From f8f1df9428b4cca488ce3069affc028cf655d546 Mon Sep 17 00:00:00 2001 From: ivana Date: Tue, 11 Nov 2025 19:59:40 +0100 Subject: [PATCH 1/7] Support an ML model with an implemented predict() for inference_evaluator.py; Compute risks for every group within the target column; Add an example notebook. --- .gitignore | 3 +- .../inference_custom_model_example.ipynb | 926 ++++++++++++++++++ .../evaluators/inference_evaluator.py | 159 ++- src/anonymeter/stats/confidence.py | 20 +- 4 files changed, 1087 insertions(+), 21 deletions(-) create mode 100644 notebooks/inference_custom_model_example.ipynb diff --git a/.gitignore b/.gitignore index 4dba1c9..0477015 100644 --- a/.gitignore +++ b/.gitignore @@ -128,7 +128,8 @@ dmypy.json # Pyre type checker .pyre/ -# vscode +# IDE .vscode/ +.idea/ **/*DS_Store* \ No newline at end of file diff --git a/notebooks/inference_custom_model_example.ipynb b/notebooks/inference_custom_model_example.ipynb new file mode 100644 index 0000000..8cb3aa6 --- /dev/null +++ b/notebooks/inference_custom_model_example.ipynb @@ -0,0 +1,926 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a6727d05-5009-49f5-8918-d5fdd22cb187", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:39.093309Z", + "start_time": "2025-05-15T11:50:39.060430Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "de41f9a83014711d", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "#### Run the cell bellow, uncommented if using autogluon" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b3766057c77adc9", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:39.113487Z", + "start_time": "2025-05-15T11:50:39.096588Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# pip install autogluon" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c64a6fab-1676-4539-b460-5b2fdb456b04", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:44.845976Z", + "start_time": "2025-05-15T11:50:39.117232Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from anonymeter.evaluators import InferenceEvaluator\n", + "from autogluon.tabular import TabularPredictor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3429c0fa-8915-4320-93f2-016fedf2b570", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:44.889226Z", + "start_time": "2025-05-15T11:50:44.848139Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "ada19947-b895-4279-aac3-9b87fac2fa6b", + "metadata": {}, + "source": [ + "## Downloading the data\n", + "\n", + "For this example, we will use the famous `Adults` (more details [here](https://archive.ics.uci.edu/ml/datasets/adult)) dataset. This dataset contains aggregated census data, where every row represent a population segment. For the purpose of demonstrating `Anonymeter`, we will use this data as if each row would in fact refer to a real individual. \n", + "\n", + "The synthetic version has been generated by `CTGAN` from [SDV](https://sdv.dev/SDV/user_guides/single_table/ctgan.html), as explained in the paper accompanying this code release. For details on the generation process, e.g. regarding hyperparameters, see Section 6.2.1 of [the accompanying paper](https://petsymposium.org/popets/2023/popets-2023-0055.php)).\n", + "\n", + "We pull these datasets from the [Statice](https://www.statice.ai/) public GC bucket:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fc128115-2f0c-43b1-9198-5c5594eae7f3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:45.192418Z", + "start_time": "2025-05-15T11:50:44.893155Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ori = pd.read_csv(\"datasets/adults_train.csv\")\n", + "syn = pd.read_csv(\"datasets/adults_syn_ctgan.csv\")\n", + "control = pd.read_csv(\"datasets/adults_control.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f6abeed8-23ae-4d4a-9cdb-006c0bba109c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:50:45.259297Z", + "start_time": "2025-05-15T11:50:45.194514Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agetype_employerfnlwgteducationeducation_nummaritaloccupationrelationshipracesexcapital_gaincapital_losshr_per_weekcountryincome
053Self-emp-not-inc13802211th7DivorcedCraft-repairNot-in-familyWhiteMale0060United-States<=50K
131Private344200HS-grad9Married-civ-spouseExec-managerialHusbandWhiteMale0040United-States>50K
228Private242482HS-grad9Never-marriedHandlers-cleanersOwn-childWhiteMale0040United-States<=50K
326Private193165Some-college10Married-civ-spouseTransport-movingHusbandWhiteMale0052United-States>50K
427Private267989Some-college10Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States<=50K
\n", + "
" + ], + "text/plain": [ + " age type_employer fnlwgt education education_num \\\n", + "0 53 Self-emp-not-inc 138022 11th 7 \n", + "1 31 Private 344200 HS-grad 9 \n", + "2 28 Private 242482 HS-grad 9 \n", + "3 26 Private 193165 Some-college 10 \n", + "4 27 Private 267989 Some-college 10 \n", + "\n", + " marital occupation relationship race sex \\\n", + "0 Divorced Craft-repair Not-in-family White Male \n", + "1 Married-civ-spouse Exec-managerial Husband White Male \n", + "2 Never-married Handlers-cleaners Own-child White Male \n", + "3 Married-civ-spouse Transport-moving Husband White Male \n", + "4 Married-civ-spouse Machine-op-inspct Husband White Male \n", + "\n", + " capital_gain capital_loss hr_per_week country income \n", + "0 0 0 60 United-States <=50K \n", + "1 0 0 40 United-States >50K \n", + "2 0 0 40 United-States <=50K \n", + "3 0 0 52 United-States >50K \n", + "4 0 0 40 United-States <=50K " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ori.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f1d19013-b7cf-48e3-a068-6c1e5449884e", + "metadata": {}, + "source": [ + "As visible the dataset contains several demographic information, as well as information regarding the education, financial situation, and personal life of some tens of thousands of \"individuals\"." + ] + }, + { + "cell_type": "markdown", + "id": "0429baae-424d-4ebe-b8ec-9205397515ba", + "metadata": {}, + "source": [ + "# Measuring the Inference Risk\n", + "\n", + "With this new implementation we allow for the user to pass their own model in order to do the attribute inference.\n", + "The requirements for the model are:\n", + "1. It is trained on the synthetic data with the `secret` as a target.\n", + "2. It supports the ::predict(x) method\n", + "\n", + "Finally, it should be passed as `ml_model=predictor` to the `InferenceEvaluator` like in the example below:\n", + "(in case we pass our own ml model and want to have the predictions for all original/control data, set sample_attacks=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c07054c-7ced-46c3-8a12-14123f6cc965", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:53:00.988964Z", + "start_time": "2025-05-15T11:50:45.261347Z" + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184928\"\n", + "Verbosity: 2 (Standard Logging)\n", + "=================== System Info ===================\n", + "AutoGluon Version: 1.4.0\n", + "Python Version: 3.11.12\n", + "Operating System: Darwin\n", + "Platform Machine: x86_64\n", + "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", + "CPU Count: 12\n", + "Memory Avail: 5.93 GB / 16.00 GB (37.0%)\n", + "Disk Space Avail: 26.36 GB / 465.63 GB (5.7%)\n", + "===================================================\n", + "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", + "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", + "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", + "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", + "\tpresets='high' : Strong accuracy with fast inference speed.\n", + "\tpresets='good' : Good accuracy with very fast inference speed.\n", + "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", + "Beginning AutoGluon training ...\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184928\"\n", + "Train Data Rows: 39040\n", + "Train Data Columns: 14\n", + "Label Column: age\n", + "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).\n", + "\tFirst 10 (of 73) unique label values: [37, 53, 17, 30, 40, 34, 36, 25, 56, 46]\n", + "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", + "Problem Type: multiclass\n", + "Preprocessing data ...\n", + "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 66 out of 73 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", + "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.999359631147541\n", + "Train Data Class Count: 66\n", + "Using Feature Generators to preprocess the data ...\n", + "Fitting AutoMLPipelineFeatureGenerator...\n", + "\tAvailable Memory: 6007.93 MB\n", + "\tTrain Data (Original) Memory Usage: 23.48 MB (0.4% of available memory)\n", + "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", + "\tStage 1 Generators:\n", + "\t\tFitting AsTypeFeatureGenerator...\n", + "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", + "\tStage 2 Generators:\n", + "\t\tFitting FillNaFeatureGenerator...\n", + "\tStage 3 Generators:\n", + "\t\tFitting IdentityFeatureGenerator...\n", + "\t\tFitting CategoryFeatureGenerator...\n", + "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", + "\tStage 4 Generators:\n", + "\t\tFitting DropUniqueFeatureGenerator...\n", + "\tStage 5 Generators:\n", + "\t\tFitting DropDuplicatesFeatureGenerator...\n", + "\tTypes of features in original data (raw dtype, special dtypes):\n", + "\t\t('int', []) : 5 | ['fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\tTypes of features in processed data (raw dtype, special dtypes):\n", + "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\t\t('int', []) : 5 | ['fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", + "\t0.3s = Fit runtime\n", + "\t14 features in original data used to generate 14 features in processed data.\n", + "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", + "Data preprocessing and feature engineering runtime = 0.36s ...\n", + "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", + "\tTo change this, specify the eval_metric parameter of Predictor()\n", + "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36516, Val Rows: 2499\n", + "User-specified model hyperparameters to be fit:\n", + "{\n", + "\t'RF': [{}],\n", + "}\n", + "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", + "Fitting model: RandomForest ...\n", + "\tFitting with cpus=12, gpus=0, mem=1.5/5.6 GB\n", + "\tWarning: Reducing model 'n_estimators' from 300 -> 50 due to low memory. Expected memory usage reduced from 89.52% -> 15.0% of available memory...\n", + "\t0.0572\t = Validation score (accuracy)\n", + "\t3.61s\t = Training runtime\n", + "\t0.04s\t = Validation runtime\n", + "Fitting model: WeightedEnsemble_L2 ...\n", + "\tEnsemble Weights: {'RandomForest': 1.0}\n", + "\t0.0572\t = Validation score (accuracy)\n", + "\t0.01s\t = Training runtime\n", + "\t0.0s\t = Validation runtime\n", + "AutoGluon training complete, total runtime = 5.76s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 55927.0 rows/s (2499 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184928\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184939\"\n", + "Verbosity: 2 (Standard Logging)\n", + "=================== System Info ===================\n", + "AutoGluon Version: 1.4.0\n", + "Python Version: 3.11.12\n", + "Operating System: Darwin\n", + "Platform Machine: x86_64\n", + "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", + "CPU Count: 12\n", + "Memory Avail: 5.83 GB / 16.00 GB (36.4%)\n", + "Disk Space Avail: 25.53 GB / 465.63 GB (5.5%)\n", + "===================================================\n", + "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", + "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", + "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", + "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", + "\tpresets='high' : Strong accuracy with fast inference speed.\n", + "\tpresets='good' : Good accuracy with very fast inference speed.\n", + "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", + "Beginning AutoGluon training ...\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184939\"\n", + "Train Data Rows: 39040\n", + "Train Data Columns: 14\n", + "Label Column: type_employer\n", + "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n", + "\t9 unique label values: ['Private', '?', 'State-gov', 'Federal-gov', 'Local-gov', 'Self-emp-not-inc', 'Self-emp-inc', 'Without-pay', 'Never-worked']\n", + "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", + "Problem Type: multiclass\n", + "Preprocessing data ...\n", + "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 8 out of 9 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", + "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9998206967213115\n", + "Train Data Class Count: 8\n", + "Using Feature Generators to preprocess the data ...\n", + "Fitting AutoMLPipelineFeatureGenerator...\n", + "\tAvailable Memory: 5809.01 MB\n", + "\tTrain Data (Original) Memory Usage: 21.38 MB (0.4% of available memory)\n", + "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", + "\tStage 1 Generators:\n", + "\t\tFitting AsTypeFeatureGenerator...\n", + "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", + "\tStage 2 Generators:\n", + "\t\tFitting FillNaFeatureGenerator...\n", + "\tStage 3 Generators:\n", + "\t\tFitting IdentityFeatureGenerator...\n", + "\t\tFitting CategoryFeatureGenerator...\n", + "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", + "\tStage 4 Generators:\n", + "\t\tFitting DropUniqueFeatureGenerator...\n", + "\tStage 5 Generators:\n", + "\t\tFitting DropDuplicatesFeatureGenerator...\n", + "\tTypes of features in original data (raw dtype, special dtypes):\n", + "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", + "\t\t('object', []) : 8 | ['education', 'marital', 'occupation', 'relationship', 'race', ...]\n", + "\tTypes of features in processed data (raw dtype, special dtypes):\n", + "\t\t('category', []) : 6 | ['education', 'marital', 'occupation', 'relationship', 'race', ...]\n", + "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", + "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", + "\t0.3s = Fit runtime\n", + "\t14 features in original data used to generate 14 features in processed data.\n", + "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", + "Data preprocessing and feature engineering runtime = 0.44s ...\n", + "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", + "\tTo change this, specify the eval_metric parameter of Predictor()\n", + "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36533, Val Rows: 2500\n", + "User-specified model hyperparameters to be fit:\n", + "{\n", + "\t'RF': [{}],\n", + "}\n", + "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", + "Fitting model: RandomForest ...\n", + "\tFitting with cpus=12, gpus=0, mem=0.2/5.6 GB\n", + "\t0.7044\t = Validation score (accuracy)\n", + "\t3.66s\t = Training runtime\n", + "\t0.08s\t = Validation runtime\n", + "Fitting model: WeightedEnsemble_L2 ...\n", + "\tEnsemble Weights: {'RandomForest': 1.0}\n", + "\t0.7044\t = Validation score (accuracy)\n", + "\t0.01s\t = Training runtime\n", + "\t0.0s\t = Validation runtime\n", + "AutoGluon training complete, total runtime = 5.78s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 29373.8 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184939\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184949\"\n", + "Verbosity: 2 (Standard Logging)\n", + "=================== System Info ===================\n", + "AutoGluon Version: 1.4.0\n", + "Python Version: 3.11.12\n", + "Operating System: Darwin\n", + "Platform Machine: x86_64\n", + "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", + "CPU Count: 12\n", + "Memory Avail: 5.65 GB / 16.00 GB (35.3%)\n", + "Disk Space Avail: 25.78 GB / 465.63 GB (5.5%)\n", + "===================================================\n", + "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", + "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", + "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", + "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", + "\tpresets='high' : Strong accuracy with fast inference speed.\n", + "\tpresets='good' : Good accuracy with very fast inference speed.\n", + "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", + "Beginning AutoGluon training ...\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184949\"\n", + "Train Data Rows: 39040\n", + "Train Data Columns: 14\n", + "Label Column: fnlwgt\n", + "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).\n", + "\tLabel info (max, min, mean, stddev): (814369, 13492, 181263.22961, 100607.94976)\n", + "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", + "Problem Type: regression\n", + "Preprocessing data ...\n", + "Using Feature Generators to preprocess the data ...\n", + "Fitting AutoMLPipelineFeatureGenerator...\n", + "\tAvailable Memory: 5794.55 MB\n", + "\tTrain Data (Original) Memory Usage: 23.49 MB (0.4% of available memory)\n", + "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", + "\tStage 1 Generators:\n", + "\t\tFitting AsTypeFeatureGenerator...\n", + "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", + "\tStage 2 Generators:\n", + "\t\tFitting FillNaFeatureGenerator...\n", + "\tStage 3 Generators:\n", + "\t\tFitting IdentityFeatureGenerator...\n", + "\t\tFitting CategoryFeatureGenerator...\n", + "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", + "\tStage 4 Generators:\n", + "\t\tFitting DropUniqueFeatureGenerator...\n", + "\tStage 5 Generators:\n", + "\t\tFitting DropDuplicatesFeatureGenerator...\n", + "\tTypes of features in original data (raw dtype, special dtypes):\n", + "\t\t('int', []) : 5 | ['age', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\tTypes of features in processed data (raw dtype, special dtypes):\n", + "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\t\t('int', []) : 5 | ['age', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", + "\t0.4s = Fit runtime\n", + "\t14 features in original data used to generate 14 features in processed data.\n", + "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", + "Data preprocessing and feature engineering runtime = 0.39s ...\n", + "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n", + "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n", + "\tTo change this, specify the eval_metric parameter of Predictor()\n", + "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36540, Val Rows: 2500\n", + "User-specified model hyperparameters to be fit:\n", + "{\n", + "\t'RF': [{}],\n", + "}\n", + "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", + "Fitting model: RandomForest ...\n", + "\tFitting with cpus=12, gpus=0, mem=0.0/5.6 GB\n", + "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", + "\t10.2s\t = Training runtime\n", + "\t0.11s\t = Validation runtime\n", + "Fitting model: WeightedEnsemble_L2 ...\n", + "\tEnsemble Weights: {'RandomForest': 1.0}\n", + "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", + "\t0.0s\t = Training runtime\n", + "\t0.0s\t = Validation runtime\n", + "AutoGluon training complete, total runtime = 13.17s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 23178.3 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184949\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_185101\"\n", + "Verbosity: 2 (Standard Logging)\n", + "=================== System Info ===================\n", + "AutoGluon Version: 1.4.0\n", + "Python Version: 3.11.12\n", + "Operating System: Darwin\n", + "Platform Machine: x86_64\n", + "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", + "CPU Count: 12\n", + "Memory Avail: 4.46 GB / 16.00 GB (27.9%)\n", + "Disk Space Avail: 25.47 GB / 465.63 GB (5.5%)\n", + "===================================================\n", + "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", + "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", + "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", + "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", + "\tpresets='high' : Strong accuracy with fast inference speed.\n", + "\tpresets='good' : Good accuracy with very fast inference speed.\n", + "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", + "Beginning AutoGluon training ...\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185101\"\n", + "Train Data Rows: 39040\n", + "Train Data Columns: 14\n", + "Label Column: education\n", + "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n", + "\tFirst 10 (of 16) unique label values: ['HS-grad', '11th', 'Masters', 'Some-college', '5th-6th', 'Bachelors', 'Assoc-acdm', '9th', 'Doctorate', 'Prof-school']\n", + "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", + "Problem Type: multiclass\n", + "Preprocessing data ...\n", + "Train Data Class Count: 16\n", + "Using Feature Generators to preprocess the data ...\n", + "Fitting AutoMLPipelineFeatureGenerator...\n", + "\tAvailable Memory: 4552.15 MB\n", + "\tTrain Data (Original) Memory Usage: 21.36 MB (0.5% of available memory)\n", + "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", + "\tStage 1 Generators:\n", + "\t\tFitting AsTypeFeatureGenerator...\n", + "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", + "\tStage 2 Generators:\n", + "\t\tFitting FillNaFeatureGenerator...\n", + "\tStage 3 Generators:\n", + "\t\tFitting IdentityFeatureGenerator...\n", + "\t\tFitting CategoryFeatureGenerator...\n", + "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", + "\tStage 4 Generators:\n", + "\t\tFitting DropUniqueFeatureGenerator...\n", + "\tStage 5 Generators:\n", + "\t\tFitting DropDuplicatesFeatureGenerator...\n", + "\tTypes of features in original data (raw dtype, special dtypes):\n", + "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", + "\t\t('object', []) : 8 | ['type_employer', 'marital', 'occupation', 'relationship', 'race', ...]\n", + "\tTypes of features in processed data (raw dtype, special dtypes):\n", + "\t\t('category', []) : 6 | ['type_employer', 'marital', 'occupation', 'relationship', 'race', ...]\n", + "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", + "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", + "\t0.3s = Fit runtime\n", + "\t14 features in original data used to generate 14 features in processed data.\n", + "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", + "Data preprocessing and feature engineering runtime = 0.39s ...\n", + "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", + "\tTo change this, specify the eval_metric parameter of Predictor()\n", + "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36540, Val Rows: 2500\n", + "User-specified model hyperparameters to be fit:\n", + "{\n", + "\t'RF': [{}],\n", + "}\n", + "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", + "Fitting model: RandomForest ...\n", + "\tFitting with cpus=12, gpus=0, mem=0.4/4.4 GB\n", + "\tWarning: Reducing model 'n_estimators' from 300 -> 214 due to low memory. Expected memory usage reduced from 21.02% -> 15.0% of available memory...\n", + "\t0.8048\t = Validation score (accuracy)\n", + "\t2.88s\t = Training runtime\n", + "\t0.06s\t = Validation runtime\n", + "Fitting model: WeightedEnsemble_L2 ...\n", + "\tEnsemble Weights: {'RandomForest': 1.0}\n", + "\t0.8048\t = Validation score (accuracy)\n", + "\t0.01s\t = Training runtime\n", + "\t0.0s\t = Validation runtime\n", + "AutoGluon training complete, total runtime = 5.02s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 37694.8 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185101\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_185111\"\n", + "Verbosity: 2 (Standard Logging)\n", + "=================== System Info ===================\n", + "AutoGluon Version: 1.4.0\n", + "Python Version: 3.11.12\n", + "Operating System: Darwin\n", + "Platform Machine: x86_64\n", + "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", + "CPU Count: 12\n", + "Memory Avail: 4.52 GB / 16.00 GB (28.3%)\n", + "Disk Space Avail: 24.84 GB / 465.63 GB (5.3%)\n", + "===================================================\n", + "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", + "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", + "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", + "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", + "\tpresets='high' : Strong accuracy with fast inference speed.\n", + "\tpresets='good' : Good accuracy with very fast inference speed.\n", + "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", + "Beginning AutoGluon training ...\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185111\"\n", + "Train Data Rows: 39040\n", + "Train Data Columns: 14\n", + "Label Column: education_num\n", + "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).\n", + "\tFirst 10 (of 14) unique label values: [9, 7, 14, 10, 13, 12, 4, 16, 15, 11]\n", + "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", + "Problem Type: multiclass\n", + "Preprocessing data ...\n", + "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 13 out of 14 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", + "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9998463114754098\n", + "Train Data Class Count: 13\n", + "Using Feature Generators to preprocess the data ...\n", + "Fitting AutoMLPipelineFeatureGenerator...\n", + "\tAvailable Memory: 4644.58 MB\n", + "\tTrain Data (Original) Memory Usage: 23.49 MB (0.5% of available memory)\n", + "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", + "\tStage 1 Generators:\n", + "\t\tFitting AsTypeFeatureGenerator...\n", + "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", + "\tStage 2 Generators:\n", + "\t\tFitting FillNaFeatureGenerator...\n", + "\tStage 3 Generators:\n", + "\t\tFitting IdentityFeatureGenerator...\n", + "\t\tFitting CategoryFeatureGenerator...\n", + "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", + "\tStage 4 Generators:\n", + "\t\tFitting DropUniqueFeatureGenerator...\n", + "\tStage 5 Generators:\n", + "\t\tFitting DropDuplicatesFeatureGenerator...\n", + "\tTypes of features in original data (raw dtype, special dtypes):\n", + "\t\t('int', []) : 5 | ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\tTypes of features in processed data (raw dtype, special dtypes):\n", + "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", + "\t\t('int', []) : 5 | ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hr_per_week']\n", + "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", + "\t0.3s = Fit runtime\n", + "\t14 features in original data used to generate 14 features in processed data.