr0wL59+8Qy0dHREAQBGRkZ8Pb2xoULFzB37lzxeExMDADgwoULEAQB/v7+SsWbkZGBLl26oHLlyuJ127JlS67rpuz4FdZvZWI6ePAgVq5cif379yM2Nhbr1q3DP//8U+AYfqhxLG7b7yusjcKkpaVBJpMp/BARERGVFSonFt7e3oiIiMCGDRtQqVIlODo6Yu3atQplTpw4gadPn2LLli2wtraGgYEBxo8fD1tbWxw4cAAA0K1bN8jlcoW/AO/YsQNdunSBoaGhUnXkFVtwcDD+/PNPWFpawsbGBlu2bMF///2HS5cuKZQdP3482rVrB6lUivHjx6Np06ZYvXq10vFnmz59OpydnaGhUfgsM29vb/j5+WHPnj2oXbs2pFIpevfujTFjxuQqq66ujjp16mDGjBkKN8x5USbeo0ePIjo6usDrVpTxK6zfysQUFhYGGxsbNG7cGDo6OmjYsKH4tOpjj2Nx285LUa7V+xYvXgypVCr+WFtbF6t9IiIiopKgcmLx+PFjODg4wMjISNzXsGFDqKn9r+pbt24hLi4OOjo6UFdXh7q6OtTU1HD37l2EhIQAAPT09NCtWzfs2LEDABAfH4/jx4/Dy8tL6Tryis3Ozk6cLgQAtra2qFSpEh4/fqxQtlGjRgrbjRs3FssUpe2aNWsqPXZ3796Fra0trKys8i2zZ88eNGrUCIaGhpBIJOjVqxfCw8MLrFeZeP39/eHg4ACpVCqe16BBA4XrVpTxK6zfysTUo0cPREVFoWnTpli8eDFu3rxZYJ3ZPsQ4FrftorRRmGnTpiE+Pl78Kcq5RERERCXtgyzelkgkBR7PysqCo6OjOLUoP/369UP37t0RFxeHgwcPQk9PD+7u7kWq4335LR4WBCFXzAX1oShta2lpFSm+gtq9desWBgwYgD///BOdOnWCkZERTpw4gc6dOxdYrzLxFtZ2dhllzy2s38rEVKVKFQQFBeH48eO4cOECPDw8UK9ePRw/fhyampoFxqnqOBbUdkHtFqWNwmhra0NbW1vp8kRERESlicpPLGrUqIGgoCDEx8eL+27fvo2srCxxu0GDBggODkZQUFCBdbVv3x5SqRT79+/Hjh070KdPH/GGUtk63lezZk2EhISI6xCAd1NeXr16herVqyuUzfkX6ps3b4plitO2Mho0aICQkBC8ePEiz+O+vr5wdHSEl5cXypcvDzU1Nfz3338KZdTU1KCmplbk8a5evTqePn2qcN3u3r2rUE9Rxk+Zviozhvr6+ujTpw82bNiAu3fv4uzZs7hx40ahdas6jgW1bWxsDAB4+/atWPbZs2dIT08vchtEREREnyuVE4vOnTvDwsICI0eOxMuXLxEUFJRrbnvnzp1Rv3599OnTBzdv3kRSUhIePnyIH374QWGuvrq6Ovr06YNVq1bh4sWL4jSootTxPk9PT9jZ2eG7775DZGQknj9/jmHDhqFx48Zo3bq1QtkVK1bg/PnziI+Px4oVK+Dr64sffvih2G0rO3Y1atTAN998Az8/P8THx2Pv3r3iWodq1aohKCgIPj4+SEpKwsGDB/Hbb78p1KGmpgZra2tcv34dcrlc6Xi7dOmCSpUqYdSoUXj16hWCg4PF/hZn/JTpa2Ex/fLLL1i9ejWePXuGlJQUnD59GhoaGqhcufJHH8eC2pZKpahduzaWL1+OuLg4hIWFYcSIEQrnK9MGERER0edM5cRCQ0MDR48eRXh4OGxsbODp6YmhQ4dCT09PoYyPjw+aNWuGrl27omLFiujfvz+qV68OZ2dnhfq8vLzg5+eHKlWqoHnz5sWq4/1zjh8/DrlcDicnJ9SqVQtGRkY4dOhQrqkz06ZNw7Rp02BpaYnff/8de/fuFV/dWpy2lR27s2fPws7ODq1bt0aVKlVw6NAh9OrVCwDg6uqKGTNmwMvLCxUqVMDSpUsxYcKEXPWsWLECW7ZsEV8bq0y8mpqaOHr0KEJDQ1G5cmV06tQJ/fv3h6ampjgdpyjjp0xfC4tpyJAhCA0NxVdffYWKFSvi999/x8GDBwtNLD7EOBbW9vbt2xEWFgZLS0t4eHigb9++CtOWlL1WRERERJ8rifCFf8UsMTERBgYGuH79Opo1a1bS4ZSoR48eoVatWggKCoKDg0NJh/PFk8lkkEqliI+Ph6GhYUmHQ0QEAIiKisKmTZvQ93UqTDO+6FsIogIZb1xS0iF8EEW5H/kgH8ijsmnZsmXw8fFBYmIiHj16hOHDh8PZ2ZlJBREREREVGROLj2jZsmWQSCR5/tSqVaukw0OXLl2wYsUKWFlZoUOHDnBycsLhw4dLOqxcSvs4EhERERGnQhGVWpwKRUSlEadCESmHU6GIiIiIiIiKgYkFERERERGpjIkFERERERGpjIkFERERERGpTKOkAyAiIqKyx3DGWBibm5d0GERUivCJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyJBRERERERqYyvmyUiIqIiky1aDZ0MoaTDKHOMNy4p6RCIPho+sSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSgFoqKi0KtXL9ja2qJBgwYlHQ4AwN7eHj4+PkqVzSt+MzMzXL58WSyTc1tVH7t+IiIiIioafiCvFJg7dy7evn2LCxcuQE9Pr6TDAQC8fPkSqampSpXNK/6XL18iLS1Nob73tz9EfB+zfiIiIiIqGiYWpcDjx4/Rvn17VKlSpaRDKZa84o+KikL58uU/WQyfuj0iIiIiUsSpUCXMysoKN27cwC+//AIzMzPMnz8fAPD69WsMHz4cDg4OcHJywg8//ID4+HjxvPDwcJiZmSEsLEyhvipVqohTmJKTk2FmZobdu3ejZ8+ecHJyQsuWLXHu3DmFc168eCFOZXJxccGhQ4dUjr9evXq4ceNGvuclJydj+vTpqFWrFpycnNC3b18EBwcr3W5OOdtLTk7GzJkzUbduXVStWhU//vgjEhMTlW5f2bHbtGkTmjZtCnt7e3Tr1g0PHjz4aH0kIiIiKs34xKKE3b59G506dYKbmxt+/PFH6OnpQRAEeHp6QltbG3v27IFcLsf333+Pnj174uzZswCAzMxMvHz5EnK5XKG+6OhocQpTVlYWXr58ifHjx2P9+vWoVasWNm/ejG7duiEkJAQmJiZiWyYmJjh8+DBkMhm+++47JCUlFTt+oOCpSYIgoGvXrtDS0sLWrVthZGSEzZs3o2XLlnjy5AmMjIyKPI7vtycIAjw8PPD69Wv89ttvqFy5Mk6fPo3169dj0qRJSrWvzNidOnUKEydOxI4dO1C7dm08evQIs2bNwpEjRz5KH4mIiIhKMyYWJaxSpUrQ1NSEvr4+zMzMAAAnT57EnTt3EBYWBktLSwDA9u3bUbNmTfj6+qJZs2ZFamPu3Lno2rUrAGDhwoVYtWoV/vvvP3h4eODkyZN4/PgxwsPDYWpqCgD4/fff0a5du2LHXxgfHx/4+vri1atXKFeuHABg6dKlOHbsGPbs2YMRI0YUqX85nTp1CpcvX8aTJ0/g6OgIAKhatSoEQShy+wWNnZ+fH6pXr47OnTsDePe0qFOnTsXuY1pamkIyJpPJVBoHIiIiok+JiUUp9ODBA9jZ2YlJBQDUqFEDJiYmePDgQZETi5o1a4r/VldXR/ny5RETEyO25ejoKCYVAODs7AyJRKJiL/J3/fp1pKamwtHRUbzZFwQBb968wdOnT1Wu/8aNG6hSpYqYVGTL7lNR2i9o7Dw8PDB//nx06dIF3bt3h6urK6ytrYvdx8WLF2PevHkq95+IiIioJDCxKIXS09OhpaWVa7+WlhbS09OLXJ+6unqufdk3uxkZGdDU1FQ4pqGhATW1j7f8Ji0tDTY2Nrhy5UquYx/irVhyuRza2tofpP2Cxq569eoICAjA3r17ceTIEYwZMwY9evTAX3/9Vaw+Tps2DePHjxe3ZTKZmKgQERERlXZcvF0KVa1aFcHBwQpTYaKjoxEVFQUnJycAgKGhIQAgISFBoUxRX7nq5OSEp0+fIjk5Wdz38OFDZGZmqtKFAtWqVQthYWHIzMyEmZmZwo+BgYHK9desWRPBwcGIjY396O2bm5tj3LhxOHz4MO7cuYPt27fD19e3WG1oa2vD0NBQ4YeIiIiorGBiUQp17doVpqamGD9+PFJTU5GUlIQxY8agevXq4tqH8uXLw97eHps2bUJWVhYSExPx008/FastQ0NDTJo0Cenp6YiLi1P4q/nH0KNHD9jb28PLywvh4eEAgFevXmHRokXw9fVVuf7u3bvD0tISQ4YMwevXryEIAv7991/89ddfH7T99evXY+/evWJS9vTpU0gkEpiamn70PhIRERGVNkwsSiFtbW0cOXIE9+/fh1QqhbGxMaKjo3Hw4EGFqTlbt27FyZMnoa+vD3t7e9StW7fAKUB50dHRwf79+3H69GkYGhrCwcEBrVq1gq6u7ofulkhbWxvnz5+HsbExHBwcYGhoiLp16yItLQ21a9dWuX4dHR2cO3cOaWlpMDc3h1QqxZw5c9CyZcsP2r67uzsOHToEMzMzSKVSjBgxAps3b4aDg8NH7yMRERFRaSMRsieMU4l58+YNtLW185x7n5CQADU1tQLXHiQkJIjTa16+fAkjIyNoa2tDEAS8fPkSFSpUUFhH8fr1a+jr64tvK8oWFxcnvgb11atXkEqlSiUqecUfHR2N8uXLi2tFcm5ny8jIQHJyMqRSaaHtvE/Z+lNTU5GVlZVvopRf+0UZu8zMTKSkpEBfX79IbRRGJpNBKpUiPj6e06KIqNSIiorCpk2b0Pd1KkwzeAtRVMYbl5R0CERFUpT7ESYWRKUUEwsiKo2YWKiGiQWVNUW5H+FUKCrQ+vXrcy0+zv5xcXH5bNokIiIiItXwdbNUoIEDB6J79+55Hsv5mtqy3CYRERERqYaJBRVIV1f3oy7kLi1tEhEREZFqOBWKiIiIiIhUxsSCiIiIiIhUxsSCiIiIiIhUxjUWREREVGSGM8bC2Ny8pMMgolKETyyIiIiIiEhlTCyIiIiIiEhlTCyIiIiIiEhlTCyIiIiIiEhlTCyIiIiIiEhlTCyIiIiIiEhlfN0sERERFZls0WroZAglHUa+jDcuKekQiL44fGJBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqUzmxmD17NhYuXPghYimSiRMn4tdff/3k7VJuX9q1aNSoEf7777+SDoOIiIioVFH5A3khISHQ0dH5ELEUydOnT5GamvrJ2y1JP/30E6ytrTF+/PiSDkXBl3Ytbt++DZlMVtJhEBEREZUq/PJ2GRIUFARBKH1fOV2+fDk0NTVLOgwiIiIiKkFFngq1ceNGtG3bFl27dsWmTZvyLHPx4kX07dsXLVu2xIABA3Dr1i3x2K5du+Dh4ZHrnIULF2L06NFK1ZGfPXv2oHPnznB2dsb333+PZ8+eKRwfPXo0Vq5ciV9//RWdOnVChw4dsG/fviLFn13PqlWrsGzZMrRt2xYjR44sNDYAuHTpEr799lu0atUKo0aNQlRUlHhs8eLFaNSoERo1aoQOHTpgzpw5SEpKEo/PmzcPV65cwa5du8Ry2f1TZqzWrVuHNm3aiNdt1qxZuaawKTN+efV7/fr12LNnT5HGMCgoCCNGjEDr1q3xzTff4Pz580qNYX4xZGZm4vfff4e7uzvatm2LiRMnIjY2VuHcN2/eYNy4cWjVqhW8vLxw/fp1fPXVV7hy5QoAICoqCo0aNUJERITCec7OzmKZvLi7u6NRo0Zo0qQJvv76a+zcuVPheEpKCho1agQfHx+MGDECzs7O2LVrl1L9JSIiIioripRYrFq1CpMnT8Y333yDUaNGYd++fdi/f79Cmd27d6N379746quv8PPPP6Nu3bpo3bo1rl+/DgBo0aIFTp48iZs3b4rnyOVyrFmzBrVr11aqjrxs2LABQ4YMQYcOHTB37lxER0ejWbNmiIuLE8sEBARg6tSpuH37NsaPH4/27dvDy8sLBw4cUDr+7HqmTJmCgIAAzJ49Gz/99FOhY7d9+3a0b98e9vb2mDdvHmrXro0RI0aIx/v164cNGzZgw4YNGDt2LC5evIhu3bqJxwcOHIjatWujXbt2YrlKlSopFe+vv/6K6dOn49tvv8WoUaNw4MAB/PrrrwgLCyvy+OXV76dPnyokIYXFlJ6eDhcXFwiCgAULFuDrr7/G0qVL4efnV+g45hdD//798ddff2HEiBGYNWsWXr58iaZNmyIlJUU8t0uXLrh69SqmTp0KDw8PfPPNN7h69arYx7S0NNy+fTvXtK7bt28rjENOS5cuxYYNG7BmzRp4eHhg/PjxWLdunXg8MzMTt2/fRp8+fVCjRg0sX74c7dq1K7SvRERERGWJRFBybo1cLoe5uTnmzZuHUaNGAQASEhJgbW2Nnj17YvPmzcjMzISVlRVWrFiBb775Rjz3p59+QkhICI4cOQIA+Oqrr9CgQQOsXLkSAHDy5El069YNUVFRkEqlStXRrVs3WFlZYe3atcjMzESlSpUwbdo0TJgwQYzX3t4eQ4YMwZw5cwAArq6uCAsLQ2BgINTU3uVUkydPxokTJ+Dn56d0/K6uroiPj1dIjgobOwsLC4wePVqMBXh3I6utrZ3nObGxsTAxMUFAQAAcHR0BAJ6ennBwcBDHTZl4MzIyYGZmhgULFojXLTExEdbW1vj666/F66bs+OXV75zXorCYnj59CkdHR7x69QomJiZKjUe2vGK4evUqXF1dERkZCWNjYwBAVlYWnJycMGPGDAwePBjnzp1Dhw4dEBISgsqVKwMAjh8/Dk9PT3h7e8PT0xNhYWGwtbVFUFAQHBwcxPp1dHSwf/9+eHp6AgAkEgnOnj0LV1fXPGPcunUrli1bhkePHonjbWBggIULF2LGjBn59i0tLQ1paWnitkwmg7W1NeLj42FoaFjguBARfSpRUVHYtGkT+r5OhWlG6Zuem81445KSDoHosyCTySCVSpW6H1F6jUVYWBhiYmIU/tJqYGCApk2bitv+/v6Ijo7G0qVLsWrVKgiCAEEQ8PLlS4UF3v369cOcOXOwfPlyqKurY8eOHejYsSPKly+PR48eKVVHzthiY2PRoUOH/3VMQwPt2rXD7du3Fcq2adNGTCoAwM3NDcuWLUNqaiqCg4OVbrt58+bKDh2ePHmC169fKzyBAKBwE/327VusWbMGvr6+iImJQVZWFiQSCUJCQsTEIidlxjssLAxv3rxRuG76+voK160o41dYv5WJycbGBo6OjujVqxdGjBgBFxcXmJubF5pU5BfDxYsXAQAdO3YU16AIgoDXr1/D398fAHDnzh04OjqKSQUAtG3bVqn2CvPw4UOsW7cOgYGBkMlkSEhIwPPnzwuNO6fFixdj3rx5HyQmIiIiok9N6cQiMTERAKCrq6uwX09PT/x39pqA2bNnK9zAAYo30b1798bYsWNx7tw5ODs74/Dhw9i2bVuR6nhfQkJCrliAdzfQoaGhCvvyil8QBCQmJhap7Zz1FCR7Oo6+vn6+ZTw8PKClpYUffvgBFhYW0NTURIsWLRSm8uSkTLzKXDdVxq84MWlqauLGjRvYsmUL/vrrL3z33Xdo3rw5du7cqfAEIz85Y0hKSoK5uTnWrl2bq2ylSpUAvBuHnOdpa2tDQ0O19xeEhISgRYsWGDp0KKZOnQojIyP4+vpi3Lhxhcad07Rp0xTe+JX9xIKIiIioLFD6rsrW1hYSiQT+/v4KNzv+/v5o0aIFAMDe3h5qampITk5Go0aN8q3L2NgYHTt2xPbt2/H69WtoaGiI00yUreN9dnZ2kEgkePz4MWxtbcX9jx49gr29vULZ7L9gZ3vy5AkMDQ1RsWJFSCSSIretDAcHB6ipqeH+/fu54gGAly9f4vr16/D390fVqlUBAKGhocjIyFAop6ampvBWKGXGKns8AgICFK5bQEAAmjVrBqBo41eYovwOTJgwARMmTEBcXByaNGmCVatWFeubKI6OjoiIiICdnR3Kly+fZxk7OztxTLPfYBUcHAy5XC6WyU6skpOTxX1v375VmJ6U09mzZ2FlZSVOTwPeTc0qDm1tbaWf2hARERGVNkov3pZKpejRowcWLFgg/lV669atePLkiVimQoUK+PbbbzFz5kyFG/ibN2/i77//VqjPy8sLhw4dwubNm9GzZ09xmkxR6shmaGiI3r17Y8GCBeL3BY4fP46LFy9i2LBhCmV9fHzg4+MDAHj9+jWWLVuGIUOGFLttZZQvXx59+vTBrFmzxCkyKSkp4gJfAwMDaGpqiusGUlNT8/xWhZmZGcLDw8VtZeI1MjJCt27dsHDhQvGGeceOHeL8f6Bo41cYZWJ69OgR/vjjD/GmXktLC2pqasX+HkrPnj1hYmKC4cOHi7+bmZmZ+Ouvv3D37l0A79aBCIKAX375RTw+e/ZshXpMTExgbm4uviksKytLYU1MXsqXL4+oqCi8ePECwLtkZfny5cXqBxEREVFZVuS3QslkMpibm8POzg6rV69Gy5YtFcps2LABbdu2Rb169eDk5ARTU1OMHTsW1atXVyjn6ekJdXV1XLx4EV5eXsWq432rV6+Gjo4OLCws4ODggD59+mD58uXiX+Wzubu74/vvv4ejoyNsbGxgZmamcPNYnLaVsXHjRtSqVQuOjo6oWrUqrK2txWk4urq6WLFiBYYNG4bq1avDzMwMurq6KFeunEIdQ4YMwYULF1CtWjXxdbPKxLtq1Sq8efNGvG7Z4/L+NCBlx08ZhcVkaWmJmzdvokKFCqhVqxYsLCxgb2+f5/QhZRgYGODMmTN4/vw5TE1NUb16dRgbG+PSpUuwsbEB8C4x3rp1K3799VdUrlxZHGNNTU2FcVi/fj1WrlwJBwcHWFhYICsrC1paWvm23b17d7Ru3RpOTk6oXr06GjVqhK+++qpY/SAiIiIqy5R+K1Q2QRAQGBgIAwMDWFhYIDQ0FGpqauINXLaEhASEhYXB0tIy3+kpAQEBSEhIQMOGDSGRSHIdL6iO4OBgaGpq5prHHxERgbdv38Le3j7XnHZXV1c0atQIS5YswfPnzyGXy2FnZ5dnbAW1HRgYCD09PVhaWuY9SAWIjY1FdHQ0HBwcck17iY+Px/Pnz2Fubo6KFSvi7t27sLW1hZGRkVgmJSUFYWFhSEpKQq1atcS/8hc23jmvW5MmTeDp6Znrr/YFjV9+/c7vWhQWU0pKCkJDQ2FqaoqKFSsWPngFxJAtMjJSjD+vJyDJyckICQmBjY0NEhISYGlpifv376NOnToKcYWFhaFy5crQ09PDnTt3YG9vD6lUCgC4desWqlatCgMDA/GciIgIxMXFwcHBARkZGQgICBCngmVlZeHOnTuoXr16rnUsBSnKWxiIiD4VvhWK6MtSlPuRIicWZdn7icWX5Pbt28jMzESTJk0AADt37oSXlxdu376N+vXrl3B0n86+ffvQpk0bVKxYEWlpaRg2bBiuXbuGoKAghTeFlRZMLIioNGJiQfRl+Sivm6X8RURE5HqV7PsOHz4MKyurTxdQDpaWlvj6668RERGBzMxMJCQkYPPmzaUuqfjY4yiVSlGvXj3o6+sjKioKlStXxsGDB0tlUkFERERU1nxRicXvv/9epKkoyjIxMcGGDRsKPF6SzMzMcPXqVURHRyMpKQlVqlSBurp6icaUl489ju3bt8fz588REhICfX19mJmZqVQfEREREf3PF5VYODk5fZR6tbW1P+jraT+W0n4j/SnGUU1NTeGr2kRERET0YXAOCBERERERqYyJBRERERERqYyJBRERERERqeyLWmNBREREH4bhjLEwNjcv6TCIqBThEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZEwsiIiIiIlIZXzdLRERERSZbtBo6GcJHqdt445KPUi8RfVx8YkFERERERCpjYkFERERERCpjYkFERERERCpjYkFERERERCpjYkFERERERCpjYkFERERERCpjYkFERERERCpjYvGJLF68GH/88Ye4PWfOHPzzzz8lGNHnJTU1Fd26dUNgYGBJh0JERET0RWJioYJFixbhzz//VKrsjRs3cP/+fXH76tWrePTo0SeN4XORlZWFbt26wc/PT9wnl8tx5MgRvHnzplTEQ0RERPSlYWKhguvXr+Phw4dKlZ02bRqGDx9eojF8LrKysnDkyBHExMSUdCgASl88RERERCVBo6QD+Fj8/f2xZcsWhIeHo3bt2hg7diz09fXF45cvX8bOnTvx5s0b1KlTB6NGjYKxsbF4fM6cOXBwcICuri7Onj2LzMxM9O3bF+3atQMArF69Grdu3cKTJ08QFhYGAFi3bh02bdoEBwcHaGpq4sSJE7CxscGCBQtw/vx5VKxYEXXq1FGIc9euXbhw4QIyMzMxaNAgtGrVSjz2008/wcXFBV27dhX3LV++HDo6Ohg9enS+MVhaWirdv5xxFubIkSPYv38/DA0N0alTJwQFBUFTUxOjR48usF65XI6///4b58+fh5qaGjp06IBvv/0WEokEALBq1Sqoq6tjzJgxAICTJ09i48aN+Pnnn1GjRg0AwKRJk+Ds7Iy9e/cCAGbOnImKFSvCysoKS5YsAQAkJydj2bJlePDgAUxNTfHjjz/Cysqq0H6lpqaib9++mD17No4fPw4/Pz94eXmhc+fOGDx4MN6+fQs1NTVYW1ujR48eaN26tXjugAEDcsWzdu1aAMDRo0dx9OhRpKeno0GDBhg5ciR0dHQKjYeIiIiorPksn1icPHkS9evXx+vXr9GxY0ckJSWhT58+4vF9+/ahXbt2kEqlcHd3x/Hjx9GsWTOkpKSIZa5evYpx48Zh+/btcHZ2homJCdq3b4+rV68CAFq2bAlra2vUqFEDgwYNwqBBgyCVSsXz/vnnH7Rv3x4dOnQAkHsqFABs3LgRmzZtQosWLWBoaIi2bdvi3Llz4vELFy4gKChI4ZybN2/i7t27BcZQlP7ljLMgW7ZsQe/evWFnZ4cGDRpg9uzZmDdvnhhPQfUOGjQIs2fPRvPmzdGwYUOMGzcOY8eOFc9TU1PDhg0bxO3du3fj2LFjOHHiBIB3N/5r1qyBsbExvvnmGwCAu7s7Bg0ahO7du4vn9evXD7GxsejQoQOePHkCZ2dnpKamFtq37KlU7u7ukMlk6NWrl5jQ9OnTB4MGDUK/fv1QoUIFdOnSBbt37xbPzS+e8ePHY8KECahbty7c3Nxw/PhxtGrVCnK5vNB4iIiIiMqaz+6JRVZWFkaMGIGRI0fit99+E/dnz73PysrChAkTMG3aNMybNw/AuxvHKlWqYN26dZg4caJ4jr29PQ4ePCj+Vf3OnTvYtWsXnJ2d0aBBA1SqVAn29vbo1q2bQgzm5ubw9vaGmlrBeZuuri5Onz4NLS0tAO9unqdPn44bN24o1de8YihK/5SNEwAyMzMxa9YszJ8/H1OmTAEAdOvWDZUrV85VNme9N2/exI4dO3Dnzh3Ur18fAFCzZk106NABY8aMQdWqVeHi4oKxY8fi9evXMDExwaVLl+Dl5YULFy5g4sSJuH79OiQSCZo1awZ1dXUA7xIrFxcXAEBiYiIAYPTo0Zg5cyYAoEuXLihfvjyuXLkCV1dXpcZ06NChWLx4scI+d3d38d9ff/01ypcvj6VLl6Jv374AgI4dO+aK5969e1i7di1CQ0NhaWkJ4N11sLe3x759+8Rk5H1paWlIS0sTt2UymVIxExEREZUGn11iERAQgPDwcHh5eSnsL1++PAAgPDwc4eHhCn/l1tXVRceOHXHt2jWFc1q2bCkmFcC7RCMyMrLQGFq3bq3UzXqHDh3EpAIAunbtik2bNiE9PV1hf1EUpX/KxgkAz58/R2RkJDp37izuq1ChAlq0aJGrbM56r127BhsbGzGpAABXV1fo6+vjxo0bqFq1KmrVqoWKFSvi4sWLaNy4MV6/fo3JkyejefPmyMzMxMWLF9GsWTNoa2sX+Bf/96eSGRgYwNTUVKlrlq1Nmza59kVGRmLLli0IDAyETCbD69evC3371OnTp6GtrY1x48ZBEAQAgCAISEtLw8OHD/NMLBYvXiwmg0RERERlzWeXWGT/lTc7kcgpNjYWAGBkZKSw39jYWFynkE1bW1thWyKRICsrq9AYDA0NlYpVKpUqbBsZGSErKwtxcXEwNTVVqo6citI/ZeMEgLdv3wLIHXPO7bzqjY2NzRVPdkzZ8UokEnz11Ve4cOECEhMT4ezsjBo1asDAwAC3b9/GxYsX0bZt20LjLO41yy/2yMhI1KtXD1999RU6deoEIyMj3L9/P1eSllNcXBzKly+fK8EdMGAA7O3t8zxn2rRpGD9+vLgtk8lgbW2tdOxEREREJemzSyxsbGwAvFu8bWtrm+t49r7g4GBUqVJF3P/06VOFbWW8/zSjOEJCQhS2g4ODoaurKyYVOjo6udYHxMTEKCxCzxnDh+zf+7LHNSQkRJzaAwChoaEKTyLyYmtri2fPnkEul0ND492vXHJyMiIjIxVicnFxwfr165GUlCQ+OWjdujVOnTqFGzduYP78+QBUH/eiOHHiBAwNDbF//35x38uXLxXK5BWPjY0NXr16BTc3N+jp6SnVlra2dq7EiIiIiKis+OwWb5uZmcHd3R3z589HXFwcgHfrDrJvDI2NjeHu7o5ffvkF6enpAID//vsPp06dQr9+/YrUlomJCV69elXsWE+ePIl79+4BeHejvXLlSnHePgBUr14d58+fF//ifvfuXVy+fLnAGD5k/95XoUIFtG3bFsuXL0dmZiYAwMfHB3fu3Cn0XE9PT2RlZWHdunXiviVLlsDY2Fhh7YOLiwuePHmC48ePi4mFi4sL1qxZI66vAAB1dXUYGxurNPbK0tHRQXx8vPgkLCYmBitXrlQok1c8PXv2hLa2NiZMmCCOFwCcO3cOT548+ehxExEREX1qn11iAQDbtm2DmpoaHBwc0K5dO9jb2+Px48fi8d9//x3Pnj2Dk5MTXFxc0KZNG/z0009o3759kdrp06cPDh8+DFdXV3Tr1g0vXrwo0vnNmjVD586d0aZNGzg5OSEpKQkLFy4Uj0+dOhX+/v6oXbs22rRpAy8vL9SsWbPQGD5U/3Jas2YNbt68iapVq+Krr77CyJEjUa9ePXExdX5MTEzwxx9/YObMmWjSpAkaNGiANWvWYNu2bTAwMBDLZa+zSEtLQ6NGjQC8SyxiYmLE9RXZvLy8MHr0aHh6eoqvqP0Yvv76a9ja2qJ69epwc3ND9erV81ywnjOeihUrwtvbGydOnICdnR3c3Nxgb2+PX375Jc/pY0RERERlnUTIXln6GXr48CEiIyNRq1Ythek7wLvXi966dQtv375FrVq1cs1lv3btGoyMjMRXjgLAgwcPkJKSgqZNm4r7IiMj8ejRIyQlJcHV1RUPHjzIdR7w7nWz5cqVE79jkV2/tbU1Hj58CLlcjubNm0NTU1PhvISEBNy5cwd6enqoXbs2Hj58CA0NDdSrVy/fGPT19YvVP2UkJyfj+vXrMDAwQO3ateHm5oY2bdqI38AoqN74+HjcvHkTampqaNKkicKUrmxXrlyBXC4X364EvPsWRJUqVXJ9A+Thw4d4/vw5dHR04OLiAm9vb7Ru3Vrhex2nT5/ONxl4X2ZmZp7nZx+7efMm4uLiUKtWLWhra+Pq1au53gb2fjzZ3zuRy+W4c+cO3r59i2rVqolTypQhk8kglUoRHx9fpPUwREQfU1RUFDZt2oS+r1NhmvFxbiGMNy75KPUSUdEV5X7ks04s6MN6/PgxypUrJ67juHDhAtq1a4d///0XLVu2LOHoPj9MLIioNGJiQfRlKcr9yGe3eJuKJzo6GiNHjsz3+IYNG6CrqwsPDw8YGRkhMzMT9+7dw/z588tEUnHkyBFs3bo1z2M2NjZYtWrVJ46IiIiI6PPCxIIAvPvmw6BBgwo8bmZmhnv37uH+/ftISkpCjRo1YGJi8umCVEHNmjXz7R+fBhARERGpjokFAQD09PRyrRnIi6ampriwuixxcHCAg4NDSYdBRERE9Nn6LN8KRUREREREnxYTCyIiIiIiUhkTCyIiIiIiUhnXWBAREVGRGc4YC2Nz85IOg4hKET6xICIiIiIilTGxICIiIiIilTGxICIiIiIilTGxICIiIiIilTGxICIiIiIilTGxICIiIiIilfF1s0RE9MVwm7qvpEMo8/QkKaivU9JREFFpxCcWRERERESkMiYWRERERESkMiYWRERERESkMiYWRERERESkMiYWRERERESkMiYWRERERESkMiYWRERERESkMiYWn8CYMWMQEBBQ5ur+UCZMmIBHjx6pXOZDtZUt59iVhbEkIiIiKq2YWHwC69atQ3h4OAAgKysLY8aMQVBQ0Aevu7TauHEjQkNDVS7zodrKlnPsysJYEhEREZVWTCw+gTVr1qBatWoA3iUW69atw4sXLz543aQajiURERFR8WmUdADFcffuXRw7dgypqalwd3dHq1atFI6fOXMG58+fh5qaGjp06IDWrVuLx9LT0zF+/HiMGTMGt2/fhp+fH0xNTTF06FBoaWlh+/btCAgIQNWqVTFo0CBoaGiodB4A+Pv7IykpCQAwdepUAMCqVauwf/9+mJmZYebMmQX29/nz59izZw9iYmJQu3Zt9OnTB5qamrnqPnz4MHx8fHKd37NnT7i4uAAAbt++DW9vb6Snp6NBgwb4+uuvIZFIlBr36dOnQyaTQU1NDdbW1ujcuXOuG3FBELB3717cunULNjY26N27d656lCmjTKzK1qOs98cy+3r/9NNPuHnzJh48eABTU1MMHjwYUqm0SHEWdP2IiIiIPhdl7onF0qVL0axZM0REREBXVxczZ87En3/+KR6fMGECevfuDYlEgoyMDHTs2BELFiwQj6enp2PdunVo164dzp49C0NDQ2zYsAFt27ZF27Zt4evri/Lly2PRokX47rvvVD4PUJxi4+joCACoXLkyqlWrBltb2wL7GxAQgJo1a8LPzw8mJiY4d+4cOnXqlGfdJiYmqFatmvijo6ODdevWiVODli1bho4dOyIlJQUGBgZYsGABPD09IQiCUmPv6OiIatWqwd7eHkFBQWjcuDFOnz6tUGbUqFEYPXo0dHR0EBQUhCZNmiAtLa3IZZSJVZl6iuL9scy+3m5ubjh+/DikUil27tyJli1bQi6XKx1nYdePiIiI6HNRpp5YPH78GNOmTcP+/fvRo0cPAMCMGTMQFhYGAHj06BFWrlwJHx8ftGnTBgDQpEkTeHl5YcCAAbCxsRHr6t+/P5YsWQIAaNWqFVq1aoW5c+dizpw5AID69evDw8MDGzZsgLa2tsrnZRs6dChGjhyJ7t27i08RCnL8+HHUrFkTf/31l7gvvzUEzs7OcHZ2BgAkJSWhRYsW6NixIwYOHIjAwEBMnz4dDx48EJ8yjBkzBg4ODjhy5Ai6detWaCyDBw9W2Lazs8OcOXPQoUMHAICfnx82btyIa9euoVmzZgAAJycnjBkzRjxHmTLKxKpMPR9Cr169sHTpUgDvrp2ZmRmuXr2K1q1bKxVnUa5fWlqaQmIkk8k+aF+IiIiIPqYylVicOHECpqamYlKRrUqVKgCAixcvwtzcXEwqgHfTgAYPHoyrV68qJBbZN8PA/54itG/fXmFfVlYWIiMjFZ4qFPe84nJycsKjR4/w999/o2vXrpBKpYXWKwgCBg0ahLS0NOzatQtqamo4duwYypUrhw0bNoh/TRcEAZqamrhz545SiUVSUhL27duHwMBAyGQyPH/+HI8fPxaPX7x4ETY2NuKNPgD069dP4WZfmTLKxKpMPR/C+9e7YsWKMDExEZ9qKBNnUa7f4sWLMW/evA8aPxEREdGnUqYSi9jYWJiZmeV7PDo6GiYmJgr7JBIJTExMEB0drbC/XLly4r/V1dXz3ZeZmflBzisuT09PrF27Fn/88QdGjBiBOnXqYMqUKbmSq/ctWLAAZ8+exX///SeuB3j9+jUMDAzg4OCgUHbq1KmoV69eoXG8efMGjRo1grm5OTp06CBeh4SEBLHMy5cvUbFiRYXzjIyMFNabKFNGmViVqedD0NXVVdhWV1cXp0IpE2dRrt+0adMwfvx4cVsmk8Ha2vqD9oeIiIjoYylTiYWFhQWeP3+OzMxM8Qb+fdbW1oiIiIAgCOLi2YyMDERHR5eaGzRlF0q/b+DAgRg4cCCSkpKwefNm9OrVCwEBAbluaAHgyJEjWLBgAby9veHk5CTuNzc3R3x8PEaMGFGshcPe3t6Qy+X4999/xbHfsmWLQhkrK6tcb7uKjo5WWJOgTBllYlWmno9N2TFV9vppa2vnOX2OiIiIqCwoU4u3u3XrhuTkZKxdu1bcl5qailu3bgEA3N3dkZiYiF27donHN2zYAC0tLYXpUSVJXV0dBgYGiIuLU6r81atX8ebNGwCAnp4e+vTpg6ysLMTGxuYq++jRI3h5eWHx4sVwd3dXONajRw/I5XIsXLgw1zkhISGFxpGZmQm5XC4+iUlJScHvv/+uUMbd3R2xsbE4dOiQuG/NmjVFLqNMrMrU87EpE2dRrh8RERFRWVamnlhYW1tj69atGDp0KA4dOgRra2vcvHkTS5cuRaNGjVC5cmX8+uuvGDJkCPbv3w+5XI4zZ87gjz/+yDVtpiR5enpi4sSJOHXqFKysrAp83WxMTAwaN26MGjVqwNTUFOfOnYOHhwcaNmyYq+zMmTMhkUgQFhamsNYg+3WzO3fuxMCBA3Hq1CnUqFEDISEhkMlkOHjwYKEx9+jRAwsXLkTjxo3RoEEDXL58GQYGBgplbGxsMG/ePHzzzTfo3LkzZDIZYmJioKWlVaQyVlZWhcaqTD0fmzJxFuX6EREREZVlEkHZd42WIq9evcK5c+eQlZWF1q1bw8rKSuF4SEgI/v33X6ipqaFNmzYK06AyMjKwceNG9OrVC5UqVQLw7qnH5s2b0bdvXzEBSUhIwF9//YX+/ftDKpUW+zwAWLt2Lbp16ybGmZmZiTNnzuD58+fQ19dHv379CuyvTCbDpUuXEBMTg+rVqyssWH6/7uPHj+f5xqFWrVqhbt26AIC4uDicP38eb9++RdWqVdGiRQuoqSn34CoxMRGnTp1CXFwc6tatC3Nzcxw+fDjXgum7d+/i9u3bsLGxwVdffYW//voL7du3FxfZK1tGmViVqSc/Oa/L+9t5XW8A2LZtG5o1a6bw/Y7C4izo+hVEJpNBKpUiPj4ehoaGSp1DRAVzm7qvpEMo8/QkKaivE4rhw4fD3Ny8pMMhoo+sKPcjZTKxIPoSMLEg+vCYWKiOiQXRl6Uo9yNlairU5+jcuXMK6wTeZ2FhgenTp39RcXwon1t/iIiIiEo7JhYlrEKFCgrTanIe+9Li+FA+t/4QERERlXZMLEpYvXr1lPqOxJcSx4fyufWHiIiIqLQrU6+bJSIiIiKi0omJBRERERERqYyJBRERERERqYyJBRERERERqYyLt4mI6Itxdkmvkg6hzIuKisKmTZtKOgwiKoX4xIKIiIiIiFTGxIKIiIiIiFTGxIKIiIiIiFTGxIKIiIiIiFTGxIKIiIiIiFTGt0IREZUAt6n7SjoEomLRk6Sgvk5JR0FEpRGfWBARERERkcqYWBARERERkcqYWBARERERkcqYWBARERERkcqYWBARERERkcqYWBARERERkcqYWBARERERkcqYWBAAYOHChQgLC/skbWVkZGDhwoV48eLFJ2mvOMpCjERERESlCROLL4wgCFi4cCGePXumsH/WrFl4+vTpJ4khLS0Ns2bNQnh4+CdprzjKQoxEREREpQkTiy9MZmYmZs2ahdDQUIX9M2bMgK2tbQlFRURERERlnUZJB0BFs3LlSiQmJkJNTQ3W1tZwdXWFubl5rnLPnz/H6dOnkZqaCldXV1SvXh0AsGLFCgDAP//8gytXrqBixYoYOXIkdHR0oK6uDgD4888/YW9vDxcXF4U6161bh/r166NFixYAgLCwMJw6dQrp6elo0KABWrZsWeT++Pj4wM/PD+bm5ujRowc0NTUBvJuKtHTpUgwePBgPHjyAn58fWrdujSZNmhQ6Bu+fGxQUhAcPHsDU1BQ9evSAlpaWUuP0vosXLxZYR2HjEB8fj6NHjyImJga1a9eGq6trkceJiIiIqLTjE4syJi0tDampqUhISMC+fftQo0YNXLt2TaHMli1b4OjoiGPHjiEwMBB9+/bFnj17xPMBID09HampqeL2+1OhAgICMGXKFIU6Q0JCMGbMGPHGf9u2bahXrx6uX7+O4OBg9OvXD4MHDy5SX4YPH465c+ciODgYU6ZMQbt27SCXy8U4Z82ahU6dOuGXX35BbGwsMjMzlRqD7HM9PDywaNEihIaGYtasWWjbti0EQVBqnLKNHDmywDoKG4fnz5/D0dER27dvx4sXL7B8+XL06NGjSONEREREVBZIhPfvkqjMmTlzJq5cuYKLFy8CeJcAVK1aFevXr8ewYcMAAHK5HP7+/qhVqxbkcjk0NTVx4cIFhScSEokEZ8+ehaurK+7fv4969eohKCgIDg4OAIAFCxZgx44d8Pf3x/Pnz+Hk5ITLly+jcePGAICYmBg4OTlh165d6NChQ4ExJyYmwsDAAO3bt8fJkyehpqaGV69ewcnJCStWrMCQIUPEMt9++y127NhRpDHIPnfEiBHYsGEDAODFixeoXLkyLl++jBYtWhQ6TsrUocw4rFy5Ev/88w9u374txnvnzh00aNAgVz/S0tLERA8AZDIZrK2tER8fD0NDwwLHgMoet6n7SjoEomLRk6Sgvk4ohg8fnucTcyL6vMhkMkilUqXuRzgVqozJzMzE2bNnERgYCJlMhvDwcDx48EA8fvToURgZGWHo0KHiPg0NDdSqVUvpNurWrYtatWphx44dmDNnDgBg586d6NevHwDgyJEj0NHRwblz5+Dj4wPg3aJwqVQKX1/fQhOLbEOGDIGa2ruHZqampujatStOnDiBIUOGiGW++eabIo9Btp49e4r/trS0hImJCUJDQ9GiRQulx6mgOpQZBwsLC4SGhuLMmTNo164d1NXV80wqAGDx4sWYN2+eMkNHREREVOpwKlQZkpSUhMaNG+PHH3+Ev78/kpOTIZfL8fbtW7HMy5cvYWlpCYlEolJb/fr1E58U3L59G/7+/mJiERkZCR0dHSQmJiIpKQlJSUlITk5Gv379xL/cKyPnX7osLCxyvd61YsWKCtvKjEE2fX19hW0NDQ1kZGQAUH6cCqpDmXHo1asXpkyZgtGjR6N8+fLo2rUrLl++nGdb06ZNQ3x8vPjDN1IRERFRWcInFmXI4cOHERUVhdDQUOjo6AB4twh7586dYhlTU1NERkbmW4eyCUe/fv0wffp03Lx5E7t27UKLFi1gZ2cHADAxMUFqairmz58vPnEojujo6FzbFhYWBZ6jzBgoo7BxUoYy4yCRSDBlyhRMmTIFERERWL9+PVxdXREUFITKlSsrlNXW1oa2trZKMRERERGVFD6xKEOSkpKgpaUlvpUoMzMTf/75p0IZT09PvHnzRmFdQlZWFvz9/QEA6urq0NPTQ2JiYoFtWVtbo1WrVvj777+xe/dueHl5ice6dOmCxMRErFu3TuGcyMjIIn1Q7u+//xYXQr958wZHjhxB+/btCzxHmTFQRmHjpAxlxuHBgwfiugkrKytMmDAB6enpfBpBREREnx0+sShDunTpgpkzZ8LNzQ1NmjSBj48PYmJiFMo4Ojrit99+w+DBg+Ht7Q1ra2tcuHABP/30E6pVqwYAcHFxwezZs3Hr1i2YmZlh5MiRebbn5eWF0aNHAwB69+4t7ndwcMD69esxevRo+Pj4oEaNGggJCcG9e/dw9OhRpfvz9OlTuLq6okGDBjh8+DAcHBwU1lcUdwyUocw4FUaZcXjw4AF69uyJ1q1bw9TUFCdOnEDTpk2LNGWMiIiIqCxgYlGGmJmZ4eHDh9i3bx/i4uIwe/ZsVKtWDX/99ZdCuR9++AHt27fHiRMnkJWVhS1btqBOnTri8T179mDv3r14/vy5+Nf0vD6Q16tXLzx79gyVK1dGhQoVFI599913aN++PY4fP463b9+iV69e2LZtG8qVK1doP7S0tDBjxgwMHz4c9+/fx6NHjzBjxgz07dtXfJ1tdhkrK6sij0F+544bNw5169ZVapyUraOwcfDy8oKLiwtOnjyJmJgYzJkzB56entDQ4H96RERE9Hnh62aJSqmivN6Nyh6+bpbKKr5ulujLwtfNUony9fUVX7+ak6mpKYYPH/6JIyIiIiKij42JBX1wmZmZSE1NzfPY+x+AIyIiIqLPBxML+uCcnZ3h7Oxc0mEQERER0SfE180SEREREZHKmFgQEREREZHKmFgQEREREZHKmFgQEREREZHKuHibiKgEnF3Sq6RDICqWqKgobNq0qaTDIKJSiE8siIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZUwsiIiIiIhIZXwrFBHRJ+Q2dV9Jh0CkEj1JCurrlHQURFQa8YkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjImFErZt24aoqKiSDuOz86HGdceOHYiIiPgAERERERFRcTGxUMLgwYPx6NEjAIAgCNi2bRuio6NLOKqy7/1xVcWIESNw79491QMqJv5OEBERETGxUMrAgQNhYWEBAMjMzMTgwYPh7+9fwlGVfe+Pa1nG3wkiIiIiQKMkGk1ISMCVK1eQmpoKZ2dnmJqaKhyPiorC1atXoaamhlatWsHExEQ8JpfLsX37dnh4eCA2NhaPHj2CqakpWrZsCYlEgnv37iEgIABOTk6oX7++yucBgIuLC4yNjQEAu3fvBgCcOnUKYWFhkEql6N69O+RyOS5fvoyYmBjUrl0b1apVU3o8CurvkSNHYGNjg3r16imcc+jQIdjZ2aFu3boAAJlMhn///Rfp6emoV68e7Ozs8ux7dHQ0/Pz8ULduXdSoUSPfmHKOl5+fH8zNzeHs7JyrbHHbfn9clRmLbIGBgbh79y5sbGzQqFGjPGMv6rUoKM49e/YgJSUFampqsLa2RpMmTaCnpyeem9/vRGFjQ0RERPQ5+eSJxbFjx9C/f3/Y2dmhcuXKmDRpEtauXQt3d3cAwJYtWzB69Gi0bNkScrkc/fv3x7Zt29CrVy8AQGpqKgYPHoxmzZohKSkJjo6OOHPmDDp37oxy5crhzp07sLOzw6lTpzB79mxMmTJFpfOAd1N2zp49C3Nzc1y9ehUAcO/ePURHR8Pc3BwtW7ZEy5Ytoa2tjRo1amD+/Plo2bIl1q9fX+h4FNbfs2fP4tGjR7hw4YJ4TmRkJHr27Inz588DAE6cOIH+/fujXr16MDIywrBhwzBq1CgsXLhQoe+urq548eIFGjRogAoVKhSYWGSf07ZtWzx//hy1atXCpUuX0K5dO+zduxcSiUTltt8fV2XGAgDWrl2LCRMmoG3btpDJZJBIJJDL5eLx169fF+taFBTntWvXEB8fj8zMTDx+/BivXr3CyZMnUatWLQDI83eie/fuhY4NERER0efkkyYWUVFR6NOnD6ZNm4aZM2cCePf04sGDBwDe3TD/8MMPWL16Nb777jsAwJIlSzBy5Ei4uroq/HXbxsYGu3btgkQiweHDh9G9e3cMHDgQd+7cgUQiwfbt2zFixAhMnDgR6urqKp+Xbc2aNdiwYQOmTp0KFxcXAO9udjU1NXHv3j2oqb2bXXby5MlCx0OZ/vbr1w/Ozs6IiIiAlZUVAGDXrl2wsrLCV199hVevXqFPnz7Yu3cvOnbsCAAIDQ1FnTp14O7ujpYtW4rtaWlp4eHDh3n2Kz/Jycnw8/ODtrY2goODUbt2bezfvx+9evX6oG0rMxaRkZGYNGkStm7dim+//RYAMH78ePHGHgD27NlTrGtRUJyrVq1SKDNmzBhMnjwZJ06cAJD370RRxiZbWloa0tLSxG2ZTKZ03EREREQl7ZOusTh48CA0NTUxdepUcZ+BgYE4vebEiRMoV64chg4dKh7/8ccfkZiYKP51PtugQYPEv5o3adIkz33JycmIjIz8IOcVxMDAALGxsQpz7LNvJguiTH+bN28OOzs77Nq1SyyzY8cOfPvtt5BIJDh48CA0NDQQGxuLf/75B//88w8uX74MCwsLXLp0SaG9YcOGFSmpAIBRo0ZBW1sbAGBvb4+uXbviwIEDAPBB21ZmLI4fPw6pVIpvvvlGLDNhwgSFeop7LQqL8+HDhzh48CC2bdsGTU1N3Lx5s8B6ijI22RYvXgypVCr+WFtbKx03ERERUUn7pE8sIiIiUKVKFWho5N3s8+fPYW1tLf6lGQB0dHRgbm6OZ8+eKZQ1MjIS/62lpZXvvvf/AqzKeQX59ttvcevWLTRv3hxmZmZwdXXFDz/8UOjcfmX7++2332LHjh2YNGkSnjx5grt37+Kff/4R61BXV4ePj49C3c2bN4etra3CPjMzM6X7lK1y5coK2zY2Nvj3338/eNvKjEV2mewkEAAsLS0Vfp+Key3yizMjIwPdunXDjRs30KJFCxgZGeHly5eIiYkptD/Kjk22adOmYfz48eK2TCZjckFERERlxidNLIyNjfH69et8j5uYmCA2NjbX/jdv3uRa4F2aaGpqYs2aNVixYgVu376NjRs3okmTJggKCkKlSpXyPU/Z/np5eWHBggV49OgRdu3ahXr16qFmzZoAAKlUCuDdNyEK8/4NubLevHmTazt7QfWHbFuZsTAxMckVj0wmU1hjUdxrkV+cR44cwY0bN/D06VMxAd21axfOnDlTYD1FGZts2tra4tMhIiIiorLmk06F6tChAyIjI3H69GmF/dkfSWvVqhUiIiLg6+srHjt+/DhSUlLQokWLTxlqvjQ0NKCjo4OUlBRx37NnzyAIAjQ1NdGsWTOsWrUKCQkJCAwMLLAuZfvr5OSERo0aYfv27di5cye8vLzEYx07dkRsbKz4ZqJsiYmJhf5VXRnZ054AICUlBceOHcNXX331wdtWZixatWqF0NBQ3L17Vyyzd+9ehXqKey3y8/r1axgbGys81dqzZ49Cmbx+Jz72dSEiIiIqbT7pE4u6deti2rRp6NGjB77//ntYW1vj9OnT6Ny5M77//nvUq1cPI0aMQJcuXfDjjz9CLpdj+fLlmDRpUql6TWeTJk2wbNkyvHjxAhUqVMCLFy+wbds2dO7cGaampjh8+DCcnJzQoEGDAuspSn+9vLwwffp0pKamKqwxqFOnDubNm4cBAwbg8uXLqFGjBkJCQnD06FHs378fFStWVKmvPj4+GDBgABo2bIhdu3ZBT08P33///QdvW5mxqF+/Pvr16wcPDw+MGzcOMpkMf//9t8JUKG9v72Jdi/y4u7tj0qRJ6N+/P5o1a4bTp0/j2rVrucrl/J3o3r37R70uRERERKXNJ/9A3s8//4zjx49DEASEhYVh3Lhx4o0qAPz+++9Yv349IiMjERMTgx07duDnn38Wj2tqamLgwIEK3zfQ0dHBwIEDUb58eXGfvr4+Bg4cCENDQ5XOA3J/yG3Pnj1wcXHBjRs38N9//2HMmDH4448/kJWVBT8/P3Tu3Bn//fefwrcO8lNYf7P17dsXvXr1wuzZs3N9VG727Nm4fPkypFIp/Pz8YG1tjatXr4rfuMir78ras2cPmjdvjqCgIHh6esLX1xe6urofpO2c46rMWGzduhXz589HaGgo9PX1cf36dQwdOlRci1Dca5FfnLa2trh58yYsLS3x8OFDdOzYEadOncLAgQNzjdP7vxPKjA0RERHR50QiCIJQ0kFQ6ZOYmAgDAwNcv34dzZo1K+lwvkgymQxSqRTx8fEKiS6VbW5T95V0CEQq0ZOkoL5OKIYPHy5+h4iIPl9FuR8pkS9vfykePXqU72tJjY2N0bVr108c0f8UFlu7du0+cUQfV2m+FkRERESfAyYWH1F4eDguXryY5zErK6sSvZktLDZ3d/diT58qjUrztSAiIiL6HHAqFFEpxalQnydOhaKyjlOhiL4sRbkf+eSLt4mIiIiI6PPDxIKIiIiIiFTGxIKIiIiIiFTGxIKIiIiIiFTGt0IREX1CZ5f0KukQiFQSFRWFTZs2lXQYRFQK8YkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjIkFERERERGpjK+bJSIqJrep+0o6BKJPTk+Sgvo6JR0FEZVGfGJBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2JBREREREQqY2LxmXnz5g2uXr2KK1euAAB8fHzw5s0b8XjObfpwLl68iJiYmJIOg4iIiKhEMLH4jJw9exa2traYMmUK1q1bBwBwc3PDnTt3xDI5t1UhCAJ8fHzw9u3bD1JfWefp6QlfX9+SDoOIiIioRDCx+IysW7cOAwcOxJUrV7Br1y4AQLt27VChQoWP0l5mZibc3Nxw//79j1I/EREREZUd/PL2Z+L8+fMIDAyEsbExfHx8YGNjA0dHR0ydOhU2NjYFnpuZmYmHDx8iPT0d1atXh4GBgVJt/vvvvwCA27dvQy6XQ09PDwBgZmYGW1tbhbK+vr4wNTWFjY0NLly4gCZNmiArKwv+/v4wMzNDlSpVihVXUFAQYmJiUL16dRgZGSkVNwBkZWXh0aNHSE1NRZ06daCtra1wPDU1Fffu3YOamhrq1asHLS2tXHW8ffsWjx8/ho2NDaysrPJsp7hjS0RERFTWMLH4TCxduhQvXrzA5cuXER4ejt69e8PR0RFubm44e/YsXF1d8zzvxo0b6Nu3L7S0tGBkZAR/f38sXrwYo0aNKrTNVatWAQB27tyJkydPonLlytDW1kZwcDDOnDkjlnv58iVatWqFkydPwtTUFG5ubujbty/OnTsHGxsbPHz4EN9//z1+++03peNKTEyEh4cH/P394ejoiKdPn2LUqFGYPXt2oXH7+vri22+/RWpqKiwtLREbG4vt27ejRYsWAABvb28MGjQIpqamkMvlSEhIwK5du9CmTRuxjr1792Lw4MGws7NDYmIiGjVqhMzMzA82tkRERERlDadCfSZOnz6N6tWrY9CgQfDx8cHw4cMLPSc+Ph6dO3fGzJkzERAQgBs3buD8+fOYOHEi7t69W+j5Bw4cAAAsX74cPj4+2LJlC4YNG4Zz587h+fPnYrm///4blpaWaNeunbjPz88PAQEBuHnzJq5cuYJ169aJyYgyce3YsQORkZF4/vw5rly5ghcvXuT71OB9b9++hYeHBzw8PBAeHi62Hx8fLx4fOHAgxo8fjydPniAoKAjffvstvLy8kJycDODdAvnhw4dj0aJFePjwIUJCQlCuXDmkpqaqNLZpaWmQyWQKP0RERERlBROLL9jBgweRkZEBe3t7XLhwARcuXEB8fDxsbW1x9uzZYtXZsGFD1K1bF9u2bRP3bd26FYMGDYJEIhH3jR07FsbGxgCARo0awdPTE3///bfScQmCALlcLt58q6urY8iQIYXGd+DAAaSnp+OXX36Buro6AMDCwgIdO3YEABw9ehRZWVmYNGmSeM7cuXPx6tUrse2jR49CXV0dY8aMAQBIJBLMnTtXoZ3ijO3ixYshlUrFH2tr60L7Q0RERFRacCrUFywoKAhZWVlYuHChwn5zc3OV1gIMGzYMy5Ytw6xZs+Dr6wt/f38MHjxYoYyjo2Ou7exX5CoTV//+/XH69GnY2NigefPmcHV1xdChQ2FqalpgbCEhIbC1tUW5cuXyPV6lShWFNRWGhoawsLBAaGgoACA0NBS2trbQ0Pjffz45t4szttOmTcP48ePFbZlMxuSCiIiIygwmFl8wPT096OrqwsfH54PW269fP0ycOBEXLlzArl270K5du1wLyBMSEnJtZy++ViYuPT09HDp0CK9fv8alS5ewefNmrFy5Ev7+/uKTkLzo6+sjLi4u3+NGRkZ5TkGSyWRifFKpNFf8KSkpkMvlCvEVdWy1tbVzLSInIiIiKis4FeoL1rZtW0RHRysstAbevcko541zXjQ0NKClpYX09HSF/VKpFD179sTatWuxZ8+ePKconTx5UqG906dPo2nTpkrHlZ0cmJiYoGfPnti7dy9evXqFhw8fFhhzmzZtEB4ejhs3bijsz663adOmCAsLw+PHj8Vj2WswGjduLJZ5+vQpnj59KpY5fvy4Qn2qji0RERFRWcMnFl+w5s2bY8SIEejVqxemTp2KGjVqICQkBNu2bcMff/yBJk2aFFpHnTp1sHnzZmRlZUEqlaJ58+YAgO+++w5fffUVjIyM0L1791zn7dixA0ZGRmjYsCH++usvJCUl4YcfflA6rt9//x2+vr7o2rUrTE1NsX//fpibm6Nu3bqF9nnQoEHo3Lkzpk+fDmtra5w+fRqOjo6YNGkSWrRoge7du6NLly6YPXs25HI5Zs2ahSFDhqBmzZoAAGdnZ3To0EFcnC2TybBo0SKoqakptKPq2BIRERGVJUwsPiNNmjTJ9f2InB/Iy7m9YcMGtG/fHocPH8a1a9dQtWpV7N69G9WrV1eqze3bt2PFihVYtWoVzM3NxcSiVatWMDc3R/fu3aGjo5PrvN27d+PKlSv4559/YGFhgWvXrilMYSosrunTp+P48eM4ePCg+B0LX19fSKXSQmPesmULduzYAW9vb1y9ehXu7u4KT1V27dqF9evX4+DBg1BTU8OsWbPw3XffKdSxb98+/Prrr9i1axdsbGxw8uRJTJ8+HSYmJh9sbImIiIjKEokgCEJJB0Gfn8DAQFSrVg23b99G/fr1xf2JiYkwMDDA9evX0axZsxKMsPSTyWSQSqWIj4+HoaFhSYdDeXCbuq+kQyD65PQkKaivE4rhw4fD3Ny8pMMhoo+sKPcjfGJB+Xr+/DkCAwPzPKavr59nYpCQkICrV69i2bJlaN++vUJS8SkUJ2YiIiIiUh0TC8rXjRs3sHHjxjyP2dra5nmTHhUVheXLl8POzg4LFizIdVxDQwPt2rVTaspScRQnZiIiIiJSHadCEZVSnApV+nEqFH2JOBWK6MtSlPsRvm6WiIiIiIhUxsSCiIiIiIhUxsSCiIiIiIhUxsSCiIiIiIhUxrdCEREV09klvUo6BKJPLioqCps2bSrpMIioFOITCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhlfN0tEH5Xb1H0lHQIRfUB6khTU1ynpKIioNOITCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTCyIiIiIiUhkTizLIz88PSUlJJR1GiXt/HLKysuDn54eUlJQSjoqIiIjoy8TEQkWhoaF49erVJ22zdu3auH79+idt81MpStL0/jgkJyejdu3auH///ieNgYiIiIjeYWKhov79++P333//pG3WrFkT+vr6n7TNT0Eul6N27dq4efOmUuU/xjgUNQYiIiIieoeJBYDXr18jJCRE/Pfjx48VjsfHxyM4OBgZGRkK+58/f47k5GS8evUKfn5+8PPzg1wuR2BgIN6+fatQ9tmzZ4iOjha3g4ODERMTA0EQEBYWhufPnytM5xEEAc+fP8/zL+e7d+9G7dq1AUDpcwAgPT0dwcHBSEtLAwA8efIECQkJRRqrV69eITw8PM9j7/clJ2Xi9Pf3B/DuKZCfnx+CgoIUzktLS0NQUBBiY2NzjUPOtsLCwnL1LSMjA35+fkhPT1fY//jxY7FsXjEUtX854wQAmUyG0NBQZGVl5Tl2RERERGUdEwsAGzduhKenJ7p3746aNWuid+/eAICEhAT07dsXVlZWcHNzg7GxMWbMmAFBEAAAy5cvR0BAAPbv34++ffuib9++iI+PR/v27XHgwAGFNkaMGIFly5aJ23369MHo0aNRrVo1tG3bFsuXLxen80ydOhUWFhZo3bo1jI2NsXDhQoW68poCVNg5J0+ehKWlJZo3bw