\n", + "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", + "Data preprocessing and feature engineering runtime = 0.38s ...\n", + "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", + "\tTo change this, specify the eval_metric parameter of Predictor()\n", + "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36534, Val Rows: 2500\n", + "User-specified model hyperparameters to be fit:\n", + "{\n", + "\t'RF': [{}],\n", + "}\n", + "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", + "Fitting model: RandomForest ...\n", + "\tFitting with cpus=12, gpus=0, mem=0.3/4.5 GB\n", + "\tWarning: Reducing model 'n_estimators' from 300 -> 288 due to low memory. Expected memory usage reduced from 15.59% -> 15.0% of available memory...\n", + "\t0.856\t = Validation score (accuracy)\n", + "\t3.71s\t = Training runtime\n", + "\t0.08s\t = Validation runtime\n", + "Fitting model: WeightedEnsemble_L2 ...\n", + "\tEnsemble Weights: {'RandomForest': 1.0}\n", + "\t0.856\t = Validation score (accuracy)\n", + "\t0.01s\t = Training runtime\n", + "\t0.0s\t = Validation runtime\n", + "AutoGluon training complete, total runtime = 5.9s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 29893.2 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185111\")\n" + ] + } + ], + "source": [ + "columns = ori.columns[:5] # Using the first 5 columns only for this example\n", + "results = []\n", + "\n", + "results_for_groups = {}\n", + "\n", + "for secret in columns:\n", + " predictor = TabularPredictor(label=secret)\n", + " predictor.fit(\n", + " train_data=syn,\n", + " hyperparameters={\n", + " 'RF': {}, \n", + " }\n", + " )\n", + " aux_cols = [col for col in columns if col != secret]\n", + " \n", + " # Needed for anonymeter (is the target categorical or numerical variable?)\n", + " regression = True if syn[secret].dtype.kind in 'iuf' else False\n", + " \n", + " # Init the InferenceEvaluator\n", + " evaluator = InferenceEvaluator(ori=ori, \n", + " syn=syn, \n", + " control=control,\n", + " aux_cols=aux_cols,\n", + " secret=secret,\n", + " n_attacks=6000, #todo important! this parameter is obsolete if sample_attacks is False\n", + " regression = regression,\n", + " ml_model=predictor,\n", + " sample_attacks=False) # Setting to true for this example\n", + " # Evaluate\n", + " evaluator.evaluate(n_jobs=-2)\n", + " \n", + " # Gather the results for the current group\n", + " results_for_groups[secret] = evaluator.risk_for_groups()\n", + " results.append((secret, evaluator.results()))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "59651586-891c-4ae6-8b28-fe7a7de66aeb", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:53:46.772503Z", + "start_time": "2025-05-15T11:53:00.992149Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAITCAYAAAAXYFiYAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgklJREFUeJzt3XdYFFfbBvB7QEBEASs27FhQsYsiKioGsRu7scQYa+y9d2MvsbfYY4m9VzQ2UGOJvaBRsSGgCAKKlOf7g495WcENm4A76P27Lq+EszPLM+zuzL1nzpxRRERAREREREkyMXYBRERERFrGsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBERGQEBQoUwPfff2/sMogoGRiWiMgg3t7eGD9+PN68eWPsUoiIPguGJSIyiLe3NyZMmMCwRERfDYYlIiLSER0djQ8fPhi7DCLNYFgiomQbP348hgwZAgAoWLAgFEWBoijInz8/ypQpk+Q6xYoVg4eHBwDg0aNHUBQFs2bNwty5c5E/f35YWlqiZs2auHHjRqJ179y5gxYtWiBLlixInz49KlasiD179vyr2jds2IAKFSrA0tISWbJkQZs2bfDkyROdZdzc3FCqVClcu3YNNWvWRIYMGVCkSBFs27YNAHDy5Ek4OzvD0tISxYoVw7FjxxL9fRRFwZ07d9CqVStYW1sja9as6NevH96/f/+PNf79999o2bIlsmTJggwZMqBKlSrYv3+/+nhYWBisrKzQr1+/ROs+ffoUpqammDp1qtr25s0b9O/fH/b29rCwsECRIkUwffp0xMbGqsskfE3mzZuHwoULw8LCArdu3UreH5boayBERMl09epVadu2rQCQuXPnyvr162X9+vUyb948ASDXr1/XWf7ChQsCQNatWyciIg8fPhQAUrp0aSlQoIBMnz5dJkyYIFmyZJHs2bOLv7+/uu6NGzfExsZGHB0dZfr06bJw4UKpUaOGKIoiO3bsMKjuyZMni6Io0rp1a1m8eLFMmDBBsmXLJgUKFJDg4GB1uZo1a0ru3LnF3t5ehgwZIgsWLBBHR0cxNTWVzZs3S86cOWX8+PEyb948yZMnj9jY2EhoaKi6/rhx49Tta9SokSxcuFDat28vAKRDhw46NeXPn186deqk/uzv7y92dnaSKVMmGTVqlMyZM0fKlCkjJiYmOtv73XffiZ2dnURHR+s834wZM0RRFHn8+LGIiISHh4uTk5NkzZpVRo4cKUuXLpWOHTuKoijSr18/db3418TR0VEKFSok06ZNk7lz56rPQ0QiDEtEZJCZM2cKAHn48KHa9ubNG0mfPr0MGzZMZ9m+ffuKlZWVhIWFicj/DsyWlpby9OlTdbnz588LABkwYIDaVqdOHSldurS8f/9ebYuNjRUXFxdxcHBIdr2PHj0SU1NTmTJlik779evXJV26dDrtNWvWFACyceNGte3OnTsCQExMTOTcuXNq++HDhwWArF69Wm2LD0uNGzfW+V29evUSAHL16lW17eOw1L9/fwEgp0+fVtvevn0rBQsWlAIFCkhMTIzO7z148KDO73BycpKaNWuqP0+aNEmsrKzk3r17OssNHz5cTE1Nxc/PT0T+95pYW1tLQEBAkn9Doq8dT8MR0X9mY2ODJk2aYNOmTRARAEBMTAy2bNmCpk2bwsrKSmf5pk2bIk+ePOrPlStXhrOzMw4cOAAAeP36NY4fP45WrVrh7du3CAoKQlBQEF69egUPDw/4+vri2bNnyaptx44diI2NRatWrdTnCQoKQs6cOeHg4IATJ07oLJ8xY0a0adNG/blYsWKwtbVFiRIl4OzsrLbH///ff/+d6Hf+9NNPOj/36dMHANTtS8qBAwdQuXJluLq66tTSrVs3PHr0SD0t5u7ujty5c+O3335Tl7tx4wauXbuG9u3bq21bt25F9erVkTlzZp3tdnd3R0xMDE6dOqXz+5s3b47s2bN/sj6ir1k6YxdARF+Gjh07YsuWLTh9+jRq1KiBY8eO4eXLl+jQoUOiZR0cHBK1FS1aFL///jsA4P79+xARjBkzBmPGjEny9wUEBOgErk/x9fWFiCT5OwHAzMxM5+e8efNCURSdNhsbG9jb2ydqA4Dg4OBEz/nx7ypcuDBMTEzw6NGjT9b5+PFjnTAWr0SJEurjpUqVgomJCb777jssWbIEERERyJAhA3777TekT58eLVu2VNfz9fXFtWvXPhmAAgICdH4uWLDgJ2sj+toxLBFRivDw8ICdnR02bNiAGjVqYMOGDciZMyfc3d0Nfq74AciDBw9WB4d/rEiRIsl+LkVRcPDgQZiamiZ6PGPGjDo/J7WMvvb4njR9Pg5f/1XHjh0xc+ZM7Nq1C23btsXGjRvRsGFDNcABcdtdt25dDB06NMnnKFq0qM7PlpaWKVoj0ZeEYYmIDPKpA7+pqSnatWuHNWvWYPr06di1axe6du2aZMjw9fVN1Hbv3j0UKFAAAFCoUCEAcb0+/yZsJVS4cGGICAoWLJgoIKQWX19fnZ6a+/fvIzY2Vt2+pOTPnx93795N1H7nzh318XilSpVCuXLl8NtvvyFv3rzw8/PDggULdNYrXLgwwsLC/vPfj4g4dQARGSh+/FFSk1J26NABwcHB6N69O8LCwnTG0CS0a9cunTFHFy5cwPnz5+Hp6QkAyJEjB9zc3LBs2TK8ePEi0fqBgYHJrvfbb7+FqakpJkyYkKgXSETw6tWrZD9Xci1atEjn5/ggE799Salfvz4uXLgAHx8ftS08PBzLly9HgQIF4OjoqLN8hw4dcOTIEcybNw9Zs2ZN9NytWrWCj48PDh8+nOh3vXnzBtHR0QZvF9HXij1LRGSQChUqAABGjRqFNm3awMzMDI0aNYKVlRXKlSuHUqVKYevWrShRogTKly+f5HMUKVIErq6u6NmzJyIjI9UDfsJTRosWLYKrqytKly6Nrl27olChQnj58iV8fHzw9OlTXL16NVn1Fi5cGJMnT8aIESPw6NEjNG3aFJkyZcLDhw+xc+dOdOvWDYMHD/7vf5gEHj58iMaNG6NevXrw8fHBhg0b0K5du0/ORQUAw4cPx6ZNm+Dp6Ym+ffsiS5YsWLt2LR4+fIjt27fDxET3u227du0wdOhQ7Ny5Ez179kw09mrIkCHYs2cPGjZsiO+//x4VKlRAeHg4rl+/jm3btuHRo0fIli1bim430RfLiFfiEVEaNWnSJMmTJ4+YmJgkmkZgxowZAkB+/vnnROvFX6Y+c+ZMmT17ttjb24uFhYVUr15d57L6eA8ePJCOHTtKzpw5xczMTPLkySMNGzaUbdu2GVzz9u3bxdXVVaysrMTKykqKFy8uP/30k9y9e1ddpmbNmlKyZMlE6+bPn18aNGiQqB2A/PTTT+rP8VMH3Lp1S1q0aCGZMmWSzJkzS+/eveXdu3eJnjPh1AHx29uiRQuxtbWV9OnTS+XKlWXfvn2f3Kb69esLAPH29k7y8bdv38qIESOkSJEiYm5uLtmyZRMXFxeZNWuWfPjwQUR0XxMiSpoikozRiUREyfTLL79gwIABePToEfLly6fz2KNHj1CwYEHMnDkzxXtztGD8+PGYMGECAgMDP0uvTbNmzXD9+nXcv38/1X8X0deMY5aIKMWICH799VfUrFkzUVCilPXixQvs378/yakZiChlccwSEf1n4eHh2LNnD06cOIHr169j9+7dn+X3+vv7633c0tJS53L6L8HDhw9x9uxZrFy5EmZmZujevbuxSyL64jEsEdF/FhgYiHbt2sHW1hYjR45E48aNP8vvzZUrl97HO3XqhDVr1nyWWj6XkydPonPnzsiXLx/Wrl2LnDlzGrskoi8exywRUZp17NgxvY/nzp070SX3RESGYlgiIiIi0oMDvImIiIj04JilFBAbG4vnz58jU6ZMKX4PKCIiIkodIoK3b98id+7ciSZ+TYhhKQU8f/480R3JiYiIKG148uQJ8ubN+8nHGZZSQKZMmQDE/bGtra2NXA0RERElR2hoKOzt7dXj+KcwLKWA+FNv1tbWDEtERERpzD8NoeEAbyIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0iPNhaVFixahQIECSJ8+PZydnXHhwgW9y2/duhXFixdH+vTpUbp0aRw4cOCTy/bo0QOKomDevHkpXDURERGlVWkqLG3ZsgUDBw7EuHHjcPnyZZQpUwYeHh4ICAhIcnlvb2+0bdsWXbp0wZUrV9C0aVM0bdoUN27cSLTszp07ce7cOeTOnTu1N4OIiIjSkDQVlubMmYOuXbuic+fOcHR0xNKlS5EhQwasWrUqyeV/+eUX1KtXD0OGDEGJEiUwadIklC9fHgsXLtRZ7tmzZ+jTpw9+++03mJmZfY5NISIiojQizYSlDx8+4NKlS3B3d1fbTExM4O7uDh8fnyTX8fHx0VkeADw8PHSWj42NRYcOHTBkyBCULFkyWbVERkYiNDRU5x8RERF9mdJMWAoKCkJMTAzs7Ox02u3s7ODv75/kOv7+/v+4/PTp05EuXTr07ds32bVMnToVNjY26j97e3sDtoSIiIjSkjQTllLDpUuX8Msvv2DNmjVQFCXZ640YMQIhISHqvydPnqRilURERGRMaSYsZcuWDaampnj58qVO+8uXL5EzZ84k18mZM6fe5U+fPo2AgADky5cP6dKlQ7p06fD48WMMGjQIBQoU+GQtFhYWsLa21vlHREREX6Y0E5bMzc1RoUIFeHl5qW2xsbHw8vJC1apVk1ynatWqOssDwNGjR9XlO3TogGvXruGvv/5S/+XOnRtDhgzB4cOHU29jiIiIKM1IZ+wCDDFw4EB06tQJFStWROXKlTFv3jyEh4ejc+fOAICOHTsiT548mDp1KgCgX79+qFmzJmbPno0GDRpg8+bNuHjxIpYvXw4AyJo1K7JmzarzO8zMzJAzZ04UK1bs824cERERaVKaCkutW7dGYGAgxo4dC39/f5QtWxaHDh1SB3H7+fnBxOR/nWUuLi7YuHEjRo8ejZEjR8LBwQG7du1CqVKljLUJRERElMYoIiLGLiKtCw0NhY2NDUJCQjh+iYiIKI1I7vE7zYxZIiIiIjIGhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4YlIiIiIj0YloiIiIj0YFgiIiIi0oNhiYiIiEgPhiUiIiIiPRiWiIiIiPRgWCIiIiLSg2GJiIiISA+GJSIiIiI9GJaIiIiI9GBYIiIiItKDYYmIiIhID4PDUlRU1CcfCwoK+k/FEBEREWmNwWGpTZs2EJFE7S9fvoSbm1tK1ERERESkGQaHJT8/P/z44486bf7+/nBzc0Px4sVTrDAiIiIiLTA4LB04cADe3t4YOHAgAOD58+eoWbMmSpcujd9//z3FCyQiIiIypnSGrpA9e3YcOXIErq6uAIB9+/ahfPny+O2332BiwvHiRERE9GUxOCwBgL29PY4ePYrq1aujbt26WL9+PRRFSenaiIiIiIwuWWEpc+bMSYahiIgI7N27F1mzZlXbXr9+nXLVERERERlZssLSvHnzUrkMIiIiIm1KVljq1KlTatdBREREpEkGj8i+fPkyrl+/rv68e/duNG3aFCNHjsSHDx9StDgiIiIiYzM4LHXv3h337t0DAPz9999o3bo1MmTIgK1bt2Lo0KEpXiARERGRMRkclu7du4eyZcsCALZu3YqaNWti48aNWLNmDbZv357S9SWyaNEiFChQAOnTp4ezszMuXLigd/mtW7eiePHiSJ8+PUqXLo0DBw6oj0VFRWHYsGEoXbo0rKyskDt3bnTs2BHPnz9P7c0gIiKiNMLgsCQiiI2NBQAcO3YM9evXBxA3nUBq3xtuy5YtGDhwIMaNG4fLly+jTJky8PDwQEBAQJLLe3t7o23btujSpQuuXLmCpk2bomnTprhx4waAuKv5Ll++jDFjxuDy5cvYsWMH7t69i8aNG6fqdhAREVHaoUhSN3rTo3bt2rC3t4e7uzu6dOmCW7duoUiRIjh58iQ6deqER48epVKpgLOzMypVqoSFCxcCAGJjY2Fvb48+ffpg+PDhiZZv3bo1wsPDsW/fPrWtSpUqKFu2LJYuXZrk7/jzzz9RuXJlPH78GPny5UtWXaGhobCxsUFISAisra3/xZYRERHR55bc47fBPUvz5s3D5cuX0bt3b4waNQpFihQBAGzbtg0uLi7/vuJ/8OHDB1y6dAnu7u5qm4mJCdzd3eHj45PkOj4+PjrLA4CHh8cnlweAkJAQKIoCW1vbTy4TGRmJ0NBQnX9ERET0ZTJ4Bm8nJyedq+HizZw5E6ampilSVFKCgoIQExMDOzs7nXY7OzvcuXMnyXX8/f2TXN7f3z/J5d+/f49hw4ahbdu2ehPm1KlTMWHCBAO3gIiIiNKiFLuZW/r06WFmZpZST/fZRUVFoVWrVhARLFmyRO+yI0aMQEhIiPrvyZMnn6lKIiIi+tyS1bOUJUsW3Lt3D9myZfvkrU/ipdbtTrJlywZTU1O8fPlSp/3ly5fImTNnkuvkzJkzWcvHB6XHjx/j+PHj/zjuyMLCAhYWFv9iK4iIiCitSVZYmjt3LjJlygTAeLc+MTc3R4UKFeDl5YWmTZsCiBvg7eXlhd69eye5TtWqVeHl5YX+/furbUePHkXVqlXVn+ODkq+vL06cOKFznzsiIiIig253Eh0dDUVR4OHhkWgs0OcwcOBAdOrUCRUrVkTlypUxb948hIeHo3PnzgCAjh07Ik+ePJg6dSoAoF+/fqhZsyZmz56NBg0aYPPmzbh48SKWL18OIC4otWjRApcvX8a+ffsQExOjjmfKkiULzM3NP/s2EhERkbYYNMA7Xbp06NGjB27fvp1a9ejVunVrBAYGYuzYsfD390fZsmVx6NAhNbj5+fnBxOR/w7BcXFywceNGjB49GiNHjoSDgwN27dqFUqVKAQCePXuGPXv2AIA60Wa8EydOwM3N7bNsFxEREWmXwfMsubm5oX///uqpMOI8S0RERGlRco/fBk8d0KtXLwwaNAhPnz5FhQoVYGVlpfO4k5OT4dUSERERaZTBPUsJT3OpT6IoEBEoioKYmJgUKy6tYM8SERFR2pNqPUsPHz78T4URERERpSUGh6X8+fOnRh1EREREmpRiM3gTERERfYkYloiIiIj0YFgiIiIi0oNhiYiIiEiPfxWW3rx5g5UrV2LEiBHqjXMvX76MZ8+epWhxRERERMZm8NVw165dg7u7O2xsbPDo0SN07doVWbJkwY4dO+Dn54d169alRp1ERERERmFwz9LAgQPx/fffw9fXF+nTp1fb69evj1OnTqVocURERETGZnBY+vPPP9G9e/dE7Xny5IG/v3+KFEVERESkFQaHJQsLC4SGhiZqv3fvHrJnz54iRRERERFphcFhqXHjxpg4cSKioqIAxN0Xzs/PD8OGDUPz5s1TvEAiIiIiYzI4LM2ePRthYWHIkSMH3r17h5o1a6JIkSLIlCkTpkyZkho1EhERERmNwVfD2djY4OjRozh79iyuXr2KsLAwlC9fHu7u7qlRHxEREZFRKSIixi4irQsNDYWNjQ1CQkJgbW1t7HKIiIgoGZJ7/Db4NFzfvn0xf/78RO0LFy5E//79DX06IiIiIk0zOCxt374d1apVS9Tu4uKCbdu2pUhRRERERFphcFh69eoVbGxsErVbW1sjKCgoRYoiIiIi0gqDw1KRIkVw6NChRO0HDx5EoUKFUqQoIiIiIq0w+Gq4gQMHonfv3ggMDETt2rUBAF5eXpg9ezbmzZuX0vURERERGZXBYemHH35AZGQkpkyZgkmTJgEAChQogCVLlqBjx44pXiARERGRMf2nqQMCAwNhaWmJjBkzpmRNaQ6nDiAiIkp7knv8NrhnKSHeC46IiIi+dAYP8H758iU6dOiA3LlzI126dDA1NdX5R0RERPQlMbhn6fvvv4efnx/GjBmDXLlyQVGU1KiLiIiISBMMDktnzpzB6dOnUbZs2VQoh4iIiEhbDD4NZ29vD95OjoiIiL4WBoelefPmYfjw4Xj06FEqlENERESkLQafhmvdujUiIiJQuHBhZMiQAWZmZjqPv379OsWKIyIiIjI2g8MSZ+kmIiKir4nBYalTp06pUQcRERGRJhk8ZgkAHjx4gNGjR6Nt27YICAgAEHcj3Zs3b6ZocURERETGZnBYOnnyJEqXLo3z589jx44dCAsLAwBcvXoV48aNS/ECiYiIiIzJ4LA0fPhwTJ48GUePHoW5ubnaXrt2bZw7dy5FiyMiIiIyNoPD0vXr19GsWbNE7Tly5EBQUFCKFEVERESkFQaHJVtbW7x48SJR+5UrV5AnT54UKYqIiIhIKwwOS23atMGwYcPg7+8PRVEQGxuLs2fPYvDgwejYsWNq1EhERERkNAaHpZ9//hnFixeHvb09wsLC4OjoiBo1asDFxQWjR49OjRqJiIiIjEYRA270JiJ48uQJsmfPjqCgIFy/fh1hYWEoV64cHBwcUrNOTQsNDYWNjQ1CQkJgbW1t7HKIiIgoGZJ7/DZoUkoRQZEiRXDz5k04ODjA3t7+PxdKREREpGUGnYYzMTGBg4MDXr16lVr1EBEREWmKwWOWpk2bhiFDhuDGjRupUQ8RERGRphg0ZgkAMmfOjIiICERHR8Pc3ByWlpY6j79+/TpFC0wLOGaJiIgo7UmVMUsAMG/evP9SFxEREVGaYnBY6tSpU2rUQURERKRJBo9ZAoAHDx5g9OjRaNu2LQICAgAABw8exM2bN1O0OCIiIiJjMzgsnTx5EqVLl8b58+exY8cOhIWFAQCuXr2KcePGpXiBRERERMZkcFgaPnw4Jk+ejKNHj8Lc3Fxtr127Ns6dO5eixREREREZm8Fh6fr162jWrFmi9hw5ciAoKChFiiIiIiLSCoPDkq2tLV68eJGo/cqVK8iTJ0+KFEVERESkFQaHpTZt2mDYsGHw9/eHoiiIjY3F2bNnMXjwYHTs2DE1aiQiIiIyGoPD0s8//4zixYvD3t4eYWFhcHR0RI0aNeDi4oLRo0enRo1ERERERpOsGbxDQ0MTzWz55MkTXL9+HWFhYShXrhwcHBxSrUit4wzeREREaU+KzuCdOXNmvHjxAjly5EDt2rWxY8cO2Nvbw97ePsUKJiIiItKiZJ2Gy5gxI169egUA+OOPPxAVFZWqRRERERFpRbLCkru7O2rVqoVatWoBAJo1a4batWsn+S+1LVq0CAUKFED69Onh7OyMCxcu6F1+69atKF68ONKnT4/SpUvjwIEDOo+LCMaOHYtcuXLB0tIS7u7u8PX1Tc1NICIiojQkWWFpw4YNGD9+PCpWrAgAKFmyJMqUKZPkv9S0ZcsWDBw4EOPGjcPly5dRpkwZeHh4qLdc+Zi3tzfatm2LLl264MqVK2jatCmaNm2KGzduqMvMmDED8+fPx9KlS3H+/HlYWVnBw8MD79+/T9VtISIiorQhWQO8E6pVqxZ27twJW1vbVCrp05ydnVGpUiUsXLgQABAbGwt7e3v06dMHw4cPT7R869atER4ejn379qltVapUQdmyZbF06VKICHLnzo1BgwZh8ODBAICQkBDY2dlhzZo1aNOmTbLq4gBvIiKitCe5x2+Dpw44ceKEUYLShw8fcOnSJbi7u6ttJiYmcHd3h4+PT5Lr+Pj46CwPAB4eHuryDx8+hL+/v84yNjY2cHZ2/uRzAkBkZCRCQ0N1/hEREdGXKVlXwyUUExODNWvWwMvLCwEBAYiNjdV5/Pjx4ylWXEJBQUGIiYmBnZ2dTrudnR3u3LmT5Dr+/v5JLu/v768+Ht/2qWWSMnXqVEyYMMHgbSAiIqK0x+Cw1K9fP6xZswYNGjRAqVKloChKatSlaSNGjMDAgQPVn0NDQzmNAhER0RfK4LC0efNm/P7776hfv35q1PNJ2bJlg6mpKV6+fKnT/vLlS+TMmTPJdXLmzKl3+fj/vnz5Erly5dJZpmzZsp+sxcLCAhYWFv9mM4iIiCiNMXjMkrm5OYoUKZIatfzj761QoQK8vLzUttjYWHh5eaFq1apJrlO1alWd5QHg6NGj6vIFCxZEzpw5dZYJDQ3F+fPnP/mcRERE9HUxOCwNGjQIv/zyCwy8iC5FDBw4ECtWrMDatWtx+/Zt9OzZE+Hh4ejcuTMAoGPHjhgxYoS6fL9+/XDo0CHMnj0bd+7cwfjx43Hx4kX07t0bAKAoCvr374/Jkydjz549uH79Ojp27IjcuXOjadOmn337iIiISHsMPg135swZnDhxAgcPHkTJkiVhZmam8/iOHTtSrLiPtW7dGoGBgRg7diz8/f1RtmxZHDp0SB2g7efnBxOT/+U/FxcXbNy4EaNHj8bIkSPh4OCAXbt2oVSpUuoyQ4cORXh4OLp164Y3b97A1dUVhw4dQvr06VNtO4iIiCjtMHiepfhenE9ZvXr1fyooLeI8S0RERGlPit5IN6GvMQwRERHR18vgMUtEREREX5Nk9SyVL18eXl5eyJw5M8qVK6d3bqXLly+nWHFERERExpassNSkSRN1XiFeJUZERERfE4MHeFNiHOBNRESU9qTajXSJiIiIviYMS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHsmaOmDgwIHJfsI5c+b862KIiIiItCZZYenKlSs6P1++fBnR0dEoVqwYAODevXswNTVFhQoVUr5CIiIiIiNKVlg6ceKE+v9z5sxBpkyZsHbtWmTOnBkAEBwcjM6dO6N69eqpUyURERGRkRg8KWWePHlw5MgRlCxZUqf9xo0b+Oabb/D8+fMULTAt4KSUREREaU+qTUoZGhqKwMDARO2BgYF4+/atoU9HREREpGkGh6VmzZqhc+fO2LFjB54+fYqnT59i+/bt6NKlC7799tvUqJGIiIjIaJI1ZimhpUuXYvDgwWjXrh2ioqLiniRdOnTp0gUzZ85M8QKJiIiIjOlf30g3PDwcDx48AAAULlwYVlZWKVpYWsIxS0RERGlPqt9I98WLF3jx4gUcHBxgZWWFf5m5iIiIiDTN4LD06tUr1KlTB0WLFkX9+vXx4sULAECXLl0waNCgFC+QiIiIyJgMDksDBgyAmZkZ/Pz8kCFDBrW9devWOHToUIoWR0RERGRsBg/wPnLkCA4fPoy8efPqtDs4OODx48cpVhgRERGRFhjcsxQeHq7ToxTv9evXsLCwSJGiiIiIiLTC4LBUvXp1rFu3Tv1ZURTExsZixowZqFWrVooWR0RERGRsBp+GmzFjBurUqYOLFy/iw4cPGDp0KG7evInXr1/j7NmzqVEjERERkdEY3LNUqlQp3Lt3D66urmjSpAnCw8Px7bff4sqVKyhcuHBq1EhERERkNAb1LEVFRaFevXpYunQpRo0alVo1EREREWmGQT1LZmZmuHbtWmrVQkRERKQ5Bp+Ga9++PX799dfUqIWIiIhIcwwe4B0dHY1Vq1bh2LFjqFChQqJ7ws2ZMyfFiiMiIiIyNoPD0o0bN1C+fHkAwL1793QeUxQlZaoiIiIi0giDw9KJEydSow4iIiIiTTJ4zBIRERHR18TgnqVatWrpPd12/Pjx/1QQERERkZYYHJbKli2r83NUVBT++usv3LhxA506dUqpuoiIiIg0weCwNHfu3CTbx48fj7CwsP9cEBEREZGWpNiYpfbt22PVqlUp9XREREREmpBiYcnHxwfp06dPqacjIiIi0gSDT8N9++23Oj+LCF68eIGLFy9izJgxKVYYERERkRYYHJZsbGx0fjYxMUGxYsUwceJEfPPNNylWGBEREZEWGByWVq9enRp1EBEREWmSwWOWnjx5gqdPn6o/X7hwAf3798fy5ctTtDAiIiIiLTA4LLVr10695Ym/vz/c3d1x4cIFjBo1ChMnTkzxAomIiIiMyeCwdOPGDVSuXBkA8Pvvv6N06dLw9vbGb7/9hjVr1qR0fURERERGZXBYioqKgoWFBQDg2LFjaNy4MQCgePHiePHiRcpWR0RERGRkBoelkiVLYunSpTh9+jSOHj2KevXqAQCeP3+OrFmzpniBRERERMZkcFiaPn06li1bBjc3N7Rt2xZlypQBAOzZs0c9PUdERET0pVBERAxdKSYmBqGhocicObPa9ujRI2TIkAE5cuRI0QLTgtDQUNjY2CAkJATW1tbGLoeIiIiSIbnHb4PnWQIAU1NTnaAEAAUKFPg3T0VERESkaf8qLG3btg2///47/Pz88OHDB53HLl++nCKFEREREWmBwWOW5s+fj86dO8POzg5XrlxB5cqVkTVrVvz999/w9PRMjRqJiIiIjMbgsLR48WIsX74cCxYsgLm5OYYOHYqjR4+ib9++CAkJSY0aiYiIiIzG4LDk5+cHFxcXAIClpSXevn0LAOjQoQM2bdqUstURERERGZnBYSlnzpx4/fo1ACBfvnw4d+4cAODhw4f4FxfWEREREWmawWGpdu3a2LNnDwCgc+fOGDBgAOrWrYvWrVujWbNmKV4gERERkTEZPM9SbGwsYmNjkS5d3IV0mzdvhre3NxwcHNC9e3eYm5unSqFaxnmWiIiI0p7kHr8N7lkyMTFRgxIAtGnTBvPnz0efPn1SNSi9fv0a3333HaytrWFra4suXbogLCxM7zrv37/HTz/9hKxZsyJjxoxo3rw5Xr58qT5+9epVtG3bFvb29rC0tESJEiXwyy+/pNo2EBERUdpjcFgCgNOnT6N9+/aoWrUqnj17BgBYv349zpw5k6LFJfTdd9/h5s2bOHr0KPbt24dTp06hW7duetcZMGAA9u7di61bt+LkyZN4/vw5vv32W/XxS5cuIUeOHNiwYQNu3ryJUaNGYcSIEVi4cGGqbQcRERGlMWKgbdu2iaWlpfz4449iYWEhDx48EBGRBQsWiKenp6FPlyy3bt0SAPLnn3+qbQcPHhRFUeTZs2dJrvPmzRsxMzOTrVu3qm23b98WAOLj4/PJ39WrVy+pVauWQfWFhIQIAAkJCTFoPSIiIjKe5B6/De5Zmjx5MpYuXYoVK1bAzMxMba9WrVqqzd7t4+MDW1tbVKxYUW1zd3eHiYkJzp8/n+Q6ly5dQlRUFNzd3dW24sWLI1++fPDx8fnk7woJCUGWLFn01hMZGYnQ0FCdf0RERPRlMjgs3b17FzVq1EjUbmNjgzdv3qRETYn4+/snukFvunTpkCVLFvj7+39yHXNzc9ja2uq029nZfXIdb29vbNmy5R9P702dOhU2NjbqP3t7++RvDBEREaUp/2qepfv37ydqP3PmDAoVKmTQcw0fPhyKouj9d+fOHUNL/Fdu3LiBJk2aYNy4cfjmm2/0LjtixAiEhISo/548efJZaiQiIqLPz+Ab6Xbt2hX9+vXDqlWroCgKnj9/Dh8fHwwePBhjxowx6LkGDRqE77//Xu8yhQoVQs6cOREQEKDTHh0djdevXyNnzpxJrpczZ058+PABb9680eldevnyZaJ1bt26hTp16qBbt24YPXr0P9ZtYWEBCwuLf1yOiIiI0j6Dw9Lw4cMRGxuLOnXqICIiAjVq1ICFhQUGDx6MPn36GPRc2bNnR/bs2f9xuapVq+LNmze4dOkSKlSoAAA4fvw4YmNj4ezsnOQ6FSpUgJmZGby8vNC8eXMAcacQ/fz8ULVqVXW5mzdvonbt2ujUqROmTJliUP1ERET05TN4Usp4Hz58wP379xEWFgZHR0dkzJgxpWvT4enpiZcvX2Lp0qWIiopC586dUbFiRWzcuBEA8OzZM9SpUwfr1q1D5cqVAQA9e/bEgQMHsGbNGlhbW6thztvbG0DcqbfatWvDw8MDM2fOVH+XqalpskJcPE5KSURElPYk9/htcM9SPHNzczg6Ov7b1Q3222+/oXfv3qhTpw5MTEzQvHlzzJ8/X308KioKd+/eRUREhNo2d+5cddnIyEh4eHhg8eLF6uPbtm1DYGAgNmzYgA0bNqjt+fPnx6NHjz7LdhEREZG2Jbtn6YcffkjWE65ateo/FZQWsWeJiIgo7UnxnqU1a9Ygf/78KFeuHP7lmTsiIiKiNCfZYalnz57YtGkTHj58iM6dO6N9+/b/OHkjERERUVqX7HmWFi1ahBcvXmDo0KHYu3cv7O3t0apVKxw+fJg9TURERPTF+tdXwz1+/Bhr1qzBunXrEB0djZs3b6b6FXFaxTFLREREaU9yj98Gz+CtrmhiAkVRICKIiYn5t09DREREpGkGhaXIyEhs2rQJdevWRdGiRXH9+nUsXLgQfn5+X22vEhEREX3Zkj3Au1evXti8eTPs7e3xww8/YNOmTciWLVtq1kZERERkdMkes2RiYoJ8+fKhXLlyUBTlk8vt2LEjxYpLKzhmiYiIKO1J8XmWOnbsqDckEREREX2JDJqUkoiIiOhr86+vhiMiIiL6GjAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpkWbC0uvXr/Hdd9/B2toatra26NKlC8LCwvSu8/79e/z000/ImjUrMmbMiObNm+Ply5dJLvvq1SvkzZsXiqLgzZs3qbAFRERElBalmbD03Xff4ebNmzh69Cj27duHU6dOoVu3bnrXGTBgAPbu3YutW7fi5MmTeP78Ob799tskl+3SpQucnJxSo3QiIiJKwxQREWMX8U9u374NR0dH/Pnnn6hYsSIA4NChQ6hfvz6ePn2K3LlzJ1onJCQE2bNnx8aNG9GiRQsAwJ07d1CiRAn4+PigSpUq6rJLlizBli1bMHbsWNSpUwfBwcGwtbVNdn2hoaGwsbFBSEgIrK2t/9vGEhER0WeR3ON3muhZ8vHxga2trRqUAMDd3R0mJiY4f/58kutcunQJUVFRcHd3V9uKFy+OfPnywcfHR227desWJk6ciHXr1sHEJHl/jsjISISGhur8IyIioi9TmghL/v7+yJEjh05bunTpkCVLFvj7+39yHXNz80Q9RHZ2duo6kZGRaNu2LWbOnIl8+fIlu56pU6fCxsZG/Wdvb2/YBhEREVGaYdSwNHz4cCiKovffnTt3Uu33jxgxAiVKlED79u0NXi8kJET99+TJk1SqkIiIiIwtnTF/+aBBg/D999/rXaZQoULImTMnAgICdNqjo6Px+vVr5MyZM8n1cubMiQ8fPuDNmzc6vUsvX75U1zl+/DiuX7+Obdu2AQDih29ly5YNo0aNwoQJE5J8bgsLC1hYWCRnE4mIiCiNM2pYyp49O7Jnz/6Py1WtWhVv3rzBpUuXUKFCBQBxQSc2NhbOzs5JrlOhQgWYmZnBy8sLzZs3BwDcvXsXfn5+qFq1KgBg+/btePfunbrOn3/+iR9++AGnT59G4cKF/+vmERER0RfAqGEpuUqUKIF69eqha9euWLp0KaKiotC7d2+0adNGvRLu2bNnqFOnDtatW4fKlSvDxsYGXbp0wcCBA5ElSxZYW1ujT58+qFq1qnol3MeBKCgoSP19hlwNR0RERF+uNBGWAOC3335D7969UadOHZiYmKB58+aYP3+++nhUVBTu3r2LiIgItW3u3LnqspGRkfDw8MDixYuNUT4RERGlUWliniWt4zxLREREac8XNc8SERERkbEwLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenBsERERESkB8MSERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREemRztgFfAlEBAAQGhpq5EqIiIgoueKP2/HH8U9hWEoBb9++BQDY29sbuRIiIiIy1Nu3b2FjY/PJxxX5pzhF/yg2NhbPnz9HpkyZoCiKscvRKzQ0FPb29njy5Amsra2NXc5/wm3Rpi9pW4Ava3u4LdrEbTEeEcHbt2+RO3dumJh8emQSe5ZSgImJCfLmzWvsMgxibW2dJt7IycFt0aYvaVuAL2t7uC3axG0xDn09SvE4wJuIiIhID4YlIiIiIj0Ylr4yFhYWGDduHCwsLIxdyn/GbdGmL2lbgC9re7gt2sRt0T4O8CYiIiLSgz1LRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEVGyJbx4lhfSEn093r17ByDu9l5fI4alL8DHb14exCi1JLz3oaIofK9RivrSDsRfyvasW7cOnTt3RlBQEExMTL6Y7TIEw1IaFxsbq97878SJE4iMjNT8zXy/Fh8HiS9lB7N69Wr06dMHANLke+3vv//GokWLMHPmTAQEBBi7nP8s4fsqMjLSiJX8d/H7sqNHj+LDhw9pPozHb8+ZM2fw9u3bNLs9T548wcOHDzFq1CgEBgZ+lYGJYSkNExH1wzhy5Eh06dIFixcvTrMfSH3S4jbFB4k5c+bg9u3bMDExSZPbkVBUVBSuXr2Ka9euISYmBkDaem1u3LiBypUrY8uWLRgzZgy+/fZbY5f0nyT8sjR//nwsWbIEf//9t5Gr+vdEBL6+vvDw8MDRo0fTZBhPSERw5swZ1K5dG69fv06zvbEjR47Ed999h3v37mH48OF49erVVxeYGJbSsPgdyZQpU7B8+XJs3LgR7dq1S7M7mPidyMWLF7FixQosWbIEXl5eANJmDwYAhIeHY8eOHZgzZw6ioqLS7HYAca+PmZkZhgwZgitXrmDBggUA0s5r8/r1a/To0QO9e/fGH3/8gYcPH+Lu3bvw9vZOkwcw4H89F0OHDsWUKVNgZWUFKysrI1f17ymKAgcHB3Tp0gVr1qxBcHCwsUv6TxRFgaurK5ydnTFhwgTExMSkmc9LvPj9VoMGDVCmTBn88ccfGDNmDF6/fv1VBSaGpTTuzZs3OHHiBKZPn44qVarAzs4OgG7XfFo5ECiKgu3bt6NevXrYvXs3tm3bhu7du2PMmDHGLu1fs7KyQuPGjXHlyhW8efMGQNo9HRe/k8+TJw+GDh2KAwcOwM/Pz8hVJV90dDQAoE2bNlAUBTY2NsiaNSvMzMzS3AEsoR07duC3337DwYMH0bVrV3UfkBZ8/FmI/9nNzQ1XrlzBs2fPAEDtxdS6j7fnw4cPAICWLVvC19cXgYGBSS6nZWZmZti8eTNatmwJPz8/mJqaYtOmTRg+fPhXNYaJYSmNCw8Px4ULF2BmZqbTbmJigvfv3yM6OjrNdP3evHkTffr0waRJk7Bv3z7MmDEDL168QEREhLFLS5ZP7TAGDhyI169fY/LkyQD+1xuQVkyfPh0jR47E7du31TYXFxfcuHED165dA5A2dv45cuTAqlWrULx4cSiKgsjISJiYmKgH4rRyQP7Y48ePUbRoUTg6OiY6Nar11yX+s/Dnn3/i2bNn6s/fffcd7O3tMXjwYACAqamp0Wo0RHz958+fh4jA3NwcANC2bVvcvXsXS5Ys0VkuLbhz5w769u2LHj16YN26dbh37x769OmDq1evYtSoUV9ND1PaecUoycBjaWmJ8uXL4/bt23j79q3OcidOnMCQIUPUwKRV8R+y+/fvo1ChQujZsycePXqEb7/9Fh07dsTs2bMBANevXzdmmf8ofge4ceNGnDt3Tr3UNl26dBg+fDj+/PNPncCRFkRERMDCwgKrV69G9+7d0aFDBwQGBqJ27dro0qULhgwZguDg4DSz8y9atKj6/5kzZ0a2bNnQqVMnAHEH5LTwpSJefK33799HQEAA0qdPD1NTU/VUT0xMDM6ePYvHjx8buVL9jh49itatW+Obb77Bxo0b1XpHjx6NV69e4dSpUwDSTg/5gQMH0K5dO5QtWxb79u2Dr68vsmfPjnHjxuHEiRO4f/++sUs0SHzvUfXq1ZExY0YAwJgxY+Du7o5NmzZh3Lhx6qDvL9mXvXVfkNjYWDXwBAYG4tWrVwCALFmyoHLlyli6dCn27NmDsLAwKIqCsLAwLF26FA8fPtTUt7KkTg+GhIQAAMzNzWFra4ubN2+iRo0aqFevHhYuXAgAOHfuHNauXYunT59+/qINEBoaiuHDh6Nv375o2LAhrly5gnfv3qFhw4Z4/vw5jhw5AkC7O/6Pvx1myJAB/fv3x5UrV/DDDz/g1q1bqFSpEnr27AlbW1vky5dPPZilJfF//0mTJsHR0RF3794FADVkaNHHr038/qBNmzZ4/fo1Zs6cCeB/vTDBwcGYNm0aLl++/HkL/Qcfb0fdunWxatUqNG/eHH369EGPHj0wbdo0lClTBpGRkTh48CAA7Y6N+/izXLVqVWzduhVlypTBhAkT0KhRIyxduhQZM2ZEQEAAHj58mOR6WhNfn5WVFTJlyoQnT54AiOuBNTMzw+TJk5E9e3Zs27YNEydO/OJ7liCUpowZM0YcHR2lZMmS0qNHD7X9xx9/lDx58ki9evWkXbt2UqVKFSlVqpR8+PBBRERiY2ONVXIi9+7dk/3794uIyNatW6V69eoSHBwsf/75p+TIkUMyZMigs20iIn369JFGjRrJmzdvjFHyJ8XExCRqCwsLk3379knTpk0la9as0qZNGzlx4oQsWbJE7O3t5eHDh5+/0GRIuC2//fabjB07VkaNGiVnz57VWW7evHnSsWNHMTU1FUVRZMiQIZ+71BQTExMjb9++lTFjxkjFihWNXc4nJfz87t69W+bPny/e3t4SHh4ub9++lZ9++kkqVqwoo0ePlsDAQLlw4YI0bNhQKlSoINHR0UasXFfC95ifn59cvXpVp93b21t+/vlnyZEjhzRo0ECqVKkiiqLIlStXjFHuP0q4PdHR0RIaGqrz+KVLl2TBggVSoEABadOmjSiKIlWqVJHg4ODPXGnyJHWc+PDhg1SpUkWqVasmT58+VdsDAwOlSZMmMmbMGHny5MnnLNMoGJY0LuGObsWKFWJnZyeLFy+WSZMmSZYsWcTT01N9/Ndff5VBgwZJq1atZNy4cRIVFSUiov5XKwYPHiyKosiwYcNEURRZu3at+tiyZctEURSZNWuW3L59Wx48eCCDBw+WzJkzy40bN4xYdWIJd5SnTp2SgwcPyrFjx3SW2bx5s/Tu3VvMzc2ldOnSoiiKbN68+XOXapBBgwaJnZ2d1KpVSz1YzZo1S0JCQtRlPnz4IAcPHpSePXtq7v1lqOjoaPn999+laNGi8ujRI2OXk0jCA9iwYcPE2tpaHB0dxdzcXAYNGiQvXryQwMBAmThxouTJk0cyZswoxYoVkxo1aqhflrQQmBJux6hRo6RUqVJiaWkpzs7OMmzYMAkLC1Mff/v2rQwePFhq1Kgh5cuX10T9H0v4+Z81a5Z8++23UrJkSVmwYIH8/fffOsveu3dPduzYIQ0bNhQ7Ozs5fvx4oucwtvjXx8vLS/r27SuNGzeWmTNnSnh4uLx8+VLy588v1apVkx07dsjFixdl+PDh4uzsLIGBgUau/PNgWEojjh49KmvXrtU50J47d05y5col9erV++SHTos7GRERNzc3MTc3l379+omI7k5jypQpkjVrVsmRI4eULVtWSpQoIZcvXzZSpf9s8ODBkitXLilSpIiYmJhIs2bN5ODBg+rjsbGxcuPGDWnXrp00adJEs6+JiMiBAwckR44ccunSJXXn+csvv4ipqamsWLFCROK25+NvoGk9MEVGRsrbt2+NXUYiCd8rFy5ckDp16oi3t7eIiKxcuVKKFi0qPXv2VL/Zh4aGyuHDh+Xq1avqZ0prr820adMka9assmPHDjlz5owMGDBAqlatKh07dpSIiAh1udjYWHn58qW6HVr93IwYMUJy5swpEyZMkLlz50rGjBmld+/ecu3atSSXd3Nzk2bNmn3mKpNnx44dYm1tLT/88INMnTpV0qdPr/bo+/v7S/Xq1aVo0aKSO3duyZ8/v1y8eNHYJX82DEsa1KhRI7l+/br68507d0RRFFEURVatWqWz7Pnz5yV37tzSqFEjnR2NVsUfZKtVqyZVqlSRDBkyyK5duxItd/36dTl+/LicP39eXr58+bnLTLYVK1ZIjhw55Ny5cxIYGCiXLl2SKlWqSMOGDeXMmTMi8r+DVXh4uLr9WjuAxVu/fr2UK1dO3r17p3Nwmjx5stja2mqy5+VLdPLkSZ2flyxZIp06dZIOHTrofLFYtWqVFC1aVH766Se5efNmoufRWs9FSEiI1K1bV3755Re1/f3797JkyRIpX768/PrrryKSOBhpaTsS2r59uxQuXFguXLggInGn3RRFkSxZskj79u3l1q1b6rLv378XEZHff/9dk6finjx5IqVKlZKFCxeKSNzrZWtrK4MHD9YJrL6+vnL58mXx9/c3ZrmfHcOSxnz48EH69OkjkZGRalt0dLTs3LlT8uTJI23atEm0zoULF9Lc2JH40NCjRw+xtLSUnTt36rSnlQ9i7969pWXLliLyvx369evXpVixYtKzZ88k19HK+LGkDkAbNmwQCwsLefHihYiIehrn+vXrkidPHjUAUurp27ev9OjRQ+d9MmLECDExMRFHR0d5/PixzvKrV68WR0dHad++vebC7MfvsaioKHF2dk5yX1W3bl1p1arV5yrtP4uNjZWDBw/KggULRERk7969YmtrKxs3bpQDBw6IoijSo0ePRL0vP/zwgxQvXlxzPZl+fn5SsWJFiYyMlAcPHkju3Lmla9eu6uM+Pj5GrM74GJY0bM6cOerBKTo6Wnbs2CFWVlbSrVu3RMveunVLs93U8Tt9X19fuXr1qpw/f17n8R49eoiVlZXs3LlT3r9/Lz///LPUrl1bIiIiNBMsRBKHnOjoaOnUqZM0atRIROIODPHhYv369WJrayvPnz/X1DYkZcuWLeo34MDAQKlevbp8++23OoM5//77b3FwcJDTp08bq8yvhq+vr/o+un37tto+d+5cyZ49u4wdO1aePXums87ChQulbdu2mu2Bef78uYiIvHv3Tlq3bi21a9cWf39/nc/G2LFjpX79+prtdY2vNWHN/v7+8vz5cwkMDJQqVarI9OnTRSSuFzl//vyiKIpMmzZNXT4yMlI6dOgg586d+7zF63Hnzh0JCgoSX19fyZMnj+zcuVMKFy4sXbt2VY8p165dkwYNGsilS5eMXK3xMCxpVHR0tNSoUUOyZMmihouYmBi9gSl+PS2J37Hs3LlTChUqJMWLF5f06dNLnz59dL4h9+7dWxRFEVdXV8mQIYOmP5Q+Pj7qt8Lff/9dFEWRvXv36iyzZcsWqVChgs6gaK1IeEB99uyZKIoi7du3l/v374tIXNCrWbOmuLm5yR9//CFeXl5Sv359cXZ21uzB+EuR8EC8YcMGcXZ2li1btqhtkyZNkrx588rEiRPVAPLxulp4jRLWcPDgQSlUqJB6Rdu9e/fExsZGWrduLQ8ePJAPHz7Iu3fvxNXV9ZP7NWNLuD0BAQGJeoUePHggJUqUkH379olIXIjq3bu37N69O9E+2ZhfnoKCgnR+vnHjhmTLlk3u3r0rIiLdu3cXc3NzadKkic5yI0eOlMqVKyd6z31NGJY07P3799KsWTOxs7NTv4nExMTIzp07xcbGRj39o3WHDh0SGxsbWbp0qYSHh8u2bdtEURTp3LmzzmX0mzZtksWLF4uvr6/xik1Cwh3l/v37pVSpUjJ9+nQJDw8XEZGffvpJLC0tZdOmTfLixQsJCAiQevXqSf369TXXq/TxFUkjRoyQwoULS7p06aRx48Zqb1L8lTvp0qWT0qVLS61atTR1ZdXX4MqVK+Lm5ib169eXrVu3qu2TJk0Se3t7mTx5cqJLtrXwfkv4edm5c6f06tVLTE1NpUqVKvLnn3+KiMjFixclW7ZsUrZsWSlfvrxUrVpVSpYsqcmpThIaN26clCpVSpycnMTd3V1u3bolsbGxcvv2bcmRI4cMGzZMdu7cKfXr15datWqp62nhM/Prr79KmzZtdK4qvnHjhjg5Oan1eXl5ibu7u1SqVEl2794tu3fvlv79+4u1tbU6zcPXimFJgxJebRQVFSWenp46gSk2NlY2btwoderU0cS3SH3evHkjnTp1kokTJ4pI3OmcwoULS+PGjSVDhgzStm1buXfvnrq81naSCetZs2aNDB06VGxtbSVPnjwyd+5cef/+vbx580aGDh0q5ubmkj9/filatKiUL19e3fFr8TWaNWuWZM6cWc6cOSN//vmn7N+/X6ytraV+/fo6B+Bbt27J06dPNXtl1Zcq4fg3d3d3qVu3rk5gmjx5sqRLl05Wr15tpAr/2cCBA6Vw4cIyceJE6dy5sxQrVkwqVKigDoZ+9uyZLFiwQEaOHCmzZ8/W5FQnCT+7v/76q9ja2sqyZctk/vz5UqtWLcmaNascOHBAROKmPcmVK5cUK1ZMXF1dNRf8Fi1aJE5OTtKjRw/1AiIfHx9xcnLSqXHv3r3SqVMnyZAhgzg5OUnt2rW/+qAkwrBkdEld9RH/xo3vto6KipJGjRpJzpw51VNyCd/cWjsYx9f2/v17iYmJkY0bN8rff/8tQUFBUrZsWenSpYuIxF36bGJiIi1atNDcwNSPjRkzRmxtbWXNmjWyefNmqVOnjpQsWVLmzJmjXuVy/vx52bVrl+zdu1d9XbW040+odevWiU55XLlyRTJlyiQtWrSQO3fuJFpHa++zL1nCv/Xdu3fF3d1dPDw8dALT6tWrNdFjkZRLly5Jvnz5xMvLS23bt2+feHp6SoUKFdRBz0mNA9Siffv2ydixY2XdunU67e3atZOsWbOqF6Q8ePBAHj16pNkvFytXrpSyZcvKjz/+KPfv35cTJ05IwYIFk7yS+tmzZxIWFpZoos2vFcOSRty9e1fng7Vjxw5RFEVOnTolInHBo2nTpqIoSpKXCGuNl5eXjB8/XkREnXX7119/lWrVqqmDU9esWSMuLi6SO3duzc4AGxsbK8+ePZMSJUroTNsQGRkpnTp1kgIFCsjcuXN1JtSLp8Udf2xsrERHR0vt2rWlXbt2anv81ZdTpkwRRVHku+++U6ds0Mo3469F/N97z549MnPmTBGJ62GqW7eueHp6Jjpga/F9dv78ebGyslLnhIq3detWsbW1lUqVKqlzpyU1b5eWXLhwQYoUKSKWlpayYcMGEfnf5yUmJkZKly4tAwcOFBHtfolNeGw5cOCAODk5SZ8+fWTmzJlSu3ZtOXbsmBw7dkyOHz8uZ86cke3bt7M36SMMS0Zy8OBB6d27t4jE3cqjcePG6qDBAwcOiKWlpSxbtkxnnXfv3snQoUM1uXP82Ny5c8XExETnqp3Ro0dL+fLl1UHPw4YNk5UrV6o9M1oVGhoqJUuWVOcfSbjjKVWqlDg4OMjs2bPl3bt3xirRYBs2bJAMGTLIjh07dNqXLl0q7du3FysrKxkwYICRqvs6xB9MEx5g4z/bW7dulXTp0ukE9OvXr0vZsmXTxOvy8OFDqVixoixcuDDR57tq1apSrlw5cXNzUy8q0LLg4GCZM2eO5MmTR+eOCVFRURIVFSX169dX9+VatmbNGrGzs5PY2FhZsWKFVKxYUYoUKSKKokjVqlUlW7Zski9fPilatKjkzZtXs7dlMhaGJSN4//69TJ06VYoVKybOzs5iY2OjXiIcGRkpI0eOlPXr1+t9Dq0Hprdv38o333wjI0aMUMPFmTNnJF26dFKnTh355ptvxNra+pOz3GpJZGSkVK9eXRo2bKi2xf/927dvL2XLlhUXFxc5dOiQiGi3JyY2NlY9QPv7+0v37t2lcOHC6qmd169fS4MGDWTjxo2ybt06sba2lrt372p2e9KyhL0OT58+leDgYLW34vbt25IhQwZZtGiRukz8a/DgwQNN9Vjoe29069ZN8ufPL/v27VPH7wQFBUmLFi1k3rx5UrZsWU2PuRL53+sUGhoqCxculEKFCknHjh11lqlYsaJmA2z86xMUFCQtW7aUWbNmqY+tXbtWypYtK23atJErV65IVFSUOnTi9evXxipZsxiWjOT9+/dSu3ZtURRFOnTokOixtOTjS5bj/zty5EgpU6aMetWYSNxtW1q2bCldu3bVmaVcC5I6CMWHouvXr0vmzJnlxx9/VHcosbGx0qZNGzl48KA4OztL48aNP3fJBol/nfbt2yfe3t5y69Yt6devn6RLl06KFi0qBQoUkJIlS0p0dLTs3btXHBwcvpr7PhnL6NGjxcHBQYoVKyaVKlWSs2fPSkBAQJKnQLR0iifhdAYJxdcYP6npt99+K0WKFJHu3bvLnDlzpEaNGupVYhUqVJDOnTt/noKTIeHf9+M5ld6+fSsxMTEyf/58yZ07t5QpU0Zat24trVu3liJFimhubFJC58+fl3r16kndunXFz89PDa4icWOYypcvLz179tS5pRS/ICXGsGQEMTExEhwcLGPGjJEBAwaIk5OTeo80EdF5M2vVypUrdSaX9PHxkf79+6s7SZG47cibN68MHTpUZ93o6Gij7+wTGjJkSKKB8wnHUVy+fFl8fX3l0KFDYm1tLZUrV5ZGjRpJ5cqVxcHBQUREJk6cKFWrVtVEj19Sk+fF17V9+3admxe/f/9ezp8/LwsXLpT169erO/0BAwZI9erVNXdLhrQu4ft+48aNkiVLFlm/fr2sWrVKWrZsKVZWVrJp0yYR0e4B68iRI6IoikyYMEFti4mJUbdt+/bt4ujoqJ7GmTBhgjRu3FjKli0rLVq0UAcT16tXT53E0diS+lvHb8/OnTvFw8NDXr9+LaGhobJgwQIpWrSolCpVSo4ePaour8XAFBMTI4sWLZISJUpI9uzZ1S+uCb+Qr169WgoWLCj9+vXTuXME6WJY+kw+FQ5CQ0Nl+vTp4ujoqBOYRET++usvTYUKkf8NeC5fvrzOnbUnTZok5cqVk8yZM8uECRPk2LFjIhJ3iXqDBg3kwYMHmhzIee3aNXFxcdEZcJpwx79t2zaxsrKSs2fPikjc/ZMGDBggXbt2lYEDB6rBtlWrVtK2bVujh6WE7xd/f395/fq1Ovjc29tbMmXKJEuXLlWX+fj1uH//vvTq1UtsbGw4wDMV7dixQ0aPHp1oXGK/fv0kQ4YM6iSBWvu8iMSdll6+fLmYm5urF3HE27hxo1hbW8uSJUt02mNiYtT3YVRUlIwePVqyZ8+ubqcxeXt7q1ced+vWTebPn68+tmXLFsmUKZMsX75cbQsNDZV58+ZJpUqVdK4oNfZn/1NCQ0NlxYoVYmdnJ02aNFFDXcJgtH79ep39OSXGsPQZfHzjy8GDB0uXLl3UbyVv376VGTNmSOnSpaVnz57qzSY/PjeuBfE77/jBzBcuXNA5qM6cOVPq1asndnZ2MnLkSFm4cKHY2dnJb7/9ZpR6k+PYsWPSpEkTqVixok5X9N69e0VRFFm8eLGIJL0z9Pf3l0GDBknWrFmNfpViwgPrlClTpFq1alKmTBkpX768XLp0Sc6dO6fzTfhj4eHhsnbtWmnZsiWDUiq6cuWKlCxZUtKnT6+GioQHrmrVqqkHYS2GJZG4XuNly5aJqampTmCqX7++zJkzR/354/ofPXokzZo1k3z58qkBxVhiY2PF399fcubMKe3bt5eOHTtKhgwZ5K+//hKRuKt4s2fPrhOe4rcnJCRE5s2bJ+XLl5e2bdsapf6kJDwN+vLlS/UCm/fv38uKFSukTJky0r59e3VfltaGfBgTw9JnNGjQIMmRI4c0btxYateuLaampjJq1CgJDg6WkJAQmT9/vuTPn1/y5s2rM6mhliTsHfrw4YMUK1ZM3NzcdC4R9vPzk127dkmpUqWkcePGoiiKuj1a2vknrOXYsWPSqFEjncC0cePGJAegxq/39OlTmT59uhQvXtzoO/6ExowZI9myZZNt27bJ5cuXpVy5cpI/f351KgB9wsPDNXeDz7Tu4/d8RESELF26VBwcHKRy5crqASsqKkpiY2OlVatW8v333xujVINERkaqgWns2LHJXu/kyZPy4MGDVKzMMFeuXJGsWbOKmZlZorFYSY3Zi389Q0NDZerUqeLq6qqJ24DE17Vjxw71Kt0cOXLI+PHjJTAwUKKiomTZsmVSrlw56dSpk2Z7wrSKYekzOXbsmOTMmVPnnmfLli2TzJkzy9SpU0Uk7sP38OFD2b9/v+YnNdy8ebPs3btXbt++LaVKlZImTZokuslqYGCg7Nu3Tzp16qR+W9OahDuMI0eOqIEpOfXGxsbK8+fPkxVCPpeXL19KtWrVZP/+/SIisnv3brG1tVWvrNLS/cO+Bgn/zjExMWowioiIkF9//VVKliwpjRs3Vt+HsbGxUqVKFenVq5dR6v2UT71fwsPDkwxMWvpSlJSEF6PcuHFDChUqJHny5JFOnTqpE2YmXO7j/08YmLR05ZiXl5ekT59efvnlF9m7d68sWrRIMmXKJF26dJHg4GB59+6dLFu2TAoWLKjZ+/BpFcPSZ7Jr1y4pVqyYBAYG6hyg582bJ5aWlknON6Kl5J9w53f9+nWxsbGRuXPnikjc2KpixYpJkyZN1LE9WqYvKBw6dEgaNmyo08Ok5R3/x7XduHFDMmfOLMHBwXLo0CHJmDGjeqonPDxcpk+frk4SSp/P1KlTpUmTJtK6dWt1VuuIiAhZsWKFFCxYUAoWLCgNGzaUdu3aSdGiRTXVq5zw87J7925ZvXq1zrQGHz58kKVLl4qpqanOoO+04PTp0+p+1tvbWwoUKCDt2rVL1o28tbhf6NOnj7Ro0UKn7fDhw2JlZSU///yziMQN+1i9ejXHKBmIYSkVJPUh2rFjh5ibm8vjx49F5H9jfgICAiRPnjyJ7lqvBUmFiuvXr8vYsWPVK9zie74SBiYfH5/PWqchEm7T6tWrpUePHjJw4ECdMVWHDh2SRo0aSaVKldTTa1rcMSac8DPhrM5NmzaV7t27i5WVlaxYsUJtv3fvntStW1edD4o+j1mzZomdnZ306tVLPDw8JF26dOpM0O/evZNVq1aJk5OTFC9eXA4fPqyup4Ve5YTv+2HDhkm+fPnUq0ArV66sHnDjxzBZWFjIoEGDjFWuQY4fPy6FCxeW4cOHq18gvLy8pGDBgtKxY0f1Clk3NzdZuXKlMUv9pI+veG3ZsqW0bt1aRP43aaZI3HswX758EhAQYJQ6vwQMSyks4cE44aDN2NhYcXNzkypVquictnny5IkUKVJEvXpMK+K34+nTp7J582b57bffZM+ePeq9kOK7cGNiYnQCU6lSpcTNzU1nWgEtGjp0qOTOnVu+//57+f777yVv3rw6lzEfOnRImjZtKvnz59e50a9WHD16VKpUqSLnzp2T/v37i6Io8vDhQ4mKipK+fftKhgwZ5Mcff1SXDwsLE09PT/nmm294Ci6Vffz3nTZtmhqCXr16JcOHDxcTExN14tmIiAhZtmyZuLi4SOvWrdUDoJZ6lufMmSO5cuVST1GtX79eHYsY//n48OGDzJ49W6pXr67JLxcf+/DhgwwePFhcXFxkxIgROoEpfsLg0qVLi4ODg6Yvqf/jjz8kKChIRERmz54tGTNmVCf7jd83r1u3TpycnJK8LRMlD8NSCkq4g5g/f760atVKBg4cqN7f7eTJk1K9enVxdHSUffv2yc6dO6VBgwZSsWJFTe0Y43f2V69elUKFComjo6OYmZlJhQoVpHHjxuLp6Sn29vY6vS7x9V+8eFEqVaqk2Xu9icRdkVioUCE5d+6ciMTd+sPMzEwsLCxk1KhR6nK7du3S7O1lXr58KeXLl5d8+fKJjY2Nzhir8PBwadiwoZQpU0a+/fZbGTRokLi6ukrp0qXV0zsMTKkj4d/14MGDsmvXLnF1ddXpOQ4NDZURI0aIqamp2sMUEREhK1euFGdnZ/H09DT6wTnhdoSEhMiPP/4omzdvFpG4z4W1tbXMnDlTypYtKxUrVlSnAIiOjk5yni9jS2rCSZG4MDF06FBxdnaWESNGqLdiOn/+vMyePVsmTZqkBg4t9PR97MOHD1KjRg1xc3OT6OhoCQgIkMaNG0u5cuV07o4QHwrjt48Mx7CUQhJ+AH/++WextraWHj16SP78+aVGjRrqVVWXL1+W5s2bS+bMmcXJyUk8PDzUA5gWDsoJg1KGDBlk6NCh8uzZM9m9e7d4eHiIi4uLzJgxQ1xdXaVx48bqJeaxsbHqzkTLl6N++PBBxo0bp/Yi7dmzR2xsbGTmzJkyduxYMTExkRkzZiRaTwuvTbz4v/PYsWPF3NxcKlSoIKdOndKpMSwsTGbPni3NmzeXNm3ayKhRozS90/8SfHzKytzcXJycnERRFBk/frzO6xMaGiqjRo0SRVHk4MGDIhL3uVmwYIHUqlVLnj59+tnrj5dwO+J7vw4fPiwvXryQy5cvS6FChdT7JK5cuVIURRF7e3vx8/NL8jm0ZM2aNTJjxgydMWEfPnyQoUOHSrFixWTcuHESGhoqIrqBUUuf/4+D3+7du6V27drqbYv++OMPadasmVhaWoqHh4fUqVNHrK2tNXXFblrEsJTCLl++LD/++KP88ccfIhI3rqRdu3bi4uIiv/76q7rc/fv3JTAwUH3ja+kA5ufnJ9myZZOWLVvqtC9ZskRsbW3l8ePHsnPnTqlTp440adJE/QajxW+UCXd48V3Qb968kfv378uTJ0/E0dFRvV/S2bNnxcrKShRF0RnAqlVHjhyRM2fOSNWqVaVGjRpy6NChJP/2Sc3kTann8uXLUrVqVTl37pzcunVLpkyZIiYmJup8XfFCQkJk6dKlOp/99+/fG3UAfsL3ysyZM8XCwkJn/rClS5eKu7u7ekn95s2bpWfPntKjRw/Nv7ciIyPV+dQWLVqUaBB9rVq1JF++fNK7d2+dWzRp0cmTJ9WrjyMiIqR169ZSt25d9fFnz57JihUrpFevXjJ27Fi5c+eOsUr9YjAspaDffvtNKleuLGXKlJFHjx6p7Y8fP5bvvvtOXF1dE+0wRbR3SuThw4dSqVIlady4sc50AEeOHJHMmTPLrVu3RCRuR/nNN99IrVq15MaNG8Yq95MS/l0XLlwoo0ePVgfYi8R9Wy5ZsqT4+/uLSNxBrn379rJ7927N7fg/dQmzSNyOsXLlylK9enU5cuSI2j5t2rTPVh/FmTp1qrRv3z7RPc+mTZuWZGCKFz/HklZ4e3vLTz/9lOhigKFDh4q9vb2Eh4dLcHCwNG7cWCZOnKg+rqXPTVL71Tdv3kjHjh2lSpUqsmDBAp3Tnf379xcnJyfp37+/pl6Ljz1+/FhMTU1FURT55Zdf5MaNGxIcHCx2dnYyevRoY5f3xWJYSkFXrlyRWrVqibW1tU4vkkhcb02HDh2kePHisnPnTuMUaIB79+5JvXr15JtvvpFbt27J27dvJXv27Inu87Z27Vpp3LixpscoDRkyROzs7GTVqlXq/apE4g4IVlZWsnjxYnnx4oXUr19f2rdvr7kBtgl3+gsWLJCuXbuKu7u77Nu3T70i7vnz51KlShVxdXWVKVOmSMOGDcXGxkYz2/C1mDVrliiKIo6OjjpXK4rEBSZzc3OZOXOmkapLnj179kjp0qWlQIEC6peg+PfRs2fPpECBApItWzYpUqSIlCpVSlPTHMRL+Jm5c+eO/P333+r0LCEhIWpg+uWXXyQ8PFxiY2Olffv2sm3bNk32kH+sW7duYmJiIu3atZO2bdvKpEmTZNOmTVKhQgX1rAalLIalFBL/4bxz5464u7tL7dq1E4Wiv//+WyZMmJBmDmD37t0TT09PqVmzpmTOnFn69++vPpZwBxl/jl+Ldu/eLXnz5tWZYTzeq1evZMiQIWJpaSkFCxaUsmXLqtulxR3lsGHDJHv27DJy5Ej57rvvpFixYjJs2DD18u0XL15I8+bNpU6dOuLp6cnB3KnsU3/XFStWqOOUPr4R8ahRo8TV1VWT7694ly9fljZt2kj69Ol1xu/F77devnwps2fPluXLl2tyHFzCv+3IkSOlWLFikjdvXsmVK5fa2/r27Vv58ccfpXz58uqVbyVKlFC3UYufmfv376vjwoKCgqRjx44ycuRI2b9/v1SoUEEKFCgghQoVkj59+qg3K6aUw7CUQhJ+uO7evSvu7u7yzTfffLIXKS0Fptq1a0v+/Pnl5MmTarsWb4qblOnTp0uNGjV0wl3Cut+9eyeXL1+WAwcOaHrW9HXr1knBggXVyfJOnz4tiqJI0aJFZeDAgerpxYiICHn9+rUmx8J9SRJ+3s+cOSP79++XAwcOqG3z5s0TRVFk8uTJiQKTlnouPg4F8TXdu3dPvvvuOylbtqysWrVKfTyp95NW92XTp0+XrFmzypEjR+Tw4cPyyy+/iKmpqfTt21dE4sYw/v777zJ8+HAZM2aMum1a256YmBh58eKFZM6cWVq1aiVr164VkbjpHHr16iWhoaESGxsr/fv3lxw5ckju3Ll51VsqYFhKAfE7mD179qhd7NevX5e6deuKp6enbNy40Zjl/We+vr5Sr1498fDwkDNnzhi7nGSJPwiMGTNGqlSpkuhUQVRUlGzfvl1nDJOIdnaUHx9I161bp37L37lzp9ja2srKlStlwoQJkiFDBhk8eLD4+vrqfQ5KeUOHDpXixYuLg4ODuLi4SKlSpdR76y1cuFAURZGff/5ZXr16pbOeFl6bhEHp119/lVGjRknr1q3Vz/iDBw+kffv24uLionOPRC32unwsKipKGjZsmGhG8d27d4uiKLJmzZok19PK5z8pR48elb59+0qhQoWkV69ecvHiRSlevLjO2MSzZ88m2qdRymBYMkD8TiKpq4u2bt0q6dKl0/kWdv36dSlbtqwMGDDg8xaaCu7duycNGzaUKlWqaHKG7k/twON3jps2bdJpf/PmjTRr1kyd50ZLEk5aumLFCnn//r0EBgbKy5cv5fnz51KhQgWZPXu2iIgEBwdLrly5JHfu3Dp3R6fUN3/+fMmWLZs6Aevs2bNFURT1vnzxyyiKovYGaNGQIUMkT5480rVrV+nQoYOYmJioB+AbN25Ihw4dpHr16mniClGRuP1zWFiYODg4yPjx40VEd/Lc7t27S7169eTdu3dpouc1OjparTM4OFiOHz8u+fPnl6ZNm0rTpk0le/bsOr3+lDoYlpIp4cH46dOnEhwcrF5Jcfv2bcmQIYPOziQ+UD148CBNfBNLjtu3b0uLFi00980l4d/32LFjsnXrVtmzZ48639OAAQPE3NxcFi9eLBcvXpSrV6+Kh4eHlCtXTnPfJI8dOya5c+eWmzdvSr9+/cTS0lLnysqLFy9K4cKF1cB648YN+f7772Xx4sWa25YvWWxsrHTr1k0NqLt27ZJMmTLJ8uXLRSRuEHH8PmDr1q2aPSjv27dP7O3t1UlNL168KIqiyJYtW9Rlbt26JQ0aNJDu3btrokfsY5/avw4ePFiKFi2qDlKPX27w4MHi6en52eozVPzf2M/PT2fOuufPn6szqH/48EEGDBggnp6eoiiK9O3bV5MD7b8kDEsGGj16tDg4OEixYsWkUqVKcvbsWQkICFAnZ0wo4Y7lSwlMxp5ZWJ/BgwdLvnz5JF++fFKgQAEpUKCAOkfMpEmTxNbWVrJnzy4lS5bUGceklZAR/x6pWrWqZM+eXTJlyqTezDf+sVOnTknRokVl1qxZ4uPjIw0bNpQ2bdqoz6GVbfkaeHh4yNy5c+XAgQOSMWNGdVqA6OhomTdvnixbtkxneWMHppUrV8qLFy902jZs2CBNmzYVEZGNGzdKpkyZ1O0ICQlRryBL+KVPi4FJJG5g+pkzZ9SA8eeff0rdunWlUaNG6nQnERERUrduXZ1bAWnR1q1bpXLlymqIffTokeTLl0+mTZumM7bqr7/+kv79+6vbR6mHYekfJAw5GzdulCxZssj69etl1apV0rJlS7GyslJP8Wh1J/I1WLVqlWTJkkUuXLggz58/l5s3b0q9evUkd+7cak/YzZs35c8//5Q///xTfV2NfQCLd/jwYZk8ebI8efJEJk2aJIqiSL58+eTKlSuJavzpp5+kUKFCkjdvXp3xWHz/pTwvLy/1ptfx4v/O48aNExcXF7G2ttaZP+nly5dSv3599VSpFly4cEEURZF+/frpnOadM2eOVK1aVQ4dOpRoO9asWSM//vijziSZWvnSN3r0aNm1a5f688CBAyVv3rxiaWkpLi4usnv3bhER2b9/v3zzzTeSKVMmcXV1FScnJ53pDrT0mYmv5c2bN1KrVi1ZsGCBiMTdPzRXrlw6PXtaqvtrwbCUTDt27JDRo0cn+rbYr18/yZAhg3pvJL6JjWPEiBHSrl07nbaQkBCpUaOGuLq6JhmKtLLjX7VqleTJk0d69uwpN2/elOvXr8vdu3fFxcVFChcuLGfOnNG555ZI3Df9CxcuaPoKvrSuXLly0qhRI/V98nHPysOHD6VYsWJSokQJuXTpkkRGRoqfn594enqKs7Oz5l6T3bt3q1eDPX/+XETiJjisWLGiKIqiM+YtIiJCGjVqJD/88IPm9mnBwcFSsGBBcXNzk6NHj8revXulZMmScuTIEbl8+bLUrVtXKlWqpF5Y8/z5c1mzZo2MGjVK5s+fr8npDuIdOXJEWrVqJU2bNlXnhDtw4IAMGjRIc6/D14ZhKRmuXLkiJUuWlPTp08uSJUtERPd0VLVq1aRbt24iwrBkLD169BBHR0f15/gQsXbtWilWrJg6S7fWbNq0STJkyCBbtmxJ8jYXrq6ukj9/fp1B9VOmTNEZy8BTbylvy5YtUqJECfW2Fx/Pv3PixAm5ceOG3LlzR/Lnzy9OTk6SO3duqVq1qlSuXFlTp3gThoIdO3aIoigyatQo8ff3l+joaPnll1+kdOnS0qFDB7l165YcOHBAPD09xcnJSV1XK/u1+DpevHghVapUEU9PTxk1apRMmTJFXebt27fSrFkzqVixoqxfvz7JoQNaeF2S4uXlJenTpxczMzN1fBJpA8NSEj7eMURERMjSpUvFwcFBKleurB6o4m9R0KpVK/n++++NUepXJygoKMn2o0ePSsmSJWXu3Lk6B4eDBw9KiRIldG7yqRUBAQHi5uam3pQ03tu3b+XMmTPq/Zzq168v+fLlk9mzZ8s333wjBQoU0OzO/kuxY8cOMTExkYCAAOnTp4+4u7urf/MdO3aIjY2NOofakydP5MCBAzJ//nw5fPiwpnr7Eu7LJkyYINOmTZPMmTOLiYmJ9OnTR96+fSsRERGyYMECqVixoqRPn14qVKggjRs31lTgE4nblujoaLWegIAAcXFxEUVRpEOHDjrLxgemqlWryqJFizSzDclx6tQpsba2ltatW3O+JA1hWPrIx/fgig9GERER8uuvv0rJkiWlcePG6ocvNjZWqlSpIr169TJKvV+TU6dOiZubW6LJMUXiuua7du0qbm5uMn78eHnz5o38/fff4unpKZ6enpr5ZpxQQECAODo66kxcunjxYmnRooUoiiLZs2eXJk2aiIhIhw4dpFatWlKvXj3OzJ3KYmNjJTIyUlq3bi2ZMmWSzJkzq2F7165doiiKejr+U6+B1g7OP//8s2TOnFmOHDkiBw8eVCfN7NWrlzovlEhcL7pWb/CdcB6xZcuWia+vrwQFBUmNGjWkZMmSsm/fvkQ3zq5Ro4Z07drVGOX+o4QTgHp5eYm3t7d626ijR4+KlZWVdO7cWfM39f1aMCx9wtSpU6VJkybSunVr8fLyEpG4wLRixQopWLCgFCxYUBo2bCjt2rWTokWL8rLNz+DOnTtSs2ZNadCggc7kmAlvw9C3b18pWbKkmJmZSalSpaR8+fKaDRcBAQGSN29e+fHHH8XLy0uaN28upUuXlp49e8qRI0dk69atYm9vrw70DAgI0ORB7EvRp08fWb9+vfpzr169RFEUsbGxUa8i8/b2lhUrVhirxH8lOjpavvnmGxk+fLhO+5YtW0RRFBk4cGCS93bU0ufl6tWrki5dOlm/fr0MGzZMbG1t1XGiL1++FGdnZ6lRo4YcPHgw0Qz9WryKL76W7du3S8GCBaVkyZLi7OwsZcqUUU+5e3l5ScaMGeXHH3+UsLAwY5ZLwrCUpFmzZomdnZ306tVLPDw8JF26dOrkhe/evZNVq1aJk5OTFC9eXA4fPqyuxwNY6ou/we/Hs4nHB6LIyEh5+/atzJgxQy5fvqypUyJJOXbsmNjY2EihQoWkTJky4uXlpZ5qfP36tZQtWzbRncS1tNP/Ujx69Ei+/fZbcXR0VOcY+vnnn2X//v3SpEkTsbW1VS/P1lKI+CfR0dHy7t07qVixogwbNkxti/88dO3aVczNzaVbt26JZhnXgvjP74sXL2TSpEliaWkpNjY26gD1+J5/f39/cXZ2lpo1a8rhw4cTfUa0+JqdPXtW5wrE+PFkkydPVpc5fvy4KIoiP/30k7HKpP/HsCSJP0jTpk1TQ9CrV69k+PDhYmJion7rjIiIkGXLlomLi4u0bt1ac3ep/9IlDEynT59W22NjY+XZs2fi6ekpPXv2VNu1/roEBASoN8NN6PXr11K9evVEV2BS6rh69ap07dpVihcvrjML9+3bt6V+/fpia2sr9+7dExHtvqc+FQrGjRsnWbJkUeftiV9u7Nix4ubmJtWqVdNcoGjRooV06tRJ/XnZsmWiKIpkyJBBZ0b0+AHc/v7+4uLiIsWLF5dz58597nKTLf54MWfOHHWs1ZMnTyRfvnw6oSgwMFBERE6ePCm3b9/+/IWSjq8+LCXcQRw8eFB27dolrq6usnfvXrU9NDRURowYIaampmoPU0REhKxcuVKcnZ3F09NT05M1fomS6mHy9/eXGjVqSOHChdP8adGAgABp0KCBODs7a/bA/KVI2Avx119/yQ8//CDFixeXzZs3q+13796V+vXrS5YsWdTApLVwkbCekydPys6dO2X37t0SGRkpwcHB0qhRI3FycpIrV66ISNw+rGHDhjq941rapoRfhGJiYiQoKEguXLggkyZNkkyZMsnSpUtFJO71i687ICBAevToocnPzMe9XePHj5euXbvKo0ePJG/evNKtWzd1mYMHD8qMGTN4+k1DvuqwlPDNO2zYMDE3NxcnJydRFEXGjx+v84ELDQ2VUaNGiaIocvDgQRGJ6wJesGCB1KpVS54+ffrZ6//axQcmT09P2bNnj9StW1dKlCihBiWtnnrTJzAwUKZOnSoNGjSQSpUqae6KpC/Nx3MoiYhcunRJDUwJb/tx9+5dadSokSiKkuQYH2NKuC8bOnSoOv+Tq6urFCtWTIKDg+XcuXPSsmVLMTMzE2dnZ3FwcJASJUpobnoAkbiglC1bNnn79q1s2LBBHB0d1Tr9/Pxk1KhRkilTJp3xYxMmTFBnHBfR5mfm3Llz6ntq2bJl4ujoKLlz51annhGJq7t79+7Ss2dPiYiIMFap9JGvOizFu3z5slStWlXOnTsnt27dkilTpoiJiYnObLYicZMcLl26VOcg/P79+yTnx6HP4969e1K/fn1RFCXNByWRuKuRGjZsKP369dP05HlfgoQByc/PT27cuKG2+fr6SufOnRMFpps3b8rgwYM19ZoknENs0aJFki1bNrlw4YKIiPzyyy+iKIocOnRIROIuqd+yZYv8/PPPMnv2bJ1bZ2jJ3bt3pUiRItKjRw85efKkFClSRGdy2SdPnsjo0aPFwsJCndqhaNGimtuOePHTHtSpU0fq16+vtnt6ekq6dOnEx8dHwsLC1LMYdnZ2PPWmMV99WJo6daq0b99eOnfurNM+bdq0JANTvPg5lsj4bt++LX369PliwkVwcDDHwaWyhJ/dMWPGiJOTk+TKlUvKly8vs2bNkvDwcLl586Z06dJFHB0d5ffff0/0HFp4n82bN0/s7e3ViwJ++uknmTVrloiI7Ny5U+fmvuHh4UmentbieywqKkrGjBkj5cuXlxMnTsilS5ekYMGC4uLiov7d/f39ZfHixeLi4iLt2rXT7FWvIv97v50/f16yZs2qnuINDQ0VZ2dnyZcvnxQqVEjq1KkjuXPnVu8JSdrx1YelWbNmiaIo4ujoKM+ePdN5bNq0aWJubi4zZ840UnVkKC0cwFIKw3jq+/nnn8XOzk7279+vfvPPnz+/emPs+EHfWbJkUacQ0YqlS5eKhYWFTs9XkyZNZMqUKbJ//36dm/vGxMTI/PnzZeHChZp9X33ck/L69WspUqSItGjRQkTizgAUKFBAJzCJxA3w1uKUGkldkRcSEiLt2rWTnj176tS6efNmmTt3rmzdulW9lyVpy1cVlj71jWPFihXqOKXg4GCdx0aNGiWurq6a3cEQUfIlvE3MmzdvxM3NTdatWycicfflypQpk3r1YXyPy6VLl2Tq1Kma6oFZvny5mJub60xoKhK3v6pdu7ZkypRJFi1apLYHBgZK/fr1Zfr06Z+50uTZs2ePKIoi9evXl0ePHqlDG44fPy7p06dXt+XSpUtSuHBhqV69eqKLarS4jz5//rxs375dp23t2rViaWkp165dM1JV9G98NWEpYVA6c+aM7N+/Xw4cOKC2xc9oO3ny5ESBiXd6Jkr7Dh8+LDNmzJDz58+LSFxYKleunAQEBMiRI0ckY8aM6r0f429xFD/xYTwtBKYTJ06IoigyYcIEnfaffvpJunfvLqVKlZIiRYqo42AePXoknp6eUrlyZU31vCR09epVyZs3r2TKlEnq168vU6ZMUa/a69Gjh1SvXl0NFxcvXpSMGTPqTA+iNbGxsfLq1Svp0KGDejuWhBOetmzZUlq3bs2r3dIQRUQEX5Fhw4Zhz549iImJQfbs2REaGgofHx9kzJgRixYtQp8+fTBlyhR0794dWbJkUdcTESiKYsTKiejfWr16NcaMGYPGjRvjhx9+QMWKFQEA1apVg6mpKa5evYo5c+agS5cuAAA/Pz907NgRPXv2ROvWrY1ZeiK+vr7o0qULMmfOjDFjxqBixYpo3rw5rl27hhs3biAgIADu7u6wsLCAv78/ihQpgtjYWJw+fRpmZmaIiYmBqampsTcDsbGxMDExQXR0NGJiYvDLL78gNDQUNjY28PPzg5eXF2bMmAELCwt07doVvXv3xuDBgxEdHY379+/DwcFBE9uhT0xMDC5evIiJEyfi2bNnMDU1xfTp03Hq1ClcvHgR8+fPR5EiRYxdJiWHkcPaZzV//nzJli2b+s1y9uzZoiiKzgR08+fPF0VRdCY9I6K0a9OmTZIhQwbZsmWLemPS+F7i/fv3S/HixaV69erq8mFhYVK/fn1xc3PTRE9SUuKnzWjQoIG4urpK+fLl5eHDh+rjAQEB4uXlJcuWLZPjx49rcib7j29uffjwYalZs6acOHFCROIurbezs5MZM2aIs7OzZMmSRZ1FPZ6WXp/499SlS5fk119/lSVLlqhj30JDQ+XmzZvSrFkzcXFxEWdnZ1EURUaOHGnMkskAX01Yio2NlW7dusn8+fNFJO6GmAmvFAkJCVHf7Fu3btXUToWI/p2AgABxc3OThQsX6rS/fftWLl++LPv375epU6eKo6OjVKxYUZo1ayaurq7i5OSk+Tmu7t27J+7u7mJjY6Nztd6n9l1a2o4LFy6IoigyePBguXPnjto+evRoyZkzp3o7k3Pnzknfvn3Fzc1NFEWRfv36Gani5Nm2bZvkzp1bXF1dpV69eqIoijomLt7hw4dl2rRpYmdnp4Yp0r6vJiyJiHh4eMjcuXPlwIEDOleKREdHy7x58xLdVoKBiShtCwgIEEdHR52B0IsXL5YWLVqIoiji4OAgTk5O4uPjI3369JF+/frJrFmz0sw0FPfv3xcPDw/x9PRMdOsfLQsODlZ7+mvUqCFTpkxRH+vUqZP06tVLQkNDRSTullMXLlyQ3r17a+b1SDgGNr6mK1euSPbs2dXjyN9//y2KosiYMWMSrSMinHAyjTEx9mnAlHb8+HG8f/9ep03+f1hWlSpVsHXrVrRp0wYzZsxAz549AQCvXr3CkSNHEBYWprNeunTpPk/RRJRqQkNDsX//fhw/fhwtWrTAkiVLkD17dhw6dAhTpkxBREQEzp07h/nz52PevHkYNGgQ0qVLh5iYGM3vAwoXLowFCxZARDBlyhScPXsWADQ/vtLW1hZ9+vTB2bNnUahQIaxcuRIuLi64dOkSqlevjvDwcNy8eRMAkDlzZlSqVAkLFixAunTpEB0dbeTqARMTEzx+/Bgior5Hnj17hqpVq6Jbt254+PAhatSoge7du2PixIkAgKCgIAD/Ox6lT5/eOMXTv2PcrJayypUrJ40aNUp0C4P4b1kPHz5UbwNw6dIliYyMFD8/P/H09BRnZ2fNfGshopRz7NgxsbGxkUKFCkmZMmXEy8tLncTx9evXUrZsWRk9erSRq/xv7t27Jw0aNJCKFSumuVM7b968kT/++EOqVKkihQsXlj59+oiDg4Omr3Z7//69VKlSRQoUKKAeX5YvXy4VKlSQmzdvSv78+aVbt27qMejQoUPyww8/yOvXr41ZNv0HX0zP0u+//473799j8+bNMDExQUxMDExMTBAbGwtFUfDHH38gPDwcu3fvRkREBDp37oyCBQuidevWePXqFU6fPq1+mySiL0edOnXg6+uLY8eO4a+//kLt2rWRNWtW9XFra2vY29sbscL/zsHBATNnzkSNGjVQqlQpY5djEBsbG9SsWRM+Pj5o3bo1Hj9+jMDAQCxduhS7du0ydnlJMjc3x8yZM5ExY0aUL18eIoKGDRvCwsIC1apVg5ubG5YtW6b28B05cgTBwcEwMfliDrlfnS9m6oCdO3eiRYsW8Pf3x6RJk3D79m0cOnQIpqam2LlzJzp37ow1a9agadOmePr0Ka5fv4779++jWLFiqFOnDkxNTREdHa35bnciShmBgYHo3LkzgoKCcPbsWc1fhm6I+Mvy04qE9V64cAH79u3D0aNH1S+xxpbU3zM2NhYXLlzA999/D2tra1y4cAHTpk3DjBkzMGzYMHTu3BkRERFYunQpVqxYgVOnTqFkyZJG2gL6r76IsCQiiIqKQseOHXHgwAGkS5cOV69ehb29PXbv3o1mzZph6dKl6Nat2yd3IlqZe4SIUldQUBBWrlyJM2fOICAgAGfPntXU/ENfK/nEXHbG/hIbf8zw9/fHo0ePUKVKFfWxqKgoXLlyBW3atEGePHlw+vRpDB8+HPv27cP9+/dRpkwZhISEYNOmTShXrpzRtoH+O+NH9v+gb9++qFy5Mtq3bw9zc3NkzZoVYWFhsLa2hpmZGQAgR44cWL58OX788UcA+OS3Le4kib4OT58+xdmzZ1GkSBHs2rVLHTSshR6Mr1lSQUkSDKA2FhMTEzx58gTlypXD69evUbNmTVStWhXu7u6oWLEiKleujC1btqBLly5wdXXFmTNnMGjQIBw/fhwODg7InTs3cubMadRtoP8uzfYsPX78GAMHDsSdO3cwbtw4tGrVClOnTkWZMmWwfPlynDx5Et7e3ihRokSa65ImotT15s0b2NjYQFEU9ijRP3r8+DGaNm2Kd+/eIVOmTChZsiS2bNmC4sWLo3Tp0mjYsCEURcGIESNQsGBBHDlyRPNXJJJh0mxYAoBr165h4cKFOH36NGbPno369esDAO7cuYNBgwbB29sbFy5cgIODA3eIRJTIp079EH3s/v37GDp0KGJjYzFixAjkypUL3t7eWLhwIaKionDjxg0ULlwYN27cQJMmTbBz506+v74gaTIsJXwDXr16FfPnz4e3tzfGjx+v3sfp3r17GDBgAM6dO4dz587BwcGBPUxERPSv3b17F/369UNsbCymTJmCSpUqAYjrqdy7dy/u3LmDgwcP4tdff+UYpS9MmgtL8YEnYfC5fPkyFi1aBG9vb0yYMAGtWrUCEBeYBg8ejH379sHPzw958+Y1ZulERJTG+fr6ok+fPgCAESNGoGbNmjqPc/zblylNhaWEAenJkycIDQ1FiRIlYGJigvv37+Pnn3+Gj4+PTmC6desWVq9ejalTp/INTERE/5mvry/69u0LEcHYsWPh4uJi7JIolaWZsJTw1NvYsWOxe/duBAYGIleuXGjXrh169uyJR48eYc6cOfDx8cH48ePRsmVLnedg4iciopTg6+uLgQMHIigoCHPnztWZUoC+PGlmAE98UJo6dSqWL1+OqVOn4smTJ8icOTMWLFiA+/fvw9HREX379kW1atXQo0cPHD9+XOc5GJSIiCglxM+anjdvXuTOndvY5VAq03zPUmRkJCwsLAAAISEhaNq0KX744Qd06NABR48eRfPmzTFr1ix069ZNveLt8uXLOHLkCIYMGcIr4IiIKNV8+PAB5ubmxi6DUpmmw9KRI0dw9epV1KxZE5UrV0ZISAhq1aqFw4cP46+//sK3336LmTNnokePHnj37h3WrVuHWrVqoWjRoupzcMoAIiIi+i80expu9erV+OGHH/Dw4UN1ULeNjQ0sLS3RvHlztGjRAvPmzUOPHj0AxN3nadOmTbhy5YrO8zAoERER0X+hyZ6lzZs3o0uXLli9ejXq1asHa2trdYD3gQMHMGjQIGTPnh2nTp0CAISHh6NVq1aIiIjAsWPHGJCIiIgoxWguLAUGBqJVq1Zo0aIFfvrpJ7U9LCwMvr6+ePHiBa5du4b169cjQ4YMsLe3R2BgIEJDQ3Hx4kXeEJOIiIhSlCYvDwsICECePHnUn5csWYLjx49j+/btKFKkCCwtLfHrr79i48aNMDExQbVq1dCvXz/eEJOIiIhSnCZTRWhoKPbv3w9ra2ssXrwY9+7dg6urKw4dOoSQkBCMHDkS586dw/z583XWi4mJYVAiIiKiFKW5ZJE9e3asWbMGzZs3x/Hjx5EpUybMmzcPZcqUQdasWREcHIyff/4Zr169SrQuT70RERFRStNcWAKAOnXqwNfXF2FhYShYsGCix62trWFvb2+EyoiIiOhro7kB3voEBgaic+fOCAoKwtmzZ9mTRERERKlOkz1LHwsKCsLKlStx5swZBAQEqEGJV70RERFRatPspJQJPX36FGfPnkWRIkXg7e0NMzMzREdHMygRERFRqkszp+HevHkDGxsbKIrCHiUiIiL6bNJMWIoXP5M3ERER0eeQJk7DJcSgRERERJ9TmgtLRERERJ8TwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtERKmsQIECmDdvnrHLIKJ/iWGJiFJVYGAgevbsiXz58sHCwgI5c+aEh4cHzp49a+zSGGKIKFnSxI10iSjtat68OT58+IC1a9eiUKFCePnyJby8vPDq1atU+50fPnyAubl5qj0/EX1d2LNERKnmzZs3OH36NKZPn45atWohf/78qFy5MkaMGIHGjRvrLPfjjz8ie/bssLa2Ru3atXH16lWd59q7dy8qVaqE9OnTI1u2bGjWrJn6WIECBTBp0iR07NgR1tbW6NatGwDgzJkzqF69OiwtLWFvb4++ffsiPDwcAODm5obHjx9jwIABUBRF790B3rx5g+7du8POzg7p06dHqVKlsG/fPvXx7du3o2TJkrCwsECBAgUwe/bsTz7Xo0ePoCgK/vrrL53nVxQFf/zxBwDgjz/+gKIoOHz4MMqVKwdLS0vUrl0bAQEBOHjwIEqUKAFra2u0a9cOERER6vO4ubmhb9++GDp0KLJkyYKcOXNi/Pjxn36BiChZGJaIKNVkzJgRGTNmxK5duxAZGfnJ5Vq2bKkGgUuXLqF8+fKoU6cOXr9+DQDYv38/mjVrhvr16+PKlSvw8vJC5cqVdZ5j1qxZKFOmDK5cuYIxY8bgwYMHqFevHpo3b45r165hy5YtOHPmDHr37g0A2LFjB/LmzYuJEyfixYsXePHiRZK1xcbGwtPTE2fPnsWGDRtw69YtTJs2Tb2Z96VLl9CqVSu0adMG169fx/jx4zFmzBisWbPmP//9xo8fj4ULF8Lb2xtPnjxBq1atMG/ePGzcuBH79+/HkSNHsGDBAp111q5dCysrK5w/fx4zZszAxIkTcfTo0f9cC9FXTYiIUtG2bdskc+bMkj59enFxcZERI0bI1atX1cdPnz4t1tbW8v79e531ChcuLMuWLRMRkapVq8p33333yd+RP39+adq0qU5bly5dpFu3bjptp0+fFhMTE3n37p263ty5c/XWf/jwYTExMZG7d+8m+Xi7du2kbt26Om1DhgwRR0dHnfrif8/Dhw8FgFy5ckV9PDg4WADIiRMnRETkxIkTAkCOHTumLjN16lQBIA8ePFDbunfvLh4eHurPNWvWFFdXV51aKlWqJMOGDdO7jUSkH3uWiChVNW/eHM+fP8eePXtQr149/PHHHyhfvrza83L16lWEhYUha9asak9UxowZ8fDhQzx48AAA8Ndff6FOnTp6f0/FihV1fr569SrWrFmj85weHh6IjY3Fw4cPk13/X3/9hbx586Jo0aJJPn779m1Uq1ZNp61atWrw9fVFTExMsn9PUpycnNT/t7OzQ4YMGVCoUCGdtoCAgE+uAwC5cuVKtAwRGYYDvIko1aVPnx5169ZF3bp1MWbMGPz4448YN24cvv/+e4SFhSFXrlzqeJ2EbG1tAQCWlpb/+DusrKx0fg4LC0P37t3Rt2/fRMvmy5cv2bUn53cbwsQk7juqiKhtUVFRSS5rZmam/r+iKDo/x7fFxsZ+cp1PLUNEhmFYIqLPztHREbt27QIAlC9fHv7+/kiXLh0KFCiQ5PJOTk7w8vJC586dk/07ypcvj1u3bqFIkSKfXMbc3Pwfe3+cnJzw9OlT3Lt3L8nepRIlSiSaBuHs2bMoWrSoOq4poezZswMAXrx4gXLlygGAzmBvItIenoYjolTz6tUr1K5dGxs2bMC1a9fw8OFDbN26FTNmzECTJk0AAO7u7qhatSqaNm2KI0eO4NGjR/D29saoUaNw8eJFAMC4ceOwadMmjBs3Drdv38b169cxffp0vb972LBh8Pb2Ru/evfHXX3/B19cXu3fvVgd4A3FX0Z06dQrPnj1DUFBQks9Ts2ZN1KhRA82bN8fRo0fx8OFDHDx4EIcOHQIADBo0CF5eXpg0aRLu3buHtWvXYuHChRg8eHCSz2dpaYkqVapg2rRpuH37Nk6ePInRo0cb/Lclos+HYYmIUk3GjBnh7OyMuXPnokaNGihVqhTGjBmDrl27YuHChQDiThMdOHAANWrUQOfOnVG0aFG0adMGjx8/hp2dHYC4S+K3bt2KPXv2oGzZsqhduzYuXLig93c7OTnh5MmTuHfvHqpXr45y5cph7NixyJ07t7rMxIkT8ejRIxQuXFjt8UnK9u3bUalSJbRt2xaOjo4YOnSo2iNVvnx5/P7779i8eTNKlSqFsWPHYuLEifj+++8/+XyrVq1CdHQ0KlSogP79+2Py5MnJ/ZMSkREokvDEORERERHpYM8SERERkR4MS0RERER6MCwRERER6cGwRERERKQHwxIRERGRHgxLRERERHowLBERERHpwbBEREREpAfDEhEREZEeDEtEREREejAsEREREenxf/QJbQitDOhCAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHPCAYAAACoQyVSAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAASjtJREFUeJzt3XlcVdX+//H3AWVywDEGRUHFnGclSMUBw6/eW5jd1G45ZNq1Ww445IBoZpGWRqZ90VtOpTmk11tdtZDMBglTcC41Q8kUFE0ITFHYvz/8eb6dIxjHDhzB1/Px4IFn7bXX/ixAzpt91t7HZBiGIQAAAJg5OboAAACAOw0BCQAAwAoBCQAAwAoBCQAAwAoBCQAAwAoBCQAAwAoBCQAAwAoBCQAAwAoBCQAAwAoBCUCJ+fzzz2UymfT55587upQiLV++XCaTSSdOnHB0KQDuIAQkAHeFl19+WZs2bXJ0GQDKCAISgLtCUQHpiSee0G+//ab69euXflEA7lgVHF0AADiSs7OznJ2dHV0GgDsMZ5AA/KGff/5ZTz75pLy8vOTq6qrmzZtr6dKlFn1OnTqliIgIVapUSffcc4/GjRunK1eu3DSWv7+/hg4delN7t27d1K1bN4u2y5cva+bMmWrcuLHc3Nzk4+Ojhx9+WMePHzf3ee211xQSEqKaNWvK3d1d7du31wcffGAxjslkUm5urlasWCGTySSTyWSuoag1SG+99ZaaN28uV1dX+fr66p///KcuXrx4U80tWrTQ4cOH1b17d3l4eKhOnTqaO3furb+ghfD399df/vIXffXVV+rUqZPc3NzUoEEDrVy50qLfzJkzZTKZbtq/sHncGPPzzz9Xhw4d5O7urpYtW5rXhG3cuFEtW7aUm5ub2rdvr5SUFJvrBsorAhKAW8rIyNB9992nbdu26dlnn9Ubb7yhRo0aafjw4YqNjZUk/fbbb+rZs6c++eQTPfvss5o2bZq+/PJLTZo06baPm5+fr7/85S964YUX1L59e82bN09jxoxRVlaWDh48aO73xhtvqG3btpo1a5ZefvllVahQQX/729/03//+19zn3Xfflaurq7p06aJ3331X7777rp5++ukijz1z5kz985//lK+vr+bNm6f+/ftr8eLFeuCBB3T16lWLvr/88ot69+6t1q1ba968eWrSpImef/55bdmyxeY5//DDD3rkkUfUq1cvzZs3T9WrV9fQoUN16NAhm8f6/ZiPPfaY/vrXvyomJka//PKL/vrXv2rVqlUaN26cHn/8cb3wwgs6fvy4Hn30URUUFNz2sYByxQCAWxg+fLjh4+NjZGZmWrQPHDjQ8PT0NC5dumTExsYakox169aZt+fm5hqNGjUyJBnbt283t9evX98YMmTITccJDQ01QkNDzY+XLl1qSDLmz59/U9+CggLzvy9dumSxLS8vz2jRooXRo0cPi/ZKlSoVetxly5YZkozU1FTDMAzj7NmzhouLi/HAAw8Y+fn55n4LFy40JBlLly61qFmSsXLlSnPblStXDG9vb6N///43HetW6tevb0gyvvjiC3Pb2bNnDVdXV2P8+PHmthkzZhiF/eq2nsfvx9y5c6e57ZNPPjEkGe7u7sbJkyfN7YsXL77pewXczTiDBKBIhmFow4YN+utf/yrDMJSZmWn+CA8PV1ZWlpKTk7V582b5+PjokUceMe/r4eGhkSNH3vaxN2zYoFq1aum55567advvX2Jyd3c3//uXX35RVlaWunTpouTk5Ns67rZt25SXl6exY8fKyen/fkWOGDFCVatWtTgzJUmVK1fW448/bn7s4uKiTp066ccff7T52M2aNVOXLl3Mj2vXrq177733tsb6/ZjBwcHmx0FBQZKkHj16qF69eje1/5ljAeUJi7QBFOncuXO6ePGilixZoiVLlhTa5+zZszp58qQaNWp009qYe++997aPffz4cd17772qUOHWv6Y+/vhjzZ49W3v37rVY81TYOp3iOHnypKSba3dxcVGDBg3M22+oW7fuTceqXr269u/fb/Oxfx9Yfj/WL7/8YvNYRY3p6ekpSfLz8yu0/c8cCyhPCEgAinRjPcrjjz+uIUOGFNqnVatWNo1ZVHDJz8+3+WqyL7/8Ug8++KC6du2qt956Sz4+PqpYsaKWLVum1atX2zTW7SqqZsMwSmSsW339bBnTnnUD5REBCUCRateurSpVqig/P19hYWFF9qtfv74OHjwowzAsnsCPHDlyU9/q1avfdDWYdP3MTYMGDcyPGzZsqKSkJF29elUVK1Ys9LgbNmyQm5ubPvnkE7m6uprbly1bdlPf4p5RunE/pCNHjljUk5eXp9TU1Ft+HUpD9erVJUkXL15UtWrVzO3WZ7YA/DmsQQJQJGdnZ/Xv318bNmywuHLshnPnzkmS+vTpo9OnT1tcXn/p0qVCX5Zr2LChvvnmG+Xl5ZnbPv74Y/30008W/fr376/MzEwtXLjwpjFunOVwdnaWyWSyOHty4sSJQm8IWalSpUKDmbWwsDC5uLhowYIFFmdT3nnnHWVlZalv375/OEZJatiwoSTpiy++MLfduIUBAPvhDBKAW3rllVe0fft2BQUFacSIEWrWrJkuXLig5ORkbdu2TRcuXNCIESO0cOFCDR48WHv27JGPj4/effddeXh43DTeU089pQ8++EC9e/fWo48+quPHj+u9994zP/HfMHjwYK1cuVKRkZHatWuXunTpotzcXG3btk3PPPOMHnroIfXt21fz589X79699dhjj+ns2bNatGiRGjVqdNMaoPbt22vbtm2aP3++fH19FRAQYF6Y/Hu1a9fWlClT9MILL6h379568MEHdeTIEb311lvq2LGjxYJsR3jggQdUr149DR8+XBMnTpSzs7OWLl2q2rVrKy0tzaG1AeWK4y6gA1BWZGRkGP/85z8NPz8/o2LFioa3t7fRs2dPY8mSJeY+J0+eNB588EHDw8PDqFWrljFmzBhj69athV46Pm/ePKNOnTqGq6urcf/99xu7d+++6TJ/w7h+Cf+0adOMgIAA83EfeeQR4/jx4+Y+77zzjhEYGGi4uroaTZo0MZYtW1bopfDff/+90bVrV8Pd3d2QZL7kv7DL4w3j+mX9TZo0MSpWrGh4eXkZo0aNMn755ReLPqGhoUbz5s1v+noNGTLEqF+/frG+tjfUr1/f6Nu3703thX1d9uzZYwQFBRkuLi5GvXr1jPnz5xd5mX9hY0oy/vnPf1q0paamGpKMV1991aa6gfLKZBisyAMAAPg91iABAABYYQ0SAJSgc+fOFXkJvnT9/ko1atQoxYoAFAcvsQFACfL397/lJfihoaHmN48FcOe4I15iW7Rokfz9/eXm5qagoCDt2rXrlv3Xr1+vJk2ayM3NTS1bttTmzZstthuGoejoaPn4+Mjd3V1hYWE6duyYRR9/f3/zu3rf+HjllVfsPjcAd7dVq1YpPj6+yI958+Y5ukQAhXD4GaS1a9dq8ODBiouLU1BQkGJjY7V+/XodOXJE99xzz039d+7cqa5duyomJkZ/+ctftHr1as2ZM0fJyclq0aKFJGnOnDmKiYnRihUrFBAQoOnTp+vAgQM6fPiw3NzcJF0PSMOHD9eIESPMY1epUkWVKlUqnYkDAIA7lsMDUlBQkDp27Gi+GVxBQYH8/Pz03HPPafLkyTf1HzBggHJzc/Xxxx+b2+677z61adNGcXFxMgxDvr6+Gj9+vCZMmCBJysrKkpeXl5YvX66BAwdKuh6Qxo4dq7Fjx5b8JAEAQJni0EXaeXl52rNnj6ZMmWJuc3JyUlhYmBITEwvdJzExUZGRkRZt4eHh5jvnpqamKj093eLtADw9PRUUFKTExERzQJKu3wDvxRdfVL169fTYY49p3LhxRb4x5pUrVyzeCLOgoEAXLlxQzZo1b/tNMQEAQOkyDEO//vqrfH195eRU9EojhwakzMxM5efny8vLy6Ldy8tL33//faH7pKenF9o/PT3dvP1GW1F9JGn06NFq166datSooZ07d2rKlCk6c+aM5s+fX+hxY2Ji9MILL9g2QQAAcEf66aefVLdu3SK337WX+f/+LFSrVq3k4uKip59+WjExMRZvennDlClTLPbJyspSvXr19NNPP6lq1aqlUjMAAPhzsrOz5efnpypVqtyyn0MDUq1ateTs7KyMjAyL9oyMDHl7exe6j7e39y373/ickZEhHx8fiz5t2rQpspagoCBdu3ZNJ06c0L333nvTdldX10KDU9WqVQlIAACUMX+0PMahl/m7uLioffv2SkhIMLcVFBQoISFBwcHBhe4THBxs0V+S4uPjzf0DAgLk7e1t0Sc7O1tJSUlFjilJe/fulZOTU6FXzgEAgLuLw19ii4yM1JAhQ9ShQwd16tRJsbGxys3N1bBhwyRdf0fvOnXqKCYmRpI0ZswYhYaGat68eerbt6/WrFmj3bt3a8mSJZKuJ8KxY8dq9uzZCgwMNF/m7+vrq4iICEnXF3onJSWpe/fuqlKlihITEzVu3Dg9/vjjql69ukO+DgAA4M7h8IA0YMAAnTt3TtHR0UpPT1ebNm20detW8yLrtLQ0i1XmISEhWr16taKiojR16lQFBgZq06ZN5nsgSdKkSZOUm5urkSNH6uLFi+rcubO2bt1qvgeSq6ur1qxZo5kzZ+rKlSsKCAjQuHHjbro6DgAA3J0cfh+ksio7O1uenp7KyspiDRIAAGVEcZ+/74i3GgEAALiTEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsOPy92AAAKA2vxx8tsbHH9WpcYmPDMTiDBAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYIWABAAAYKWCowsAULa9Hn+0RMYd16txiYwLAMXBGSQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArd0RAWrRokfz9/eXm5qagoCDt2rXrlv3Xr1+vJk2ayM3NTS1bttTmzZstthuGoejoaPn4+Mjd3V1hYWE6duxYoWNduXJFbdq0kclk0t69e+01JQAAUIY5PCCtXbtWkZGRmjFjhpKTk9W6dWuFh4fr7NmzhfbfuXOnBg0apOHDhyslJUURERGKiIjQwYMHzX3mzp2rBQsWKC4uTklJSapUqZLCw8N1+fLlm8abNGmSfH19S2x+AACg7HF4QJo/f75GjBihYcOGqVmzZoqLi5OHh4eWLl1aaP833nhDvXv31sSJE9W0aVO9+OKLateunRYuXCjp+tmj2NhYRUVF6aGHHlKrVq20cuVKnT59Wps2bbIYa8uWLfr000/12muvlfQ0AQBAGeLQgJSXl6c9e/YoLCzM3Obk5KSwsDAlJiYWuk9iYqJFf0kKDw83909NTVV6erpFH09PTwUFBVmMmZGRoREjRujdd9+Vh4fHH9Z65coVZWdnW3wAAIDyyaEBKTMzU/n5+fLy8rJo9/LyUnp6eqH7pKen37L/jc+36mMYhoYOHap//OMf6tChQ7FqjYmJkaenp/nDz8+vWPsBAICyx+EvsTnCm2++qV9//VVTpkwp9j5TpkxRVlaW+eOnn34qwQoBAIAjOTQg1apVS87OzsrIyLBoz8jIkLe3d6H7eHt737L/jc+36vPZZ58pMTFRrq6uqlChgho1aiRJ6tChg4YMGVLocV1dXVW1alWLDwAAUD45NCC5uLioffv2SkhIMLcVFBQoISFBwcHBhe4THBxs0V+S4uPjzf0DAgLk7e1t0Sc7O1tJSUnmPgsWLNC+ffu0d+9e7d2713ybgLVr1+qll16y6xwBAEDZU8HRBURGRmrIkCHq0KGDOnXqpNjYWOXm5mrYsGGSpMGDB6tOnTqKiYmRJI0ZM0ahoaGaN2+e+vbtqzVr1mj37t1asmSJJMlkMmns2LGaPXu2AgMDFRAQoOnTp8vX11cRERGSpHr16lnUULlyZUlSw4YNVbdu3VKaOQAAuFM5PCANGDBA586dU3R0tNLT09WmTRtt3brVvMg6LS1NTk7/d6IrJCREq1evVlRUlKZOnarAwEBt2rRJLVq0MPeZNGmScnNzNXLkSF28eFGdO3fW1q1b5ebmVurzAwAAZY/JMAzD0UWURdnZ2fL09FRWVhbrkXBXez3+aImMO65X4xIZF3evkvpZlfh5LUuK+/x9V17FBgAAcCsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACsEJAAAACs2B6SrV68WuS0zM/NPFQMAAHAnsDkgDRw4UIZh3NSekZGhbt262aMmAAAAh7I5IKWlpempp56yaEtPT1e3bt3UpEkTuxUGAADgKDYHpM2bN2vnzp2KjIyUJJ0+fVqhoaFq2bKl1q1bZ/cCAQAASlsFW3eoXbu2Pv30U3Xu3FmS9PHHH6tdu3ZatWqVnJxY8w0AAMo+mwOSJPn5+Sk+Pl5dunRRr1699O6778pkMtm7NgAAAIcoVkCqXr16oQHo0qVL+uijj1SzZk1z24ULF+xXHQAAgAMUKyDFxsaWcBkAAAB3jmIFpCFDhpR0HQAAAHcMm1dVJycn68CBA+bH//nPfxQREaGpU6cqLy/PrsUBAAA4gs0B6emnn9bRo0clST/++KMGDBggDw8PrV+/XpMmTbJ7gQAAAKXN5oB09OhRtWnTRpK0fv16hYaGavXq1Vq+fLk2bNhg7/oAAABKnc0ByTAMFRQUSJK2bdumPn36SLp+6T/vxQYAAMoDmwNShw4dNHv2bL377rvasWOH+vbtK0lKTU2Vl5eX3QsEAAAobTYHpNjYWCUnJ+vZZ5/VtGnT1KhRI0nSBx98oJCQELsXCAAAUNpsvpN2q1atLK5iu+HVV1+Vs7OzXYoCAABwpNt6q5HCuLm52WsoAAAAhypWQKpRo4aOHj2qWrVqFfm2IzfwViMAAKCsK1ZAev3111WlShVJvO0IAAAo/2x6q5Fr167JZDIpPDycK9YAAEC5ZdNVbBUqVNA//vEPXb58uaTqAQAAcDibL/Pv1KmTUlJSSqIWAACAO4LNV7E988wzGj9+vE6dOqX27durUqVKFttbtWplt+IAAAAcweaANHDgQEnS6NGjzW0mk0mGYchkMik/P99+1QEAADiAzQEpNTW1JOoAAAC4Y9gckOrXr18SdQAAANwxbF6kDQAAUN4RkAAAAKwQkAAAAKwQkAAAAKzcVkC6ePGi3n77bU2ZMsX85rTJycn6+eef7VocAACAI9h8Fdv+/fsVFhYmT09PnThxQiNGjFCNGjW0ceNGpaWlaeXKlSVRJwAAQKmx+QxSZGSkhg4dqmPHjsnNzc3c3qdPH33xxRd2LQ4AAMARbA5I3377rZ5++umb2uvUqaP09HS7FAUAAOBINgckV1dXZWdn39R+9OhR1a5d2y5FAQAAOJLNAenBBx/UrFmzdPXqVUnX34ctLS1Nzz//vPr372/3AgEAAEqbzQFp3rx5ysnJ0T333KPffvtNoaGhatSokapUqaKXXnqpJGoEAAAoVTZfxebp6an4+Hh9/fXX2rdvn3JyctSuXTuFhYWVRH0AAAClzuaAdMP999+v+++/3561AAAA3BFsfolt9OjRWrBgwU3tCxcu1NixY+1REwAAgEPZHJA2bNhQ6JmjkJAQffDBB3YpCgAAwJFsDkjnz5+Xp6fnTe1Vq1ZVZmamXYoCAABwJJsDUqNGjbR169ab2rds2aIGDRrcVhGLFi2Sv7+/3NzcFBQUpF27dt2y//r169WkSRO5ubmpZcuW2rx5s8V2wzAUHR0tHx8fubu7KywsTMeOHbPo8+CDD6pevXpyc3OTj4+PnnjiCZ0+ffq26gcAAOXLbb3VyKRJkzRjxgzt2LFDO3bsUHR0tCZPnqxx48bZXMDatWsVGRmpGTNmKDk5Wa1bt1Z4eLjOnj1baP+dO3dq0KBBGj58uFJSUhQREaGIiAgdPHjQ3Gfu3LlasGCB4uLilJSUpEqVKik8PFyXL1829+nevbvWrVunI0eOaMOGDTp+/LgeeeQRm+sHAADlj8kwDMPWnf73f/9XL730kvmMi7+/v2bOnKnBgwfbXEBQUJA6duyohQsXSpIKCgrk5+en5557TpMnT76p/4ABA5Sbm6uPP/7Y3HbfffepTZs2iouLk2EY8vX11fjx4zVhwgRJUlZWlry8vLR8+XINHDiw0Do+/PBDRURE6MqVK6pYseIf1p2dnS1PT09lZWWpatWqNs8bKC9ejz9aIuOO69W4RMbF3aukflYlfl7LkuI+f9t8BkmSRo0apVOnTikjI0PZ2dn68ccfbysc5eXlac+ePRb3UHJyclJYWJgSExML3ScxMfGmey6Fh4eb+6empio9Pd2ij6enp4KCgooc88KFC1q1apVCQkKKDEdXrlxRdna2xQcAACifbisg3VC7dm1Vrlz5tvfPzMxUfn6+vLy8LNq9vLyKfOPb9PT0W/a/8bk4Yz7//POqVKmSatasqbS0NP3nP/8pstaYmBh5enqaP/z8/Io3SQAAUObYHJAyMjL0xBNPyNfXVxUqVJCzs7PFR1kyceJEpaSk6NNPP5Wzs7MGDx6sol5xnDJlirKysswfP/30UylXCwAASovNd9IeOnSo0tLSNH36dPn4+MhkMt32wWvVqiVnZ2dlZGRYtGdkZMjb27vQfby9vW/Z/8bnjIwM+fj4WPRp06bNTcevVauWGjdurKZNm8rPz0/ffPONgoODbzquq6urXF1dbZ4jAAAoe2wOSF999ZW+/PLLm8LG7XBxcVH79u2VkJCgiIgISdcXaSckJOjZZ58tdJ/g4GAlJCRY3LU7Pj7eHGoCAgLk7e2thIQEc43Z2dlKSkrSqFGjiqyloKBA0vW1RgAA4O5mc0Dy8/Mr8mWo2xEZGakhQ4aoQ4cO6tSpk2JjY5Wbm6thw4ZJkgYPHqw6deooJiZGkjRmzBiFhoZq3rx56tu3r9asWaPdu3dryZIlkiSTyaSxY8dq9uzZCgwMVEBAgKZPny5fX19zCEtKStK3336rzp07q3r16jp+/LimT5+uhg0bFnr2CAAA3F1sDkixsbGaPHmyFi9eLH9//z9dwIABA3Tu3DlFR0crPT1dbdq00datW82LrNPS0uTk9H9LpUJCQrR69WpFRUVp6tSpCgwM1KZNm9SiRQtzn0mTJik3N1cjR47UxYsX1blzZ23dulVubm6SJA8PD23cuFEzZsxQbm6ufHx81Lt3b0VFRfEyGgAAsP0+SNWrV9elS5d07do1eXh43HRZ/IULF+xa4J2K+yAB13EfJJQV3AcJUvGfv2/rDBIAAEB5ZnNAGjJkSEnUAQAAcMe4rRtFHj9+XFFRURo0aJD5PdO2bNmiQ4cO2bU4AAAAR7A5IO3YsUMtW7ZUUlKSNm7cqJycHEnSvn37NGPGDLsXCAAAUNpsDkiTJ0/W7NmzFR8fLxcXF3N7jx499M0339i1OAAAAEewOSAdOHBA/fr1u6n9nnvuUWZmpl2KAgAAcCSbA1K1atV05syZm9pTUlJUp04duxQFAADgSDYHpIEDB+r5559Xenq6TCaTCgoK9PXXX2vChAkaPHhwSdQIAABQqmwOSC+//LKaNGkiPz8/5eTkqFmzZuratatCQkIUFRVVEjUCAACUKpvug2QYhtLT07VgwQJFR0frwIEDysnJUdu2bRUYGFhSNQIAAJQqmwNSo0aNdOjQIQUGBsrPz6+k6gIAAHAYm15ic3JyUmBgoM6fP19S9QAAADiczWuQXnnlFU2cOFEHDx4siXoAAAAczub3Yhs8eLAuXbqk1q1by8XFRe7u7hbbL1y4YLfiAAAAHMHmgBQbG1sCZQAAANw5bA5IQ4YMKYk6AAAA7hg2r0GSpOPHjysqKkqDBg3S2bNnJUlbtmzRoUOH7FocAACAI9gckHbs2KGWLVsqKSlJGzduVE5OjiRp3759mjFjht0LBAAAKG02B6TJkydr9uzZio+Pl4uLi7m9R48e+uabb+xaHAAAgCPYHJAOHDigfv363dR+zz33KDMz0y5FAQAAOJLNAalatWo6c+bMTe0pKSmqU6eOXYoCAABwJJsD0sCBA/X8888rPT1dJpNJBQUF+vrrrzVhwgQNHjy4JGoEAAAoVTYHpJdffllNmjSRn5+fcnJy1KxZM3Xt2lUhISGKiooqiRoBAABKVbHug5Sdna2qVatKklxcXPSvf/1L0dHROnDggHJyctS2bVsFBgaWaKEAAAClpVgBqXr16jpz5ozuuece9ejRQxs3bpSfn5/8/PxKuj4AAIBSV6yX2CpXrqzz589Lkj7//HNdvXq1RIsCAABwpGKdQQoLC1P37t3VtGlTSVK/fv0s7oH0e5999pn9qgMAAHCAYgWk9957TytWrNDx48e1Y8cONW/eXB4eHiVdGwAAgEMUKyC5u7vrH//4hyRp9+7dmjNnjqpVq1aSdQEAADhMsQLS723fvr0k6gAAALhj2ByQ8vPztXz5ciUkJOjs2bMqKCiw2M4aJAAAUNbZHJDGjBmj5cuXq2/fvmrRooVMJlNJ1AUAAOAwNgekNWvWaN26derTp09J1AMAAOBwNr/ViIuLixo1alQStQAAANwRbA5I48eP1xtvvCHDMEqiHgAAAIez+SW2r776Stu3b9eWLVvUvHlzVaxY0WL7xo0b7VYcAACAI9gckKpVq6Z+/fqVRC0AAAB3BJsD0rJly0qiDgAAgDuGzWuQAAAAyrtinUFq166dEhISVL16dbVt2/aW9z5KTk62W3EAAACOUKyA9NBDD8nV1VWSFBERUZL1AAAAOFyxAtKMGTMK/TcAAEB5xBokAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAK8W6ii0yMrLYA86fP/+2iwEAALgTFCsgpaSkWDxOTk7WtWvXdO+990qSjh49KmdnZ7Vv397+FQIAAJSyYgWk7du3m/89f/58ValSRStWrFD16tUlSb/88ouGDRumLl26lEyVAAAApcjmNUjz5s1TTEyMORxJUvXq1TV79mzNmzfPrsUBAAA4gs0BKTs7W+fOnbup/dy5c/r111/tUhQAAIAj2RyQ+vXrp2HDhmnjxo06deqUTp06pQ0bNmj48OF6+OGHS6JGAACAUlWsNUi/FxcXpwkTJuixxx7T1atXrw9SoYKGDx+uV1991e4FAgAAlDabA5KHh4feeustvfrqqzp+/LgkqWHDhqpUqZLdiwMAAHCE275R5JkzZ3TmzBkFBgaqUqVKMgzDnnUBAAA4jM0B6fz58+rZs6caN26sPn366MyZM5Kk4cOHa/z48XYvEAAAoLTZHJDGjRunihUrKi0tTR4eHub2AQMGaOvWrXYtDgAAwBFsXoP06aef6pNPPlHdunUt2gMDA3Xy5Em7FQYAAOAoNp9Bys3NtThzdMOFCxfk6upql6IAAAAcyeaA1KVLF61cudL82GQyqaCgQHPnzlX37t3tWhwAAIAj2PwS29y5c9WzZ0/t3r1beXl5mjRpkg4dOqQLFy7o66+/LokaAQAASpXNZ5BatGiho0ePqnPnznrooYeUm5urhx9+WCkpKWrYsGFJ1AgAAFCqbApIV69eVc+ePXX27FlNmzZN69at0+bNmzV79mz5+PjcdhGLFi2Sv7+/3NzcFBQUpF27dt2y//r169WkSRO5ubmpZcuW2rx5s8V2wzAUHR0tHx8fubu7KywsTMeOHTNvP3HihIYPH66AgAC5u7urYcOGmjFjhvLy8m57DgAAoPywKSBVrFhR+/fvt2sBa9euVWRkpGbMmKHk5GS1bt1a4eHhOnv2bKH9d+7cqUGDBmn48OFKSUlRRESEIiIidPDgQXOfuXPnasGCBYqLi1NSUpIqVaqk8PBwXb58WZL0/fffq6CgQIsXL9ahQ4f0+uuvKy4uTlOnTrXr3AAAQNlkMmy8Bfa4cePk6uqqV155xS4FBAUFqWPHjlq4cKEkqaCgQH5+fnruuec0efLkm/oPGDBAubm5+vjjj81t9913n9q0aaO4uDgZhiFfX1+NHz9eEyZMkCRlZWXJy8tLy5cv18CBAwut49VXX9X//u//6scffyxW3dnZ2fL09FRWVpaqVq1q67SBcuP1+KMlMu64Xo1LZFzcvUrqZ1Xi57UsKe7zt82LtK9du6alS5dq27Ztat++/U3vwTZ//vxij5WXl6c9e/ZoypQp5jYnJyeFhYUpMTGx0H0SExMVGRlp0RYeHq5NmzZJklJTU5Wenq6wsDDzdk9PTwUFBSkxMbHIgJSVlaUaNWoUWeuVK1d05coV8+Ps7Ow/nB8AACibbA5IBw8eVLt27SRJR49apnGTyWTTWJmZmcrPz5eXl5dFu5eXl77//vtC90lPTy+0f3p6unn7jbai+lj74Ycf9Oabb+q1114rstaYmBi98MILt54QAAAoF2wOSNu3by+JOhzm559/Vu/evfW3v/1NI0aMKLLflClTLM5cZWdny8/PrzRKBAAApczmy/ztqVatWnJ2dlZGRoZFe0ZGhry9vQvdx9vb+5b9b3wuzpinT59W9+7dFRISoiVLltyyVldXV1WtWtXiAwAAlE82n0Hq3r37LV9K++yzz4o9louLi9q3b6+EhARFRERIur5IOyEhQc8++2yh+wQHByshIUFjx441t8XHxys4OFiSFBAQIG9vbyUkJKhNmzaSrp/tSUpK0qhRo8z7/Pzzz+revbvat2+vZcuWycnJoVkRAADcQWwOSDdCxw1Xr17V3r17dfDgQQ0ZMsTmAiIjIzVkyBB16NBBnTp1UmxsrHJzczVs2DBJ0uDBg1WnTh3FxMRIksaMGaPQ0FDNmzdPffv21Zo1a7R7927zGSCTyaSxY8dq9uzZCgwMVEBAgKZPny5fX19zCPv555/VrVs31a9fX6+99prOnTtnrqeoM1cAAODuYXNAev311wttnzlzpnJycmwuYMCAATp37pyio6OVnp6uNm3aaOvWreZF1mlpaRZnd0JCQrR69WpFRUVp6tSpCgwM1KZNm9SiRQtzn0mTJik3N1cjR47UxYsX1blzZ23dulVubm6Srp9x+uGHH/TDDz+obt26FvXYeNcDAABQDtl8H6Si/PDDD+rUqZMuXLhgj+HueNwHCbiO+yChrOA+SJCK//xtt4U3iYmJ5jM0AAAAZZnNL7E9/PDDFo8Nw9CZM2e0e/duTZ8+3W6FAQAAOIrNAcnT09PisZOTk+69917NmjVLDzzwgN0KAwAAcBSbA9KyZctKog4AAIA7hs1rkH766SedOnXK/HjXrl0aO3bsH95oEQAAoKywOSA99thj5rcbufGmsLt27dK0adM0a9YsuxcIAABQ2mwOSAcPHlSnTp0kSevWrVPLli21c+dOrVq1SsuXL7d3fQAAAKXO5oB09epVubq6SpK2bdumBx98UJLUpEkTnTlzxr7VAQAAOIDNAal58+aKi4vTl19+qfj4ePXu3VvS9Td+rVmzpt0LBAAAKG02B6Q5c+Zo8eLF6tatmwYNGqTWrVtLkj788EPzS28AAABlmc2X+Xfr1k2ZmZnKzs5W9erVze0jR46Uh4eHXYsDAABwBJsDkiQ5OztbhCNJ8vf3t0c9AAAADndbAemDDz7QunXrlJaWpry8PIttycnJdikMAADAUWxeg7RgwQINGzZMXl5eSklJUadOnVSzZk39+OOP+p//+Z+SqBEAAKBU2RyQ3nrrLS1ZskRvvvmmXFxcNGnSJMXHx2v06NHKysoqiRoBAABKlc0BKS0tTSEhIZIkd3d3/frrr5KkJ554Qu+//759qwMAAHAAmwOSt7e3Lly4IEmqV6+evvnmG0lSamqqDMOwb3UAAAAOYHNA6tGjhz788ENJ0rBhwzRu3Dj16tVLAwYMUL9+/exeIAAAQGmz+Sq2JUuWqKCgQJL0z3/+UzVr1tTOnTv14IMP6umnn7Z7gQAAAKXN5oDk5OQkJ6f/O/E0cOBADRw40K5FAQAAOJLNL7FJ0pdffqnHH39cwcHB+vnnnyVJ7777rr766iu7FgcAAOAINgekDRs2KDw8XO7u7kpJSdGVK1ckSVlZWXr55ZftXiAAAEBpszkgzZ49W3FxcfrXv/6lihUrmtvvv/9+7qINAADKBZsD0pEjR9S1a9eb2j09PXXx4kV71AQAAOBQt3UfpB9++OGm9q+++koNGjSwS1EAAACOZHNAGjFihMaMGaOkpCSZTCadPn1aq1at0oQJEzRq1KiSqBEAAKBU2XyZ/+TJk1VQUKCePXvq0qVL6tq1q1xdXTVhwgQ999xzJVEjAABAqbI5IJlMJk2bNk0TJ07UDz/8oJycHDVr1kyVK1cuifoAAABKnc0B6QYXFxc1a9bMnrUAAADcEYodkJ588sli9Vu6dOltFwMAAHAnKHZAWr58uerXr6+2bdvKMIySrAkAANyG1+OPltjY43o1LrGx70TFDkijRo3S+++/r9TUVA0bNkyPP/64atSoUZK1AQAAOESxL/NftGiRzpw5o0mTJumjjz6Sn5+fHn30UX3yySecUQIAAOWKTfdBcnV11aBBgxQfH6/Dhw+refPmeuaZZ+Tv76+cnJySqhEAAKBU2XyjSPOOTk4ymUwyDEP5+fn2rAkAAMChbApIV65c0fvvv69evXqpcePGOnDggBYuXKi0tDTugwQAAMqNYi/SfuaZZ7RmzRr5+fnpySef1Pvvv69atWqVZG0AAAAOUeyAFBcXp3r16qlBgwbasWOHduzYUWi/jRs32q04AAAARyh2QBo8eLBMJlNJ1gIAAHBHsOlGkQAAAHeD276KDQAAoLwiIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFghIAEAAFip4OgCgNLyevzREht7XK/GJTY2AKD0cQYJAADACgEJAADACgEJAADAisMD0qJFi+Tv7y83NzcFBQVp165dt+y/fv16NWnSRG5ubmrZsqU2b95ssd0wDEVHR8vHx0fu7u4KCwvTsWPHLPq89NJLCgkJkYeHh6pVq2bvKQEAgDLOoQFp7dq1ioyM1IwZM5ScnKzWrVsrPDxcZ8+eLbT/zp07NWjQIA0fPlwpKSmKiIhQRESEDh48aO4zd+5cLViwQHFxcUpKSlKlSpUUHh6uy5cvm/vk5eXpb3/7m0aNGlXicwQAAGWPQwPS/PnzNWLECA0bNkzNmjVTXFycPDw8tHTp0kL7v/HGG+rdu7cmTpyopk2b6sUXX1S7du20cOFCSdfPHsXGxioqKkoPPfSQWrVqpZUrV+r06dPatGmTeZwXXnhB48aNU8uWLYtd65UrV5SdnW3xAQAAyieHBaS8vDzt2bNHYWFh/1eMk5PCwsKUmJhY6D6JiYkW/SUpPDzc3D81NVXp6ekWfTw9PRUUFFTkmMUVExMjT09P84efn9+fGg8AANy5HBaQMjMzlZ+fLy8vL4t2Ly8vpaenF7pPenr6Lfvf+GzLmMU1ZcoUZWVlmT9++umnPzUeAAC4c3GjyGJydXWVq6uro8sAAAClwGFnkGrVqiVnZ2dlZGRYtGdkZMjb27vQfby9vW/Z/8ZnW8YEAACw5rCA5OLiovbt2yshIcHcVlBQoISEBAUHBxe6T3BwsEV/SYqPjzf3DwgIkLe3t0Wf7OxsJSUlFTkmAACANYe+xBYZGakhQ4aoQ4cO6tSpk2JjY5Wbm6thw4ZJkgYPHqw6deooJiZGkjRmzBiFhoZq3rx56tu3r9asWaPdu3dryZIlkiSTyaSxY8dq9uzZCgwMVEBAgKZPny5fX19FRESYj5uWlqYLFy4oLS1N+fn52rt3rySpUaNGqly5cql+DQAAwJ3HoQFpwIABOnfunKKjo5Wenq42bdpo69at5kXWaWlpcnL6v5NcISEhWr16taKiojR16lQFBgZq06ZNatGihbnPpEmTlJubq5EjR+rixYvq3Lmztm7dKjc3N3Of6OhorVixwvy4bdu2kqTt27erW7duJTxrAABwpzMZhmE4uoiyKDs7W56ensrKylLVqlUdXQ6K4fX4oyU29rhejUts7DtdSX1d7+avKUrG3fA74G6Y459V3Odvh7/VCAAAwJ2GgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGClgqMLAAAAZdPr8UdLbOxxvRqX2NjFwRkkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKxUcXQBQXr0ef7TExh7Xq3GJjQ0A4AwSAADATTiDBKBM4cwcgNLAGSQAAAArBCQAAAArBCQAAAArBCQAAAArLNIGAKAEcEFB2XZHnEFatGiR/P395ebmpqCgIO3ateuW/devX68mTZrIzc1NLVu21ObNmy22G4ah6Oho+fj4yN3dXWFhYTp27JhFnwsXLujvf/+7qlatqmrVqmn48OHKycmx+9wAAEDZ4/CAtHbtWkVGRmrGjBlKTk5W69atFR4errNnzxbaf+fOnRo0aJCGDx+ulJQURUREKCIiQgcPHjT3mTt3rhYsWKC4uDglJSWpUqVKCg8P1+XLl819/v73v+vQoUOKj4/Xxx9/rC+++EIjR44s8fkCAIA7n8MD0vz58zVixAgNGzZMzZo1U1xcnDw8PLR06dJC+7/xxhvq3bu3Jk6cqKZNm+rFF19Uu3bttHDhQknXzx7FxsYqKipKDz30kFq1aqWVK1fq9OnT2rRpkyTpu+++09atW/X2228rKChInTt31ptvvqk1a9bo9OnTpTV1AABwh3LoGqS8vDzt2bNHU6ZMMbc5OTkpLCxMiYmJhe6TmJioyMhIi7bw8HBz+ElNTVV6errCwsLM2z09PRUUFKTExEQNHDhQiYmJqlatmjp06GDuExYWJicnJyUlJalfv343HffKlSu6cuWK+XFWVpYkKTs72/aJwyEu55bcS6iF/RyU9vEcpaTmWdQc75avK+zvbvgdcDfM0V7jGoZxy34ODUiZmZnKz8+Xl5eXRbuXl5e+//77QvdJT08vtH96erp5+422W/W55557LLZXqFBBNWrUMPexFhMToxdeeOGmdj8/v6Kmh7vI1HJ+PEdwxBzvhq8rSsbd8DugvM3x119/laenZ5HbuYqtmKZMmWJx5qqgoEAXLlxQzZo1ZTKZJF1PpX5+fvrpp59UtWpVR5Vaophj+XA3zFG6O+bJHMsH5lh6DMPQr7/+Kl9f31v2c2hAqlWrlpydnZWRkWHRnpGRIW9v70L38fb2vmX/G58zMjLk4+Nj0adNmzbmPtaLwK9du6YLFy4UeVxXV1e5urpatFWrVq3QvlWrVi23P+A3MMfy4W6Yo3R3zJM5lg/MsXTc6szRDQ5dpO3i4qL27dsrISHB3FZQUKCEhAQFBwcXuk9wcLBFf0mKj4839w8ICJC3t7dFn+zsbCUlJZn7BAcH6+LFi9qzZ4+5z2effaaCggIFBQXZbX4AAKBscvhLbJGRkRoyZIg6dOigTp06KTY2Vrm5uRo2bJgkafDgwapTp45iYmIkSWPGjFFoaKjmzZunvn37as2aNdq9e7eWLFkiSTKZTBo7dqxmz56twMBABQQEaPr06fL19VVERIQkqWnTpurdu7dGjBihuLg4Xb16Vc8++6wGDhz4h6fcAABA+efwgDRgwACdO3dO0dHRSk9PV5s2bbR161bzIuu0tDQ5Of3fia6QkBCtXr1aUVFRmjp1qgIDA7Vp0ya1aNHC3GfSpEnKzc3VyJEjdfHiRXXu3Flbt26Vm5ubuc+qVav07LPPqmfPnnJyclL//v21YMGCPzUXV1dXzZgx46aX4soT5lg+3A1zlO6OeTLH8oE53nlMxh9d5wYAAHCXcfiNIgEAAO40BCQAAAArBCQAAAArBCQAAAArBCQAAAArBCQAAAArBKRiKCgoUH5+vqPLKBXl+a4PZ86c0eHDhx1dRom6dOmS8vLyHF0GAJR5BKQ/cPjwYQ0ePFjh4eEaNWqUdu7c6eiS7C43N1e//vqrsrOzzW+8W978/PPPatmypaKiorR7925Hl1MiDh48qEcffVTffPONrly54uhySsSpU6e0bt06bdy4UQcOHHB0OaWqPP/xUl5duHBB586dc3QZJeqHH37Qv//973L5hxkB6RaOHDmikJAQ5efnq2PHjkpMTNSYMWP+9B237ySHDx/Www8/rNDQUDVt2lSrVq2SVP5+GR87dkxZWVnKysrSm2++qeTkZPO28jDXQ4cOqUuXLqpbt64CAgLKzJ1qbXHgwAF17txZr776qp555hlNmzZNx48fd3RZdnf06FE9//zzGjZsmN544w0dO3ZM0vW3USoPP6uSlJqaqtdff13jx4/X2rVrHV1Oifjxxx/VsWNHvfnmmzp9+rSjyykR+/fvV0hIiLZs2aLMzExHl2N/BgpVUFBgTJ061Xj00UfNbdnZ2cbs2bONNm3aGHPmzHFgdfZx6NAho2bNmsa4ceOMVatWGZGRkUbFihWNlJQUR5dmd+fPnzcefPBBY/HixUa7du2Mv//978bBgwcNwzCM/Px8B1f35+Tk5BgPPPCAMWrUKHPbd999Z6SkpBgnT550YGX2c+LECaNOnTrG5MmTjZycHGPz5s2Gt7e3kZSU5OjS7OrQoUOGp6en0bt3b6N///6Gp6enERYWZvzrX/8y9ykoKHBghX/e/v37jbp16xo9e/Y0QkJCDCcnJ2Pu3LmOLsvu4uLiDJPJZLRt29Z46aWXjDNnzpi3FRQUlPnv48mTJ4169eoZEydOLLJPWZ8jAekWhg4danTt2tWiLTs723jttdeMDh06GO+9956DKvvzzp8/bzzwwAPG6NGjLdq7detmPPfcc4ZhlP0f7huuXbtmnD171mjcuLFx6tQpY+PGjUbHjh2NESNGGCEhIUb//v0dXeKfcvnyZaNz585GcnKyce3aNSM8PNzo2LGjUaVKFeO+++4z3n77bUeX+KctXrzY6Natm8XPZJ8+fYzFixcbK1asMD777DMHVmcfV65cMR5//HFjxIgR5rZjx44ZAwYMMO677z7jjTfecGB19nHixAmjUaNGxqRJk8x/mLzzzjuGl5eXcfToUQdXZ1/79u0zhgwZYsyePdvw9fU1XnzxReOXX35xdFl289FHHxl9+vQxDMMw8vLyjGnTphkRERHGU089ZaxYscLcryw/jzj8zWrvRIZhyGQyqV27djp27JiOHDmie++9V5JUpUoVPfnkkzpy5Ijeeust9evXTx4eHg6u2HZXr17VxYsX9cgjj0i6vhDdyclJAQEBunDhgiSVm/VITk5Oql27tjp27KiDBw+qX79+cnV11ZAhQ3TlyhWNGDHC0SX+KRcvXtSRI0eUmZmpiRMnSpLefvttnT59Wp999pmioqLk6elp/l6XRYZhKC0tTXv37lXbtm310ksvacuWLcrLy1NWVpZOnjypOXPmaOjQoY4u9ba5uLgoIyNDAQEBkq7PuVGjRpo7d65mzJihDz74QAEBAfrrX//q4EpvT0FBgdasWaNGjRpp6tSp5jch79ixoypWrKiCggIHV2hfhmFo586dWrZsmfLz87VkyRJVqVJFO3bsUNOmTfXSSy85usQ/JTk52fxc0adPH127dk2tW7fW4cOHtXv3bn3//fd6+eWXy/TzCGuQCnHjG9qnTx8dOXJEc+fOVU5OjqTrP/TVq1fX9OnTlZiYqC+++MKRpd42Ly8vvffee+rSpYskma/Sq1OnjvkX1w035l5W3fh+Ojs76/PPP5ckbdy4Ufn5+fLz89OXX36pXbt2ObDCP+eee+5Rz5499eGHH+rYsWMaN26cWrVqpd69e2v06NEKCwtTQkKC8vPzy+walgceeEDe3t569NFH9cgjj2j69On697//rU8//VQff/yxBg4cqBUrVuj8+fNlco75+fm6evWq6tatqwsXLpgX2RcUFKhevXqaPn26rl27Zl4jWBY5OTkpODhYbdq0kaenp7m9efPmqlChgs6cOePA6uyvdevWCggI0MmTJxUdHa3nnntO06ZN02effabQ0FBHl/enhYSEyMPDQ++8845MJpPee+89xcbGav369erXr5+2b99e5q8aJiDdQsOGDbVu3TqtWrVKkydPVmZmpvnJtmLFimrVqpXFf/SyJjAwUNL1X8IVK1aUdD0Anj171twnJiZGS5Ys0bVr1xxSoz3ceMLs0aOHXF1d9cwzz2jz5s3as2ePZs+erR07dmjZsmW6fPmygyu9PSaTSePHj9eyZcv03//+1+Jqkrp168rLy0uHDx+Wk5NTmf1rLiAgQO+9955eeukltWjRQv3799dDDz0kk8mke+65R76+vvrll19UqVKlMjXHG3+YODs7q2LFihoyZIj+/e9/a/HixTKZTHJyclJ+fr4aNGigmJgYrV+/XocOHXJw1bb5/S1SunbtqpiYGEmWF0eYTCZdvXrV/DghIaFMXf1V1G1g8vLyzH9EHzlyRM7OznJ3d9e+ffvK3MJt6znWrVtX33//vebPny/DMFSnTh1Jkqenp4YNG6b9+/dr3759jijVbghIf6B79+5av3693n77bT399NNau3atvvvuO73xxhs6e/as/Pz8HF3in+bk5GTxy+rGGaTo6GhNmzZNPXv2VIUKZffV2BtPmAEBAZo1a5b+/e9/66OPPlJAQID69eun1157TZMmTZKbm5uDK719HTp00JYtWyRJS5YssXgSvXr1qho3blymQ650/fv36KOPqm7duvrtt98sgmBGRob8/f3L1P3Kjh49qtjYWIszJ6GhoZozZ47GjRunt99+W9L18CRdf3n/3nvvVaVKlRxS7+0obI43fteYTCZdu3ZNv/32m5ydnVW1alVJ0tSpU9WrVy+LwHQnK2yON2oPCgqSk5OTRo8erS1btmjv3r0aPXq0Zs6cqTVr1pSZn9fC5tikSRMtWbJER48e1f79+5WYmGje5uXlpfvuu081atRwRLn245CVT2XQnj17jNDQUKN+/fpGw4YNjcaNGxvJycmOLstubiyYnDFjhjFy5Ejj1VdfNVxdXY09e/Y4uDL7ycvLM9555x1j3759hmGU7cWDRdmxY4fh6+trdOrUyRg+fLjxxBNPGJ6ensaBAwccXZrd3LjSa+7cucbKlSuNSZMmGdWqVTP279/v6NKK7dixY0aNGjUMk8lkTJkyxTh37px5W25urvHCCy8YJpPJiIqKMpKTk43z588bkydPNho1amScPXvWgZUX363meEN+fr7x22+/GQ0bNjR2795tzJo1y6hUqZKxa9cuB1Rsuz+a49KlSw2TyWT4+PgY3377rbl9zpw5ZWZR+h/N8f333zecnJyM8PBw4/333zeOHTtmTJ482fD19TXS0tIcVLV9EJBskJWVZaSmphr79+8v9D97eTB79mzDZDIZnp6eFv+hy4uyfkl/cXz//fdGVFSUERYWZowaNapchaMbPvvsM6Nhw4ZGYGCg0a1bN3PoLQtycnKMJ5980hg6dKixaNEiw2QyGRMnTrQIPvn5+caKFSsMb29vo06dOkaTJk0MX1/fMvMHS1FzLOr3Ztu2bY2OHTsaLi4uZeb3TnHmeOTIESMqKsp865Sy9vunuN/Hbdu2GcHBwYaXl5fRpEmTcnMCgYAEC99++61hMpmMQ4cOOboU/En5+fll7heyLc6fP2+kp6eXuUunL126ZCxatMhYs2aNYRiGsXbt2kJDkmEYRmpqqrFjxw5jy5YtxqlTpxxR7m251Rx//+R67do14/z584anp6fh7Oxcps4CFneOubm55n+XtbPWxZ2jYRhGZmamcfToUSMlJaXcnEAgIOEmOTk5ji4BKNes/4+tWbPGMJlMxoQJE8xPLlevXi3TN/q81RwzMzMNw7g+x3Pnzhlbt24137i1LLnVHG+E3fz8fOPHH390RHl2UdzvY2pqqgOqK1lld+UtSkxZWgQKlEU3/o/l5+fLyclJAwYMkGEYeuyxx2QymTR27Fi99tprOnnypFauXCkPD48ydXWeVPw5njhxQu+9916ZvJ+cLd/Hd999t9zPsaz+rBbFZBhl8KYhAFBOGNfP5MvJyUlr167VE088oQYNGuj48eP69ttv1aZNG0eX+KcVNccffvhBu3fvLtdzvBu+j+Vpjr9HQAIABzN+d+l7z549tXfvXn3++edq2bKlgyuzH+ZYPtwNc7yBl9gAwMFMJpPy8/M1ceJEbd++XXv37i13TzjMsXy4G+Z4AzeKBIA7RPPmzZWcnKxWrVo5upQSwxzLh7thjrzEBgB3COP/v1F2ecYcy4e7YY4EJAAAACu8xAYAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAAGCFgAQAJcDf31+xsbGOLgPAbSIgAbC7c+fOadSoUapXr55cXV3l7e2t8PBwff31144ujeACoFh4LzYAdte/f3/l5eVpxYoVatCggTIyMpSQkKDz58+X2DHz8vLk4uJSYuMDuLtwBgmAXV28eFFffvml5syZo+7du6t+/frq1KmTpkyZogcffNCi31NPPaXatWuratWq6tGjh/bt22cx1kcffaSOHTvKzc1NtWrVUr9+/czb/P399eKLL2rw4MGqWrWqRo4cKUn66quv1KVLF7m7u8vPz0+jR49Wbm6uJKlbt246efKkxo0bJ5PJdMu3Srh48aKefvppeXl5yc3NTS1atNDHH39s3r5hwwY1b95crq6u8vf317x584oc68SJEzKZTNq7d6/F+CaTSZ9//rkk6fPPP5fJZNInn3yitm3byt3dXT169NDZs2e1ZcsWNW3aVFWrVtVjjz2mS5cumcfp1q2bRo8erUmTJqlGjRry9vbWzJkzi/4GASgWAhIAu6pcubIqV66sTZs26cqVK0X2+9vf/mZ+8t+zZ4/atWunnj176sKFC5Kk//73v+rXr5/69OmjlJQUJSQkqFOnThZjvPbaa2rdurVSUlI0ffp0HT9+XL1791b//v21f/9+rV27Vl999ZWeffZZSdLGjRtVt25dzZo1S2fOnNGZM2cKra2goED/8z//o6+//lrvvfeeDh8+rFdeeUXOzs6SpD179ujRRx/VwIEDdeDAAc2cOVPTp0/X8uXL//TXb+bMmVq4cKF27typn376SY8++qhiY2O1evVq/fe//9Wnn36qN99802KfFStWqFKlSkpKStLcuXM1a9YsxcfH/+lagLuaAQB29sEHHxjVq1c33NzcjJCQEGPKlCnGvn37zNu//PJLo2rVqsbly5ct9mvYsKGxePFiwzAMIzg42Pj73/9e5DHq169vREREWLQNHz7cGDlypEXbl19+aTg5ORm//fabeb/XX3/9lvV/8sknhpOTk3HkyJFCtz/22GNGr169LNomTpxoNGvWzKK+G8dJTU01JBkpKSnm7b/88oshydi+fbthGIaxfft2Q5Kxbds2c5+YmBhDknH8+HFz29NPP22Eh4ebH4eGhhqdO3e2qKVjx47G888/f8s5Arg1ziABsLv+/fvr9OnT+vDDD9W7d299/vnnateunfkMy759+5STk6OaNWuazzhVrlxZqampOn78uCRp79696tmz5y2P06FDB4vH+/bt0/Llyy3GDA8PV0FBgVJTU4td/969e1W3bl01bty40O3fffed7r//fou2+++/X8eOHVN+fn6xj1OYVq1amf/t5eUlDw8PNWjQwKLt7NmzRe4jST4+Pjf1AWAbFmkDKBFubm7q1auXevXqpenTp+upp57SjBkzNHToUOXk5MjHx8e8/ub3qlWrJklyd3f/w2NUqlTJ4nFOTo6efvppjR49+qa+9erVK3btxTm2LZycrv8tahiGue3q1auF9q1YsaL53yaTyeLxjbaCgoIi9ymqDwDbEJAAlIpmzZpp06ZNkqR27dopPT1dFSpUkL+/f6H9W7VqpYSEBA0bNqzYx2jXrp0OHz6sRo0aFdnHxcXlD8/ytGrVSqdOndLRo0cLPYvUtGnTm25Z8PXXX6tx48bmdUq/V7t2bUnSmTNn1LZtW0myWLAN4M7DS2wA7Or8+fPq0aOH3nvvPe3fv1+pqalav3695s6dq4ceekiSFBYWpuDgYEVEROjTTz/ViRMntHPnTk2bNk27d++WJM2YMUPvv/++ZsyYoe+++04HDhzQnDlzbnns559/Xjt37tSzzz6rvXv36tixY/rPf/5jXqQtXb/67YsvvtDPP/+szMzMQscJDQ1V165d1b9/f8XHxys1NVVbtmzR1q1bJUnjx49XQkKCXnzxRR09elQrVqzQwoULNWHChELHc3d313333adXXnlF3333nXbs2KGoqCibv7YASg8BCYBdVa5cWUFBQXr99dfVtWtXtWjRQtOnT9eIESO0cOFCSddfAtq8ebO6du2qYcOGqXHjxho4cKBOnjwpLy8vSdcvX1+/fr0+/PBDtWnTRj169NCuXbtueexWrVppx44dOnr0qLp06aK2bdsqOjpavr6+5j6zZs3SiRMn1LBhQ/OZncJs2LBBHTt21KBBg9SsWTNNmjTJfOapXbt2WrdundasWaMWLVooOjpas2bN0tChQ4scb+nSpbp27Zrat2+vsWPHavbs2cX9kgJwAJPx+xfFAQAAwBkkAAAAawQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAKwQkAAAAK/8PZZYipiPH5woAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for secret in columns:\n", + " curr_res = results_for_groups[secret]\n", + "\n", + " fig, ax = plt.subplots()\n", + "\n", + " risks = [res[\"risk\"].value for _, res in curr_res.items()]\n", + " columns = [key for key,_ in curr_res.items()]\n", + "\n", + " ax.bar(x=columns, height=risks, alpha=0.5, ecolor='black', capsize=10)\n", + "\n", + " plt.xticks(rotation=45, ha='right')\n", + " plt.title(secret)\n", + " ax.set_ylabel(\"Measured inference risk\")\n", + " _ = ax.set_xlabel(\"Secret column\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c790d894-e3d9-4cee-bd08-f4ef5f364d4e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T11:53:47.147242Z", + "start_time": "2025-05-15T11:53:46.776658Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "risks = [res[1].risk().value for res in results]\n", + "columns = [res[0] for res in results]\n", + "\n", + "ax.bar(x=columns, height=risks, alpha=0.5, ecolor='black', capsize=10)\n", + "\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.title(\"Risk scores per secret\")\n", + "ax.set_ylabel(\"Measured inference risk\")\n", + "_ = ax.set_xlabel(\"Secret column\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.12" + }, + "vscode": { + "interpreter": { + "hash": "237cf5f6b3dcd73bf2688629baee50bd53e43ee0aa8f2bde7060bbd4d3c193da" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/anonymeter/evaluators/inference_evaluator.py b/src/anonymeter/evaluators/inference_evaluator.py index 94c87dd..a32169c 100644 --- a/src/anonymeter/evaluators/inference_evaluator.py +++ b/src/anonymeter/evaluators/inference_evaluator.py @@ -3,11 +3,12 @@ # See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. """Privacy evaluator that measures the inference risk.""" -from typing import Optional +from typing import Optional, Union, Tuple, Any import numpy as np import numpy.typing as npt import pandas as pd +from pandas import DataFrame from anonymeter.neighbors.mixed_types_kneighbors import MixedTypeKNeighbors from anonymeter.stats.confidence import EvaluationResults, PrivacyRisk @@ -22,25 +23,40 @@ def _run_attack( n_jobs: int, naive: bool, regression: Optional[bool], -) -> int: + ml_model: Optional[Any], + sample_attacks: bool, +) -> tuple[int, Union[Tuple[npt.NDArray, npt.NDArray], npt.NDArray, None], DataFrame]: if regression is None: regression = pd.api.types.is_numeric_dtype(target[secret]) - targets = target.sample(n_attacks, replace=False) + # We only sample if `ml_model` is set to None, i.e MixedTypeKNeighbors is used, + # or `sample_attacks` is set to True by default. + if ml_model is None or sample_attacks: + targets = target.sample(n_attacks, replace=False) + else: + # When an `ml_model` is passed we don't want to sample, but rather predict for all targets + # as we assume it can scale to all target samples, better rather than using MixedTypeKNeighbors + targets = target if naive: guesses = syn.sample(n_attacks)[secret] - + targets = target.sample(n_attacks, replace=False) # we need this after dropping nans in the synthetic data else: - nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=syn[aux_cols]) + if ml_model is not None: + guesses = ml_model.predict(targets) + else: + nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=syn[aux_cols]) + guesses_idx = nn.kneighbors(queries=targets[aux_cols]) + if isinstance(guesses_idx, tuple): + raise RuntimeError("guesses_idx cannot be a tuple") - guesses_idx = nn.kneighbors(queries=targets[aux_cols]) - if isinstance(guesses_idx, tuple): - raise RuntimeError("guesses_idx cannot be a tuple") + guesses = syn.iloc[guesses_idx.flatten()][secret] - guesses = syn.iloc[guesses_idx.flatten()][secret] + assert len(guesses) == len(targets), "Predictions and targets have different lengths" + guesses.index = targets.index - return evaluate_inference_guesses(guesses=guesses, secrets=targets[secret], regression=regression).sum() + return evaluate_inference_guesses(guesses=guesses, secrets=targets[secret], + regression=regression).sum(), guesses, targets def evaluate_inference_guesses( @@ -142,6 +158,13 @@ class InferenceEvaluator: the variable. n_attacks : int, default is 500 Number of attack attempts. + ml_model: Any + An ml model fitted on `syn` as training data, and `secret` as target, that supports ::predict(x). + If not None, it will be used over the MixedTypeKNeighbors in the attack. + sample_attacks: bool, optional + Specifies whether we should sample `n_attacks` samples from the `ori` or `control` dataset + in the attack phase. When a custom `ml_model` is being passed which can scale to more attacks, + `sample_attacks` can be set to False so that we predict the values for all samples in `ori` and `control`. """ @@ -154,11 +177,23 @@ def __init__( regression: Optional[bool] = None, n_attacks: int = 500, control: Optional[pd.DataFrame] = None, + ml_model: Optional[Any] = None, + sample_attacks: Optional[bool] = True, ): self._ori = ori self._syn = syn self._control = control self._n_attacks = n_attacks + self._ml_model = ml_model + self._sample_attacks = sample_attacks + if not self._sample_attacks: + self._n_attacks_ori = self._ori.shape[0] + self._n_attacks_baseline = min(self._syn.shape[0], self._n_attacks_ori) + self._n_attacks_control = self._control.shape[0] + else: + self._n_attacks_ori = self._n_attacks + self._n_attacks_baseline = self._n_attacks + self._n_attacks_control = self._n_attacks # check if secret is a string column if not isinstance(secret, str): @@ -172,17 +207,22 @@ def __init__( self._regression = regression self._aux_cols = aux_cols self._evaluated = False + self._data_groups = self._ori[self._secret].unique().tolist() - def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int) -> int: + def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int, n_attacks: int) -> tuple[ + int, Union[Tuple[npt.NDArray, npt.NDArray], + pd.Series, None], DataFrame]: return _run_attack( target=target, syn=self._syn, - n_attacks=self._n_attacks, + n_attacks=n_attacks, aux_cols=self._aux_cols, secret=self._secret, n_jobs=n_jobs, naive=naive, regression=self._regression, + ml_model=self._ml_model, + sample_attacks=self._sample_attacks ) def evaluate(self, n_jobs: int = -2) -> "InferenceEvaluator": @@ -199,10 +239,17 @@ def evaluate(self, n_jobs: int = -2) -> "InferenceEvaluator": The evaluated ``InferenceEvaluator`` object. """ - self._n_baseline = self._attack(target=self._ori, naive=True, n_jobs=n_jobs) - self._n_success = self._attack(target=self._ori, naive=False, n_jobs=n_jobs) - self._n_control = ( - None if self._control is None else self._attack(target=self._control, naive=False, n_jobs=n_jobs) + # n_attacks is effective here + self._n_baseline, _, _ = self._attack(target=self._ori, naive=True, n_jobs=n_jobs, + n_attacks=self._n_attacks_baseline) + + # n_attacks is not effective here, just needed for the baseline + self._n_success, self.guesses_success, self.target = self._attack(target=self._ori, naive=False, n_jobs=n_jobs, + n_attacks=self._n_attacks_ori) + # n_attacks is not effective here, just needed for the baseline + self._n_control, self.guesses_control, self.target_control = ( + None if self._control is None else self._attack(target=self._control, naive=False, n_jobs=n_jobs, + n_attacks=self._n_attacks_control) ) self._evaluated = True @@ -227,6 +274,9 @@ def results(self, confidence_level: float = 0.95) -> EvaluationResults: return EvaluationResults( n_attacks=self._n_attacks, + n_attacks_ori=self._n_attacks_ori, + n_attacks_baseline=self._n_attacks_baseline, + n_attacks_control=self._n_attacks_control, n_success=self._n_success, n_baseline=self._n_baseline, n_control=self._n_control, @@ -258,3 +308,80 @@ def risk(self, confidence_level: float = 0.95, baseline: bool = False) -> Privac """ results = self.results(confidence_level=confidence_level) return results.risk(baseline=baseline) + + def risk_for_groups(self, confidence_level: float = 0.95) -> dict[ + str, dict[str, Union[EvaluationResults, PrivacyRisk]]]: + """Compute the attack risks on a group level, for every unique value of `self._data_groups`. + + Parameters + ---------- + confidence_level : float, default is 0.95 + Confidence level for the error bound calculation. + + Returns + ------- + dict[str, dict[str, EvaluationResults | PrivacyRisk]] + The group as a key, and then for every group the results (EvaluationResults), + and the risks (PrivacyRisk). + + """ + if not self._evaluated: + self.evaluate(n_jobs=-2) + + all_results = {} + + # For every unique group in `self._data_groups` + for group in self._data_groups: + # Get the targets for the current group + target = self.target[self.target[self._secret] == group] + + assert len(self.guesses_success) == len(self.target) + assert (self.guesses_success.index == self.target.index).all() + + # Get the guesses for the current group + guess = self.guesses_success.loc[target.index] + + # Count the number of success attacks + n_success = evaluate_inference_guesses(guesses=guess, + secrets=target[self._secret], + regression=self._regression).sum() + + if self._control is not None: + # Get the targets for the current control group + target_control = self.target_control[self.target_control[self._secret] == group] + + + assert len(self.guesses_control) == len(self.target_control) + assert (self.guesses_control.index == self.target_control.index).all() + + # Get the guesses for the current control group + guesses_control = self.guesses_control.loc[target_control.index] + + # Count the number of success control attacks + n_control = evaluate_inference_guesses(guesses=guesses_control, + secrets=target_control[self._secret], + regression=self._regression).sum() + else: + n_control = None + + # Recreate the EvaluationResults for the current group + results = EvaluationResults( + n_attacks=self._n_attacks, # passing for + n_attacks_ori=self._n_attacks_ori, + n_attacks_baseline=self._n_attacks_baseline, + # We leave the overall n_attacks_baseline here, it doesn't change the risk + n_attacks_control=self._n_attacks_control, + n_success=n_success, + n_baseline=self._n_baseline, # We leave the overall _n_baseline here, it doesn't change the risk + n_control=n_control, + confidence_level=confidence_level, + ) + # Compute the risk + risk = results.risk() + + all_results[group] = { + "results": results, + "risk": risk + } + + return all_results diff --git a/src/anonymeter/stats/confidence.py b/src/anonymeter/stats/confidence.py index b95049c..7f3469c 100644 --- a/src/anonymeter/stats/confidence.py +++ b/src/anonymeter/stats/confidence.py @@ -199,18 +199,30 @@ def __init__( n_baseline: int, n_control: Optional[int] = None, confidence_level: float = 0.95, + n_attacks_ori: Optional[int] = None, + n_attacks_baseline: Optional[int] = None, + n_attacks_control: Optional[int] = None, ): - self.attack_rate = success_rate(n_total=n_attacks, n_success=n_success, confidence_level=confidence_level) + self.n_attacks = n_attacks + if None in [n_attacks_ori, n_attacks_baseline, n_attacks_control]: + # Setting all to self.n_attacks because of at least one missing value.. + n_attacks_ori = self.n_attacks + n_attacks_baseline = self.n_attacks + n_attacks_control = self.n_attacks + + self.attack_rate = success_rate(n_total=n_attacks_ori, n_success=n_success, confidence_level=confidence_level) - self.baseline_rate = success_rate(n_total=n_attacks, n_success=n_baseline, confidence_level=confidence_level) + self.baseline_rate = success_rate(n_total=n_attacks_baseline, n_success=n_baseline, confidence_level=confidence_level) self.control_rate = ( None if n_control is None - else success_rate(n_total=n_attacks, n_success=n_control, confidence_level=confidence_level) + else success_rate(n_total=n_attacks_control, n_success=n_control, confidence_level=confidence_level) ) - self.n_attacks = n_attacks + self.n_attacks_ori = n_attacks_ori + self.n_attacks_baseline = n_attacks_baseline + self.n_attacks_control = n_attacks_control self.n_success = n_success self.n_baseline = n_baseline self.n_control = n_control From f490f7a40e6031f10648cbd5df5906e06d3e1dec Mon Sep 17 00:00:00 2001 From: ivana Date: Fri, 14 Nov 2025 13:12:07 +0100 Subject: [PATCH 2/7] Ruff - Tuple. --- src/anonymeter/evaluators/inference_evaluator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/anonymeter/evaluators/inference_evaluator.py b/src/anonymeter/evaluators/inference_evaluator.py index a32169c..865e8a6 100644 --- a/src/anonymeter/evaluators/inference_evaluator.py +++ b/src/anonymeter/evaluators/inference_evaluator.py @@ -3,7 +3,7 @@ # See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. """Privacy evaluator that measures the inference risk.""" -from typing import Optional, Union, Tuple, Any +from typing import Optional, Union, Any import numpy as np import numpy.typing as npt @@ -25,7 +25,7 @@ def _run_attack( regression: Optional[bool], ml_model: Optional[Any], sample_attacks: bool, -) -> tuple[int, Union[Tuple[npt.NDArray, npt.NDArray], npt.NDArray, None], DataFrame]: +) -> tuple[int, Union[tuple[npt.NDArray, npt.NDArray], npt.NDArray, None], DataFrame]: if regression is None: regression = pd.api.types.is_numeric_dtype(target[secret]) @@ -210,7 +210,7 @@ def __init__( self._data_groups = self._ori[self._secret].unique().tolist() def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int, n_attacks: int) -> tuple[ - int, Union[Tuple[npt.NDArray, npt.NDArray], + int, Union[tuple[npt.NDArray, npt.NDArray], pd.Series, None], DataFrame]: return _run_attack( target=target, From 83386c9afc36da742163a13732431c30675077fa Mon Sep 17 00:00:00 2001 From: ivana Date: Fri, 21 Nov 2025 10:34:54 +0100 Subject: [PATCH 3/7] Add an InferencePredictor Protocol; Create class wrappers for a KNN and the ml model; Update example inference_custom_model_example.ipynb. --- .../inference_custom_model_example.ipynb | 190 +++++++++--------- src/anonymeter/evaluators/__init__.py | 3 +- .../evaluators/inference_evaluator.py | 99 ++++----- .../evaluators/inference_predictor.py | 45 +++++ 4 files changed, 184 insertions(+), 153 deletions(-) create mode 100644 src/anonymeter/evaluators/inference_predictor.py diff --git a/notebooks/inference_custom_model_example.ipynb b/notebooks/inference_custom_model_example.ipynb index 8cb3aa6..a2ff806 100644 --- a/notebooks/inference_custom_model_example.ipynb +++ b/notebooks/inference_custom_model_example.ipynb @@ -66,7 +66,7 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", - "from anonymeter.evaluators import InferenceEvaluator\n", + "from anonymeter.evaluators import InferenceEvaluator, MLModelInference\n", "from autogluon.tabular import TabularPredictor\n" ] }, @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "6c07054c-7ced-46c3-8a12-14123f6cc965", "metadata": { "ExecuteTime": { @@ -336,17 +336,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184928\"\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092829\"\n", "Verbosity: 2 (Standard Logging)\n", "=================== System Info ===================\n", "AutoGluon Version: 1.4.0\n", - "Python Version: 3.11.12\n", + "Python Version: 3.9.22\n", "Operating System: Darwin\n", "Platform Machine: x86_64\n", "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", "CPU Count: 12\n", - "Memory Avail: 5.93 GB / 16.00 GB (37.0%)\n", - "Disk Space Avail: 26.36 GB / 465.63 GB (5.7%)\n", + "Memory Avail: 4.33 GB / 16.00 GB (27.0%)\n", + "Disk Space Avail: 11.13 GB / 465.63 GB (2.4%)\n", "===================================================\n", "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", @@ -356,7 +356,7 @@ "\tpresets='good' : Good accuracy with very fast inference speed.\n", "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184928\"\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092829\"\n", "Train Data Rows: 39040\n", "Train Data Columns: 14\n", "Label Column: age\n", @@ -370,8 +370,8 @@ "Train Data Class Count: 66\n", "Using Feature Generators to preprocess the data ...\n", "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 6007.93 MB\n", - "\tTrain Data (Original) Memory Usage: 23.48 MB (0.4% of available memory)\n", + "\tAvailable Memory: 4553.59 MB\n", + "\tTrain Data (Original) Memory Usage: 23.48 MB (0.5% of available memory)\n", "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", "\tStage 1 Generators:\n", "\t\tFitting AsTypeFeatureGenerator...\n", @@ -393,10 +393,10 @@ "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", "\t\t('int', []) : 5 | ['fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.3s = Fit runtime\n", + "\t0.5s = Fit runtime\n", "\t14 features in original data used to generate 14 features in processed data.\n", "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.36s ...\n", + "Data preprocessing and feature engineering runtime = 0.62s ...\n", "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", "\tTo change this, specify the eval_metric parameter of Predictor()\n", "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36516, Val Rows: 2499\n", @@ -406,29 +406,29 @@ "}\n", "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=1.5/5.6 GB\n", - "\tWarning: Reducing model 'n_estimators' from 300 -> 50 due to low memory. Expected memory usage reduced from 89.52% -> 15.0% of available memory...\n", - "\t0.0572\t = Validation score (accuracy)\n", - "\t3.61s\t = Training runtime\n", - "\t0.04s\t = Validation runtime\n", + "\tFitting with cpus=12, gpus=0, mem=1.5/4.6 GB\n", + "\tWarning: Reducing model 'n_estimators' from 300 -> 42 due to low memory. Expected memory usage reduced from 106.04% -> 15.0% of available memory...\n", + "\t0.0576\t = Validation score (accuracy)\n", + "\t1.51s\t = Training runtime\n", + "\t0.06s\t = Validation runtime\n", "Fitting model: WeightedEnsemble_L2 ...\n", "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.0572\t = Validation score (accuracy)\n", - "\t0.01s\t = Training runtime\n", + "\t0.0576\t = Validation score (accuracy)\n", + "\t0.02s\t = Training runtime\n", "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 5.76s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 55927.0 rows/s (2499 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184928\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184939\"\n", + "AutoGluon training complete, total runtime = 3.95s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 42137.1 rows/s (2499 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092829\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092838\"\n", "Verbosity: 2 (Standard Logging)\n", "=================== System Info ===================\n", "AutoGluon Version: 1.4.0\n", - "Python Version: 3.11.12\n", + "Python Version: 3.9.22\n", "Operating System: Darwin\n", "Platform Machine: x86_64\n", "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", "CPU Count: 12\n", - "Memory Avail: 5.83 GB / 16.00 GB (36.4%)\n", - "Disk Space Avail: 25.53 GB / 465.63 GB (5.5%)\n", + "Memory Avail: 5.53 GB / 16.00 GB (34.5%)\n", + "Disk Space Avail: 24.42 GB / 465.63 GB (5.2%)\n", "===================================================\n", "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", @@ -438,7 +438,7 @@ "\tpresets='good' : Good accuracy with very fast inference speed.\n", "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184939\"\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092838\"\n", "Train Data Rows: 39040\n", "Train Data Columns: 14\n", "Label Column: type_employer\n", @@ -452,7 +452,7 @@ "Train Data Class Count: 8\n", "Using Feature Generators to preprocess the data ...\n", "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 5809.01 MB\n", + "\tAvailable Memory: 5521.62 MB\n", "\tTrain Data (Original) Memory Usage: 21.38 MB (0.4% of available memory)\n", "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", "\tStage 1 Generators:\n", @@ -475,10 +475,10 @@ "\t\t('category', []) : 6 | ['education', 'marital', 'occupation', 'relationship', 'race', ...]\n", "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.3s = Fit runtime\n", + "\t0.4s = Fit runtime\n", "\t14 features in original data used to generate 14 features in processed data.\n", "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.44s ...\n", + "Data preprocessing and feature engineering runtime = 0.55s ...\n", "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", "\tTo change this, specify the eval_metric parameter of Predictor()\n", "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36533, Val Rows: 2500\n", @@ -488,28 +488,28 @@ "}\n", "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.2/5.6 GB\n", + "\tFitting with cpus=12, gpus=0, mem=0.2/5.3 GB\n", "\t0.7044\t = Validation score (accuracy)\n", - "\t3.66s\t = Training runtime\n", - "\t0.08s\t = Validation runtime\n", + "\t4.2s\t = Training runtime\n", + "\t0.11s\t = Validation runtime\n", "Fitting model: WeightedEnsemble_L2 ...\n", "\tEnsemble Weights: {'RandomForest': 1.0}\n", "\t0.7044\t = Validation score (accuracy)\n", "\t0.01s\t = Training runtime\n", "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 5.78s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 29373.8 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184939\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_184949\"\n", + "AutoGluon training complete, total runtime = 6.66s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 23297.2 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092838\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092850\"\n", "Verbosity: 2 (Standard Logging)\n", "=================== System Info ===================\n", "AutoGluon Version: 1.4.0\n", - "Python Version: 3.11.12\n", + "Python Version: 3.9.22\n", "Operating System: Darwin\n", "Platform Machine: x86_64\n", "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", "CPU Count: 12\n", - "Memory Avail: 5.65 GB / 16.00 GB (35.3%)\n", - "Disk Space Avail: 25.78 GB / 465.63 GB (5.5%)\n", + "Memory Avail: 5.02 GB / 16.00 GB (31.4%)\n", + "Disk Space Avail: 23.66 GB / 465.63 GB (5.1%)\n", "===================================================\n", "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", @@ -519,7 +519,7 @@ "\tpresets='good' : Good accuracy with very fast inference speed.\n", "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184949\"\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092850\"\n", "Train Data Rows: 39040\n", "Train Data Columns: 14\n", "Label Column: fnlwgt\n", @@ -530,8 +530,8 @@ "Preprocessing data ...\n", "Using Feature Generators to preprocess the data ...\n", "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 5794.55 MB\n", - "\tTrain Data (Original) Memory Usage: 23.49 MB (0.4% of available memory)\n", + "\tAvailable Memory: 5106.42 MB\n", + "\tTrain Data (Original) Memory Usage: 23.49 MB (0.5% of available memory)\n", "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", "\tStage 1 Generators:\n", "\t\tFitting AsTypeFeatureGenerator...\n", @@ -556,7 +556,7 @@ "\t0.4s = Fit runtime\n", "\t14 features in original data used to generate 14 features in processed data.\n", "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.39s ...\n", + "Data preprocessing and feature engineering runtime = 0.46s ...\n", "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n", "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n", "\tTo change this, specify the eval_metric parameter of Predictor()\n", @@ -567,28 +567,28 @@ "}\n", "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.0/5.6 GB\n", + "\tFitting with cpus=12, gpus=0, mem=0.0/5.0 GB\n", "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", - "\t10.2s\t = Training runtime\n", - "\t0.11s\t = Validation runtime\n", + "\t10.78s\t = Training runtime\n", + "\t0.13s\t = Validation runtime\n", "Fitting model: WeightedEnsemble_L2 ...\n", "\tEnsemble Weights: {'RandomForest': 1.0}\n", "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", "\t0.0s\t = Training runtime\n", "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 13.17s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 23178.3 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_184949\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_185101\"\n", + "AutoGluon training complete, total runtime = 12.3s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 19535.7 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092850\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_093000\"\n", "Verbosity: 2 (Standard Logging)\n", "=================== System Info ===================\n", "AutoGluon Version: 1.4.0\n", - "Python Version: 3.11.12\n", + "Python Version: 3.9.22\n", "Operating System: Darwin\n", "Platform Machine: x86_64\n", "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", "CPU Count: 12\n", - "Memory Avail: 4.46 GB / 16.00 GB (27.9%)\n", - "Disk Space Avail: 25.47 GB / 465.63 GB (5.5%)\n", + "Memory Avail: 6.53 GB / 16.00 GB (40.8%)\n", + "Disk Space Avail: 35.48 GB / 465.63 GB (7.6%)\n", "===================================================\n", "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", @@ -598,7 +598,7 @@ "\tpresets='good' : Good accuracy with very fast inference speed.\n", "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185101\"\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093000\"\n", "Train Data Rows: 39040\n", "Train Data Columns: 14\n", "Label Column: education\n", @@ -610,8 +610,8 @@ "Train Data Class Count: 16\n", "Using Feature Generators to preprocess the data ...\n", "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 4552.15 MB\n", - "\tTrain Data (Original) Memory Usage: 21.36 MB (0.5% of available memory)\n", + "\tAvailable Memory: 6593.09 MB\n", + "\tTrain Data (Original) Memory Usage: 21.36 MB (0.3% of available memory)\n", "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", "\tStage 1 Generators:\n", "\t\tFitting AsTypeFeatureGenerator...\n", @@ -633,10 +633,10 @@ "\t\t('category', []) : 6 | ['type_employer', 'marital', 'occupation', 'relationship', 'race', ...]\n", "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.3s = Fit runtime\n", + "\t0.4s = Fit runtime\n", "\t14 features in original data used to generate 14 features in processed data.\n", "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.39s ...\n", + "Data preprocessing and feature engineering runtime = 0.56s ...\n", "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", "\tTo change this, specify the eval_metric parameter of Predictor()\n", "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36540, Val Rows: 2500\n", @@ -646,29 +646,28 @@ "}\n", "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.4/4.4 GB\n", - "\tWarning: Reducing model 'n_estimators' from 300 -> 214 due to low memory. Expected memory usage reduced from 21.02% -> 15.0% of available memory...\n", - "\t0.8048\t = Validation score (accuracy)\n", - "\t2.88s\t = Training runtime\n", - "\t0.06s\t = Validation runtime\n", + "\tFitting with cpus=12, gpus=0, mem=0.4/6.4 GB\n", + "\t0.8064\t = Validation score (accuracy)\n", + "\t4.32s\t = Training runtime\n", + "\t0.12s\t = Validation runtime\n", "Fitting model: WeightedEnsemble_L2 ...\n", "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.8048\t = Validation score (accuracy)\n", + "\t0.8064\t = Validation score (accuracy)\n", "\t0.01s\t = Training runtime\n", "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 5.02s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 37694.8 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185101\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251111_185111\"\n", + "AutoGluon training complete, total runtime = 7.13s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 20566.3 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093000\")\n", + "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_093013\"\n", "Verbosity: 2 (Standard Logging)\n", "=================== System Info ===================\n", "AutoGluon Version: 1.4.0\n", - "Python Version: 3.11.12\n", + "Python Version: 3.9.22\n", "Operating System: Darwin\n", "Platform Machine: x86_64\n", "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", "CPU Count: 12\n", - "Memory Avail: 4.52 GB / 16.00 GB (28.3%)\n", - "Disk Space Avail: 24.84 GB / 465.63 GB (5.3%)\n", + "Memory Avail: 5.17 GB / 16.00 GB (32.3%)\n", + "Disk Space Avail: 35.78 GB / 465.63 GB (7.7%)\n", "===================================================\n", "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", @@ -678,7 +677,7 @@ "\tpresets='good' : Good accuracy with very fast inference speed.\n", "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185111\"\n", + "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093013\"\n", "Train Data Rows: 39040\n", "Train Data Columns: 14\n", "Label Column: education_num\n", @@ -692,8 +691,8 @@ "Train Data Class Count: 13\n", "Using Feature Generators to preprocess the data ...\n", "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 4644.58 MB\n", - "\tTrain Data (Original) Memory Usage: 23.49 MB (0.5% of available memory)\n", + "\tAvailable Memory: 5330.31 MB\n", + "\tTrain Data (Original) Memory Usage: 23.49 MB (0.4% of available memory)\n", "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", "\tStage 1 Generators:\n", "\t\tFitting AsTypeFeatureGenerator...\n", @@ -715,10 +714,10 @@ "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", "\t\t('int', []) : 5 | ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hr_per_week']\n", "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.3s = Fit runtime\n", + "\t0.4s = Fit runtime\n", "\t14 features in original data used to generate 14 features in processed data.\n", "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.38s ...\n", + "Data preprocessing and feature engineering runtime = 0.44s ...\n", "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", "\tTo change this, specify the eval_metric parameter of Predictor()\n", "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36534, Val Rows: 2500\n", @@ -728,18 +727,17 @@ "}\n", "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.3/4.5 GB\n", - "\tWarning: Reducing model 'n_estimators' from 300 -> 288 due to low memory. Expected memory usage reduced from 15.59% -> 15.0% of available memory...\n", - "\t0.856\t = Validation score (accuracy)\n", - "\t3.71s\t = Training runtime\n", - "\t0.08s\t = Validation runtime\n", + "\tFitting with cpus=12, gpus=0, mem=0.3/5.1 GB\n", + "\t0.8564\t = Validation score (accuracy)\n", + "\t4.43s\t = Training runtime\n", + "\t0.14s\t = Validation runtime\n", "Fitting model: WeightedEnsemble_L2 ...\n", "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.856\t = Validation score (accuracy)\n", - "\t0.01s\t = Training runtime\n", + "\t0.8564\t = Validation score (accuracy)\n", + "\t0.02s\t = Training runtime\n", "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 5.9s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 29893.2 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251111_185111\")\n" + "AutoGluon training complete, total runtime = 7.3s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 17247.9 rows/s (2500 batch size)\n", + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093013\")\n" ] } ], @@ -757,6 +755,7 @@ " 'RF': {}, \n", " }\n", " )\n", + " inference_model = MLModelInference(predictor)\n", " aux_cols = [col for col in columns if col != secret]\n", " \n", " # Needed for anonymeter (is the target categorical or numerical variable?)