4zMzOMGTMGDRs2xKVLl5QaoydPnqBZs2awsbFBixYtUKtWLTx48EA8fuXKFTg4OKBhw4aoU6cOqlevjjt37ojHlYnzu+++AwDMnz8fffv2xYQJE8TzJk+eDCsrK3Tu3BknT57MNQ7Ztm/fDhMTE7Rq1QoVK1bEkiVLxGMvXrxA7dq1cyUGDRo0EMchrxiK0r+ccaanp6NPnz4wNzeHm5sbTExMsH79eqXGnIiIiKgsYWLx/548eYLmzZuLTx8AYMiQIUhLS0NkZCRCQkLg5+eHv//+G1u3bgUArFq1CvXr18eoUaPEJxYVKlRQus3jx49j165dCAkJwapVq8T9vr6+8PPzQ2hoKI4cOYLZs2cr/OU8LwWdk5CQAC8vL4wYMQKvXr3Cq1ev8OLFC6UXOicmJsLNzQ12dnZ48+YNwsPDsWfPHgQEBAAAkpKS0Lt3b3Tp0gUxMTGIjY1F06ZN0bt371xPBwqK8/LlywCArVu3ws/PD0ePHhXPu3z5Mh4/fgx/f394eXnlG+vJkydx//59hIeHw9vbGzNmzMC1a9eU6md+MRSlfznj3LFjB65fv47w8HA8ffoUz549Q3x8fJ5tp6WlQSaTKfwQERERlRVMLP6fiYkJJk6cKG5HRERg//79GDJkCF68eIEnT54gOTkZHTp0wOHDhz9Im3379kWDBg1y7Z8yZYqYoHTs2BFSqVTh6UBeCjrn8OHDyMzMxKxZswAAmpqaCn/JL8zBgwfx5s0brF+/HuXKlQPwbn1Dr169xPoTEhKwaNEiSCQSqKurY9myZQgLC4OPj4/KfQOAiRMnwsTEpNBy48ePh5WVFQCgffv2cHd3xx9//KF0X/NSlP7ljDMxMRHlypUTx01fXx9Tp07Ns53FixdDKpWKP9bW1irFTURERPQpaZR0AKWFjY0N1NT+l2dlz7WfOnUqJBKJQtnq1at/kDZtbW3z3G9hYaGwraenV+haiILOCQ4Ohr29PbS1tcXjjo6O0NBQ7vIHBATAzs4OUqk0z+NPnz6Fra0tdHV1xX0VK1aEubk5nj59qnScBclvrHKqUaOGwnbNmjVVfoNWUfqXM04vLy/s3LkT1tbW6NChA1xdXdG7d2/o6enlamfatGkYP368uC2TyZhcEBERUZnBxOL/qaurK2xraWkBALy9vWFnZ1ekunImIsC7tw0V1ubHoqurm2vaU3p6ep4x5UVHRwfJycn5HtfT08tzWlVycnKeN9DFoexY5Yzj/Rjyui6CICAzM7PAOovSv5xxGhsb4/r163j06BEuXLiA33//HYsWLcLdu3dhYGCgUFZbW1sh+SMiIiIqSzgVKh/169eHnp4e9u7dm+vY+/PqdXR0cr0tysTEBJGRkQrlHz169PGCLUS9evUQFBSkENO///6r9PnOzs549uyZuPYkW3a/GzRogNDQUISGhorH7t69izdv3qB+/fpKt6Ourg4NDY1c41kUFy5cEP8tCAIuXrwoxpA9Ren9cXj48KFCgpVXDKr0L/t3pWbNmhgzZgzOnDmD4OBg3L17t9h9JCIiIiqN+MQiHwYGBvj5558xefJkZGRkwNXVFTExMTh8+DDs7e0xffp0AO+mRfn4+KBbt24oV64cqlWrBk9PT6xfvx7169eHVCrFypUrFV49+qm1b98eDRo0QK9evbBgwQIkJCRgwoQJkEgkef4VP6e2bduiU6dO6Nq1KxYtWgRra2ucPn0aurq6mDp1Ktq2bYvWrVujZ8+eWLx4MeRyOcaPH48ePXrkuYYkPxKJBNWqVcO+fftgYmICPT09mJubF6mvGzZsgLW1NRo2bIgtW7YgPDwc48aNA/DuyU2bNm0wffp0LFmyBHFxcZg1a5bCGOQVgyr9W7RoEaKiotC1a1eYmppi7969MDIyyjVli4iIiKisY2IBwNTUFPb29rn2jx07Fo6Ojti8eTMOHz4Ma2trdO/eHf379xfLzJgxA9OnT8eoUaOQkpKCS5cuYerUqcjKysKSJUtgZGSEPn36wNTUVOEm2cHBIddiZHV1ddSsWVNhLj8AVKtWDcbGxuL2+x+GU/Ycb29vzJw5E5MnT4aNjQ02bNgAd3d3cVFxYQ4cOIDVq1dj/fr1yMrKgru7u8J6gMOHD+Pnn3/G7Nmzoaamhr59+2LKlClF7tuWLVuwcOFCDB48GNbW1tizZ0+e5+U3DkuWLMGhQ4ewc+dOmJmZ4eLFizAzMxPP2blzJ2bOnIkJEyagSpUqWL9+PUaPHg1DQ8N8Yzh69Gix+zd79mxs27YNv//+O2JiYlC9enX8+++/qFixolLjTkRERFRWSITsjzLQZ00ulyss1r569SpatWqFiIiIXAuqqXSQyWSQSqWIj49XSHzKGrep+0o6BCL6gPQkKaivE4rhw4cX+akyEZU9Rbkf4ROLL8Ts2bNhamqKZs2a4dmzZ5g2bRp69eoFCwsLxMbGIioqKs/zdHR04ODg8ImjJSIiIqKyhonFF2Ly5Mn4+eefMXHiROjr62P06NHi2gNvb2+Fr4K/z9HREYcOHfqUoRIRERFRGcTE4gthZGSEX375Jc9jgwYNwqBBgz5tQERERET0WeHrZomIiIiISGVMLIiIiIiISGVMLIiIiIiISGVMLIiIiIiISGVcvE1EH9XZJb1KOgQi+oCioqKwadOmkg6DiEohPrEgIiIiIiKVMbEgIiIiIiKVMbEgIiIiIiKVMbEgIiIiIiKVMbEgIiIiIiKVMbEgIiIiIiKV8XWzRF84t6n7SjoEIipD9CQpqK9T0lEQUWnEJxZERERERKQyJhZERERERKQyJhZERERERKQyJhZERERERKQyJhZERERERKQyJhZERERERKSyUpdYJCcnIyUl5ZO3m5SUVCLtUm68FkRERERlT6lLLIYPH44ffvjhk7fbr18/TJo06ZO3W5JK6w38l3gtiIiIiMq6UpdY0KfTp08fTJs2raTDyEVPTw+6urolHQYRERERFUGp+PJ2eno6NDU1IZFICiyXmpoKHR3Fz32mpKQgMzMT+vr6CvvT0tKQnp4OAwODQusoSFZWFtLS0lCuXLlcx5KSkqCurg4dHR1kZmZCEARoaOQ/pPm1/X49crkcaWlp0NPTUzrGtLQ0aGtrK+xLSUlBWloaAMDAwADq6uoKx5OTk8W24uLiAACGhoZQU/tfrlnYWL1/3ZKTkyGRSHKNk7Lj936/N23apBDH+5SJSUtLK9/jBclrHFXpx/vnZmZmQlNTs1hxEREREZUFJfrEIiEhAb169YKuri7Kly+P3r174+3btwplMjMzMXv2bFSsWBFSqRQWFhZYtmyZeHz37t2wsbFBRkaGwnleXl4YPHiwUnXkJTk5Gd999x0MDAxgaGgIR0dHHDlyRKFM165dMWzYMHTo0AHGxsbQ09PDiBEjFGJRpu2uXbviu+++g7u7OwwNDdGnT59Cxy4zMxNz5syBqakpDAwM4OTkhBMnTojHZ8yYgSpVqqBKlSrQ19eHs7Mz7t+/Lx6fOHEizp07hy1btojlgoODlYo3Pj4e3bt3R7ly5cTr1qtXL4UpbMqOX179zjkVSpmY9u/fD3t7exgaGsLU1BTTp09Henq6yuOoSj+ioqLQo0cP6OrqQiqVol69erh06VKhMRERERGVRSWaWEyaNAmPHz9GUFAQoqOjUa1aNYWbOgCYPn06Tpw4gWvXriEtLQ0nT57Eb7/9hi1btgAAvv76ayQnJ+PUqVPiOTKZDMeOHYOXl5dSdeRl8uTJuHjxIm7fvo3k5GSMHj0aPXv2REBAgEK5HTt24Ouvv8abN2/w33//4dixY/jll1+Ujj/bzp07MXjwYCQkJODYsWNKjd0ff/yBffv2ITU1FcePH1e4aV2xYgXi4uIQFxeHN2/ewNnZGV9//bWY9Pz+++/o0KEDvv/+e7Gco6OjUvFOmDABQUFBePr0KaKjo1GzZs1c103Z8VOm34XFJJPJ8O2332Lu3LlISUlBQEAAjIyMcrVVnHEsbj8yMjLg5uYGCwsLvHr1CgkJCRg7diw8PT3x/PnzPGNJS0uDTCZT+CEiIiIqKySCIAgl0XBycjKMjIywd+9edOvWDcC7KSN2dnZwdXXF5s2bkZycjAoVKuDgwYNo1aoVBEGAIAj49ddfcfXqVZw/fx4A0Lt3b6ipqWH37t0AgK1bt2LixImIioqCXC5Xqo5u3brBysoKa9euRUpKCgwNDbF9+3aFpwdNmjRBkyZNsHbtWgCAq6srUlNTceXKFbHM6tWrsWjRIrx8+VLp+F1dXaGjo6NUQgEAiYmJqFChAjZu3IhBgwYVWFYQBCQkJCA1NRUWFha4desW6tWrBwDw9PSEg4MDVq5cKV6TwuJNSkqCkZER9u3bp3DdHBwc0LZtW2zevLlI45dXv9+/FsrEFBoaCjs7OwQFBcHBwUGpMVRmHFXpx8GDBzF06FCEhYWJ07oEQYCLiwsGDRqEsWPH5mpv7ty5mDdvXq798fHxMDQ0VLpfReU2dd9Hq5uIPj96khTU1wnF8OHDYW5uXtLhENFHJpPJIJVKlbofKbE1FiEhIcjIyBBvcgFATU0NdevWFbf9/f2RmpqKvn375lp/YWtrK/7by8sLffv2RUJCAgwMDLB9+3b06tULWlpa8PPzU6qO9wUHB0Mul6Nhw4YK+xs3bpzrL9Xvxw8A9evXx6tXrxAXF4eQkBCl265atWqeseQlICAA6enpaNGiRb5lbt26hfHjx+O///6Duro6NDU1kZmZiYiIiFwxZ1NmvENCQiCXy3Ndtzp16ojbRRm/wvqtTEy2trbo27cvmjRpgu7du6NNmzbw9PSEkZFRgXUXNo6q9OPevXtISEiAjY1NrnpfvXqVZ3vTpk3D+PHjxW2ZTAZra+sC+0BERERUWpRYYpG9mDgzM1Nhv1wuz1X2ypUrqF27dr51dezYEbq6ujh48CDc3Nxw8eLFXH/5LayO92UvwM4Zi1wuz7U4O7/43y+nTNtFWdib/RfwnOtKsmVlZaFz587o378/vL29IZVKkZmZKS4uLkxB8Wb3q6DrVpTxU7bfhY3hrl27cP/+fZw9exZ//PEHxo4dCx8fHzRo0CDfcwobR1X7YWNjg+Dg4Hzbz0lbWzvfxeNEREREpV2JrbGws7ODrq4url+/Lu5LT0/HnTt3xO0aNWpAX18f3t7eBdalqamJXr16YceOHdi1axcqV64MZ2fnItWRV2zXrl0T9wmCgKtXr6JWrVoKZd+PP3vbxsYG+vr6xWpbGdWrV4e+vj4uXLiQ5/HIyEhER0djxIgRkEqlAID//vsv1w2ylpaWQoKgTLy2trYoV64cfH19xX0ZGRkK160o41eYooxh3bp1MXHiRFy6dAnVqlXDjh07Cixf2Diq0o8mTZogNDQUjx49KjRuIiIios9BiT2x0NbWxtixYzFt2jRYWlrCxsYGCxcuRHR0tFhGR0cHc+bMwZw5c2BsbAwPDw+8ffsWJ06cQHJyMhYsWCCW7devH1xcXBASEoJ+/fqJ02aKUkc2LS0tTJo0CTNmzICVlRWcnJywZs0ahIWF4ccff1Qoe//+fUyfPh0jRozAvXv38Msvv2DRokXFblsZOjo6mDZtGmbNmgWpVAoXFxcEBATg77//xt9//w0zMzOYmppi5cqVmDp1KkJCQjB8+PBc9djZ2eHy5cuIiIiAvr4+DA0NC41XR0cHo0ePxvTp02FpaYnKlStj6dKliIqKKtb4KdPXwmK6du0aVq1ahVGjRqFq1arw9/dHUFAQhg0bptI4qtKPTp06oWXLlujZsydWr16NGjVqICQkBJs2bcKQIUPQpk2bIo0DERERUWlXot+xmD9/PtLT0zFgwAAYGBjAw8MDAwYMUPg42sSJE2FhYYF169Zh7ty5sLCwgKenZ64vMzs7O6N69eoIDw8X3wZVlDpyfpRt1qxZ0NDQwNixY/H27VvUqVMH58+fh6WlpULd33//PWJiYuDm5ga5XI5JkyZh1KhRRWpbX18/z+8jFGT69OkwMTHBypUrMXnyZNStWxdLly4F8G4Kz+HDhzFx4kQ0aNAA5ubmmDx5MmbNmqUwZeenn36Cv78/mjZtiqSkJNy8eVOpeBctWoSMjAz0799fvG6dOnVS+L6EMuOXX79zXovCYmrevDkiIiIwZ84cBAYGolKlSpg+fTqGDBmi0jiq0g81NTWcOnUKCxcuxLhx4/D27VtUrVoVw4cPh4uLS6FxEREREZU1JfZWqM+Bq6srGjVqhCVLlpR0KCUqMzMTTk5OGDt2LMaNG1fS4Xw2ivIWBlXwrVBEVBR8KxTRl6VMvBWK8peVlVXgNwxyfiH7Uztz5gxu3bqFXr16ITMzE8uXL8fr16/Ru3fvEospL6V9HImIiIg+J0wsVFCcKUzKePbsGerXr5/v8bt37+b7qtxPoU2bNrh69Sq6d++OpKQk1K1bF76+vqXuL1elfRyJiIiIPiecCkVUSnEqFBGVRpwKRfRlKcr9COeBEBERERGRyphYEBERERGRyphYEBERERGRyphYEBERERGRyvhWKKIv3NklvUo6BCIqQ6KiorBp06aSDoOISiE+sSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSAiIiIiIpUxsSD6v/buNCqqI+0D+B9ooNlRQEDZRREPA2hYkrgMCqhxw+DSbnHJGKMxMeaI0egwJi6JWY4aNVFjFGeMiuCGy7gLCggiBuMSVIwILUhEUEACAdrn/cDLnTQ0TTfNIvD8zvFDV9+qeqputXTdW3WbMcYYY4xpjCcWjDHGGGOMMY2JWjsAxphiRAQAKC4ubuVIGGPsf0pKSlBeXo6SkhIYGRm1djiMsWZW8z2k5nuJMlqkylGMsRb38OFD2Nvbt3YYjDHGGGOQSqWws7NTegxPLBh7Sb148QK5ubkwMTGBlpZWa4fT5hUXF8Pe3h5SqRSmpqatHQ5TgM9R28Dn6eXH56htaCvniYhQUlKCrl27Qltb+S4KXgrF2EtKW1u7wSsDTH2mpqYv9X/gjM9RW8Hn6eXH56htaAvnyczMTKXjePM2Y4wxxhhjTGM8sWCMMcYYY4xpjCcWjLEOQV9fH8uXL4e+vn5rh8LqweeobeDz9PLjc9Q2tMfzxJu3GWOMMcYYYxrjOxaMMcYYY4wxjfHEgjHGGGOMMaYxnlgwxhhjjDHGNMa/Y8EYa/dyc3Nx//59eHp6qvys8OzsbDx+/Bg9e/Z86Z8v3h5UVFTg5s2bMDIygpubm9Jjq6qqkJycXCe9V69esLS0bK4QO5ScnBw8evQI3bt3R6dOnZotD2u8qqoq3Lx5E3p6enB3d2/wh1QTEhLqpLm6usLGxqa5QmQACgoKkJ6eDjc3N1hZWamUJy8vD1KpFC4uLrCwsGjmCJsYMcZYO3X58mV68803ycrKigBQfHx8g3n++OMPCgkJIUNDQ+rVqxcZGBjQd9991wLRdlynTp0iS0tLcnZ2ps6dO1OfPn3o4cOH9R6fn59PAMjb25v69esn/Dt79mwLRt0+VVRU0OTJk0ksFpO7uzuJxWJas2ZNk+dhmklISCBbW1tycHAgKysr6t27N927d6/e4ysrKwkAeXh4yH1mDh8+3IJRdyy3b9+madOmka2tLQGgXbt2NZhHJpPR7NmzSV9fn3r37k36+vq0dOnSFoi26fDEgjHWbm3fvp32799PGRkZKk8swsLCyMHBgfLy8oiIKDo6mrS0tCg1NbW5w+2QCgoKyMzMjP71r38REVF5eTn169ePgoOD681TM7FIS0troSg7jlWrVlGXLl3owYMHRER0+vRp0tLSonPnzjVpHtZ4z58/JxsbG5o/fz4REVVVVdHQoUPJ19e33jw1E4vY2NgWipIdOnSIIiIi6I8//lB5YrFp0yYyMzOj9PR0IiK6dOkS6erq0oEDB5o73CbDEwvGWLuXmZmp0sTixYsXZGFhQatWrZJLd3d3p3nz5jVniB3WDz/8QGKxmEpKSoS0w4cPEwDKzs5WmKdmYnHkyBG6evUqPXv2rKXCbfdcXFwoLCxMLu3VV1+lKVOmNGke1nhRUVGkra1Nv//+u5AWFxdHAOjGjRsK89RMLHbv3k2pqalUUFDQUuEyIpUnFn379qVZs2bJpQ0bNoxGjBjRXKE1Od68zRhj/08qlaKgoACvvPKKXLqvry/S0tJaKar2LS0tDW5ubjA2NhbS/Pz8AADXrl1TmnfWrFmYPn06rKysMHXqVBQXFzdnqO1ecXEx7t+/X2f8+/n51Tv+G5OHaSYtLQ329vbo0qWLkFbzmWmoz+fPn4+ZM2fC1tYWY8eORUFBQbPGylQnk8lw48aNNv9Z4s3bjLE24+7du3j8+LHSY3x9fRv9K6aFhYUAUGeznIWFBVJSUhpVZkdTUVHRYF9ZWVkJG7QLCwsV9nfNe4ro6elh7969mDhxIoDqcREYGIiPPvoI27dv17QJHZay8V/fuWhMHqYZRZ8ZAwMDGBgY1NvnWlpa2L59O2bOnAktLS1kZWUhODgYs2fPxoEDB1oibNaAkpISVFZWtvnPEk8sGGNtxr59+3Dq1Cmlx+zfv7/RTznR1dUFAJSXl8ull5WVQU9Pr1FldjRFRUVYsmSJ0mOCgoLw6aefAqjuc0X9DaDePjc1NRUmFQDQs2dPLFy4EP/85z/x448/Nvh0HKZYY8Y/f2ZanqLPDBGhoqKi3j7X0dHB22+/Lbx2dHTE0qVLMWvWLJSXl0MsFjdrzKxh7eWzxBMLxlibER4ejvDw8GYr38HBAVpaWsjJyZFLz8nJgYODQ7PV255YWVkpfKxlfRwdHREfHy+XVtP/6vS5tbU1SktLUVRUBHNzc5Xzsf+xsbGBvr6+WuO/MXmYZhwdHfHo0SMQkTCJfvToEWQymdqfGZlMhry8PDg5OTVTtExVRkZGsLCwaPOfJd5jwRjr0DIyMoS1/CYmJvD398eRI0eE90tKSnD+/HkEBwe3UoTtW3BwMLKysnD9+nUhLSYmBubm5vD19QUAVFZWIiEhAU+ePAEAlJaW1inn9OnTsLOz40mFBnR0dDBo0CC58f/nn3/i5MmTcuM/MzMTqampauVhTSc4OBhPnz6Vm5DHxMRALBZjwIABAKrvYCQkJCAvLw9A/Z8Zc3Nz2NnZtUzgrI7s7Gy53+QJDg7G0aNHhdcymQzHjx9vW5+lVt48zhhjzSYvL4/i4+MpOjqaAND3339P8fHxck8bmj59Onl5eQmvz507RyKRiJYuXUqHDx+mwYMHU48ePej58+et0IKO4Y033qBevXpRVFQUbdy4kcRiMW3YsEF4/9GjRwSA9u7dS0REX375JU2ZMoX27NlDx44do3fffZdEIhHt2bOntZrQbqSkpJC+vj4tWLCAjhw5QiNGjCA7Ozu5pwh9+OGH5OjoqFYe1rQmTZpETk5OtGfPHvrhhx/IxMSEVqxYIbxfVlZGAGjz5s1ERLR161YKDQ2lXbt20X//+1/68MMPSSQS0ZYtW1qrCe3es2fPKD4+nuLj4wkAhYeHU3x8PGVkZAjHLF++nMzMzITXt27dIiMjI5o9ezYdOXKExo8fT1ZWVkp/1+dlo0VE1LpTG8YYax7Hjh3DmjVr6qTPnj0b06ZNAwB8/vnnyMjIQEREhPB+fHw8vv/+ezx+/BheXl5YsmSJ3BNYWNMqKyvD2rVrceHCBRgaGmLSpEmQSCTC+4WFhRg9ejQ+++wzBAYGAgAOHTqEgwcP4smTJ3B1dcW7774LDw+P1mpCu3LlyhVs2LABubm5cHd3x5IlS+Suam/YsAHx8fGIjo5WOQ9rWhUVFdi4cSNOnz4NPT09jB07FjNmzJB7f/DgwVi0aBFCQkIAACdOnMC+ffuQl5cHFxcX/OMf/6jzBCLWdFJTU7FgwYI66aNGjcLixYsBADt27EB0dDROnDghvH/jxg2sXbsW2dnZ6NGjBz7++GO4uLi0VNga44kFY4wxxhhjTGO8x4IxxhhjjDGmMZ5YMMYYY4wxxjTGEwvGGGOMMcaYxnhiwRhjjDHGGNMYTywYY4wxxhhjGuOJBWOMMcYYY0xjPLFgjDHGGGOMaYwnFoyxdu348eP47bff1MoTExODrKysJqlfJpMhKioKf/75Z5OU15ROnTqFu3fvNkvZlZWViIyMxLNnzwBU/wheZGQkSkpKmqU+RXXW1hIxtITGtKOwsBBHjhxpxqhUU1JSgsjISJSVlTV7Xar0kyrxNPe4SU5Oxo0bN5ql7L/ivmctonV/+JsxxppX9+7dafPmzWrlsba2pl27dik95siRI/Tbb781WNamTZto6NChKtWraplNxcvLi9atW9csZT99+pQAUFpaGhERSaVSAkDp6enNUp+iOmtriRhaQmPaUVVVRb169aLjx483Y2QNS09PJwAklUqbva7a/fT8+XPau3cvlZSUqBVPc4+bKVOm0LJly5ql7L/ivmctge9YMMbatZEjR8LV1bXJy/3ggw9w/vx5pceUl5fj008/xbJly5qszLbK0NAQEokEpqamrR1Kh6Sjo4NFixbhk08+ae1QWk1+fj4mTZqEvLw8tfLx2NUc933HIWrtABhjrMYvv/yCoqIiDBw4EADw5MkTnD17Fv369YO9vT0A4MqVK5DJZHj11VcBVC9/SU5OxrNnz+Du7l5nEhEcHAxnZ2e5tN9//x3JycmwtbWFt7c3YmNj4ezsjJ49e9Y57tq1azA2Noa/vz9Eour/Mk+fPo3S0lJcuXIFxsbG0NbWxoQJE+q0JzIyEmZmZhgwYIBcelpaGqRSKVxdXdG7d2+lZZ47dw75+fnQ0tIS4q39RzYmJgbe3t4Qi8UK462Rn5+PpKQk2NjYwNvbu0686tSlo6ODq1evwt7eHn379gUA3LlzB3fu3IGrqyu6du0ql8/AwABjxoyBiYmJkFZRUYHLly+juLgYffr0qZOnqKgISUlJ0NbWhre3N7p06VInZmV1qio3NxdXrlyBoaEhXn/9dRgZGdU5Jj09HRkZGXB1dUWPHj1w4MABDBs2DObm5grLbKhtxcXFuHz5MgDgtddeg7GxMQCAiLBv3z4AgEgkgqOjI/r06VPnXCrSUH+NHz8e8+bNQ0JCAvr3799geaq4e/dunc+Nqu7du4fbt2/DyckJHh4edd5X1h51+6mqqgpHjx4FUL080traGjY2NrCxsWkwHkVjt6H4SkpKcPz4cYSEhCAjIwMZGRnw9/eHnZ2d2v1U8//ghAkToK1dfT24oqICBw8eFMbgX+vLyclR2q/K2qpK29p739+5cwdubm4qHcsUaOU7JowxJti5cyfZ29sLr7ds2UIAKDw8XEh79dVX6fPPPyciouvXr5OzszN5e3vTqFGjyNLSkmbNmkUvXrwQjq+9FComJoYMDAzI39+fBg4cSF5eXmRnZye3JMja2pqGDBlCLi4uNHLkSOratSu9/vrrVFlZSURES5cuJUNDQ/L19SWJREKTJ09W2J6xY8fSrFmzhNd//vknBQUFkZ2dHYWEhJCnpyeFhISQTCart8zly5eTRCKhCRMmkK+vL1laWlJcXJxcPQ3FS0R04sQJMjIyIj8/PxowYIDCdqtaV3BwMDk6OtKYMWNox44dRET02WefkVgspqCgIHJ3d6c33nhD6VKozMxMcnBwIE9PTxo1ahQ5OzvTmjVrhHr27NlD5ubmFBAQQEOHDiUzMzP64Ycf5GJpqM7aFC2r+Oabb8jAwIAGDRpEnp6eZGlpSYmJiXL5li1bRvr6+hQUFES9e/dusJ6G2vbTTz+RiYkJ9e3bl4KDg6lnz5509epVIiKSyWQkkUhIIpFQaGgoubi4kKenJ+Xm5ipthyr9RUTUv39/Wrx4scK4VZWfn0/r1q0jDw8P8vDwUCtvzfKX0aNHk4eHB40YMYIMDQ1p0aJFcsc11B51+6m8vJxGjhxJAGjEiBEkkUho1apVKsXTmP6uKXfkyJHk7u5O48ePp+TkZIV90tBSqPj4eAJAZWVlQlp+fr7cGFSlHdz3qjE3Nyc/Pz/aunUrFRUVqZyPVeOJBWPspZGVlUUAhH0GEydOJB8fHxowYAARVa/T1dXVpUuXLlFlZSV1796dvv76ayH/kydPqFu3bnL7I/46sSgrK6OuXbvK/RHfuHEjAagzsXjllVfo+fPnRFT9R9zY2JiioqKEYxwdHWnbtm1K2+Ps7Exr164VXp88eZJMTU3l1hkfPXqUKioqVC7zyy+/pF69esmlNRRveXk52dvby7V73bp1ddqtal3u7u5yf3Bv3rxJ2tradPr0aSKq/uIRGhqqdGKxZMkSGjx4sFBGVVUVxcTEEBHR7du3ycjIiC5duiS8n5iYSGKxWBgbqtRZW+0Ybt26RTo6OnT48GHhmHfeeYd69uwpnJNffvmFtLS06MyZM0I948ePV1qPsrbdunWLRCIRbdmyRXg/NzeXUlJSFJZVVVVFI0eOpDlz5tTbDlX6q8bcuXPlYlOVTCajkydP0vjx40lPT4+6d+9OK1asoKysLLXKqfnSN3fuXOECwMmTJ0lbW5sePXqkdntqqNJPmZmZBIAyMjLUiqcx/V1T7ltvvSV3oUORppxYKGsH971qbt++TYsXL6auXbuSoaEhTZ8+nS5evKh2OR0VL4VijL00HBwc4OTkhNjYWLi4uODChQvYtm0bxo4diz/++AMJCQnQ09ODj48PLl68iPv378PGxgb79+8HVV8ogaurK2JjYzF16tQ65SclJSE3NxdhYWFC2pw5c7B06dI6x06bNk1YEmNpaQkPDw/cuXNHrfY8efIEnTp1El4bGBigoqIC6enp8PX1BVC9B6QhUqkU6enpePr0KUQiEW7fvo3i4mK5ZUrK4k1KSoJUKpVr97x58xAeHt6oumbMmCH3Ojo6Gp6enggODgYAaGtrIywsDAcPHqy3TQYGBnj8+DEePXoEW1tb6OjoYPTo0QCA3bt3w8bGBjk5OYiOjgYRAQBMTEyQmJgIFxeXRtVZW3R0NNzc3BASEiKkLVu2DNu2bUNaWhr8/Pxw4MABeHl5ISgoSKhn4cKFiI6ObnTbnJ2d8e677wrH29rawtbWVq6MmzdvIjMzE6WlpbC2tkZKSkq99anSXzU6deqEJ0+eqNxHDx48QEREBCIiIlBYWIjx48fjzJkzwnLFxpozZw60tLQAAAEBAXjx4gXu3bsHGxsbtdqjTj81Np7a1Inv/fffF8ptCaq0g/teOTc3N6xZswarV6/GqVOnEBERgaCgIDg5OeHtt9/G9OnTFcbGqvHEgjH2UgkICEBsbCz69++P0tJSDBs2DM7Ozrh06RLi4uLQr18/6Orq4sGDBxCJRDh27Jhcfhsbm3rXx0qlUpibm8utixeJROjWrVudYzt37iz3Wl9fH+Xl5Wq1xdjYGKWlpcLrgQMHYuHChRgyZAisrKwQGBiId955R9ijoMhHH32Ebdu2wdfXF5aWlkIM+fn5cl/ulcWbnZ1dp926urp12q1qXbW/BGdnZ8PJyUkurfa+ltrmz5+Pmzdvonv37vDw8MCQIUPw/vvvw8bGBg8ePEB5eTn2798vl2fw4MHCRK0xddaWlZUl90UEqJ7cikQiZGVlwc/PD1KptE49tV+r07bs7Gz06NGj3rylpaUYPnw4fv31V/j4+MDU1BQPHjzA48eP682jSn/9tfza69WV6dOnD0pLS7Fy5UrMmzdP2AuiSFxcnLA5V1dXF2PHjq332L+OV319fQAQxpsq7WlMPymjLJ7a1Onv2p+V5qZKO7jvqzU0XnV0dDB8+HAMHz4cBQUF+OKLL/DJJ5/gp59+apHHA7dVPLFgjL1UAgICsGzZMgwYMAADBw6Ejo4OAgICEBcXh7i4OOHKr6mpKSorK7F161aYmZmpVHbnzp1RUlICmUwGHR0dIf3p06fN0paePXsiMzNTLm3VqlX49NNPcfXqVezduxf+/v64evUqPD096+S/fv061q9fjzt37ggbZFNTU3Hs2DHhSp0qLCwsUFJSgqqqKrkNln9ttzp11b4KaGFhgfv378ulNdSn5ubmiIqKwvPnz5GQkID169fD19cX9+7dg6mpKbp06YLIyEilbVK3ztosLS3r3IWq6SdLS0sA1WOm9jnUpG3m5ubIyMioN+/27duRk5OD7OxsGBgYAAC++eYbrF+/vt48qvRXjczMTLU2pq5atQpbt25FeHg4kpOTMXPmTAwfPlzhRt3ExEThC5dYLFY6sVBGlfY0pp+aijr93RR3K2o2bL948UJIU/cih6o6Ut+rMl7Lyspw6NAhRERE4Pz58/Dz88P8+fMbF3wHwY+bZYy9VAYNGoScnBz8+OOPCAgIAFA92Th27BiuXr0qpP3973+HWCzG1q1b5fLLZDL8/vvvCsv28fGBSCTC8ePHhbSUlJR6j1fG2Ni4wT/ugYGBSExMFF7n5eVBJpNBJBLB398f69evR+fOnZGamqqwzLy8POjr68tdia99pU4VPj4+0NXVlbu7k5iYKHeFUZO6+vfvj+TkZLlHSTa0JCknJwdAdZuHDRuGdevW4eHDh8jJycGwYcNw7do1JCcny+UpKioS7gA1pk5FcV+5cgVSqVRIi46OhrGxMby8vAAA/fr1Q3JystwYiYmJaXTbhgwZgtTUVPz6669yeWqWJ+Xl5cHBwUH4wkZEDbZLlf6qKSspKUlY1qWKefPm4fr160hMTIS1tTXeeustdOvWDQsXLsTNmzfljl22bBkiIyMRGRmJnTt3qlxHY9rTmH6qudui6ZdyVfu7qdTcWbx3756QFhsb2+T1AB2r75WN18uXL2POnDmwtbXFhx9+iL/97W+4fv06kpOTMXnyZI3a0N7xHQvG2EulZp9FamqqMGkICAjAxIkTYWRkBB8fHwCAlZUVNm7ciLlz5+Lu3bt47bXXkJOTgwMHDmDFihVy6+Zr2NjYYMGCBZg+fTqWLFkCPT09bNiwAcbGxmpfWfTx8UFERARMTU0hFosVPm525syZWLlyJTIzM+Hs7IzLly8jPDwc48aNg4ODAy5evIjKykoEBgYqLHPo0KHo1KkTxo0bh5CQEKSkpChd218fa2trLFy4EDNmzBDa/e2338otifH39290XaNHj4avry8CAwPx3nvvQSqV4t///rfSPOvXr8f169cxdOhQmJqa4j//+Q98fHzg7OwMFxcXTJs2DUOHDsUHH3wAZ2dnpKenIyYmBnFxcTAyMmpUnYriHjRoEAIDA/HBBx+goKAAX331FVavXi0szxgzZgy8vb0RFBSEuXPn4uHDh0I99Y0ZZW1zdnbGuHHjEBAQgPnz58PCwgIHDx7E3LlzERoailGjRuGrr75CWFgY3NzcsH//fqSnpyt8BG6NkSNHNthfAHD+/Hm8ePECb775plr9BAC+vr7w9fXF2rVrsX//fmzfvh1r167FmDFjcOjQIbXLU0aV9jSmnywtLeHk5ITVq1dj1KhR6Nq1a6PWyqva303F0dERAwcOxPTp04Wxvnv37iatowb3ffVY//nnnzFkyBD8+OOPGD16NPT09DQut6PgOxaMsZfOggULMHXqVOG3FqytrfH+++8jLCwMurq6wnGzZs1CamoqrKyscPHiReH56n+dVNT+gbwvvvgC3377LW7fvo3Hjx/j0KFDMDc3l/uSPWbMmDrr6AcPHiy3XGndunUIDQ3FuXPn6r2CbWdnhxkzZmDDhg0AgJCQEERFRaGqqgoXL16Ek5MTrl27BkdHR4VlmpmZITk5Gb169cKFCxfQrVs3XLx4ERKJRO14V65ciU2bNuHOnTvIz89HTEwM3nvvPWFZjCZ1aWlp4eTJk5g2bRpSU1NhYmKCpKQkSCQSYd1z7R+6+vrrr7Fo0SI8fPgQKSkpkEgkiI2NFb6s79y5E3v27EFRUREuXbqEbt264fLly8LVW1XqrE3Rj20dPXoUYWFh+Pnnn4Xx8NFHH8m17cyZM5g8eTKuXLkCIyMj4U5OfXsVlLVNS0sLe/fuxffffy9slF+6dClCQ0MBVP+mRVxcHMrLy5GUlITQ0FBERUXJjWlF7WiovwBg48aNWLhwIcRiscK4VWFoaIhp06bhwoULuHv3LoYMGaJWflNTU0gkEhgaGsqlSyQSuS+aDbWnsf108uRJ2NnZ4fjx40hMTFQpnsb0d33lNtaxY8cwYcIEJCcnw8jICLGxsXJjXZV2cN+rZtKkScjKysKJEycwbtw4nlSoSYvUWajLGGNtXGFhodxmwRs3bsDLywtpaWnC8pemlJ+fj48//hhbtmwRNiaytqX2mNm0aROWL1+O/Px8Yf37yy4/Px+LFy/G5s2beRy+ZKZOnQonJyesWrWqtUNhTGO8FIox1qEcOHAAp0+fRlBQEAoLC7FhwwaMGzeuWSYVQPWSrYiIiGYpm7WMjz/+GIaGhvDy8sKtW7ewefNmfPPNN21mUgFUj8MdO3a0dhiMsXaO71gwxjqcw4cP4+zZs9DR0cFrr70GiUTSos+aZ21LeXk5IiIi8PPPP8PCwgKjR4/G66+/3tphsXbiu+++g5WVlcJ9Woy1NTyxYIwxxhhjjGms7dzHZYwxxhhjjL20eGLBGGOMMcYY0xhPLBhjjDHGGGMa44kFY4wxxhhjTGM8sWCMMcYYY4xpjCcWjDHGGGOMMY3xxIIxxhhjjDGmMZ5YMMYYY4wxxjTGEwvGGGOMMcaYxv4P6+Tej0tGmFIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intercept: -1.023\n" + ] + } + ], + "source": [ + "model = make_model().fit(X, y)\n", + "coefs = pd.Series(model[-1].coef_[0], index=selected).sort_values()\n", + "\n", + "plt.figure(figsize=(8, 5))\n", + "colors = [UNHEALTHY if c > 0 else HEALTHY for c in coefs]\n", + "plt.barh(coefs.index, coefs.values, color=colors)\n", + "plt.axvline(0, color=\"grey\", linewidth=1)\n", + "plt.xlabel(\"weight (standardised log scale) <- healthier | unhealthier ->\")\n", + "plt.title(\"Logistic regression weights\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(\"intercept:\", round(model[-1].intercept_[0], 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "009fec37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "developer_categories_core -1.277484\n", + "returning_contributors -1.167053\n", + "file_types_code -0.905957\n", + "commits_over_periods_rate -0.841944\n", + "active_branches -0.834069\n", + "commit_size_added_lines -0.389225\n", + "contributor_growth_rate 0.114363\n", + "developer_categories_regular 0.193106\n", + "found_file_license 0.376334\n", + "developer_categories_casual 0.958613\n", + "days_since_last_commit 1.081051\n", + "dtype: float64" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coefs" + ] + }, + { + "cell_type": "markdown", + "id": "4749cdaf", + "metadata": {}, + "source": [ + "### What is the distribution of the scores?\n", + "\n", + "During the first iterations of the model and due to the huge amount of dead (or almost dead)\n", + "projects in npm, we find the distribution strongly bimodal. We have most of the values close \n", + "to 0 or close to 1. Almost nothing in between." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "cf52c686", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "count 166.000\n", + "mean 0.462\n", + "std 0.452\n", + "min 0.000\n", + "25% 0.010\n", + "50% 0.229\n", + "75% 0.972\n", + "max 0.981\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "bins = np.linspace(0, 1, 21)\n", + "plt.figure(figsize=(9, 4))\n", + "plt.hist([score_cv[y == 0], score_cv[y == 1]], bins=bins, stacked=True,\n", + " color=[HEALTHY, UNHEALTHY], label=[\"Expert: Healthy\", \"Expert: Unhealthy\"])\n", + "plt.axvline(0.5, color=\"grey\", linestyle=\"--\", linewidth=1)\n", + "plt.xlabel(\"score (0 = healthy, 1 = unhealthy)\")\n", + "plt.ylabel(\"packages\")\n", + "plt.title(\"Cross-validated scores by expert label\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(pd.Series(score_cv).describe().round(3))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "abcd", + "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": "3.13.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model/npm/notebooks/phase1_metrics_cleaning.ipynb b/model/npm/notebooks/phase1_metrics_cleaning.ipynb deleted file mode 100644 index 7796dec..0000000 --- a/model/npm/notebooks/phase1_metrics_cleaning.ipynb +++ /dev/null @@ -1,764 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "60e7fe1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (3.0.3)\n", - "Requirement already satisfied: seaborn in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (0.13.2)\n", - "Requirement already satisfied: matplotlib in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (3.11.0)\n", - "Requirement already satisfied: numpy in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (2.5.1)\n", - "Requirement already satisfied: statsmodels in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (0.14.6)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (1.3.3)\n", - "Requirement already satisfied: cycler>=0.10 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (4.63.0)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (1.5.0)\n", - "Requirement already satisfied: packaging>=20.0 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (26.2)\n", - "Requirement already satisfied: pillow>=9 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (12.3.0)\n", - "Requirement already satisfied: pyparsing>=3 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from matplotlib) (3.3.2)\n", - "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from statsmodels) (1.18.0)\n", - "Requirement already satisfied: patsy>=0.5.6 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from statsmodels) (1.0.2)\n", - "Requirement already satisfied: six>=1.5 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n" - ] - } - ], - "source": [ - "!pip install pandas seaborn matplotlib numpy statsmodels" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e35933aa", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c84bdea9", - "metadata": {}, - "outputs": [], - "source": [ - "# 1. Load the data from GrimoireLab and build a dataframe\n", - "\n", - "with open('../data/metrics.json', 'r') as f:\n", - " data = json.load(f)\n", - "\n", - "records = []\n", - "for pkg, info in data['packages'].items():\n", - " metrics = info.get('metrics', {})\n", - " # metrics['package'] = pkg # Keep the package name just in case\n", - " records.append(metrics)\n", - "\n", - "df = pd.DataFrame(records)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "be46cb90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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total_commits200.0240.1250001.164178e+030.0000000.0000004.00000040.0000001.430100e+04
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total_organizations200.08.9700003.834175e+010.0000000.0000001.0000003.0000002.670000e+02
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recent_organizations200.06.0900002.394722e+010.0000000.0000001.0000003.0000001.650000e+02
recent_contributors200.020.0000009.171860e+010.0000000.0000001.0000005.0000006.290000e+02
recent_commits200.0162.8900008.148622e+020.0000000.0000002.00000029.7500001.018600e+04
contributor_growth200.0-5.0150003.119762e+01-215.000000-1.0000000.0000001.0000001.600000e+01
contributor_growth_rate200.00.3902041.377968e+00-1.000000-0.0344830.0000000.2598048.000000e+00
active_branches200.09.2600009.521619e+010.0000000.0000001.0000003.0000001.347000e+03
days_since_last_commit122.083.2131151.142487e+020.0000002.00000023.000000135.5000004.810000e+02
casual_regular_contributors_rate200.00.5514711.218157e+000.0000000.0000000.0000000.6666679.000000e+00
returning_contributors200.03.0950001.265451e+010.0000000.0000000.0000001.0000008.500000e+01
commits_over_periods_rate200.00.4484774.206525e-010.0000000.0000000.5100680.8628571.000000e+00
coefficient_of_variation122.02.1995011.275881e+000.2162211.3049432.0122743.2984374.123106e+00
file_types_code200.0477.0750003.234830e+030.0000000.0000000.00000017.2500004.347500e+04
file_types_binary200.00.0100009.974843e-020.0000000.0000000.0000000.0000001.000000e+00
file_types_other200.05007.6900003.660997e+040.0000000.0000006.00000092.0000004.910180e+05
commit_size_added_lines200.0147287.4300001.137573e+060.0000000.00000068.0000005702.0000001.524937e+07
commit_size_removed_lines200.0123533.8000008.978386e+050.0000000.00000027.5000002804.7500001.211097e+07
message_size_total200.048822.4750003.910615e+050.0000000.000000235.0000004828.2500005.479443e+06
message_size_mean200.0165.5368165.942889e+020.0000000.00000037.81250098.4666674.546360e+03
message_size_median200.0104.5150004.451961e+020.0000000.00000030.00000061.0000004.323000e+03
developer_categories_core200.09.9750005.980500e+010.0000000.0000001.0000001.0000004.270000e+02
developer_categories_regular200.014.2350008.122028e+010.0000000.0000000.0000002.0000005.780000e+02
developer_categories_casual200.06.4150003.368287e+010.0000000.0000000.0000002.0000004.290000e+02
commits_per_week200.03.1477061.526076e+010.0000000.0000000.0524340.5243451.874663e+02
commits_per_month200.013.4901696.540324e+010.0000000.0000000.2247192.2471918.034270e+02
commits_per_year200.0164.1303847.957394e+020.0000000.0000002.73408227.3408249.775028e+03
found_file_license200.00.7400004.397350e-010.0000000.0000001.0000001.0000001.000000e+00
found_file_adopters200.00.0000000.000000e+000.0000000.0000000.0000000.0000000.000000e+00
\n", - "
" - ], - "text/plain": [ - " count mean std \\\n", - "total_commits 200.0 240.125000 1.164178e+03 \n", - "total_contributors 200.0 30.625000 1.454316e+02 \n", - "total_organizations 200.0 8.970000 3.834175e+01 \n", - "pony_factor 200.0 1.375000 3.461834e+00 \n", - "elephant_factor 200.0 0.770000 7.278384e-01 \n", - "recent_organizations 200.0 6.090000 2.394722e+01 \n", - "recent_contributors 200.0 20.000000 9.171860e+01 \n", - "recent_commits 200.0 162.890000 8.148622e+02 \n", - "contributor_growth 200.0 -5.015000 3.119762e+01 \n", - "contributor_growth_rate 200.0 0.390204 1.377968e+00 \n", - "active_branches 200.0 9.260000 9.521619e+01 \n", - "days_since_last_commit 122.0 83.213115 1.142487e+02 \n", - "casual_regular_contributors_rate 200.0 0.551471 1.218157e+00 \n", - "returning_contributors 200.0 3.095000 1.265451e+01 \n", - "commits_over_periods_rate 200.0 0.448477 4.206525e-01 \n", - "coefficient_of_variation 122.0 2.199501 1.275881e+00 \n", - "file_types_code 200.0 477.075000 3.234830e+03 \n", - "file_types_binary 200.0 0.010000 9.974843e-02 \n", - "file_types_other 200.0 5007.690000 3.660997e+04 \n", - "commit_size_added_lines 200.0 147287.430000 1.137573e+06 \n", - "commit_size_removed_lines 200.0 123533.800000 8.978386e+05 \n", - "message_size_total 200.0 48822.475000 3.910615e+05 \n", - "message_size_mean 200.0 165.536816 5.942889e+02 \n", - "message_size_median 200.0 104.515000 4.451961e+02 \n", - "developer_categories_core 200.0 9.975000 5.980500e+01 \n", - "developer_categories_regular 200.0 14.235000 8.122028e+01 \n", - "developer_categories_casual 200.0 6.415000 3.368287e+01 \n", - "commits_per_week 200.0 3.147706 1.526076e+01 \n", - "commits_per_month 200.0 13.490169 6.540324e+01 \n", - "commits_per_year 200.0 164.130384 7.957394e+02 \n", - "found_file_license 200.0 0.740000 4.397350e-01 \n", - "found_file_adopters 200.0 0.000000 0.000000e+00 \n", - "\n", - " min 25% 50% \\\n", - "total_commits 0.000000 0.000000 4.000000 \n", - "total_contributors 0.000000 0.000000 1.000000 \n", - "total_organizations 0.000000 0.000000 1.000000 \n", - "pony_factor 0.000000 0.000000 1.000000 \n", - "elephant_factor 0.000000 0.000000 1.000000 \n", - "recent_organizations 0.000000 0.000000 1.000000 \n", - "recent_contributors 0.000000 0.000000 1.000000 \n", - "recent_commits 0.000000 0.000000 2.000000 \n", - "contributor_growth -215.000000 -1.000000 0.000000 \n", - "contributor_growth_rate -1.000000 -0.034483 0.000000 \n", - "active_branches 0.000000 0.000000 1.000000 \n", - "days_since_last_commit 0.000000 2.000000 23.000000 \n", - "casual_regular_contributors_rate 0.000000 0.000000 0.000000 \n", - "returning_contributors 0.000000 0.000000 0.000000 \n", - "commits_over_periods_rate 0.000000 0.000000 0.510068 \n", - "coefficient_of_variation 0.216221 1.304943 2.012274 \n", - "file_types_code 0.000000 0.000000 0.000000 \n", - "file_types_binary 0.000000 0.000000 0.000000 \n", - "file_types_other 0.000000 0.000000 6.000000 \n", - "commit_size_added_lines 0.000000 0.000000 68.000000 \n", - "commit_size_removed_lines 0.000000 0.000000 27.500000 \n", - "message_size_total 0.000000 0.000000 235.000000 \n", - "message_size_mean 0.000000 0.000000 37.812500 \n", - "message_size_median 0.000000 0.000000 30.000000 \n", - "developer_categories_core 0.000000 0.000000 1.000000 \n", - "developer_categories_regular 