\n", @@ -770,8 +769,7 @@ " secret=secret,\n", " n_attacks=6000, #todo important! this parameter is obsolete if sample_attacks is False\n", " regression = regression,\n", - " ml_model=predictor,\n", - " sample_attacks=False) # Setting to true for this example\n", + " inference_model=inference_model) # Setting to true for this example\n", " # Evaluate\n", " evaluator.evaluate(n_jobs=-2)\n", " \n", @@ -782,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "59651586-891c-4ae6-8b28-fe7a7de66aeb", "metadata": { "ExecuteTime": { @@ -794,7 +792,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAHPCAYAAABz+I9wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAABU7klEQVR4nO3de1hU5f4+/nsG5KiARxAlQcUEBRFJgvA8BmU7MbcKmhiRtE3yQGpqCG3EKErF05bMbR5JQ/vyKTOK8JRKGAdPaGmFYuKASEBgcprn94c/V02gMbMHB8b7dV1z4TzrvdZ6r1mYd2ueWSMTQggQERERkUbk+m6AiIiIqC1iiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLDFFEREREWmCIIiIiItICQxQRERGRFhiiiIiIiLTAEEVERESkBYYoInqoXblyBa+88goeffRRmJubo3Pnzpg0aRIuX77cqPbMmTMYMWIEzM3N0bNnT8TFxeHDDz+ETCZrVP/FF19g2LBhsLS0RIcOHTBu3Djk5+c/mIMiogfCWN8NEBHp03fffYcTJ04gKCgIPXv2xOXLl7Fx40aMHDkS58+fh4WFBQDg2rVrGDVqFGQyGZYsWQJLS0ts3rwZpqamjba5Y8cOzJgxA/7+/njnnXdw69YtbNy4EX5+fsjLy4Ojo+MDPkoiagkyIYTQdxNERPry+++/w9zcXG3s22+/hY+PD7Zv347p06cDAObMmYP169cjNzcXHh4eAICysjI4OzujrKwMBQUFcHR0RFVVFRwcHDBp0iRs2rRJ2mZxcTEeffRRTJ48WW2ciNouvp1HRA+1Pweouro63Lx5E3379oWNjQ1yc3OlZWlpafDx8ZECFAB06tQJ06ZNU9teeno6ysvLERwcjNLSUulhZGQEb29vHDp0qMWPiYgeDL6dR0QPtd9//x3x8fH48MMPce3aNfz54nxFRYX05ytXrsDHx6fR+n379lV7funSJQDA6NGjm9yflZWVLtomolaAIYqIHmqvvvoqPvzwQ8ybNw8+Pj6wtraGTCZDUFAQVCqVxtu7u86OHTtgZ2fXaLmxMf+zS2Qo+LeZiB5qe/fuxYwZM7By5Upp7Pbt2ygvL1er69WrF3788cdG6/91rE+fPgCAbt26QaFQ6L5hImo1OCeKiB5qRkZG+Ovna9atW4eGhga1MX9/f2RmZuLUqVPSWFlZGXbt2tWozsrKCm+99Rbq6uoa7e/GjRu6a56I9IpXoojoofbMM89gx44dsLa2hqurKzIzM/H111+jc+fOanWLFi3Czp07MXbsWLz66qvSLQ4eeeQRlJWVQSaTAbgz52njxo2YPn06PD09ERQUhK5du6KwsBCff/45nnjiCaxfv14fh0pEOsYQRUQPtTVr1sDIyAi7du3C7du38cQTT+Drr7+Gv7+/Wp2DgwMOHTqEOXPm4K233kLXrl0xe/ZsWFpaYs6cOTAzM5Nqp06dCnt7e7z99tt49913UVNTgx49emDYsGEIDQ190IdIRC2E94kiIvofzJs3D++//z6qqqpgZGSk73aI6AHinCgiomb6/fff1Z7fvHkTO3bsgJ+fHwMU0UOIb+cRETWTj48PRo4cCRcXFxQXF+O///0vKisrsWzZMn23RkR6wBBFRNRMTz/9NPbu3YtNmzZBJpPB09MT//3vfzF8+HB9t0ZEesA5UURERERa4JwoIiIiIi0wRBERERFpgXOiWpBKpUJRURE6dOgg3YiPiIiIWjchBH777TfY29tDLr/39SaGqBZUVFQEBwcHfbdBREREWrh69Sp69ux5z+UMUS2oQ4cOAO6cBCsrKz13Q0RERM1RWVkJBwcH6d/xe2GIakF//i4thigiIqK25e+m4nBiOREREZEWGKKIiIiItMAQRURERKQFhigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLxvpugIioOVanX2w0Nn9sPz10QkR0B69EEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLDFFEREREWmCIIiIiItICQxQRERGRFhiiiIiIiLTAEEVERESkBYYoIiIiIi0wRBERERFpgSGKiIiISAt6D1EbNmyAo6MjzMzM4O3tjZMnT963PiUlBf3794eZmRnc3Nxw4MABteVCCERHR6N79+4wNzeHQqHApUuX1GpWrFgBX19fWFhYwMbG5r77u3nzJnr27AmZTIby8nJtDpGIiIgMkF5D1J49exAZGYmYmBjk5uZi0KBB8Pf3R0lJSZP1J06cQHBwMMLCwpCXl4fAwEAEBgbi3LlzUk1CQgLWrl2LpKQkZGVlwdLSEv7+/rh9+7ZUU1tbi0mTJmHWrFl/22NYWBjc3d3/94MlIiIigyITQgh97dzb2xuPPfYY1q9fDwBQqVRwcHDAq6++isWLFzeqnzJlCqqrq7F//35p7PHHH4eHhweSkpIghIC9vT1ee+01LFiwAABQUVEBW1tbbN26FUFBQWrb27p1K+bNm3fPK0wbN27Enj17EB0djTFjxuDXX3/92ytXf1ZZWQlra2tUVFTAysqq2esRUWOr0y82Gps/tp8eOiEiQ9fcf7/1diWqtrYWOTk5UCgUfzQjl0OhUCAzM7PJdTIzM9XqAcDf31+qLygogFKpVKuxtraGt7f3Pbd5L+fPn0dsbCy2b98Oubx5L1NNTQ0qKyvVHkRERGSY9BaiSktL0dDQAFtbW7VxW1tbKJXKJtdRKpX3rb/7U5NtNqWmpgbBwcF499138cgjjzR7vfj4eFhbW0sPBweHZq9LREREbYveJ5a3RkuWLIGLiwuef/55jderqKiQHlevXm2hDomIiEjf9BaiunTpAiMjIxQXF6uNFxcXw87Orsl17Ozs7lt/96cm22zKwYMHkZKSAmNjYxgbG2PMmDFSzzExMfdcz9TUFFZWVmoPIiIiMkx6C1EmJiYYMmQIMjIypDGVSoWMjAz4+Pg0uY6Pj49aPQCkp6dL9U5OTrCzs1OrqaysRFZW1j232ZR9+/bh9OnTOHXqFE6dOoXNmzcDAL755hvMnj272dshIiIiw2Wsz51HRkZixowZ8PLywtChQ5GYmIjq6mqEhoYCAEJCQtCjRw/Ex8cDAObOnYsRI0Zg5cqVGDduHHbv3o3s7Gxs2rQJACCTyTBv3jzExcXB2dkZTk5OWLZsGezt7REYGCjtt7CwEGVlZSgsLERDQwNOnToFAOjbty/at2+PPn36qPVZWloKAHBxcdHo03lERERkuPQaoqZMmYIbN24gOjoaSqUSHh4eSEtLkyaGFxYWqn0yztfXF8nJyYiKisLSpUvh7OyM1NRUDBw4UKpZtGgRqqurER4ejvLycvj5+SEtLQ1mZmZSTXR0NLZt2yY9Hzx4MADg0KFDGDlyZAsfNRERERkCvd4nytDxPlFEusP7RBHRg9Lq7xNFRERE1JYxRBERERFpgSGKiIiISAsMUURERERaYIgiIiIi0gJDFBEREZEWGKKIiIiItMAQRURERKQFhigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLDFFEREREWmCIIiIiItICQxQRERGRFhiiiIiIiLTAEEVERESkBYYoIiIiIi0wRBERERFpgSGKiIiISAsMUURERERaYIgiIiIi0gJDFBEREZEWjPXdABG1LavTLzYamz+2nx46ISLSL16JIiIiItICQxQRERGRFhiiiIiIiLTAEEVERESkBYYoIiIiIi3oPURt2LABjo6OMDMzg7e3N06ePHnf+pSUFPTv3x9mZmZwc3PDgQMH1JYLIRAdHY3u3bvD3NwcCoUCly5dUqtZsWIFfH19YWFhARsbm0b7OH36NIKDg+Hg4ABzc3O4uLhgzZo1//OxEhERkeHQa4jas2cPIiMjERMTg9zcXAwaNAj+/v4oKSlpsv7EiRMIDg5GWFgY8vLyEBgYiMDAQJw7d06qSUhIwNq1a5GUlISsrCxYWlrC398ft2/flmpqa2sxadIkzJo1q8n95OTkoFu3bti5cyfy8/PxxhtvYMmSJVi/fr1uXwAiIiJqs2RCCKGvnXt7e+Oxxx6TwolKpYKDgwNeffVVLF68uFH9lClTUF1djf3790tjjz/+ODw8PJCUlAQhBOzt7fHaa69hwYIFAICKigrY2tpi69atCAoKUtve1q1bMW/ePJSXl/9tr7Nnz8aFCxdw8ODBZh9fZWUlrK2tUVFRASsrq2avR9Sa6es+Ubw/FRE9KM3991tvV6Jqa2uRk5MDhULxRzNyORQKBTIzM5tcJzMzU60eAPz9/aX6goICKJVKtRpra2t4e3vfc5vNVVFRgU6dOt23pqamBpWVlWoPIiIiMkx6C1GlpaVoaGiAra2t2ritrS2USmWT6yiVyvvW3/2pyTab48SJE9izZw/Cw8PvWxcfHw9ra2vp4eDgoPU+iYiIqHXT+8Ty1u7cuXMYP348YmJi8OSTT963dsmSJaioqJAeV69efUBdEhER0YOmtxDVpUsXGBkZobi4WG28uLgYdnZ2Ta5jZ2d33/q7PzXZ5v2cP38eY8aMQXh4OKKiov623tTUFFZWVmoPIiIiMkx6C1EmJiYYMmQIMjIypDGVSoWMjAz4+Pg0uY6Pj49aPQCkp6dL9U5OTrCzs1OrqaysRFZW1j23eS/5+fkYNWoUZsyYgRUrVmi0LhERERk+Y33uPDIyEjNmzICXlxeGDh2KxMREVFdXIzQ0FAAQEhKCHj16ID4+HgAwd+5cjBgxAitXrsS4ceOwe/duZGdnY9OmTQAAmUyGefPmIS4uDs7OznBycsKyZctgb2+PwMBAab+FhYUoKytDYWEhGhoacOrUKQBA37590b59e5w7dw6jR4+Gv78/IiMjpflURkZG6Nq164N7gYiIiKjV0muImjJlCm7cuIHo6GgolUp4eHggLS1NmhheWFgIufyPi2W+vr5ITk5GVFQUli5dCmdnZ6SmpmLgwIFSzaJFi1BdXY3w8HCUl5fDz88PaWlpMDMzk2qio6Oxbds26fngwYMBAIcOHcLIkSOxd+9e3LhxAzt37sTOnTulul69euHy5cst9XIQERFRG6LX+0QZOt4nigwR7xNFRIau1d8nioiIiKgtY4giIiIi0gJDFBEREZEWGKKIiIiItMAQRURERKQFhigiIiIiLTBEEREREWlBrzfbJGpJvK8QERG1JI2vRNXV1d1zWWlp6f/UDBEREVFboXGICgoKQlM3OS8uLsbIkSN10RMRERFRq6dxiCosLMRLL72kNqZUKjFy5Ej0799fZ40RERERtWYah6gDBw7gxIkTiIyMBAAUFRVhxIgRcHNzw8cff6zzBomIiIhaI40nlnft2hVfffUV/Pz8AAD79++Hp6cndu3aBbmcH/YjIiKih4NWn85zcHBAeno6hg0bhrFjx2LHjh2QyWS67o2IiIio1WpWiOrYsWOTIenWrVv47LPP0LlzZ2msrKxMd90RERERtVLNClGJiYkt3AYRERFR29KsEDVjxoyW7oOIiIioTdF4Jnhubi7Onj0rPf+///s/BAYGYunSpaitrdVpc0REREStlcYh6uWXX8bFi3e+TuPnn3/GlClTYGFhgZSUFCxatEjnDRIRERG1RhqHqIsXL8LDwwMAkJKSghEjRiA5ORlbt27Fvn37dN0fERERUaukcYgSQkClUgEAvv76azz99NMA7tz2gN+dR0RERA8LjUOUl5cX4uLisGPHDhw5cgTjxo0DABQUFMDW1lbnDRIRERG1RhqHqMTEROTm5iIiIgJvvPEG+vbtCwDYu3cvfH19dd4gERERUWuk8R3L3d3d1T6dd9e7774LIyMjnTRFRERE1Npp9bUvTTEzM9PVpoiIiIhavWaFqE6dOuHixYvo0qXLPb8C5i5+7QsRERE9DJoVolavXo0OHToA4FfAEBEREQEafu1LfX09ZDIZ/P39+Uk8IiIieqhp9Ok8Y2Nj/Otf/8Lt27dbqh8iIiKiNkHjWxwMHToUeXl5LdELERERUZuh8afzXnnlFbz22mv45ZdfMGTIEFhaWqotd3d311lzRERERK2VxiEqKCgIADBnzhxpTCaTQQgBmUyGhoYG3XVHRERE1EppHKIKCgpaog8iIiKiNkXjENWrV6+W6IOIiIioTdF4YjkRERERMUQRERERaUXvIWrDhg1wdHSEmZkZvL29cfLkyfvWp6SkoH///jAzM4ObmxsOHDigtlwIgejoaHTv3h3m5uZQKBS4dOmSWs2KFSvg6+sLCwsL2NjYNLmfwsJCjBs3DhYWFujWrRsWLlyI+vr6/+lYiYiIyHDoNUTt2bMHkZGRiImJQW5uLgYNGgR/f3+UlJQ0WX/ixAkEBwcjLCwMeXl5CAwMRGBgIM6dOyfVJCQkYO3atUhKSkJWVhYsLS3h7++vdoPQ2tpaTJo0CbNmzWpyPw0NDRg3bhxqa2tx4sQJbNu2DVu3bkV0dLRuXwAiIiJqs7QKUeXl5di8eTOWLFkifeFwbm4url27ptF2Vq1ahZkzZyI0NBSurq5ISkqChYUFtmzZ0mT9mjVrEBAQgIULF8LFxQXLly+Hp6cn1q9fD+DOVajExERERUVh/PjxcHd3x/bt21FUVITU1FRpO//+978xf/58uLm5Nbmfr776CufPn8fOnTvh4eGBp556CsuXL8eGDRtQW1ur0TESERGRYdI4RJ05cwb9+vXDO++8g/feew/l5eUAgE8++QRLlixp9nZqa2uRk5MDhULxRzNyORQKBTIzM5tcJzMzU60eAPz9/aX6goICKJVKtRpra2t4e3vfc5v32o+bm5va9wP6+/ujsrIS+fn591yvpqYGlZWVag8iIiIyTBqHqMjISLzwwgu4dOkSzMzMpPGnn34aR48ebfZ2SktL0dDQ0OiLjG1tbaFUKptcR6lU3rf+7k9NtqnJfv68j6bEx8fD2tpaejg4ODR7n0RERNS2aByivvvuO7z88suNxnv06KFRUDFES5YsQUVFhfS4evWqvlsiIiKiFqJxiDI1NW3ybaqLFy+ia9euzd5Oly5dYGRkhOLiYrXx4uJi2NnZNbmOnZ3dfevv/tRkm5rs58/7aIqpqSmsrKzUHkRERGSYNA5Rzz77LGJjY1FXVwfgzvfmFRYW4vXXX8fEiRObvR0TExMMGTIEGRkZ0phKpUJGRgZ8fHyaXMfHx0etHgDS09OleicnJ9jZ2anVVFZWIisr657bvNd+zp49q/YpwfT0dFhZWcHV1bXZ2yEiIiLDpXGIWrlyJaqqqtCtWzf8/vvvGDFiBPr27YsOHTpgxYoVGm0rMjISH3zwAbZt24YLFy5g1qxZqK6uRmhoKAAgJCREbbL63LlzkZaWhpUrV+L777/Hm2++iezsbERERAC4E+jmzZuHuLg4fPrppzh79ixCQkJgb2+PwMBAaTuFhYU4deoUCgsL0dDQgFOnTuHUqVOoqqoCADz55JNwdXXF9OnTcfr0aXz55ZeIiorC7NmzYWpqqulLRkRERAZI4+/Os7a2Rnp6Oo4fP47Tp0+jqqoKnp6ejT411xxTpkzBjRs3EB0dDaVSCQ8PD6SlpUmTuAsLCyGX/5HzfH19kZycjKioKCxduhTOzs5ITU3FwIEDpZpFixahuroa4eHhKC8vh5+fH9LS0tQmwUdHR2Pbtm3S88GDBwMADh06hJEjR8LIyAj79+/HrFmz4OPjA0tLS8yYMQOxsbEaHyMREREZJpkQQui7CUNVWVkJa2trVFRUcH6UHqxOv9hobP7YfnroxLDo63Xl+SSiB6W5/35r/HbenDlzsHbt2kbj69evx7x58zTdHBEREVGbpHGI2rdvH5544olG476+vti7d69OmiIiIiJq7TQOUTdv3oS1tXWjcSsrK5SWluqkKSIiIqLWTuMQ1bdvX6SlpTUa/+KLL9C7d2+dNEVERETU2mn86bzIyEhERETgxo0bGD16NAAgIyMDK1euRGJioq77IyIiImqVNA5RL774ImpqarBixQosX74cAODo6IiNGzciJCRE5w0SERERtUYahygAmDVrFmbNmoUbN27A3Nwc7du313VfRERERK2aViHqLk2+K4+IiIjIkGg8sby4uBjTp0+Hvb09jI2NYWRkpPYgIiIiehhofCXqhRdeQGFhIZYtW4bu3btDJpO1RF9ERERErZrGIerYsWP45ptv4OHh0QLtEBEREbUNGr+d5+DgAH7dHhERET3sNA5RiYmJWLx4MS5fvtwC7RARERG1DRq/nTdlyhTcunULffr0gYWFBdq1a6e2vKysTGfNEREREbVWGoco3pWciIiISIsQNWPGjJbog6hVWJ1+Ue35/LH99NQJERG1dhrPiQKAn376CVFRUQgODkZJSQmAO19AnJ+fr9PmiIiIiForjUPUkSNH4ObmhqysLHzyySeoqqoCAJw+fRoxMTE6b5CIiIioNdI4RC1evBhxcXFIT0+HiYmJND569Gh8++23Om2OiIiIqLXSOESdPXsWEyZMaDTerVs3lJaW6qQpIiIiotZO4xBlY2OD69evNxrPy8tDjx49dNIUERERUWuncYgKCgrC66+/DqVSCZlMBpVKhePHj2PBggUICQlpiR6JiIiIWh2NQ9Rbb72F/v37w8HBAVVVVXB1dcXw4cPh6+uLqKioluiRiIiIqNXR6D5RQggolUqsXbsW0dHROHv2LKqqqjB48GA4Ozu3VI9ERERErY7GIapv377Iz8+Hs7MzHBwcWqovIiIiolZNo7fz5HI5nJ2dcfPmzZbqh4iIiKhN0HhO1Ntvv42FCxfi3LlzLdEPERERUZug8XfnhYSE4NatWxg0aBBMTExgbm6utrysrExnzRERERG1VhqHqMTExBZog4iIiKht0ThEzZgxoyX6ICIiImpTNJ4TBQA//fQToqKiEBwcjJKSEgDAF198gfz8fJ02R0RERNRaaRyijhw5Ajc3N2RlZeGTTz5BVVUVAOD06dOIiYnReYNERERErZHGIWrx4sWIi4tDeno6TExMpPHRo0fj22+/1WlzRERERK2VxnOizp49i+Tk5Ebj3bp1Q2lpqU6aIt1bnX6x0dj8sf300AkREZFh0PhKlI2NDa5fv95oPC8vDz169NBJU0REREStncYhKigoCK+//jqUSiVkMhlUKhWOHz+OBQsWICQkpCV6JCIiImp1NA5Rb731Fvr37w8HBwdUVVXB1dUVw4cPh6+vL6KiojRuYMOGDXB0dISZmRm8vb1x8uTJ+9anpKSgf//+MDMzg5ubGw4cOKC2XAiB6OhodO/eHebm5lAoFLh06ZJaTVlZGaZNmwYrKyvY2NggLCxMmiB/15dffonHH38cHTp0QNeuXTFx4kRcvnxZ4+MjIiIiw9SsEFVZWSn92cTEBB988AF+/vln7N+/Hzt37sT333+PHTt2wMjISKOd79mzB5GRkYiJiUFubi4GDRoEf39/6bYJf3XixAkEBwcjLCwMeXl5CAwMRGBgoNpX0CQkJGDt2rVISkpCVlYWLC0t4e/vj9u3b0s106ZNQ35+PtLT07F//34cPXoU4eHh0vKCggKMHz8eo0ePxqlTp/Dll1+itLQUzz33nEbHR0RERIarWSGqY8eOUrAZPXo0ysvL4eDggKeffhqTJ0+Gs7OzVjtftWoVZs6cidDQULi6uiIpKQkWFhbYsmVLk/Vr1qxBQEAAFi5cCBcXFyxfvhyenp5Yv349gDtXoRITExEVFYXx48fD3d0d27dvR1FREVJTUwEAFy5cQFpaGjZv3gxvb2/4+flh3bp12L17N4qKigAAOTk5aGhoQFxcHPr06QNPT08sWLAAp06dQl1dnVbHSkRERIalWSGqffv2uHnzJgDg8OHDOgkStbW1yMnJgUKh+KMZuRwKhQKZmZlNrpOZmalWDwD+/v5SfUFBAZRKpVqNtbU1vL29pZrMzEzY2NjAy8tLqlEoFJDL5cjKygIADBkyBHK5HB9++CEaGhpQUVGBHTt2QKFQoF27dvc8ppqaGlRWVqo9iIiIyDA16xYHCoUCo0aNgouLCwBgwoQJaveI+rODBw82a8elpaVoaGiAra2t2ritrS2+//77JtdRKpVN1iuVSmn53bH71XTr1k1tubGxMTp16iTVODk54auvvsLkyZPx8ssvo6GhAT4+Po3mX/1VfHw8/v3vf9+3hoiIiAxDs0LUzp07sW3bNvz00084cuQIBgwYAAsLi5buTW+USiVmzpyJGTNmIDg4GL/99huio6Pxz3/+E+np6ZDJZE2ut2TJEkRGRkrPKysr4eDg8KDaJiIiogeoWSHK3Nwc//rXvwAA2dnZeOedd2BjY/M/7bhLly4wMjJCcXGx2nhxcTHs7OyaXMfOzu6+9Xd/FhcXo3v37mo1Hh4eUs1fJ67X19ejrKxMWn/Dhg2wtrZGQkKCVLNz5044ODggKysLjz/+eJP9mZqawtTU9O8OnYiIiAyAxrc4OHTo0P8coIA7n/IbMmQIMjIypDGVSoWMjAz4+Pg0uY6Pj49aPQCkp6dL9U5OTrCzs1OrqaysRFZWllTj4+OD8vJy5OTkSDUHDx6ESqWCt7c3AODWrVuQy9VfmrufPFSpVNoeMhERERkQjb/2paGhAVu3bkVGRgZKSkoahYrmzokCgMjISMyYMQNeXl4YOnQoEhMTUV1djdDQUABASEgIevTogfj4eADA3LlzMWLECKxcuRLjxo3D7t27kZ2djU2bNgEAZDIZ5s2bh7i4ODg7O8PJyQnLli2Dvb09AgMDAQAuLi4ICAjAzJkzkZSUhLq6OkRERCAoKAj29vYAgHHjxmH16tWIjY2V3s5bunQpevXqhcGDB2v6khEREZEB0jhEzZ07F1u3bsW4ceMwcODAe84Pao4pU6bgxo0biI6OhlKphIeHB9LS0qSJ4YWFhWpXhHx9fZGcnIyoqCgsXboUzs7OSE1NxcCBA6WaRYsWobq6GuHh4SgvL4efnx/S0tJgZmYm1ezatQsREREYM2YM5HI5Jk6ciLVr10rLR48ejeTkZCQkJCAhIQEWFhbw8fFBWloazM3NtT5eIiIiMhwyIYTQZIUuXbpg+/btePrpp1uqJ4NRWVkJa2trVFRUwMrKSq+9PIxfQKzNMf91HUN/jbShr9+lh/F3mIj0o7n/fms8J8rExAR9+/b9n5ojIiIiaus0DlGvvfYa1qxZAw0vYBEREREZFI3nRB07dgyHDh3CF198gQEDBjS6g/cnn3yis+aIiIiIWiuNQ5SNjQ0mTJjQEr0QERERtRkah6gPP/ywJfogIiIialM0nhNFRERERM28EuXp6YmMjAx07NgRgwcPvu+9oXJzc3XWHBEREVFr1awQNX78eOk74e7e+ZuIiIjoYdasEBUTE9Pkn4mIiIgeVpwTRURERKQFhigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpo1qfzIiMjm73BVatWad0MERERUVvRrBCVl5en9jw3Nxf19fV49NFHAQAXL16EkZERhgwZovsOiYiIiFqhZoWoQ4cOSX9etWoVOnTogG3btqFjx44AgF9//RWhoaEYNmxYy3RJRERE1MpoPCdq5cqViI+PlwIUAHTs2BFxcXFYuXKlTpsjIiIiaq00DlGVlZW4ceNGo/EbN27gt99+00lTRERERK2dxiFqwoQJCA0NxSeffIJffvkFv/zyC/bt24ewsDA899xzLdEjERERUavTrDlRf5aUlIQFCxZg6tSpqKuru7MRY2OEhYXh3Xff1XmDRERERK2RxiHKwsIC//nPf/Duu+/ip59+AgD06dMHlpaWOm+OiIiIqLXSOETddf36dVy/fh3Dhw+Hubk5hBCQyWS67I0eUqvTLzYamz+2nx46ISIiujeNQ9TNmzcxefJkHDp0CDKZDJcuXULv3r0RFhaGjh078hN6RPfBgEhEZDg0nlg+f/58tGvXDoWFhbCwsJDGp0yZgrS0NJ02R0RERNRaaXwl6quvvsKXX36Jnj17qo07OzvjypUrOmuMiIiIqDXT+EpUdXW12hWou8rKymBqaqqTpoiIiIhaO41D1LBhw7B9+3bpuUwmg0qlQkJCAkaNGqXT5oiIiIhaK43fzktISMCYMWOQnZ2N2tpaLFq0CPn5+SgrK8Px48dbokciIiKiVkfjK1EDBw7ExYsX4efnh/Hjx6O6uhrPPfcc8vLy0KdPn5bokYiIiKjV0ehKVF1dHQICApCUlIQ33nijpXoiIiIiavU0uhLVrl07nDlzpqV6ISIiImozNH477/nnn8d///vfluiFiIiIqM3QeGJ5fX09tmzZgq+//hpDhgxp9J15q1at0llzRERERK2VxiHq3Llz8PT0BABcvKj+FRb87jwiIiJ6WGgcog4dOtQSfRARERG1KRrPidK1DRs2wNHREWZmZvD29sbJkyfvW5+SkoL+/fvDzMwMbm5uOHDggNpyIQSio6PRvXt3mJubQ6FQ4NKlS2o1ZWVlmDZtGqysrGBjY4OwsDBUVVU12s57772Hfv36wdTUFD169MCKFSt0c9BERETU5ml8JWrUqFH3fdvu4MGDzd7Wnj17EBkZiaSkJHh7eyMxMRH+/v744Ycf0K1bt0b1J06cQHBwMOLj4/HMM88gOTkZgYGByM3NxcCBAwHcuRno2rVrsW3bNjg5OWHZsmXw9/fH+fPnYWZmBgCYNm0arl+/jvT0dNTV1SE0NBTh4eFITk6W9jV37lx89dVXeO+99+Dm5oaysjKUlZU1+9iIiIjIsGkcojw8PNSe19XV4dSpUzh37hxmzJih0bZWrVqFmTNnIjQ0FACQlJSEzz//HFu2bMHixYsb1a9ZswYBAQFYuHAhAGD58uVIT0/H+vXrkZSUBCEEEhMTERUVhfHjxwMAtm/fDltbW6SmpiIoKAgXLlxAWloavvvuO3h5eQEA1q1bh6effhrvvfce7O3tceHCBWzcuBHnzp3Do48+CgBwcnLS6NiIiIjIsGkcolavXt3k+JtvvtnoLbH7qa2tRU5ODpYsWSKNyeVyKBQKZGZmNrlOZmYmIiMj1cb8/f2RmpoKACgoKIBSqYRCoZCWW1tbw9vbG5mZmQgKCkJmZiZsbGykAAUACoUCcrkcWVlZmDBhAj777DP07t0b+/fvR0BAAIQQUCgUSEhIQKdOne55TDU1NaipqZGeV1ZWNvv10NTq9IuNxuaP7ddi+yMiIiJ1OpsT9fzzz2PLli3Nri8tLUVDQwNsbW3Vxm1tbaFUKptcR6lU3rf+7s+/q/nrW4XGxsbo1KmTVPPzzz/jypUrSElJwfbt27F161bk5OTgn//8532PKT4+HtbW1tLDwcHhvvVERETUduksRGVmZkpzjto6lUqFmpoabN++HcOGDcPIkSPx3//+F4cOHcIPP/xwz/WWLFmCiooK6XH16tUH2DURERE9SBq/nffcc8+pPRdC4Pr168jOzsayZcuavZ0uXbrAyMgIxcXFauPFxcWws7Nrch07O7v71t/9WVxcjO7du6vV3J3LZWdnh5KSErVt1NfXo6ysTFq/e/fuMDY2Rr9+f7w95uLiAgAoLCyU5kn9lampKUxNTe973ERERGQYNL4S9ee3q6ytrdGpUyeMHDkSBw4cQExMTLO3Y2JigiFDhiAjI0MaU6lUyMjIgI+PT5Pr+Pj4qNUDQHp6ulTv5OQEOzs7tZrKykpkZWVJNT4+PigvL0dOTo5Uc/DgQahUKnh7ewMAnnjiCdTX1+Onn36Sau7eWLRXr17NPkYiIiIyXBpfifrwww91tvPIyEjMmDEDXl5eGDp0KBITE1FdXS19Wi8kJAQ9evRAfHw8gDu3HRgxYgRWrlyJcePGYffu3cjOzsamTZsA3Llj+rx58xAXFwdnZ2fpFgf29vYIDAwEcOeKUkBAAGbOnImkpCTU1dUhIiICQUFBsLe3B3BnormnpydefPFFJCYmQqVSYfbs2Rg7dqza1SkiIiJ6eGkcoq5evQqZTIaePXsCAE6ePInk5GS4uroiPDxco21NmTIFN27cQHR0NJRKJTw8PJCWliZNDC8sLIRc/sfFMl9fXyQnJyMqKgpLly6Fs7MzUlNTpXtEAcCiRYtQXV2N8PBwlJeXw8/PD2lpaWrztXbt2oWIiAiMGTMGcrkcEydOxNq1a6Xlcrkcn332GV599VUMHz4clpaWeOqpp7By5UpNXy4iIiIyUBqHqKlTpyI8PBzTp0+XbicwcOBA7Nq1C0qlEtHR0RptLyIiAhEREU0uO3z4cKOxSZMmYdKkSffcnkwmQ2xsLGJjY+9Z06lTJ7UbazbF3t4e+/btu28NERERPbw0nhN17tw5DB06FADw8ccfw83NDSdOnMCuXbuwdetWXfdHRERE1CppHKLq6uqkT6B9/fXXePbZZwEA/fv3x/Xr13XbHREREVErpXGIGjBgAJKSkvDNN98gPT0dAQEBAICioiJ07txZ5w0SERERtUYah6h33nkH77//PkaOHIng4GAMGjQIAPDpp59Kb/MRERERGTqNJ5aPHDkSpaWlqKysRMeOHaXx8PBwWFhY6LQ5IiIiotZK4xAFAEZGRmoBCgAcHR110Q8RERFRm6BViNq7dy8+/vhjFBYWora2Vm1Zbm6uThojIsO2Ov1io7H5Y7W7me1ft6XtdoiINKHxnKi1a9ciNDQUtra2yMvLw9ChQ9G5c2f8/PPPeOqpp1qiRyIiIqJWR+MQ9Z///AebNm3CunXrYGJigkWLFiE9PR1z5sxBRUVFS/RIRERE1OpoHKIKCwvh6+sLADA3N8dvv/0GAJg+fTo++ugj3XZHRERE1EppHKLs7OxQVlYGAHjkkUfw7bffAgAKCgoghNBtd0REREStlMYTy0ePHo1PP/0UgwcPRmhoKObPn4+9e/ciOzsbzz33XEv0SER6oMuJ30REhkjjELVp0yaoVCoAwOzZs9G5c2ecOHECzz77LF5++WWdN0ia4T98RERED4bGIUoul0Mu/+NdwKCgIAQFBem0KSIiIqLWTuM5UQDwzTff4Pnnn4ePjw+uXbsGANixYweOHTum0+aIiIiIWiuNQ9S+ffvg7+8Pc3Nz5OXloaamBgBQUVGBt956S+cNEhEREbVGGoeouLg4JCUl4YMPPkC7du2k8SeeeIJ3KyciIqKHhsYh6ocffsDw4cMbjVtbW6O8vFwXPRERERG1elrdJ+rHH39sNH7s2DH07t1bJ00RERERtXYah6iZM2di7ty5yMrKgkwmQ1FREXbt2oUFCxZg1qxZLdEjERERUauj8S0OFi9eDJVKhTFjxuDWrVsYPnw4TE1NsWDBArz66qst0SMRERFRq6NxiJLJZHjjjTewcOFC/Pjjj6iqqoKrqyvat2/fEv0RERERtUoah6i7TExM4OrqqsteiIiIiNqMZoeoF198sVl1W7Zs0boZIm3wq26IiEgfmh2itm7dil69emHw4MEQQrRkT0REREStXrND1KxZs/DRRx+hoKAAoaGheP7559GpU6eW7I2IiIio1Wr2LQ42bNiA69evY9GiRfjss8/g4OCAyZMn48svv+SVKSIiInroaHSfKFNTUwQHByM9PR3nz5/HgAED8Morr8DR0RFVVVUt1SMRERFRq6PxzTalFeVyyGQyCCHQ0NCgy56IiIiIWj2NQlRNTQ0++ugjjB07Fv369cPZs2exfv16FBYW8j5RRERE9FBp9sTyV155Bbt374aDgwNefPFFfPTRR+jSpUtL9kbUavA2CkRE9FfNDlFJSUl45JFH0Lt3bxw5cgRHjhxpsu6TTz7RWXNERERErVWzQ1RISAhkMllL9kJERETUZmh0s00iIiIiukPrT+cRERERPcwYooiIiIi00CpC1IYNG+Do6AgzMzN4e3vj5MmT961PSUlB//79YWZmBjc3Nxw4cEBtuRAC0dHR6N69O8zNzaFQKHDp0iW1mrKyMkybNg1WVlawsbFBWFjYPW8Y+uOPP6JDhw6wsbH5n46T6F5Wp19s9CAiotZN7yFqz549iIyMRExMDHJzczFo0CD4+/ujpKSkyfoTJ04gODgYYWFhyMvLQ2BgIAIDA3Hu3DmpJiEhAWvXrkVSUhKysrJgaWkJf39/3L59W6qZNm0a8vPzkZ6ejv379+Po0aMIDw9vtL+6ujoEBwdj2LBhuj94IiIiarP0HqJWrVqFmTNnIjQ0FK6urkhKSoKFhQW2bNnSZP2aNWsQEBCAhQsXwsXFBcuXL4enpyfWr18P4M5VqMTERERFRWH8+PFwd3fH9u3bUVRUhNTUVADAhQsXkJaWhs2bN8Pb2xt+fn5Yt24ddu/ejaKiIrX9RUVFoX///pg8eXKLvg5ERETUtug1RNXW1iInJwcKhUIak8vlUCgUyMzMbHKdzMxMtXoA8Pf3l+oLCgqgVCrVaqytreHt7S3VZGZmwsbGBl5eXlKNQqGAXC5HVlaWNHbw4EGkpKRgw4YNzTqempoaVFZWqj2IiIjIMOk1RJWWlqKhoQG2trZq47a2tlAqlU2uo1Qq71t/9+ff1XTr1k1tubGxMTp16iTV3Lx5Ey+88AK2bt0KKyurZh1PfHw8rK2tpYeDg0Oz1iMiIqK2R+9v57VWM2fOxNSpUzF8+PBmr7NkyRJUVFRIj6tXr7Zgh0RERKRPeg1RXbp0gZGREYqLi9XGi4uLYWdn1+Q6dnZ2962/+/Pvav46cb2+vh5lZWVSzcGDB/Hee+/B2NgYxsbGCAsLQ0VFBYyNje85X8vU1BRWVlZqDyIiIjJMeg1RJiYmGDJkCDIyMqQxlUqFjIwM+Pj4NLmOj4+PWj0ApKenS/VOTk6ws7NTq6msrERWVpZU4+Pjg/LycuTk5Eg1Bw8ehEqlgre3N4A786ZOnTolPWJjY9GhQwecOnUKEyZM0M0LQERERG1Ws7/2paVERkZixowZ8PLywtChQ5GYmIjq6mqEhoYCuPOdfT169EB8fDwAYO7cuRgxYgRWrlyJcePGYffu3cjOzsamTZsAADKZDPPmzUNcXBycnZ3h5OSEZcuWwd7eHoGBgQAAFxcXBAQEYObMmUhKSkJdXR0iIiIQFBQEe3t7qebPsrOzIZfLMXDgwAf0yhAREVFrpvcQNWXKFNy4cQPR0dFQKpXw8PBAWlqaNDG8sLAQcvkfF8x8fX2RnJyMqKgoLF26FM7OzkhNTVULN4sWLUJ1dTXCw8NRXl4OPz8/pKWlwczMTKrZtWsXIiIiMGbMGMjlckycOBFr1659cAdOREREbZreQxQAREREICIiosllhw8fbjQ2adIkTJo06Z7bk8lkiI2NRWxs7D1rOnXqhOTk5Gb3+MILL+CFF15odj0REREZNn46j4iIiEgLDFFEREREWmCIIiIiItICQxQRERGRFhiiiIiIiLTAEEVERESkBYYoIiIiIi0wRBERERFpgSGKiIiISAsMUURERERaYIgiIiIi0gJDFBEREZEWGKKIiIiItMAQRURERKQFhigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLxvpugIjoz1anX2w0Nn9sPz10QkR0f7wSRURERKQFhigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiIttIoQtWHDBjg6OsLMzAze3t44efLkfetTUlLQv39/mJmZwc3NDQcOHFBbLoRAdHQ0unfvDnNzcygUCly6dEmtpqysDNOmTYOVlRVsbGwQFhaGqqoqafnhw4cxfvx4dO/eHZaWlvDw8MCuXbt0d9BERETUpuk9RO3ZsweRkZGIiYlBbm4uBg0aBH9/f5SUlDRZf+LECQQHByMsLAx5eXkIDAxEYGAgzp07J9UkJCRg7dq1SEpKQlZWFiwtLeHv74/bt29LNdOmTUN+fj7S09Oxf/9+HD16FOHh4Wr7cXd3x759+3DmzBmEhoYiJCQE+/fvb7kXg4h0anX6xUYPIiJd0XuIWrVqFWbOnInQ0FC4uroiKSkJFhYW2LJlS5P1a9asQUBAABYuXAgXFxcsX74cnp6eWL9+PYA7V6ESExMRFRWF8ePHw93dHdu3b0dRURFSU1MBABcuXEBaWho2b94Mb29v+Pn5Yd26ddi9ezeKiooAAEuXLsXy5cvh6+uLPn36YO7cuQgICMAnn3zyQF4XIiIiat30GqJqa2uRk5MDhUIhjcnlcigUCmRmZja5TmZmplo9APj7+0v1BQUFUCqVajXW1tbw9vaWajIzM2FjYwMvLy+pRqFQQC6XIysr6579VlRUoFOnTvdcXlNTg8rKSrUHERERGSa9hqjS0lI0NDTA1tZWbdzW1hZKpbLJdZRK5X3r7/78u5pu3bqpLTc2NkanTp3uud+PP/4Y3333HUJDQ+95PPHx8bC2tpYeDg4O96wlIiKitk3vb+e1BYcOHUJoaCg++OADDBgw4J51S5YsQUVFhfS4evXqA+ySiIiIHiS9hqguXbrAyMgIxcXFauPFxcWws7Nrch07O7v71t/9+Xc1f524Xl9fj7Kyskb7PXLkCP7xj39g9erVCAkJue/xmJqawsrKSu1BREREhkmvIcrExARDhgxBRkaGNKZSqZCRkQEfH58m1/Hx8VGrB4D09HSp3snJCXZ2dmo1lZWVyMrKkmp8fHxQXl6OnJwcqebgwYNQqVTw9vaWxg4fPoxx48bhnXfeUfvkHhEREZGxvhuIjIzEjBkz4OXlhaFDhyIxMRHV1dXS3KOQkBD06NED8fHxAIC5c+dixIgRWLlyJcaNG4fdu3cjOzsbmzZtAgDIZDLMmzcPcXFxcHZ2hpOTE5YtWwZ7e3sEBgYCAFxcXBAQEICZM2ciKSkJdXV1iIiIQFBQEOzt7QHceQvvmWeewdy5czFx4kRprpSJicl9J5cTERHRw0HvIWrKlCm4ceMGoqOjoVQq4eHhgbS0NGlieGFhIeTyPy6Y+fr6Ijk5GVFRUVi6dCmcnZ2RmpqKgQMHSjWLFi1CdXU1wsPDUV5eDj8/P6SlpcHMzEyq2bVrFyIiIjBmzBjI5XJMnDgRa9eulZZv27YNt27dQnx8vBTgAGDEiBE4fPhwC74iRERE1BboPUQBQEREBCIiIppc1lRgmTRpEiZNmnTP7clkMsTGxiI2NvaeNZ06dUJycvI9l2/duhVbt26953IiIiJ6uPHTeURERERaYIgiIiIi0kKreDuP2pamvn9s/th+euiEiIhIfxiiiIioEf7PEtHf49t5RERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgLDFFEREREWmCIIiIiItICQxQRERGRFvjdeUREf4PfI0dETeGVKCIiIiItMEQRERERaYEhioiIiEgLDFFEREREWmCIIiIiItICP51H1AL4aS5qK/i7SqQ9XokiIiIi0gJDFBEREZEWGKKIiIiItMAQRURERKQFhigiIiIiLfDTeUREDwF+Co9I93glioiIiEgLDFFEREREWmCIIiIiItICQxQRERGRFhiiiIiIiLTAT+eR3vDTQkSGj3/PyZDxShQRERGRFhiiiIiIiLTQKkLUhg0b4OjoCDMzM3h7e+PkyZP3rU9JSUH//v1hZmYGNzc3HDhwQG25EALR0dHo3r07zM3NoVAocOnSJbWasrIyTJs2DVZWVrCxsUFYWBiqqqrUas6cOYNhw4bBzMwMDg4OSEhI0M0BExERUZun9xC1Z88eREZGIiYmBrm5uRg0aBD8/f1RUlLSZP2JEycQHByMsLAw5OXlITAwEIGBgTh37pxUk5CQgLVr1yIpKQlZWVmwtLSEv78/bt++LdVMmzYN+fn5SE9Px/79+3H06FGEh4dLyysrK/Hkk0+iV69eyMnJwbvvvos333wTmzZtarkXg4iIiNoMvYeoVatWYebMmQgNDYWrqyuSkpJgYWGBLVu2NFm/Zs0aBAQEYOHChXBxccHy5cvh6emJ9evXA7hzFSoxMRFRUVEYP3483N3dsX37dhQVFSE1NRUAcOHCBaSlpWHz5s3w9vaGn58f1q1bh927d6OoqAgAsGvXLtTW1mLLli0YMGAAgoKCMGfOHKxateqBvC5ERETUuun103m1tbXIycnBkiVLpDG5XA6FQoHMzMwm18nMzERkZKTamL+/vxSQCgoKoFQqoVAopOXW1tbw9vZGZmYmgoKCkJmZCRsbG3h5eUk1CoUCcrkcWVlZmDBhAjIzMzF8+HCYmJio7eedd97Br7/+io4dOzbqraamBjU1NdLziooKAHeuauna7eqqRmOVlZUaj+ty37rajqbb1+Ux/3Wdu/Ut3ZMuz4+utHSvunyNdHXeNO21Ndpw8MdGY7NH99Xb72Rbeu2I7rr7OyqEuH+h0KNr164JAOLEiRNq4wsXLhRDhw5tcp127dqJ5ORktbENGzaIbt26CSGEOH78uAAgioqK1GomTZokJk+eLIQQYsWKFaJfv36Ntt21a1fxn//8RwghxNixY0V4eLja8vz8fAFAnD9/vsneYmJiBAA++OCDDz744MMAHlevXr1XhBFCCMH7ROnQkiVL1K6SqVQqlJWVoXPnzpDJZHrs7O9VVlbCwcEBV69ehZWVlb7beSB4zDxmQ8Vj5jEbsgdx3EII/Pbbb7C3t79vnV5DVJcuXWBkZITi4mK18eLiYtjZ2TW5jp2d3X3r7/4sLi5G9+7d1Wo8PDykmr9OXK+vr0dZWZnadpraz5/38VempqYwNTVVG7OxsWmytrWysrJ6qP4yAjzmhwWP+eHAY354tPRxW1tb/22NXieWm5iYYMiQIcjIyJDGVCoVMjIy4OPj0+Q6Pj4+avUAkJ6eLtU7OTnBzs5OraayshJZWVlSjY+PD8rLy5GTkyPVHDx4ECqVCt7e3lLN0aNHUVdXp7afRx99tMn5UERERPRw0fun8yIjI/HBBx9g27ZtuHDhAmbNmoXq6mqEhoYCAEJCQtQmns+dOxdpaWlYuXIlvv/+e7z55pvIzs5GREQEAEAmk2HevHmIi4vDp59+irNnzyIkJAT29vYIDAwEALi4uCAgIAAzZ87EyZMncfz4cURERCAoKEi6dDd16lSYmJggLCwM+fn52LNnD9asWdNoUjsRERE9pO47Y+oBWbdunXjkkUeEiYmJGDp0qPj222+lZSNGjBAzZsxQq//4449Fv379hImJiRgwYID4/PPP1ZarVCqxbNkyYWtrK0xNTcWYMWPEDz/8oFZz8+ZNERwcLNq3by+srKxEaGio+O2339RqTp8+Lfz8/ISpqano0aOHePvtt3V74K3I7du3RUxMjLh9+7a+W3lgeMwPBx7zw4HH/PBoTcctE+LvPr9HRERERH+l97fziIiIiNoihigiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkPUQ4J3sng48DwTET04/AJiA3X9+nVcvXoVv/76KxQKBYyMjPTdkt4IIVr9F0Bri+f5D4Z8ngHg6tWruHDhAkpKSjBu3DhYWlrCxMRE3209cDzPhqctHzNvtmmAzpw5g2effRampqbSFzFHR0fD398fnTp10nd7LebHH3/E3r17UVFRAXd3d/zjH/9A+/btARjmf3h5nh+O8wzcOdf+/v7o2rUrrly5AhsbG4SHh2PGjBno2bOnvttrETzPD8d5bvPHrJ8bpVNLKSkpEf379xdLly4VP/30k7h27ZqYMmWKcHFxETExMaKkpETfLbaIc+fOCRsbGzFixAgxfPhwYWxsLCZOnCjS0tKkGpVKpccOdYvn+eE4z0IIUVZWJjw9PcWiRYtEcXGxaGhoEK+99prw9vYWISEh4vLly/puUed4nh+O82wIx8wQZWDy8/OFo6OjyM7OVht//fXXhZubm0hISBDV1dV66q5l3Lp1SzzzzDNi9uzZ0lhOTo7w8vISCoVCfPLJJ3rsrmXwPN9h6OdZCCGuXLkievXqJb7++mu18XXr1gkfHx/xyiuviBs3buipO93jeX44zrMQhnHMnFhuYOrq6lBfX49bt24BAH7//XcAwNtvv41Ro0Zh48aN+PHHHwEYziRkc3NzlJWVoUuXLgAAlUoFT09P7NixA/X19di0aRNOnz6t5y51q6amhuf5ITjPACCXy2FhYYGioiIAQH19PQAgIiICzz33HA4dOoTjx48DMIxzbW5ujps3b/I8G/h5BgCZTAZzc/O2fcx6jXDUIh577DExatQo6fmfv+nay8tLBAUF6aOtFvPbb7+JUaNGiX/9619CCCHq6+tFXV2dEOLOFZuePXuKuXPn6rFD3SgqKhL5+fnScy8vr4fiPDc0NAghhKisrBSjRo0Ss2bNEkIY7nkWQojq6mpRU1MjPX/22WfF4MGDRXl5uRBCSMcthBBPPfWU2u9BW3X16lXx3Xffifr6+ofmPP/VM888Y/DnuaGhQfo7LYQQkyZNEm5ubm32mBmi2riqqipRWVkpKioqpLHc3FzRrVs3ERwcLI3d/cWMjIwU//jHPx54n7p28+ZNceHCBfHDDz8IIYT47LPPhEwmE/v27RNC3PmLWltbK4QQIjk5WXTs2FFcuXJFb/3+r3755RfRuXNnMWHCBJGZmSmEECIvL0906dLFoM9zXl6eeOaZZ0RVVZUQQoiUlBSDPs9CCHH27Fkxbtw4ceTIEem4b9y4IZycnMTYsWPVwpUQQiQmJophw4aJ+vp6fbSrE+fOnRMODg5i/vz5QgghPvroI4M/z1evXhV79uwR+/btE7m5uUIIwz/P+fn5Yvr06WLUqFEiNDRUHDhwQJSUlIhBgwaJUaNGtclj5tt5bdj58+fx3HPPYcSIEXBxccGuXbsAAC4uLlizZg3S09MxadIk1NXVQS6/c6pLSkpgaWmJ+vr61nt59G+cO3cOCoUCkydPxsCBAxEbG4uxY8ciIiICU6dOxf79+yGXy9GuXTsAgI2NDezs7GBpaannzrV36dIlVFRUoKKiAhs3bkReXh48PDywfv16pKWlYcKECQZ3nk+fPg1fX18MGDBAOneBgYGYPXs2pk6dis8++8zgznN+fj6GDRuGnj17wsnJSTqWLl26IDk5Gfn5+XjyySdx6dIl3L59GwBw9uxZdOjQAQ0NDfpsXWunT5/G0KFDYWxsjOTkZCiVSgQFBUl/nz///HODO89nz56Fn58f3n33XbzyyiuIiYnBxYsXpfN84cIFgzvP33//Pfz8/GBiYoJnnnkGRUVFiIiIwIoVK/Cf//wHJSUlGD16dNs7Zn2nONJOfn6+6Ny5s5g/f77YtWuXiIyMFO3atZP+j6a6ulp8+umnomfPnqJ///4iMDBQTJ48WVhaWoqzZ8/quXvt3T3uBQsWiPz8fPHee+8JmUwmrl27Jq5duyZmzpwp2rVrJzZu3CiuX78ufv/9d7F48WIxaNAgUVZWpu/2tXbz5k3x7LPPivfff194enqKqVOniosXLwohhEhNTRWurq7i0UcfNZjzfPr0aWFpaSkWLlyoNl5fXy9KS0vF7NmzDe48V1VViSeffFJ6G0sIIS5cuCDy8vLE1atXhRB3rti4uroKZ2dnMXToUDF+/HjRvn17cfr0aX21/T85deqUMDc3F0uXLhU3btwQrq6uIi4uTgghxM8//yzCw8NFu3btxPvvv28w5/ny5cuiR48eYvHixaKqqkocOHBA2NnZiaysLKnG0M7z7du3xbRp08ScOXOksd9//114eHgImUwmgoODxZkzZ4S3t7fo3bt3mzpm3ieqDSorK0NwcDD69++PNWvWSOOjRo2Cm5sb1q5dK4399ttviIuLQ1lZGczMzDBr1iy4urrqo+3/WWlpKSZOnIjBgwcjMTERwJ3Jhk899RT+/e9/w8LCArdv30Z2djbmzZuHHj16oEOHDrh+/Tq+/PJLDB48WL8HoKWGhgaUlZXBz88PBw8exMmTJxEfHw93d3f8+OOPsLW1xebNmxEbG4vy8vI2f56VSiUGDx6MQYMGIS0tDQ0NDViwYAF++OEHXLlyBbNmzcLAgQNx9uxZLFiwwGDOc01NDRQKBdauXQt3d3eMGzcOZWVluHDhAgYMGICZM2ciLCwMALBu3ToUFRXB1NQUwcHBePTRR/XcvebOnDmDoUOH4rXXXsOKFSugUqkwZcoUFBQUIDs7G8Cdm8lu3boVb775Jnr27In27du3+fO8adMmfPTRRzh48KB0r6tx48Zh/PjxMDU1Ra9evTBy5EgAhnGe71IoFBg2bBhiYmJw+/ZtmJmZ4fXXX8ePP/6IK1eu4MUXX8Qrr7yC9evX49q1a23nmPUc4kgLSqVSDB06VBw9elQI8cfE29DQUDFt2jQhxJ17qPx58t6f69qq0tJS8dZbb0lXYIQQIjY2VshkMuHu7i4eeeQRERAQIM6fPy++//57sWfPHrF79+42ca+R+7l7P5xp06ZJ98n5/PPPRZcuXUT79u3F5s2b1erb+nm+fv26mDBhgvDy8hKpqakiICBAjBkzRrz22mvilVdeEX369BEvvfSSqKqqEqdPnzaY86xUKkXXrl3FV199JebPny/8/f3F6dOnxRdffCEWLlwo7OzsRHJysr7b1JmTJ0+KZcuWCSH++J39/vvvhbW1tVi/fr1arSGd56SkJNG7d2/pXYO4uDghk8mEQqEQXl5eolu3bmLTpk167lJ3VCqVqK6uFsOGDRPTp0+X5m3+8ssvolevXmLLli3i+eefF8OGDdNzp9phiGqj/hwk7k64jIqKEtOnT1er+/OEc0O4OV1lZaX057uTT/fs2SNu3rwpDh8+LLy8vER0dLQeO2w5ISEhYvHixUIIIcLCwkTHjh2Fq6urePHFF6XJ5kIYxnkuKioSISEhwtzcXIwdO1aUlpZKy3bu3Cmsra3FZ599pscOdU+lUomgoCAREREhnnnmGbUbS169elU8//zz4l//+peoq6uTQochnOu7VCqVKC8vl96Svnucbf1/Cv7q559/Fr6+vqJv375i4sSJQiaTidTUVKFSqURxcbGYM2eOGDlypLhx44ZBnedjx44JuVwuhg8fLqZPny4sLS3FSy+9JIS482GKDh06iAsXLkiTyNvKMXNieRvl7OwM4M49VO5OuBRCoKSkRKqJj4/H5s2bpXtvGMLXJHTo0EH6s4+PD7KzszF58mR06tQJI0aMgK2tLXJzc/XYoe6J//8d99GjR8PU1BSvvPIKDhw4gJycHMTFxeHIkSPYtm0bampqABjGee7evTvi4+Mxb948LF68GJ07d5Zeh2nTpqFr1644cuSInrvULZlMhtdeew0ffvghPv/8c9TW1krLevbsCVtbW5w/fx5GRkbSBwgM4VzfJZPJYG1tjenTpyMlJQVZWVmQy+XSsRoKJycn7Ny5EytWrMDAgQMxceJEjB8/HjKZDN26dYO9vT1+/fVXtG/f3qDO8xNPPIFvv/0WjzzyCExNTZGQkIAPPvgAAPDzzz+jZ8+e6N69u/T9n23lmPkFxG2cXC5X+x6pu3/poqOjERcXh7y8PBgbG+Zp7tWrF3r16gXgTpisra1F+/bt4e7urufOdOvuuXVyckJoaChsbW2xf/9+ODk5wcnJCTKZDIMGDYKpqameO9Ute3t7LF68GGZmZgDuvA5CCJSVlaFr165tdk7M/Xh5eeGLL77AiBEjsGnTJvTu3RsDBgwAcOdGuv369UN9fb30P06G6JlnnsHYsWOxceNGeHp6wtzcXN8t6dzdv7ubN29GdnY2amtrpS/cLS4uhqOjY+v+RJqWHnvsMWzfvr1RQPrmm29ga2vbZoLTnxnmv64PmbshytjYGA4ODnjvvfeQkJCA7OxsDBo0SN/tPRByuRxvvfUWMjMzsXz5cn230yJ8fHywefNmeHl5wd3dXTrvgYGB+m6txVhZWak9l8lkWLt2LUpLS/HEE0/oqauWNWzYMBw+fBjBwcF48cUX4ebmhtraWnz66ac4duyYQQcoADAxMcGoUaMQHx+PiooKgwxRd/n6+mLBggVYs2YN7OzscO7cOXz44Yc4evRom76Fw/38OSidPXsWSUlJ2LlzJ44ePdro73tbwBBlAO5efWrXrh0++OADWFlZ4dixY/D09NRzZw9GSkoKjhw5gt27dyM9PV16q9PQtGvXDi+88IJBXeLXxO7du3Ho0CGkpKQgIyNDugppiIYPH46DBw9i586d+Pbbb+Hs7Ixjx45h4MCB+m6tRd39H4OXX34Ze/fule4XZKhcXV3x//7f/8PMmTMhl8vRo0cPHDlyBG5ubvpurcXV1NTgxx9/RFlZGb755ps2+w4Cb3FgQLKzszF06FCcO3euzX68XRv5+fmIjY3Fm2++CRcXF323Qy3kzJkzWLp0Kd555x3pLa6HgUqlAgCDmxt0P0II3Lp1y2CvxvxVWVkZ6urqYGpqChsbG32388Dc/Q7QtnyeGaIMTHV1dZv+hdRWXV2dwb/NQVCbO0JEpG8MUURERERaeHiuDxMRERHpEEMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhiohITxwdHZGYmKjvNohISwxRRKQXN27cwKxZs6Rvdbezs4O/vz+OHz+u79YYboioWfjdeUSkFxMnTkRtbS22bduG3r17o7i4GBkZGbh582aL7ZN3PCciXeKVKCJ64MrLy/HNN9/gnXfewahRo9CrVy8MHToUS5YswbPPPqtW99JLL6Fr166wsrLC6NGjcfr0abVtffbZZ3jsscdgZmaGLl26YMKECdIyR0dHLF++HCEhIbCyskJ4eDgA4NixYxg2bBjMzc3h4OCAOXPmoLq6GgAwcuRIXLlyBfPnz4dMJrvvFz2Xl5fj5Zdfhq2tLczMzDBw4EDs379fWr5v3z4MGDAApqamcHR0xMqVK++5rcuXL0Mmk+HUqVNq25fJZDh8+DAA4PDhw5DJZPjyyy8xePBgmJubY/To0SgpKcEXX3wBFxcXWFlZYerUqbh165a0nZEjR2LOnDlYtGgROnXqBDs7O7z55pv3PkFE1CwMUUT0wLVv3x7t27dHamoqampq7lk3adIkKSDk5OTA09MTY8aMQVlZGQDg888/x4QJE/D0008jLy8PGRkZGDp0qNo23nvvPQwaNAh5eXlYtmwZfvrpJwQEBGDixIk4c+YM9uzZg2PHjiEiIgIA8Mknn6Bnz56IjY3F9evXcf369SZ7U6lUeOqpp3D8+HHs3LkT58+fx9tvvw0jIyMAQE5ODiZPnoygoCCcPXsWb775JpYtW4atW7f+z6/fm2++ifXr1+PEiRO4evUqJk+ejMTERCQnJ+Pzzz/HV199hXXr1qmts23bNlhaWiIrKwsJCQmIjY1Fenr6/9wL0UNNEBHpwd69e0XHjh2FmZmZ8PX1FUuWLBGnT5+Wln/zzTfCyspK3L59W229Pn36iPfff18IIYSPj4+YNm3aPffRq1cvERgYqDYWFhYmwsPD1ca++eYbIZfLxe+//y6tt3r16vv2/+WXXwq5XC5++OGHJpdPnTpVjB07Vm1s4cKFwtXVVa2/u/spKCgQAEReXp60/NdffxUAxKFDh4QQQhw6dEgAEF9//bVUEx8fLwCIn376SRp7+eWXhb+/v/R8xIgRws/PT62Xxx57TLz++uv3PUYiuj9eiSIivZg4cSKKiorw6aefIiAgAIcPH4anp6d0peb06dOoqqpC586dpStX7du3R0FBAX766ScAwKlTpzBmzJj77sfLy0vt+enTp7F161a1bfr7+0OlUqGgoKDZ/Z86dQo9e/ZEv379mlx+4cIFPPHEE2pjTzzxBC5duoSGhoZm76cp7u7u0p9tbW1hYWGB3r17q42VlJTccx0A6N69e6MaItIMJ5YTkd6YmZlh7NixGDt2LJYtW4aXXnoJMTExeOGFF1BVVYXu3btL84H+zMbGBgBgbm7+t/uwtLRUe15VVYWXX34Zc+bMaVT7yCOPNLv35uxbE3L5nf+nFUJIY3V1dU3WtmvXTvqzTCZTe353TKVS3XOde9UQkWYYooio1XB1dUVqaioAwNPTE0qlEsbGxnB0dGyy3t3dHRkZGQgNDW32Pjw9PXH+/Hn07dv3njUmJiZ/e7XI3d0dv/zyCy5evNjk1SgXF5dGt2s4fvw4+vXrJ82b+rOuXbsCAK5fv47BgwcDgNokcyJqffh2HhE9cDdv3sTo0aOxc+dOnDlzBgUFBUhJSUFCQgLGjx8PAFAoFPDx8UFgYCC++uorXL58GSdOnMAbb7yB7OxsAEBMTAw++ugjxMTE4MKFCzh79izeeeed++779ddfx4kTJxAREYFTp07h0qVL+L//+z9pYjlw51N9R48exbVr11BaWtrkdkaMGIHhw4dj4sSJSE9PR0FBAb744gukpaUBAF577TVkZGRg+fLluHjxIrZt24b169djwYIFTW7P3Nwcjz/+ON5++21cuHABR44cQVRUlMavLRE9OAxRRPTAtW/fHt7e3li9ejWGDx+OgQMHYtmyZZg5cybWr18P4M7bTQcOHMDw4cMRGhqKfv36ISgoCFeuXIGtrS2AOx/dT0lJwaeffgoPDw+MHj0aJ0+evO++3d3dceTIEVy8eBHDhg3D4MGDER0dDXt7e6kmNjYWly9fRp8+faQrRE3Zt28fHnvsMQQHB8PV1RWLFi2SrmB5enri448/xu7duzFw4EBER0cjNjYWL7zwwj23t2XLFtTX12PIkCGYN28e4uLimvuSEpEeyMSf34AnIiIiombhlSgiIiIiLTBEEREREWmBIYqIiIhICwxRRERERFpgiCIiIiLSAkMUERERkRYYooiIiIi0wBBFREREpAWGKCIiIiItMEQRERERaYEhioiIiEgL/x9jLsU1HaMEiwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -804,7 +802,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -814,7 +812,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -824,7 +822,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -834,7 +832,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -862,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "c790d894-e3d9-4cee-bd08-f4ef5f364d4e", "metadata": { "ExecuteTime": { @@ -873,7 +871,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -895,6 +893,14 @@ "ax.set_ylabel(\"Measured inference risk\")\n", "_ = ax.set_xlabel(\"Secret column\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79503a44-6fc3-4ba4-a56d-97cb8994d176", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -913,7 +919,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.12" + "version": "3.9.22" }, "vscode": { "interpreter": { diff --git a/src/anonymeter/evaluators/__init__.py b/src/anonymeter/evaluators/__init__.py index 26b2848..d31cb0a 100644 --- a/src/anonymeter/evaluators/__init__.py +++ b/src/anonymeter/evaluators/__init__.py @@ -5,5 +5,6 @@ from anonymeter.evaluators.inference_evaluator import InferenceEvaluator from anonymeter.evaluators.linkability_evaluator import LinkabilityEvaluator from anonymeter.evaluators.singling_out_evaluator import SinglingOutEvaluator +from anonymeter.evaluators.inference_predictor import KNNInference, MLModelInference -__all__ = ["InferenceEvaluator", "LinkabilityEvaluator", "SinglingOutEvaluator"] +__all__ = ["InferenceEvaluator", "LinkabilityEvaluator", "SinglingOutEvaluator", "KNNInference", "MLModelInference"] diff --git a/src/anonymeter/evaluators/inference_evaluator.py b/src/anonymeter/evaluators/inference_evaluator.py index 865e8a6..341a40c 100644 --- a/src/anonymeter/evaluators/inference_evaluator.py +++ b/src/anonymeter/evaluators/inference_evaluator.py @@ -2,55 +2,48 @@ # Copyright (c) 2022 Anonos IP LLC. # See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. """Privacy evaluator that measures the inference risk.""" - -from typing import Optional, Union, Any +from typing import Optional, Union import numpy as np import numpy.typing as npt import pandas as pd from pandas import DataFrame -from anonymeter.neighbors.mixed_types_kneighbors import MixedTypeKNeighbors from anonymeter.stats.confidence import EvaluationResults, PrivacyRisk +from anonymeter.evaluators.inference_predictor import InferencePredictor, KNNInference + def _run_attack( - target: pd.DataFrame, - syn: pd.DataFrame, - n_attacks: int, - aux_cols: list[str], - secret: str, - n_jobs: int, - naive: bool, - regression: Optional[bool], - ml_model: Optional[Any], - sample_attacks: bool, + target: pd.DataFrame, + syn: pd.DataFrame, + n_attacks: int, + aux_cols: list[str], + secret: str, + n_jobs: int, + naive: bool, + regression: Optional[bool], + inference_model: Optional[InferencePredictor], ) -> tuple[int, Union[tuple[npt.NDArray, npt.NDArray], npt.NDArray, None], DataFrame]: if regression is None: regression = pd.api.types.is_numeric_dtype(target[secret]) - # We only sample if `ml_model` is set to None, i.e MixedTypeKNeighbors is used, - # or `sample_attacks` is set to True by default. - if ml_model is None or sample_attacks: - targets = target.sample(n_attacks, replace=False) - else: - # When an `ml_model` is passed we don't want to sample, but rather predict for all targets - # as we assume it can scale to all target samples, better rather than using MixedTypeKNeighbors - targets = target - if naive: guesses = syn.sample(n_attacks)[secret] - targets = target.sample(n_attacks, replace=False) # we need this after dropping nans in the synthetic data + targets = target.sample(n_attacks, replace=False) else: - if ml_model is not None: - guesses = ml_model.predict(targets) + # Instantiate the default KNN model if no other model is passed through `inference_model`. + if inference_model is None: + inference_model = KNNInference(data=syn, columns=aux_cols, target_col=secret, n_jobs=n_jobs) + + # Depending on the `inference_model.sample_targets` we might not need to sample, + # but rather predict for all targets as we assume it can scale to all target samples. + if inference_model.sample_targets: + targets = target.sample(n_attacks, replace=False) else: - nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=syn[aux_cols]) - guesses_idx = nn.kneighbors(queries=targets[aux_cols]) - if isinstance(guesses_idx, tuple): - raise RuntimeError("guesses_idx cannot be a tuple") + targets = target - guesses = syn.iloc[guesses_idx.flatten()][secret] + guesses = inference_model.predict(targets) assert len(guesses) == len(targets), "Predictions and targets have different lengths" guesses.index = targets.index @@ -60,7 +53,7 @@ def _run_attack( def evaluate_inference_guesses( - guesses: pd.Series, secrets: pd.Series, regression: bool, tolerance: float = 0.05 + guesses: pd.Series, secrets: pd.Series, regression: bool, tolerance: float = 0.05 ) -> npt.NDArray: """Evaluate the success of an inference attack. @@ -158,35 +151,29 @@ class InferenceEvaluator: the variable. n_attacks : int, default is 500 Number of attack attempts. - ml_model: Any + inference_model: InferencePredictor An ml model fitted on `syn` as training data, and `secret` as target, that supports ::predict(x). If not None, it will be used over the MixedTypeKNeighbors in the attack. - sample_attacks: bool, optional - Specifies whether we should sample `n_attacks` samples from the `ori` or `control` dataset - in the attack phase. When a custom `ml_model` is being passed which can scale to more attacks, - `sample_attacks` can be set to False so that we predict the values for all samples in `ori` and `control`. """ def __init__( - self, - ori: pd.DataFrame, - syn: pd.DataFrame, - aux_cols: list[str], - secret: str, - regression: Optional[bool] = None, - n_attacks: int = 500, - control: Optional[pd.DataFrame] = None, - ml_model: Optional[Any] = None, - sample_attacks: Optional[bool] = True, + self, + ori: pd.DataFrame, + syn: pd.DataFrame, + aux_cols: list[str], + secret: str, + regression: Optional[bool] = None, + n_attacks: int = 500, + control: Optional[pd.DataFrame] = None, + inference_model: Optional[InferencePredictor] = None ): self._ori = ori self._syn = syn self._control = control self._n_attacks = n_attacks - self._ml_model = ml_model - self._sample_attacks = sample_attacks - if not self._sample_attacks: + self._inference_model = inference_model + if self._inference_model is not None and not self._inference_model.sample_targets: self._n_attacks_ori = self._ori.shape[0] self._n_attacks_baseline = min(self._syn.shape[0], self._n_attacks_ori) self._n_attacks_control = self._control.shape[0] @@ -221,8 +208,7 @@ def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int, n_attacks: int n_jobs=n_jobs, naive=naive, regression=self._regression, - ml_model=self._ml_model, - sample_attacks=self._sample_attacks + inference_model=self._inference_model, ) def evaluate(self, n_jobs: int = -2) -> "InferenceEvaluator": @@ -335,9 +321,6 @@ def risk_for_groups(self, confidence_level: float = 0.95) -> dict[ # Get the targets for the current group target = self.target[self.target[self._secret] == group] - assert len(self.guesses_success) == len(self.target) - assert (self.guesses_success.index == self.target.index).all() - # Get the guesses for the current group guess = self.guesses_success.loc[target.index] @@ -350,17 +333,13 @@ def risk_for_groups(self, confidence_level: float = 0.95) -> dict[ # Get the targets for the current control group target_control = self.target_control[self.target_control[self._secret] == group] - - assert len(self.guesses_control) == len(self.target_control) - assert (self.guesses_control.index == self.target_control.index).all() - # Get the guesses for the current control group guesses_control = self.guesses_control.loc[target_control.index] # Count the number of success control attacks n_control = evaluate_inference_guesses(guesses=guesses_control, - secrets=target_control[self._secret], - regression=self._regression).sum() + secrets=target_control[self._secret], + regression=self._regression).sum() else: n_control = None diff --git a/src/anonymeter/evaluators/inference_predictor.py b/src/anonymeter/evaluators/inference_predictor.py new file mode 100644 index 0000000..8b5e62c --- /dev/null +++ b/src/anonymeter/evaluators/inference_predictor.py @@ -0,0 +1,45 @@ +from typing import Protocol + +import pandas as pd + +from anonymeter.neighbors.mixed_types_kneighbors import MixedTypeKNeighbors + + +class InferencePredictor(Protocol): + def predict(self, X: pd.DataFrame) -> pd.Series: + ... + + @property + def sample_targets(self) -> bool: + ... + + +class MLModelInference: + def __init__(self, model, sample_targets: bool = False): + self._model = model + self._sample_targets = sample_targets + + @property + def sample_targets(self) -> bool: + return self._sample_targets + + def predict(self, x: pd.DataFrame) -> pd.Series: + return self._model.predict(x) + + +class KNNInference: + def __init__(self, data: pd.DataFrame, columns: list[str], target_col: str, n_jobs: int): + self._nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=data[columns]) + self._data = data + self._target_col = target_col + self._columns = columns + + @property + def sample_targets(self) -> bool: + return True + + def predict(self, x: pd.DataFrame) -> pd.Series: + guesses_idx = self._nn.kneighbors(queries=x[self._columns]) + if isinstance(guesses_idx, tuple): + raise RuntimeError("guesses_idx cannot be a tuple") + return self._data.iloc[guesses_idx.flatten()][self._target_col] \ No newline at end of file From 46c3fa35c2e7613cd532621d3edac766cff22fec Mon Sep 17 00:00:00 2001 From: ivana Date: Thu, 4 Dec 2025 15:35:45 +0100 Subject: [PATCH 4/7] Add a custom inference model in the inference_evaluator.py; Add Inference model wrappers extending InferencePredictor in inference_predictor.py; Support different number of attacks; Add tests. --- .../inference_custom_model_example.ipynb | 932 ------------------ src/anonymeter/evaluators/__init__.py | 3 +- .../evaluators/inference_evaluator.py | 130 +-- .../evaluators/inference_predictor.py | 37 - .../evaluators/sklearn_inference_predictor.py | 41 + .../neighbors/mixed_types_kneighbors.py | 50 +- src/anonymeter/stats/confidence.py | 30 +- tests/test_inference_evaluator.py | 6 +- tests/test_sklearn_inference_model.py | 56 ++ 9 files changed, 182 insertions(+), 1103 deletions(-) delete mode 100644 notebooks/inference_custom_model_example.ipynb create mode 100644 src/anonymeter/evaluators/sklearn_inference_predictor.py create mode 100644 tests/test_sklearn_inference_model.py diff --git a/notebooks/inference_custom_model_example.ipynb b/notebooks/inference_custom_model_example.ipynb deleted file mode 100644 index a2ff806..0000000 --- a/notebooks/inference_custom_model_example.ipynb +++ /dev/null @@ -1,932 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a6727d05-5009-49f5-8918-d5fdd22cb187", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:39.093309Z", - "start_time": "2025-05-15T11:50:39.060430Z" - } - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "id": "de41f9a83014711d", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "#### Run the cell bellow, uncommented if using autogluon" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b3766057c77adc9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:39.113487Z", - "start_time": "2025-05-15T11:50:39.096588Z" - }, - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# pip install autogluon" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c64a6fab-1676-4539-b460-5b2fdb456b04", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:44.845976Z", - "start_time": "2025-05-15T11:50:39.117232Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "from anonymeter.evaluators import InferenceEvaluator, MLModelInference\n", - "from autogluon.tabular import TabularPredictor\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3429c0fa-8915-4320-93f2-016fedf2b570", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:44.889226Z", - "start_time": "2025-05-15T11:50:44.848139Z" - } - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "ada19947-b895-4279-aac3-9b87fac2fa6b", - "metadata": {}, - "source": [ - "## Downloading the data\n", - "\n", - "For this example, we will use the famous `Adults` (more details [here](https://archive.ics.uci.edu/ml/datasets/adult)) dataset. This dataset contains aggregated census data, where every row represent a population segment. For the purpose of demonstrating `Anonymeter`, we will use this data as if each row would in fact refer to a real individual. \n", - "\n", - "The synthetic version has been generated by `CTGAN` from [SDV](https://sdv.dev/SDV/user_guides/single_table/ctgan.html), as explained in the paper accompanying this code release. For details on the generation process, e.g. regarding hyperparameters, see Section 6.2.1 of [the accompanying paper](https://petsymposium.org/popets/2023/popets-2023-0055.php)).\n", - "\n", - "We pull these datasets from the [Statice](https://www.statice.ai/) public GC bucket:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fc128115-2f0c-43b1-9198-5c5594eae7f3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:45.192418Z", - "start_time": "2025-05-15T11:50:44.893155Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "ori = pd.read_csv(\"datasets/adults_train.csv\")\n", - "syn = pd.read_csv(\"datasets/adults_syn_ctgan.csv\")\n", - "control = pd.read_csv(\"datasets/adults_control.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f6abeed8-23ae-4d4a-9cdb-006c0bba109c", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:50:45.259297Z", - "start_time": "2025-05-15T11:50:45.194514Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
agetype_employerfnlwgteducationeducation_nummaritaloccupationrelationshipracesexcapital_gaincapital_losshr_per_weekcountryincome
053Self-emp-not-inc13802211th7DivorcedCraft-repairNot-in-familyWhiteMale0060United-States<=50K
131Private344200HS-grad9Married-civ-spouseExec-managerialHusbandWhiteMale0040United-States>50K
228Private242482HS-grad9Never-marriedHandlers-cleanersOwn-childWhiteMale0040United-States<=50K
326Private193165Some-college10Married-civ-spouseTransport-movingHusbandWhiteMale0052United-States>50K
427Private267989Some-college10Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States<=50K
\n", - "
" - ], - "text/plain": [ - " age type_employer fnlwgt education education_num \\\n", - "0 53 Self-emp-not-inc 138022 11th 7 \n", - "1 31 Private 344200 HS-grad 9 \n", - "2 28 Private 242482 HS-grad 9 \n", - "3 26 Private 193165 Some-college 10 \n", - "4 27 Private 267989 Some-college 10 \n", - "\n", - " marital occupation relationship race sex \\\n", - "0 Divorced Craft-repair Not-in-family White Male \n", - "1 Married-civ-spouse Exec-managerial Husband White Male \n", - "2 Never-married Handlers-cleaners Own-child White Male \n", - "3 Married-civ-spouse Transport-moving Husband White Male \n", - "4 Married-civ-spouse Machine-op-inspct Husband White Male \n", - "\n", - " capital_gain capital_loss hr_per_week country income \n", - "0 0 0 60 United-States <=50K \n", - "1 0 0 40 United-States >50K \n", - "2 0 0 40 United-States <=50K \n", - "3 0 0 52 United-States >50K \n", - "4 0 0 40 United-States <=50K " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ori.head()" - ] - }, - { - "cell_type": "markdown", - "id": "f1d19013-b7cf-48e3-a068-6c1e5449884e", - "metadata": {}, - "source": [ - "As visible the dataset contains several demographic information, as well as information regarding the education, financial situation, and personal life of some tens of thousands of \"individuals\"." - ] - }, - { - "cell_type": "markdown", - "id": "0429baae-424d-4ebe-b8ec-9205397515ba", - "metadata": {}, - "source": [ - "# Measuring the Inference Risk\n", - "\n", - "With this new implementation we allow for the user to pass their own model in order to do the attribute inference.\n", - "The requirements for the model are:\n", - "1. It is trained on the synthetic data with the `secret` as a target.\n", - "2. It supports the ::predict(x) method\n", - "\n", - "Finally, it should be passed as `ml_model=predictor` to the `InferenceEvaluator` like in the example below:\n", - "(in case we pass our own ml model and want to have the predictions for all original/control data, set sample_attacks=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6c07054c-7ced-46c3-8a12-14123f6cc965", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:53:00.988964Z", - "start_time": "2025-05-15T11:50:45.261347Z" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092829\"\n", - "Verbosity: 2 (Standard Logging)\n", - "=================== System Info ===================\n", - "AutoGluon Version: 1.4.0\n", - "Python Version: 3.9.22\n", - "Operating System: Darwin\n", - "Platform Machine: x86_64\n", - "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", - "CPU Count: 12\n", - "Memory Avail: 4.33 GB / 16.00 GB (27.0%)\n", - "Disk Space Avail: 11.13 GB / 465.63 GB (2.4%)\n", - "===================================================\n", - "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", - "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", - "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", - "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", - "\tpresets='high' : Strong accuracy with fast inference speed.\n", - "\tpresets='good' : Good accuracy with very fast inference speed.\n", - "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", - "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092829\"\n", - "Train Data Rows: 39040\n", - "Train Data Columns: 14\n", - "Label Column: age\n", - "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).\n", - "\tFirst 10 (of 73) unique label values: [37, 53, 17, 30, 40, 34, 36, 25, 56, 46]\n", - "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", - "Problem Type: multiclass\n", - "Preprocessing data ...\n", - "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 66 out of 73 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", - "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.999359631147541\n", - "Train Data Class Count: 66\n", - "Using Feature Generators to preprocess the data ...\n", - "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 4553.59 MB\n", - "\tTrain Data (Original) Memory Usage: 23.48 MB (0.5% of available memory)\n", - "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", - "\tStage 1 Generators:\n", - "\t\tFitting AsTypeFeatureGenerator...\n", - "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", - "\tStage 2 Generators:\n", - "\t\tFitting FillNaFeatureGenerator...\n", - "\tStage 3 Generators:\n", - "\t\tFitting IdentityFeatureGenerator...\n", - "\t\tFitting CategoryFeatureGenerator...\n", - "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", - "\tStage 4 Generators:\n", - "\t\tFitting DropUniqueFeatureGenerator...\n", - "\tStage 5 Generators:\n", - "\t\tFitting DropDuplicatesFeatureGenerator...\n", - "\tTypes of features in original data (raw dtype, special dtypes):\n", - "\t\t('int', []) : 5 | ['fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\tTypes of features in processed data (raw dtype, special dtypes):\n", - "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\t\t('int', []) : 5 | ['fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.5s = Fit runtime\n", - "\t14 features in original data used to generate 14 features in processed data.\n", - "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.62s ...\n", - "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", - "\tTo change this, specify the eval_metric parameter of Predictor()\n", - "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36516, Val Rows: 2499\n", - "User-specified model hyperparameters to be fit:\n", - "{\n", - "\t'RF': [{}],\n", - "}\n", - "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", - "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=1.5/4.6 GB\n", - "\tWarning: Reducing model 'n_estimators' from 300 -> 42 due to low memory. Expected memory usage reduced from 106.04% -> 15.0% of available memory...\n", - "\t0.0576\t = Validation score (accuracy)\n", - "\t1.51s\t = Training runtime\n", - "\t0.06s\t = Validation runtime\n", - "Fitting model: WeightedEnsemble_L2 ...\n", - "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.0576\t = Validation score (accuracy)\n", - "\t0.02s\t = Training runtime\n", - "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 3.95s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 42137.1 rows/s (2499 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092829\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092838\"\n", - "Verbosity: 2 (Standard Logging)\n", - "=================== System Info ===================\n", - "AutoGluon Version: 1.4.0\n", - "Python Version: 3.9.22\n", - "Operating System: Darwin\n", - "Platform Machine: x86_64\n", - "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", - "CPU Count: 12\n", - "Memory Avail: 5.53 GB / 16.00 GB (34.5%)\n", - "Disk Space Avail: 24.42 GB / 465.63 GB (5.2%)\n", - "===================================================\n", - "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", - "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", - "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", - "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", - "\tpresets='high' : Strong accuracy with fast inference speed.\n", - "\tpresets='good' : Good accuracy with very fast inference speed.\n", - "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", - "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092838\"\n", - "Train Data Rows: 39040\n", - "Train Data Columns: 14\n", - "Label Column: type_employer\n", - "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n", - "\t9 unique label values: ['Private', '?', 'State-gov', 'Federal-gov', 'Local-gov', 'Self-emp-not-inc', 'Self-emp-inc', 'Without-pay', 'Never-worked']\n", - "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", - "Problem Type: multiclass\n", - "Preprocessing data ...\n", - "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 8 out of 9 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", - "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9998206967213115\n", - "Train Data Class Count: 8\n", - "Using Feature Generators to preprocess the data ...\n", - "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 5521.62 MB\n", - "\tTrain Data (Original) Memory Usage: 21.38 MB (0.4% of available memory)\n", - "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", - "\tStage 1 Generators:\n", - "\t\tFitting AsTypeFeatureGenerator...\n", - "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", - "\tStage 2 Generators:\n", - "\t\tFitting FillNaFeatureGenerator...\n", - "\tStage 3 Generators:\n", - "\t\tFitting IdentityFeatureGenerator...\n", - "\t\tFitting CategoryFeatureGenerator...\n", - "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", - "\tStage 4 Generators:\n", - "\t\tFitting DropUniqueFeatureGenerator...\n", - "\tStage 5 Generators:\n", - "\t\tFitting DropDuplicatesFeatureGenerator...\n", - "\tTypes of features in original data (raw dtype, special dtypes):\n", - "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", - "\t\t('object', []) : 8 | ['education', 'marital', 'occupation', 'relationship', 'race', ...]\n", - "\tTypes of features in processed data (raw dtype, special dtypes):\n", - "\t\t('category', []) : 6 | ['education', 'marital', 'occupation', 'relationship', 'race', ...]\n", - "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", - "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.4s = Fit runtime\n", - "\t14 features in original data used to generate 14 features in processed data.\n", - "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.55s ...\n", - "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", - "\tTo change this, specify the eval_metric parameter of Predictor()\n", - "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36533, Val Rows: 2500\n", - "User-specified model hyperparameters to be fit:\n", - "{\n", - "\t'RF': [{}],\n", - "}\n", - "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", - "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.2/5.3 GB\n", - "\t0.7044\t = Validation score (accuracy)\n", - "\t4.2s\t = Training runtime\n", - "\t0.11s\t = Validation runtime\n", - "Fitting model: WeightedEnsemble_L2 ...\n", - "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.7044\t = Validation score (accuracy)\n", - "\t0.01s\t = Training runtime\n", - "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 6.66s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 23297.2 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092838\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_092850\"\n", - "Verbosity: 2 (Standard Logging)\n", - "=================== System Info ===================\n", - "AutoGluon Version: 1.4.0\n", - "Python Version: 3.9.22\n", - "Operating System: Darwin\n", - "Platform Machine: x86_64\n", - "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", - "CPU Count: 12\n", - "Memory Avail: 5.02 GB / 16.00 GB (31.4%)\n", - "Disk Space Avail: 23.66 GB / 465.63 GB (5.1%)\n", - "===================================================\n", - "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", - "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", - "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", - "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", - "\tpresets='high' : Strong accuracy with fast inference speed.\n", - "\tpresets='good' : Good accuracy with very fast inference speed.\n", - "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", - "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092850\"\n", - "Train Data Rows: 39040\n", - "Train Data Columns: 14\n", - "Label Column: fnlwgt\n", - "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).\n", - "\tLabel info (max, min, mean, stddev): (814369, 13492, 181263.22961, 100607.94976)\n", - "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", - "Problem Type: regression\n", - "Preprocessing data ...\n", - "Using Feature Generators to preprocess the data ...\n", - "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 5106.42 MB\n", - "\tTrain Data (Original) Memory Usage: 23.49 MB (0.5% of available memory)\n", - "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", - "\tStage 1 Generators:\n", - "\t\tFitting AsTypeFeatureGenerator...