0.000000 0.000000 0.000000 \n", - "developer_categories_casual 0.000000 0.000000 0.000000 \n", - "commits_per_week 0.000000 0.000000 0.052434 \n", - "commits_per_month 0.000000 0.000000 0.224719 \n", - "commits_per_year 0.000000 0.000000 2.734082 \n", - "found_file_license 0.000000 0.000000 1.000000 \n", - "found_file_adopters 0.000000 0.000000 0.000000 \n", - "\n", - " 75% max \n", - "total_commits 40.000000 1.430100e+04 \n", - "total_contributors 5.000000 1.005000e+03 \n", - "total_organizations 3.000000 2.670000e+02 \n", - "pony_factor 1.000000 2.400000e+01 \n", - "elephant_factor 1.000000 3.000000e+00 \n", - "recent_organizations 3.000000 1.650000e+02 \n", - "recent_contributors 5.000000 6.290000e+02 \n", - "recent_commits 29.750000 1.018600e+04 \n", - "contributor_growth 1.000000 1.600000e+01 \n", - "contributor_growth_rate 0.259804 8.000000e+00 \n", - "active_branches 3.000000 1.347000e+03 \n", - "days_since_last_commit 135.500000 4.810000e+02 \n", - "casual_regular_contributors_rate 0.666667 9.000000e+00 \n", - "returning_contributors 1.000000 8.500000e+01 \n", - "commits_over_periods_rate 0.862857 1.000000e+00 \n", - "coefficient_of_variation 3.298437 4.123106e+00 \n", - "file_types_code 17.250000 4.347500e+04 \n", - "file_types_binary 0.000000 1.000000e+00 \n", - "file_types_other 92.000000 4.910180e+05 \n", - "commit_size_added_lines 5702.000000 1.524937e+07 \n", - "commit_size_removed_lines 2804.750000 1.211097e+07 \n", - "message_size_total 4828.250000 5.479443e+06 \n", - "message_size_mean 98.466667 4.546360e+03 \n", - "message_size_median 61.000000 4.323000e+03 \n", - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 2. Data cleaning\n", - "\n", - "corr = df.corr(method=\"pearson\")\n", - "\n", - "plt.figure(figsize=(16, 14))\n", - "\n", - "sns.heatmap(\n", - " corr,\n", - " cmap=\"coolwarm\",\n", - " center=0,\n", - " square=True,\n", - " linewidths=0.5,\n", - " cbar_kws={\"shrink\": .75}\n", - ")\n", - "\n", - "plt.title(\"Correlation Matrix\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1164d993", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Features flagged for removal (correlation > 0.8):\n", - "['total_organizations', 'pony_factor', 'recent_organizations', 'recent_contributors', 'recent_commits', 'contributor_growth', 'active_branches', 'returning_contributors', 'file_types_code', 'commit_size_added_lines', 'commit_size_removed_lines', 'message_size_total', 'message_size_median', 'developer_categories_core', 'developer_categories_regular', 'developer_categories_casual', 'commits_per_week', 'commits_per_month', 'commits_per_year']\n", - "\n", - "Original shape: (200, 32)\n", - "New shape after removing correlated features: (200, 13)\n" - ] - } - ], - "source": [ - "# Time to remove collinear or multicollinear features to simplify ther model, \n", - "# reduce overfitting, and make the data more interpretable.\n", - "\n", - "threshold = 0.8\n", - "\n", - "corr_matrix = corr.abs()\n", - "\n", - "# Create a mask to select the upper triangle of the matrix\n", - "# This prevents comparing a feature to itself or checking pairs twice (e.g., A vs B and B vs A)\n", - "upper = corr_matrix.where(\n", - " np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)\n", - ")\n", - "\n", - "high_corr = upper.columns[(upper > threshold).any()].tolist()\n", - "print(f\"Features flagged for removal (correlation > {threshold}):\\n{high_corr}\")\n", - "\n", - "df_low_corr = df.drop(columns=high_corr)\n", - "\n", - "print(f\"\\nOriginal shape: {df.shape}\")\n", - "print(f\"New shape after removing correlated features: {df_low_corr.shape}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "e7c6cf3e", - "metadata": {}, - "source": [ - "# Warning\n", - "\n", - "We have it done, but \"Correlation only detects pairwise relationships. Logistic regression is more affected by multicollinearity, which VIF measures.\"\n", - "\n", - "But, since the end goal is prediction with logistic regression, we won't remove variables based on VIF alone. The recommendation is to:\n", - " - Remove constant variables.\n", - " - Remove perfectly redundant variables (e.g., counts that are exact sums of others).\n", - " - Fit a regularized logistic regression (L1/Lasso or Elastic Net), which can handle correlated predictors by shrinking or eliminating less informative ones. Comparing the performance of a regularized model to a manually reduced feature set is often more informative than relying solely on VIF." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b70722aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully saved 13 metric names to 'low_correlated_metric_names.csv'!\n", - "['total_commits', 'total_contributors', 'elephant_factor', 'contributor_growth_rate', 'days_since_last_commit', 'casual_regular_contributors_rate', 'commits_over_periods_rate', 'coefficient_of_variation', 'file_types_binary', 'file_types_other', 'message_size_mean', 'found_file_license', 'found_file_adopters']\n" - ] - } - ], - "source": [ - "metric_names = pd.Series(df_low_corr.columns, name=\"metric_name\")\n", - "\n", - "# Save just the names to a CSV\n", - "output_file = 'low_correlated_metric_names.csv'\n", - "metric_names.to_csv(output_file, index=False)\n", - "\n", - "print(f\"Successfully saved {len(metric_names)} metric names to '{output_file}'!\")\n", - "print(list(df_low_corr.columns))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9db4e2de", - "metadata": {}, - "outputs": [], - "source": [ - "# https://stats.stackexchange.com/questions/271954/vifcollinearity-vs-correlation\n", - "# https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_multiple_logistic_results_multicollinearity.htm\n", - "\n", - "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", - "\n", - "def calculate_vif(df):\n", - " return pd.DataFrame({\n", - " \"Variable\": df.columns,\n", - " \"VIF\": [\n", - " variance_inflation_factor(df.values, i)\n", - " for i in range(df.shape[1])\n", - " ]\n", - " }).sort_values(\"VIF\", ascending=False)\n", - "\n", - "X = df_numeric.drop(columns=[\n", - " \"file_types_binary\",\n", - " \"found_file_adopters\"\n", - "]).fillna(0)\n", - "\n", - "while True:\n", - " vif = calculate_vif(X)\n", - "\n", - " max_vif = vif.iloc[0][\"VIF\"]\n", - "\n", - " if max_vif < 10:\n", - " break\n", - "\n", - " feature = vif.iloc[0][\"Variable\"]\n", - "\n", - " print(f\"Removing {feature} (VIF={max_vif:.2f})\")\n", - "\n", - " X = X.drop(columns=feature)\n", - "\n", - "calculate_vif(X)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "abcd", - "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": "3.13.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/model/npm/notebooks/phase2_prepare_key_metrics.ipynb b/model/npm/notebooks/phase2_prepare_key_metrics.ipynb deleted file mode 100644 index 1cceded..0000000 --- a/model/npm/notebooks/phase2_prepare_key_metrics.ipynb +++ /dev/null @@ -1,719 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1d9b11bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (3.0.3)\n", - "Collecting scikit-learn\n", - " Using cached scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (11 kB)\n", - "Requirement already satisfied: numpy>=1.26.0 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from pandas) (2.5.1)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n", - "Requirement already satisfied: scipy>=1.10.0 in /home/luis/python-virtual-environments/abcd/lib/python3.13/site-packages (from scikit-learn) (1.18.0)\n", - "Collecting joblib>=1.4.0 (from scikit-learn)\n", - " Using cached joblib-1.5.3-py3-none-any.whl.metadata (5.5 kB)\n", - "Collecting narwhals>=2.0.1 (from scikit-learn)\n", - " Downloading narwhals-2.24.0-py3-none-any.whl.metadata (15 kB)\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8d66bf48", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.impute import SimpleImputer" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e166a077", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# 1. Read metrics.json and keep only the low-correlated metrics\n", - "\n", - "# Load the low correlated metric names from CSV\n", - "low_corr_df = pd.read_csv(\"low_correlated_metric_names.csv\")\n", - "selected_metrics = set(low_corr_df[\"metric_name\"].tolist())\n", - "\n", - "with open(\"../data/metrics.json\", \"r\") as f:\n", - " metrics_data = json.load(f)\n", - "\n", - "# Parse the JSON structure into rows containing repository and filtered metrics\n", - "parsed_rows = []\n", - "for package_id, package_info in metrics_data.get(\"packages\", {}).items():\n", - " repo = package_info.get(\"repository\")\n", - " metrics = package_info.get(\"metrics\", {})\n", - "\n", - " # Filter metrics to only include those in the allowed list\n", - " filtered_metrics = {k: v for k, v in metrics.items() if k in selected_metrics}\n", - "\n", - " # Add repository to the record to allow joining later\n", - " filtered_metrics[\"repository\"] = repo\n", - " parsed_rows.append(filtered_metrics)\n", - "\n", - "# Create the metrics DataFrame\n", - "df_metrics = pd.DataFrame(parsed_rows)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6b25eaae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " total_commits total_contributors elephant_factor \\\n", - "0 0 0 0 \n", - "1 0 0 0 \n", - "2 25 2 1 \n", - "3 19 3 1 \n", - "4 0 0 0 \n", - "\n", - " contributor_growth_rate days_since_last_commit \\\n", - "0 0.0 NaN \n", - "1 0.0 NaN \n", - "2 1.0 49.0 \n", - "3 0.0 13.0 \n", - "4 0.0 NaN \n", - "\n", - " casual_regular_contributors_rate commits_over_periods_rate \\\n", - "0 0.0 0.000000 \n", - "1 0.0 0.000000 \n", - "2 1.0 1.000000 \n", - "3 0.5 0.526316 \n", - "4 0.0 0.000000 \n", - "\n", - " coefficient_of_variation file_types_binary file_types_other \\\n", - "0 NaN 0 0 \n", - "1 NaN 0 0 \n", - "2 2.365418 0 33 \n", - "3 2.008983 0 28 \n", - "4 NaN 0 0 \n", - "\n", - " message_size_mean found_file_license found_file_adopters \\\n", - "0 0.000000 1 0 \n", - "1 0.000000 1 0 \n", - "2 24.320000 1 0 \n", - "3 132.105263 1 0 \n", - "4 0.000000 0 0 \n", - "\n", - " clean_url Result \n", - "0 https://github.com/moment/moment Unhealthy \n", - "1 https://github.com/epoberezkin/fast-json-stabl... Unhealthy \n", - "2 https://github.com/holepunchto/b4a Healthy \n", - "3 https://github.com/lydell/js-tokens Healthy \n", - "4 https://github.com/sindresorhus/array-union Unhealthy " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. Standardize URL fields and merge datasets\n", - "\n", - "def clean_repo_url(url):\n", - " \"\"\"Standardizes URLs by removing trailing slashes and '.git' extensions.\n", - " \"\"\"\n", - "\n", - " url = url.strip()\n", - " if url.endswith(\"/\"):\n", - " url = url[:-1]\n", - " if url.endswith(\".git\"):\n", - " url = url[:-4]\n", - " return url.lower()\n", - "\n", - "\n", - "# Apply the standardization to the matching keys\n", - "df_metrics[\"clean_url\"] = df_metrics[\"repository\"].apply(clean_repo_url)\n", - "df_expert_filtered[\"clean_url\"] = df_expert_filtered[\"downloadLocation\"].apply(clean_repo_url)\n", - "\n", - "# Merge the dataframes on the cleaned URL column\n", - "# Using an inner join to keep only matching records (change to 'left' or 'outer' if needed)\n", - "df_merged = pd.merge(df_metrics, df_expert_filtered, on=\"clean_url\", how=\"inner\")\n", - "\n", - "# Drop the temporary matching key\n", - "df_merged = df_merged.drop(columns=[\"repository\", \"downloadLocation\"])\n", - "\n", - "# Display the merged DataFrame summary\n", - "df_merged.head()" - ] - }, - { - "cell_type": "markdown", - "id": "1f7fe4b8", - "metadata": {}, - "source": [ - "### FIXME: Issues at this point\n", - "\n", - "1. we identified NaN values in days_since_last_commit and coefficient_of_variation at this point\n", - "2. StandardScaler will calculate a standard deviation of 0 for the fields that have a 0 in every single row, like file_types_binary or found_file_adopters. It is recommended to drop them" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2d7281ea", - "metadata": {}, - "outputs": [], - "source": [ - "## issue: we identified NaN values in days_since_last_commit and coefficient_of_variation at this point\n", - "\n", - "x = df_merged.drop(columns=[\"clean_url\",\"Result\"])\n", - "y = df_merged[\"Result\"]\n", - "\n", - "# handle missing values (NaN) ---\n", - "imputer = SimpleImputer(strategy='constant', fill_value=0)\n", - "x_imputed = imputer.fit_transform(x)\n", - "\n", - "# Rescale features (Mean = 0, Std Dev = 1)\n", - "scaler = StandardScaler()\n", - "x_scaled = scaler.fit_transform(x_imputed)\n", - "\n", - "x_scaled_df = pd.DataFrame(x_scaled, columns=x.columns)\n", - "\n", - "# now we rebuild the dataframe with the missing columns\n", - "df_tracking = df_merged[['clean_url', 'Result']].reset_index(drop=True)\n", - "final_df = pd.concat([df_tracking, x_scaled_df], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "56014280", - "metadata": {}, - "outputs": [], - "source": [ - "output_file = 'relevant_metrics_project.csv'\n", - "final_df.to_csv(output_file, index=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "abcd", - "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": "3.13.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/model/npm/notebooks/phase3_final_calculation.ipynb b/model/npm/notebooks/phase3_final_calculation.ipynb deleted file mode 100644 index 8f2d31e..0000000 --- a/model/npm/notebooks/phase3_final_calculation.ipynb +++ /dev/null @@ -1,131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "1b4ace85", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install pandas scikit-learn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1897801f", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import classification_report, accuracy_score\n", - "\n", - "file_path = 'relevant_metrics_project.csv'\n", - "df = pd.read_csv(file_path)\n", - "\n", - "\n", - "x = df.drop(columns=['clean_url', 'Result'])\n", - "y = df['Result']\n", - "\n", - "# split the dataset into training and testing sets\n", - "# 'test_size=0.25' means 25% of the data will be used to test the model\n", - "# 'stratify=y' ensures balanced distribution of Healthy/Unhealthy in both sets\n", - "x_train, x_test, y_train, y_test = train_test_split(\n", - " x, y, test_size=0.25, random_state=42, stratify=y\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3419cb6d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Accuracy: 83.33%\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " Healthy 0.89 0.80 0.84 10\n", - " Unhealthy 0.78 0.88 0.82 8\n", - "\n", - " accuracy 0.83 18\n", - " macro avg 0.83 0.84 0.83 18\n", - "weighted avg 0.84 0.83 0.83 18\n", - "\n" - ] - } - ], - "source": [ - "# \n", - "model = LogisticRegression(max_iter=1000)\n", - "model.fit(x_train, y_train)\n", - "\n", - "# Make predictions on the test set\n", - "y_pred = model.predict(x_test)\n", - "\n", - "# Evaluate the model performance\n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "print(f\"Model Accuracy: {accuracy:.2%}\\n\")\n", - "print(\"Classification Report:\")\n", - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "770d04ad", - "metadata": {}, - "outputs": [], - "source": [ - "# Trying with penalty='l2' handles multicollinearity by shrinking coefficients together.\n", - "model = LogisticRegression(l1_ratio=0, C=1.0, solver='lbfgs', max_iter=1000)\n", - "model.fit(x_train, y_train)\n", - "\n", - "# Predict\n", - "y_pred = model.predict(x_test)\n", - "\n", - "# Evaluate\n", - "\n", - "print(f\"Regularized Model Accuracy: {accuracy_score(y_test, y_pred):.2%}\\n\")\n", - "print(\"Classification Report:\")\n", - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1cecff5e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "abcd", - "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": "3.13.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/grimoirelab_metrics/metrics.py b/src/grimoirelab_metrics/metrics.py index 817bfe2..2d2183d 100644 --- a/src/grimoirelab_metrics/metrics.py +++ b/src/grimoirelab_metrics/metrics.py @@ -27,6 +27,8 @@ from collections import Counter +from math import sqrt + from opensearchpy import OpenSearch, Search, Q from grimoirelab_toolkit.datetime import ( @@ -262,7 +264,12 @@ def get_commit_coefficient_of_variation(self): if mean == 0: raise ZeroDivisionError cv = stdev / mean except ZeroDivisionError as e: - return 0.0 + # the activity in commits is zero, + # so we will return the worst possible value, which is + # the cv for a distribution where all the commits where + # made in a single month + months = len(commits_list) + return sqrt(months - 1) return cv @@ -362,12 +369,16 @@ def get_analysis_metadata(self): return metadata def get_days_since_last_commit(self): - """Return the number of days since the last commit.""" - - if not self.last_commit_date: - return 99999 + """ + Return the number of days since the last commit, if no commits + are found it returns the number of days of the monitored timeframe.""" - days_since_last_commit = (self.to_date - self.last_commit_date).days + if self.last_commit_date: + days_since_last_commit = (self.to_date - self.last_commit_date).days + elif not self.last_commit_date: + # when the repository was not active during the monitored timeframe + # we set the worst possible value within the timeframe + days_since_last_commit = (self.to_date - self.from_date).days return days_since_last_commit diff --git a/src/grimoirelab_metrics/metrics_model.py b/src/grimoirelab_metrics/metrics_model.py index 0dc73bb..b43dbb5 100644 --- a/src/grimoirelab_metrics/metrics_model.py +++ b/src/grimoirelab_metrics/metrics_model.py @@ -30,23 +30,25 @@ class npmModel: # Model Name MODEL_NAME = "health" # Model Version - MODEL_VERSION = "0.1" + MODEL_VERSION = "0.2" # We have dropped the low-impact metrics, those with a coefficient close to 0 COEFFICIENTS = { - 'elephant_factor': -1.635941, - 'coefficient_of_variation': -1.404157, - 'total_contributors': -0.991894, - 'days_since_last_commit': 0.865738, - 'contributor_growth_rate': 0.435875, - 'commits_over_periods_rate': -0.410393, - 'total_commits': -0.330035, - 'message_size_mean': -0.320026, - 'found_file_license': 0.266483 + 'active_branches': -0.834069, + 'commits_over_periods_rate': -0.841944, + 'commit_size_added_lines': -0.389225, + 'contributor_growth_rate': 0.114363, + 'days_since_last_commit': 1.081051, + 'developer_categories_casual': 0.958613, + 'developer_categories_core': -1.277484, + 'developer_categories_regular': 0.193106, + 'file_types_code': -0.905957, + 'found_file_license': 0.376334, + 'returning_contributors': -1.167053, } # Model Intercept - Z = -0.549873845969752 + Z = -1.023426 def __init__(self): self.coefficients = self.COEFFICIENTS.copy()