\n", - "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", - "\tStage 2 Generators:\n", - "\t\tFitting FillNaFeatureGenerator...\n", - "\tStage 3 Generators:\n", - "\t\tFitting IdentityFeatureGenerator...\n", - "\t\tFitting CategoryFeatureGenerator...\n", - "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", - "\tStage 4 Generators:\n", - "\t\tFitting DropUniqueFeatureGenerator...\n", - "\tStage 5 Generators:\n", - "\t\tFitting DropDuplicatesFeatureGenerator...\n", - "\tTypes of features in original data (raw dtype, special dtypes):\n", - "\t\t('int', []) : 5 | ['age', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\tTypes of features in processed data (raw dtype, special dtypes):\n", - "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\t\t('int', []) : 5 | ['age', 'education_num', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.4s = Fit runtime\n", - "\t14 features in original data used to generate 14 features in processed data.\n", - "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.46s ...\n", - "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n", - "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n", - "\tTo change this, specify the eval_metric parameter of Predictor()\n", - "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36540, Val Rows: 2500\n", - "User-specified model hyperparameters to be fit:\n", - "{\n", - "\t'RF': [{}],\n", - "}\n", - "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", - "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.0/5.0 GB\n", - "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", - "\t10.78s\t = Training runtime\n", - "\t0.13s\t = Validation runtime\n", - "Fitting model: WeightedEnsemble_L2 ...\n", - "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t-96603.1958\t = Validation score (-root_mean_squared_error)\n", - "\t0.0s\t = Training runtime\n", - "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 12.3s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 19535.7 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_092850\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_093000\"\n", - "Verbosity: 2 (Standard Logging)\n", - "=================== System Info ===================\n", - "AutoGluon Version: 1.4.0\n", - "Python Version: 3.9.22\n", - "Operating System: Darwin\n", - "Platform Machine: x86_64\n", - "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", - "CPU Count: 12\n", - "Memory Avail: 6.53 GB / 16.00 GB (40.8%)\n", - "Disk Space Avail: 35.48 GB / 465.63 GB (7.6%)\n", - "===================================================\n", - "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", - "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", - "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", - "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", - "\tpresets='high' : Strong accuracy with fast inference speed.\n", - "\tpresets='good' : Good accuracy with very fast inference speed.\n", - "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", - "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093000\"\n", - "Train Data Rows: 39040\n", - "Train Data Columns: 14\n", - "Label Column: education\n", - "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n", - "\tFirst 10 (of 16) unique label values: ['HS-grad', '11th', 'Masters', 'Some-college', '5th-6th', 'Bachelors', 'Assoc-acdm', '9th', 'Doctorate', 'Prof-school']\n", - "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", - "Problem Type: multiclass\n", - "Preprocessing data ...\n", - "Train Data Class Count: 16\n", - "Using Feature Generators to preprocess the data ...\n", - "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 6593.09 MB\n", - "\tTrain Data (Original) Memory Usage: 21.36 MB (0.3% of available memory)\n", - "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", - "\tStage 1 Generators:\n", - "\t\tFitting AsTypeFeatureGenerator...\n", - "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", - "\tStage 2 Generators:\n", - "\t\tFitting FillNaFeatureGenerator...\n", - "\tStage 3 Generators:\n", - "\t\tFitting IdentityFeatureGenerator...\n", - "\t\tFitting CategoryFeatureGenerator...\n", - "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", - "\tStage 4 Generators:\n", - "\t\tFitting DropUniqueFeatureGenerator...\n", - "\tStage 5 Generators:\n", - "\t\tFitting DropDuplicatesFeatureGenerator...\n", - "\tTypes of features in original data (raw dtype, special dtypes):\n", - "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", - "\t\t('object', []) : 8 | ['type_employer', 'marital', 'occupation', 'relationship', 'race', ...]\n", - "\tTypes of features in processed data (raw dtype, special dtypes):\n", - "\t\t('category', []) : 6 | ['type_employer', 'marital', 'occupation', 'relationship', 'race', ...]\n", - "\t\t('int', []) : 6 | ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', ...]\n", - "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.4s = Fit runtime\n", - "\t14 features in original data used to generate 14 features in processed data.\n", - "\tTrain Data (Processed) Memory Usage: 2.09 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.56s ...\n", - "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", - "\tTo change this, specify the eval_metric parameter of Predictor()\n", - "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36540, Val Rows: 2500\n", - "User-specified model hyperparameters to be fit:\n", - "{\n", - "\t'RF': [{}],\n", - "}\n", - "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", - "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.4/6.4 GB\n", - "\t0.8064\t = Validation score (accuracy)\n", - "\t4.32s\t = Training runtime\n", - "\t0.12s\t = Validation runtime\n", - "Fitting model: WeightedEnsemble_L2 ...\n", - "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.8064\t = Validation score (accuracy)\n", - "\t0.01s\t = Training runtime\n", - "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 7.13s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 20566.3 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093000\")\n", - "No path specified. Models will be saved in: \"AutogluonModels/ag-20251121_093013\"\n", - "Verbosity: 2 (Standard Logging)\n", - "=================== System Info ===================\n", - "AutoGluon Version: 1.4.0\n", - "Python Version: 3.9.22\n", - "Operating System: Darwin\n", - "Platform Machine: x86_64\n", - "Platform Version: Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:17 PDT 2025; root:xnu-11417.140.69~1/RELEASE_X86_64\n", - "CPU Count: 12\n", - "Memory Avail: 5.17 GB / 16.00 GB (32.3%)\n", - "Disk Space Avail: 35.78 GB / 465.63 GB (7.7%)\n", - "===================================================\n", - "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n", - "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n", - "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n", - "\tpresets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n", - "\tpresets='high' : Strong accuracy with fast inference speed.\n", - "\tpresets='good' : Good accuracy with very fast inference speed.\n", - "\tpresets='medium' : Fast training time, ideal for initial prototyping.\n", - "Beginning AutoGluon training ...\n", - "AutoGluon will save models to \"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093013\"\n", - "Train Data Rows: 39040\n", - "Train Data Columns: 14\n", - "Label Column: education_num\n", - "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).\n", - "\tFirst 10 (of 14) unique label values: [9, 7, 14, 10, 13, 12, 4, 16, 15, 11]\n", - "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n", - "Problem Type: multiclass\n", - "Preprocessing data ...\n", - "Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 13 out of 14 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.\n", - "Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9998463114754098\n", - "Train Data Class Count: 13\n", - "Using Feature Generators to preprocess the data ...\n", - "Fitting AutoMLPipelineFeatureGenerator...\n", - "\tAvailable Memory: 5330.31 MB\n", - "\tTrain Data (Original) Memory Usage: 23.49 MB (0.4% of available memory)\n", - "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n", - "\tStage 1 Generators:\n", - "\t\tFitting AsTypeFeatureGenerator...\n", - "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n", - "\tStage 2 Generators:\n", - "\t\tFitting FillNaFeatureGenerator...\n", - "\tStage 3 Generators:\n", - "\t\tFitting IdentityFeatureGenerator...\n", - "\t\tFitting CategoryFeatureGenerator...\n", - "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n", - "\tStage 4 Generators:\n", - "\t\tFitting DropUniqueFeatureGenerator...\n", - "\tStage 5 Generators:\n", - "\t\tFitting DropDuplicatesFeatureGenerator...\n", - "\tTypes of features in original data (raw dtype, special dtypes):\n", - "\t\t('int', []) : 5 | ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('object', []) : 9 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\tTypes of features in processed data (raw dtype, special dtypes):\n", - "\t\t('category', []) : 7 | ['type_employer', 'education', 'marital', 'occupation', 'relationship', ...]\n", - "\t\t('int', []) : 5 | ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hr_per_week']\n", - "\t\t('int', ['bool']) : 2 | ['sex', 'income']\n", - "\t0.4s = Fit runtime\n", - "\t14 features in original data used to generate 14 features in processed data.\n", - "\tTrain Data (Processed) Memory Usage: 1.83 MB (0.0% of available memory)\n", - "Data preprocessing and feature engineering runtime = 0.44s ...\n", - "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n", - "\tTo change this, specify the eval_metric parameter of Predictor()\n", - "Automatically generating train/validation split with holdout_frac=0.06403688524590163, Train Rows: 36534, Val Rows: 2500\n", - "User-specified model hyperparameters to be fit:\n", - "{\n", - "\t'RF': [{}],\n", - "}\n", - "Fitting 1 L1 models, fit_strategy=\"sequential\" ...\n", - "Fitting model: RandomForest ...\n", - "\tFitting with cpus=12, gpus=0, mem=0.3/5.1 GB\n", - "\t0.8564\t = Validation score (accuracy)\n", - "\t4.43s\t = Training runtime\n", - "\t0.14s\t = Validation runtime\n", - "Fitting model: WeightedEnsemble_L2 ...\n", - "\tEnsemble Weights: {'RandomForest': 1.0}\n", - "\t0.8564\t = Validation score (accuracy)\n", - "\t0.02s\t = Training runtime\n", - "\t0.0s\t = Validation runtime\n", - "AutoGluon training complete, total runtime = 7.3s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 17247.9 rows/s (2500 batch size)\n", - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/Users/ivanananevski/projects/anonymeter-copy/anonymeter/notebooks/AutogluonModels/ag-20251121_093013\")\n" - ] - } - ], - "source": [ - "columns = ori.columns[:5] # Using the first 5 columns only for this example\n", - "results = []\n", - "\n", - "results_for_groups = {}\n", - "\n", - "for secret in columns:\n", - " predictor = TabularPredictor(label=secret)\n", - " predictor.fit(\n", - " train_data=syn,\n", - " hyperparameters={\n", - " 'RF': {}, \n", - " }\n", - " )\n", - " inference_model = MLModelInference(predictor)\n", - " aux_cols = [col for col in columns if col != secret]\n", - " \n", - " # Needed for anonymeter (is the target categorical or numerical variable?)\n", - " regression = True if syn[secret].dtype.kind in 'iuf' else False\n", - " \n", - " # Init the InferenceEvaluator\n", - " evaluator = InferenceEvaluator(ori=ori, \n", - " syn=syn, \n", - " control=control,\n", - " aux_cols=aux_cols,\n", - " secret=secret,\n", - " n_attacks=6000, #todo important! this parameter is obsolete if sample_attacks is False\n", - " regression = regression,\n", - " inference_model=inference_model) # Setting to true for this example\n", - " # Evaluate\n", - " evaluator.evaluate(n_jobs=-2)\n", - " \n", - " # Gather the results for the current group\n", - " results_for_groups[secret] = evaluator.risk_for_groups()\n", - " results.append((secret, evaluator.results()))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "59651586-891c-4ae6-8b28-fe7a7de66aeb", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:53:46.772503Z", - "start_time": "2025-05-15T11:53:00.992149Z" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHPCAYAAACoQyVSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKTElEQVR4nO3deVyU5f7/8feAsrngGouiomLuu5LkLoZfPacwO6mdcsm0Y4sLLrngklmkpaJpRz3lVppLejzVUQvJbJE0xd1SM5RMQdGEwBKF6/eHP+c0IxhjwAi+no8HD5zrvu7r/lzDyLy5514sxhgjAAAAWLk4uwAAAIA7DQEJAADADgEJAADADgEJAADADgEJAADADgEJAADADgEJAADADgEJAADADgEJAADADgEJQIH57LPPZLFY9Nlnnzm7lFwtW7ZMFotFJ0+edHYpAO4gBCQAd4VXXnlFGzdudHYZAIoIAhKAu0JuAemJJ57Qr7/+qurVqxd+UQDuWCWcXQAAOJOrq6tcXV2dXQaAOwx7kAD8oZ9++klPPvmkfHx85O7urgYNGmjJkiU2fU6fPq3w8HCVKlVK99xzj0aOHKkrV67cNFaNGjU0YMCAm9o7duyojh072rT99ttvmjp1qurUqSMPDw/5+fnp4Ycf1okTJ6x9Xn/9dYWEhKhixYry9PRUixYt9P7779uMY7FYlJGRoeXLl8tischisVhryO0YpDfffFMNGjSQu7u7/P399eyzz+rSpUs31dywYUMdOXJEnTp1kpeXl6pUqaKZM2fe+gnNQY0aNfSXv/xFX375pVq3bi0PDw/VrFlTK1assOk3depUWSyWm9bPaR43xvzss8/UsmVLeXp6qlGjRtZjwjZs2KBGjRrJw8NDLVq00N69ex2uGyiuCEgAbik5OVn33Xeftm7dqueee05z585V7dq1NWjQIEVHR0uSfv31V3Xp0kUff/yxnnvuOU2cOFFffPGFxo4de9vbzcrK0l/+8he9+OKLatGihWbNmqXhw4crNTVVhw4dsvabO3eumjVrpmnTpumVV15RiRIl9Le//U3//e9/rX3eeecdubu7q127dnrnnXf0zjvv6Omnn85121OnTtWzzz4rf39/zZo1S7169dKiRYv0wAMP6OrVqzZ9f/75Z3Xr1k1NmjTRrFmzVLduXb3wwgvavHmzw3P+/vvv9cgjj6hr166aNWuWypcvrwEDBujw4cMOj/X7MR977DH99a9/VVRUlH7++Wf99a9/1cqVKzVy5Eg9/vjjevHFF3XixAk9+uijys7Ovu1tAcWKAYBbGDRokPHz8zMpKSk27X369DHe3t7m8uXLJjo62kgya9eutS7PyMgwtWvXNpLMtm3brO3Vq1c3/fv3v2k7HTp0MB06dLA+XrJkiZFkZs+efVPf7Oxs678vX75ssywzM9M0bNjQdO7c2aa9VKlSOW536dKlRpJJSEgwxhhz7tw54+bmZh544AGTlZVl7Td//nwjySxZssSmZklmxYoV1rYrV64YX19f06tXr5u2dSvVq1c3ksznn39ubTt37pxxd3c3o0aNsrZNmTLF5PSr234evx9zx44d1raPP/7YSDKenp7m1KlT1vZFixbd9LMC7mbsQQKQK2OM1q9fr7/+9a8yxiglJcX6FRYWptTUVMXHx2vTpk3y8/PTI488Yl3Xy8tLQ4YMue1tr1+/XpUqVdLzzz9/07Lff8Tk6elp/ffPP/+s1NRUtWvXTvHx8be13a1btyozM1MjRoyQi8v/fkUOHjxYZcuWtdkzJUmlS5fW448/bn3s5uam1q1b64cffnB42/Xr11e7du2sjytXrqx77733tsb6/Zht2rSxPg4ODpYkde7cWdWqVbup/c9sCyhOOEgbQK7Onz+vS5cuafHixVq8eHGOfc6dO6dTp06pdu3aNx0bc++99972tk+cOKF7771XJUrc+tfURx99pOnTp2vfvn02xzzldJxOXpw6dUrSzbW7ubmpZs2a1uU3VK1a9aZtlS9fXgcOHHB4278PLL8f6+eff3Z4rNzG9Pb2liQFBATk2P5ntgUUJwQkALm6cTzK448/rv79++fYp3Hjxg6NmVtwycrKcvhssi+++EIPPvig2rdvrzfffFN+fn4qWbKkli5dqlWrVjk01u3KrWZjTIGMdavnz5Ex87NuoDgiIAHIVeXKlVWmTBllZWUpNDQ0137Vq1fXoUOHZIyxeQM/evToTX3Lly9/09lg0vU9NzVr1rQ+rlWrlnbu3KmrV6+qZMmSOW53/fr18vDw0Mcffyx3d3dr+9KlS2/qm9c9Sjeuh3T06FGbejIzM5WQkHDL56EwlC9fXpJ06dIllStXztpuv2cLwJ/DMUgAcuXq6qpevXpp/fr1NmeO3XD+/HlJUvfu3XXmzBmb0+svX76c48dytWrV0tdff63MzExr20cffaQff/zRpl+vXr2UkpKi+fPn3zTGjb0crq6uslgsNntPTp48meMFIUuVKpVjMLMXGhoqNzc3zZs3z2Zvyttvv63U1FT16NHjD8coSLVq1ZIkff7559a2G5cwAJB/2IME4JZeffVVbdu2TcHBwRo8eLDq16+vixcvKj4+Xlu3btXFixc1ePBgzZ8/X/369dOePXvk5+end955R15eXjeN99RTT+n9999Xt27d9Oijj+rEiRN69913rW/8N/Tr108rVqxQRESEdu3apXbt2ikjI0Nbt27VM888o4ceekg9evTQ7Nmz1a1bNz322GM6d+6cFixYoNq1a990DFCLFi20detWzZ49W/7+/goMDLQemPx7lStX1vjx4/Xiiy+qW7duevDBB3X06FG9+eabatWqlc0B2c7wwAMPqFq1aho0aJDGjBkjV1dXLVmyRJUrV1ZiYqJTawOKFeedQAegqEhOTjbPPvusCQgIMCVLljS+vr6mS5cuZvHixdY+p06dMg8++KDx8vIylSpVMsOHDzdbtmzJ8dTxWbNmmSpVqhh3d3dz//33m927d990mr8x10/hnzhxogkMDLRu95FHHjEnTpyw9nn77bdNUFCQcXd3N3Xr1jVLly7N8VT47777zrRv3954enoaSdZT/nM6Pd6Y66f1161b15QsWdL4+PiYoUOHmp9//tmmT4cOHUyDBg1uer769+9vqlevnqfn9obq1aubHj163NSe0/OyZ88eExwcbNzc3Ey1atXM7Nmzcz3NP6cxJZlnn33Wpi0hIcFIMq+99ppDdQPFlcUYjsgDAAD4PY5BAgAAsMMxSABQgM6fP5/rKfjS9esrVahQoRArApAXfMQGAAWoRo0atzwFv0OHDtabxwK4c9wRH7EtWLBANWrUkIeHh4KDg7Vr165b9l+3bp3q1q0rDw8PNWrUSJs2bbJZbozR5MmT5efnJ09PT4WGhur48eM2fWrUqGG9q/eNr1dffTXf5wbg7rZy5UrFxMTk+jVr1ixnlwggB07fg7RmzRr169dPCxcuVHBwsKKjo7Vu3TodPXpU99xzz039d+zYofbt2ysqKkp/+ctftGrVKs2YMUPx8fFq2LChJGnGjBmKiorS8uXLFRgYqEmTJungwYM6cuSIPDw8JF0PSIMGDdLgwYOtY5cpU0alSpUqnIkDAIA7ltMDUnBwsFq1amW9GFx2drYCAgL0/PPPa9y4cTf17927tzIyMvTRRx9Z2+677z41bdpUCxculDFG/v7+GjVqlEaPHi1JSk1NlY+Pj5YtW6Y+ffpIuh6QRowYoREjRhT8JAEAQJHi1IO0MzMztWfPHo0fP97a5uLiotDQUMXFxeW4TlxcnCIiImzawsLCrFfOTUhIUFJSks3tALy9vRUcHKy4uDhrQJKuXwDvpZdeUrVq1fTYY49p5MiRud4Y88qVKzY3wszOztbFixdVsWLF274pJgAAKFzGGP3yyy/y9/eXi0vuRxo5NSClpKQoKytLPj4+Nu0+Pj767rvvclwnKSkpx/5JSUnW5TfacusjScOGDVPz5s1VoUIF7dixQ+PHj9fZs2c1e/bsHLcbFRWlF1980bEJAgCAO9KPP/6oqlWr5rr8rj3N//d7oRo3biw3Nzc9/fTTioqKsrnp5Q3jx4+3WSc1NVXVqlXTjz/+qLJlyxZKzQAA4M9JS0tTQECAypQpc8t+Tg1IlSpVkqurq5KTk23ak5OT5evrm+M6vr6+t+x/43tycrL8/Pxs+jRt2jTXWoKDg3Xt2jWdPHlS9957703L3d3dcwxOZcuWJSABAFDE/NHhMU49zd/NzU0tWrRQbGystS07O1uxsbFq06ZNjuu0adPGpr8kxcTEWPsHBgbK19fXpk9aWpp27tyZ65iStG/fPrm4uOR45hwAALi7OP0jtoiICPXv318tW7ZU69atFR0drYyMDA0cOFDS9Tt6V6lSRVFRUZKk4cOHq0OHDpo1a5Z69Oih1atXa/fu3Vq8eLGk64lwxIgRmj59uoKCgqyn+fv7+ys8PFzS9QO9d+7cqU6dOqlMmTKKi4vTyJEj9fjjj6t8+fJOeR4AAMCdw+kBqXfv3jp//rwmT56spKQkNW3aVFu2bLEeZJ2YmGhzlHlISIhWrVqlyMhITZgwQUFBQdq4caP1GkiSNHbsWGVkZGjIkCG6dOmS2rZtqy1btlivgeTu7q7Vq1dr6tSpunLligIDAzVy5Mibzo4DAAB3J6dfB6moSktLk7e3t1JTUzkGCQCAIiKv7993xK1GAAAA7iQEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADtOvxcbAACFYU7MsQIbe2TXOgU2NpyDPUgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2Sji7AABF25yYYwUy7siudQpkXADIC/YgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2CEgAQAA2LkjAtKCBQtUo0YNeXh4KDg4WLt27bpl/3Xr1qlu3bry8PBQo0aNtGnTJpvlxhhNnjxZfn5+8vT0VGhoqI4fP57jWFeuXFHTpk1lsVi0b9++/JoSAAAowpwekNasWaOIiAhNmTJF8fHxatKkicLCwnTu3Lkc++/YsUN9+/bVoEGDtHfvXoWHhys8PFyHDh2y9pk5c6bmzZunhQsXaufOnSpVqpTCwsL022+/3TTe2LFj5e/vX2DzAwAARY/TA9Ls2bM1ePBgDRw4UPXr19fChQvl5eWlJUuW5Nh/7ty56tatm8aMGaN69erppZdeUvPmzTV//nxJ1/ceRUdHKzIyUg899JAaN26sFStW6MyZM9q4caPNWJs3b9Ynn3yi119/vaCnCQAAihCnBqTMzEzt2bNHoaGh1jYXFxeFhoYqLi4ux3Xi4uJs+ktSWFiYtX9CQoKSkpJs+nh7eys4ONhmzOTkZA0ePFjvvPOOvLy8/rDWK1euKC0tzeYLAAAUT04NSCkpKcrKypKPj49Nu4+Pj5KSknJcJykp6Zb9b3y/VR9jjAYMGKB//OMfatmyZZ5qjYqKkre3t/UrICAgT+sBAICix+kfsTnDG2+8oV9++UXjx4/P8zrjx49Xamqq9evHH38swAoBAIAzOTUgVapUSa6urkpOTrZpT05Olq+vb47r+Pr63rL/je+36vPpp58qLi5O7u7uKlGihGrXri1Jatmypfr375/jdt3d3VW2bFmbLwAAUDw5NSC5ubmpRYsWio2NtbZlZ2crNjZWbdq0yXGdNm3a2PSXpJiYGGv/wMBA+fr62vRJS0vTzp07rX3mzZun/fv3a9++fdq3b5/1MgFr1qzRyy+/nK9zBAAARU8JZxcQERGh/v37q2XLlmrdurWio6OVkZGhgQMHSpL69eunKlWqKCoqSpI0fPhwdejQQbNmzVKPHj20evVq7d69W4sXL5YkWSwWjRgxQtOnT1dQUJACAwM1adIk+fv7Kzw8XJJUrVo1mxpKly4tSapVq5aqVq1aSDMHAAB3KqcHpN69e+v8+fOaPHmykpKS1LRpU23ZssV6kHViYqJcXP63oyskJESrVq1SZGSkJkyYoKCgIG3cuFENGza09hk7dqwyMjI0ZMgQXbp0SW3bttWWLVvk4eFR6PMDAABFj8UYY5xdRFGUlpYmb29vpaamcjwS7mpzYo4VyLgju9YpkHFx9yqo16rE67Uoyev79115FhsAAMCtEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsOByQrl69muuylJSUP1UMAADAncDhgNSnTx8ZY25qT05OVseOHfOjJgAAAKdyOCAlJibqqaeesmlLSkpSx44dVbdu3XwrDAAAwFkcDkibNm3Sjh07FBERIUk6c+aMOnTooEaNGmnt2rX5XiAAAEBhK+HoCpUrV9Ynn3yitm3bSpI++ugjNW/eXCtXrpSLC8d8AwCAos/hgCRJAQEBiomJUbt27dS1a1e98847slgs+V0bAACAU+QpIJUvXz7HAHT58mV9+OGHqlixorXt4sWL+VcdAACAE+QpIEVHRxdwGQAAAHeOPAWk/v37F3QdAAAAdwyHj6qOj4/XwYMHrY//85//KDw8XBMmTFBmZma+FgcAAOAMDgekp59+WseOHZMk/fDDD+rdu7e8vLy0bt06jR07Nt8LBAAAKGwOB6Rjx46padOmkqR169apQ4cOWrVqlZYtW6b169fnd30AAACFzuGAZIxRdna2JGnr1q3q3r27pOun/nMvNgAAUBw4HJBatmyp6dOn65133tH27dvVo0cPSVJCQoJ8fHzyvUAAAIDC5nBAio6OVnx8vJ577jlNnDhRtWvXliS9//77CgkJyfcCAQAACpvDV9Ju3LixzVlsN7z22mtydXXNl6IAAACc6bZuNZITDw+P/BoKAADAqfIUkCpUqKBjx46pUqVKud525AZuNQIAAIq6PAWkOXPmqEyZMpK47QgAACj+HLrVyLVr12SxWBQWFsYZawAAoNhy6Cy2EiVK6B//+Id+++23gqoHAADA6Rw+zb9169bau3dvQdQCAABwR3D4LLZnnnlGo0aN0unTp9WiRQuVKlXKZnnjxo3zrTgAAABncDgg9enTR5I0bNgwa5vFYpExRhaLRVlZWflXHQAAgBM4HJASEhIKog4AAIA7hsMBqXr16gVRBwAAwB3D4YO0AQAAijsCEgAAgB0CEgAAgB0CEgAAgJ3bCkiXLl3SW2+9pfHjx1tvThsfH6+ffvopX4sDAABwBofPYjtw4IBCQ0Pl7e2tkydPavDgwapQoYI2bNigxMRErVixoiDqBAAAKDQO70GKiIjQgAEDdPz4cXl4eFjbu3fvrs8//zxfiwMAAHAGhwPSN998o6effvqm9ipVqigpKSlfigIAAHAmhwOSu7u70tLSbmo/duyYKleunC9FAQAAOJPDAenBBx/UtGnTdPXqVUnX78OWmJioF154Qb169cr3AgEAAAqbwwFp1qxZSk9P1z333KNff/1VHTp0UO3atVWmTBm9/PLLBVEjAABAoXL4LDZvb2/FxMToq6++0v79+5Wenq7mzZsrNDS0IOoDAAAodA4HpBvuv/9+3X///flZCwAAwB3B4Y/Yhg0bpnnz5t3UPn/+fI0YMSI/agIAAHAqhwPS+vXrc9xzFBISovfffz9figIAAHAmhwPShQsX5O3tfVN72bJllZKSki9FAQAAOJPDAal27drasmXLTe2bN29WzZo1b6uIBQsWqEaNGvLw8FBwcLB27dp1y/7r1q1T3bp15eHhoUaNGmnTpk02y40xmjx5svz8/OTp6anQ0FAdP37cps+DDz6oatWqycPDQ35+fnriiSd05syZ26ofAAAUL7d1q5GxY8dqypQp2r59u7Zv367Jkydr3LhxGjlypMMFrFmzRhEREZoyZYri4+PVpEkThYWF6dy5czn237Fjh/r27atBgwZp7969Cg8PV3h4uA4dOmTtM3PmTM2bN08LFy7Uzp07VapUKYWFhem3336z9unUqZPWrl2ro0ePav369Tpx4oQeeeQRh+sHAADFj8UYYxxd6Z///Kdefvll6x6XGjVqaOrUqerXr5/DBQQHB6tVq1aaP3++JCk7O1sBAQF6/vnnNW7cuJv69+7dWxkZGfroo4+sbffdd5+aNm2qhQsXyhgjf39/jRo1SqNHj5YkpaamysfHR8uWLVOfPn1yrOODDz5QeHi4rly5opIlS/5h3WlpafL29lZqaqrKli3r8LyB4mJOzLECGXdk1zoFMi7uXgX1WpV4vRYleX3/dngPkiQNHTpUp0+fVnJystLS0vTDDz/cVjjKzMzUnj17bK6h5OLiotDQUMXFxeW4Tlxc3E3XXAoLC7P2T0hIUFJSkk0fb29vBQcH5zrmxYsXtXLlSoWEhOQajq5cuaK0tDSbLwAAUDzdVkC6oXLlyipduvRtr5+SkqKsrCz5+PjYtPv4+OR649ukpKRb9r/xPS9jvvDCCypVqpQqVqyoxMRE/ec//8m11qioKHl7e1u/AgIC8jZJAABQ5DgckJKTk/XEE0/I399fJUqUkKurq81XUTJmzBjt3btXn3zyiVxdXdWvXz/l9onj+PHjlZqaav368ccfC7laAABQWBy+kvaAAQOUmJioSZMmyc/PTxaL5bY3XqlSJbm6uio5OdmmPTk5Wb6+vjmu4+vre8v+N74nJyfLz8/Ppk/Tpk1v2n6lSpVUp04d1atXTwEBAfr666/Vpk2bm7br7u4ud3d3h+cIAACKHocD0pdffqkvvvjiprBxO9zc3NSiRQvFxsYqPDxc0vWDtGNjY/Xcc8/luE6bNm0UGxtrc9XumJgYa6gJDAyUr6+vYmNjrTWmpaVp586dGjp0aK61ZGdnS7p+rBEAALi7ORyQAgICcv0Y6nZERESof//+atmypVq3bq3o6GhlZGRo4MCBkqR+/fqpSpUqioqKkiQNHz5cHTp00KxZs9SjRw+tXr1au3fv1uLFiyVJFotFI0aM0PTp0xUUFKTAwEBNmjRJ/v7+1hC2c+dOffPNN2rbtq3Kly+vEydOaNKkSapVq1aOe48AAMDdxeGAFB0drXHjxmnRokWqUaPGny6gd+/eOn/+vCZPnqykpCQ1bdpUW7ZssR5knZiYKBeX/x0qFRISolWrVikyMlITJkxQUFCQNm7cqIYNG1r7jB07VhkZGRoyZIguXbqktm3basuWLfLw8JAkeXl5acOGDZoyZYoyMjLk5+enbt26KTIyko/RAACA49dBKl++vC5fvqxr167Jy8vrptPiL168mK8F3qm4DhJwHddBQlHBdZAg5f39+7b2IAEAABRnDgek/v37F0QdAAAAd4zbulDkiRMnFBkZqb59+1rvmbZ582YdPnw4X4sDAABwBocD0vbt29WoUSPt3LlTGzZsUHp6uiRp//79mjJlSr4XCAAAUNgcDkjjxo3T9OnTFRMTIzc3N2t7586d9fXXX+drcQAAAM7gcEA6ePCgevbseVP7Pffco5SUlHwpCgAAwJkcDkjlypXT2bNnb2rfu3evqlSpki9FAQAAOJPDAalPnz564YUXlJSUJIvFouzsbH311VcaPXq0+vXrVxA1AgAAFCqHA9Irr7yiunXrKiAgQOnp6apfv77at2+vkJAQRUZGFkSNAAAAhcqh6yAZY5SUlKR58+Zp8uTJOnjwoNLT09WsWTMFBQUVVI0AAACFyuGAVLt2bR0+fFhBQUEKCAgoqLoAAACcxqGP2FxcXBQUFKQLFy4UVD0AAABO5/AxSK+++qrGjBmjQ4cOFUQ9AAAATufwvdj69euny5cvq0mTJnJzc5Onp6fN8osXL+ZbcQAAAM7gcECKjo4ugDIAAADuHA4HpP79+xdEHQAAAHcMh49BkqQTJ04oMjJSffv21blz5yRJmzdv1uHDh/O1OAAAAGdwOCBt375djRo10s6dO7Vhwwalp6dLkvbv368pU6bke4EAAACFzeGANG7cOE2fPl0xMTFyc3Oztnfu3Flff/11vhYHAADgDA4HpIMHD6pnz543td9zzz1KSUnJl6IAAACcyeGAVK5cOZ09e/am9r1796pKlSr5UhQAAIAzORyQ+vTpoxdeeEFJSUmyWCzKzs7WV199pdGjR6tfv34FUSMAAEChcjggvfLKK6pbt64CAgKUnp6u+vXrq3379goJCVFkZGRB1AgAAFCo8nQdpLS0NJUtW1aS5Obmpn/961+aPHmyDh48qPT0dDVr1kxBQUEFWigAAEBhyVNAKl++vM6ePat77rlHnTt31oYNGxQQEKCAgICCrg8AAKDQ5ekjttKlS+vChQuSpM8++0xXr14t0KIAAACcKU97kEJDQ9WpUyfVq1dPktSzZ0+bayD93qeffpp/1QEAADhBngLSu+++q+XLl+vEiRPavn27GjRoIC8vr4KuDQAAwCnyFJA8PT31j3/8Q5K0e/duzZgxQ+XKlSvIugAAAJwmTwHp97Zt21YQdQAAANwxHA5IWVlZWrZsmWJjY3Xu3DllZ2fbLOcYJAAAUNQ5HJCGDx+uZcuWqUePHmrYsKEsFktB1AUAAOA0Dgek1atXa+3aterevXtB1AMAAOB0Dt9qxM3NTbVr1y6IWgAAAO4IDgekUaNGae7cuTLGFEQ9AAAATufwR2xffvmltm3bps2bN6tBgwYqWbKkzfINGzbkW3EAAADO4HBAKleunHr27FkQtQAAANwRHA5IS5cuLYg6AAAA7hgOH4MEAABQ3OVpD1Lz5s0VGxur8uXLq1mzZre89lF8fHy+FQcAAOAMeQpIDz30kNzd3SVJ4eHhBVkPAACA0+UpIE2ZMiXHfwMAABRHHIMEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgJ09nsUVEROR5wNmzZ992MQAAAHeCPAWkvXv32jyOj4/XtWvXdO+990qSjh07JldXV7Vo0SL/KwQAAChkeQpI27Zts/579uzZKlOmjJYvX67y5ctLkn7++WcNHDhQ7dq1K5gqAQAACpHDxyDNmjVLUVFR1nAkSeXLl9f06dM1a9asfC0OAADAGRwOSGlpaTp//vxN7efPn9cvv/ySL0UBAAA4k8MBqWfPnho4cKA2bNig06dP6/Tp01q/fr0GDRqkhx9+uCBqBAAAKFR5Ogbp9xYuXKjRo0frscce09WrV68PUqKEBg0apNdeey3fCwQAAChsDgckLy8vvfnmm3rttdd04sQJSVKtWrVUqlSpfC8OAADAGW77QpFnz57V2bNnFRQUpFKlSskYk591AQAAOI3DAenChQvq0qWL6tSpo+7du+vs2bOSpEGDBmnUqFH5XiAAAEBhczggjRw5UiVLllRiYqK8vLys7b1799aWLVvytTgAAABncPgYpE8++UQff/yxqlatatMeFBSkU6dO5VthAAAAzuLwHqSMjAybPUc3XLx4Ue7u7vlSFAAAgDM5HJDatWunFStWWB9bLBZlZ2dr5syZ6tSpU74WBwAA4AwOf8Q2c+ZMdenSRbt371ZmZqbGjh2rw4cP6+LFi/rqq68KokYAAIBC5fAepIYNG+rYsWNq27atHnroIWVkZOjhhx/W3r17VatWrYKoEQAAoFA5FJCuXr2qLl266Ny5c5o4caLWrl2rTZs2afr06fLz87vtIhYsWKAaNWrIw8NDwcHB2rVr1y37r1u3TnXr1pWHh4caNWqkTZs22Sw3xmjy5Mny8/OTp6enQkNDdfz4cevykydPatCgQQoMDJSnp6dq1aqlKVOmKDMz87bnAAAAig+HAlLJkiV14MCBfC1gzZo1ioiI0JQpUxQfH68mTZooLCxM586dy7H/jh071LdvXw0aNEh79+5VeHi4wsPDdejQIWufmTNnat68eVq4cKF27typUqVKKSwsTL/99psk6bvvvlN2drYWLVqkw4cPa86cOVq4cKEmTJiQr3MDAABFk8U4eAnskSNHyt3dXa+++mq+FBAcHKxWrVpp/vz5kqTs7GwFBATo+eef17hx427q37t3b2VkZOijjz6ytt13331q2rSpFi5cKGOM/P39NWrUKI0ePVqSlJqaKh8fHy1btkx9+vTJsY7XXntN//znP/XDDz/kqe60tDR5e3srNTVVZcuWdXTaQLExJ+ZYgYw7smudAhkXd6+Ceq1KvF6Lkry+fzt8kPa1a9e0ZMkSbd26VS1atLjpHmyzZ8/O81iZmZnas2ePxo8fb21zcXFRaGio4uLiclwnLi5OERERNm1hYWHauHGjJCkhIUFJSUkKDQ21Lvf29lZwcLDi4uJyDUipqamqUKFCrrVeuXJFV65csT5OS0v7w/kBAICiyeGAdOjQITVv3lySdOyYbRq3WCwOjZWSkqKsrCz5+PjYtPv4+Oi7777LcZ2kpKQc+yclJVmX32jLrY+977//Xm+88YZef/31XGuNiorSiy++eOsJAQCAYsHhgLRt27aCqMNpfvrpJ3Xr1k1/+9vfNHjw4Fz7jR8/3mbPVVpamgICAgqjRAAAUMgcPs0/P1WqVEmurq5KTk62aU9OTpavr2+O6/j6+t6y/43veRnzzJkz6tSpk0JCQrR48eJb1uru7q6yZcvafAEAgOLJ4T1InTp1uuVHaZ9++mmex3Jzc1OLFi0UGxur8PBwSdcP0o6NjdVzzz2X4zpt2rRRbGysRowYYW2LiYlRmzZtJEmBgYHy9fVVbGysmjZtKun63p6dO3dq6NCh1nV++uknderUSS1atNDSpUvl4uLUrAgAAO4gDgekG6HjhqtXr2rfvn06dOiQ+vfv73ABERER6t+/v1q2bKnWrVsrOjpaGRkZGjhwoCSpX79+qlKliqKioiRJw4cPV4cOHTRr1iz16NFDq1ev1u7du617gCwWi0aMGKHp06crKChIgYGBmjRpkvz9/a0h7KefflLHjh1VvXp1vf766zp//ry1ntz2XAEAgLuHwwFpzpw5ObZPnTpV6enpDhfQu3dvnT9/XpMnT1ZSUpKaNm2qLVu2WA+yTkxMtNm7ExISolWrVikyMlITJkxQUFCQNm7cqIYNG1r7jB07VhkZGRoyZIguXbqktm3basuWLfLw8JB0fY/T999/r++//15Vq1a1qcfBqx4AAIBiyOHrIOXm+++/V+vWrXXx4sX8GO6Ox3WQgOu4DhKKCq6DBCnv79/5duBNXFycdQ8NAABAUebwR2wPP/ywzWNjjM6ePavdu3dr0qRJ+VYYAACAszgckLy9vW0eu7i46N5779W0adP0wAMP5FthAAAAzuJwQFq6dGlB1AEAAHDHcPgYpB9//FGnT5+2Pt61a5dGjBjxhxdaBAAAKCocDkiPPfaY9XYjN24Ku2vXLk2cOFHTpk3L9wIBAAAKm8MB6dChQ2rdurUkae3atWrUqJF27NihlStXatmyZfldHwAAQKFzOCBdvXpV7u7ukqStW7fqwQcflCTVrVtXZ8+ezd/qAAAAnMDhgNSgQQMtXLhQX3zxhWJiYtStWzdJ12/8WrFixXwvEAAAoLA5HJBmzJihRYsWqWPHjurbt6+aNGkiSfrggw+sH70BAAAUZQ6f5t+xY0elpKQoLS1N5cuXt7YPGTJEXl5e+VocAACAMzgckCTJ1dXVJhxJUo0aNfKjHgAAAKe7rYD0/vvva+3atUpMTFRmZqbNsvj4+HwpDAAAwFkcPgZp3rx5GjhwoHx8fLR37161bt1aFStW1A8//KD/+7//K4gaAQAACpXDAenNN9/U4sWL9cYbb8jNzU1jx45VTEyMhg0bptTU1IKoEQAAoFA5HJASExMVEhIiSfL09NQvv/wiSXriiSf03nvv5W91AAAATuBwQPL19dXFixclSdWqVdPXX38tSUpISJAxJn+rAwAAcAKHA1Lnzp31wQcfSJIGDhyokSNHqmvXrurdu7d69uyZ7wUCAAAUNofPYlu8eLGys7MlSc8++6wqVqyoHTt26MEHH9TTTz+d7wUCAAAUNocDkouLi1xc/rfjqU+fPurTp0++FgUAAOBMDn/EJklffPGFHn/8cbVp00Y//fSTJOmdd97Rl19+ma/FAQAAOIPDAWn9+vUKCwuTp6en9u7dqytXrkiSUlNT9corr+R7gQAAAIXN4YA0ffp0LVy4UP/6179UsmRJa/v999/PVbQBAECx4HBAOnr0qNq3b39Tu7e3ty5dupQfNQEAADjVbV0H6fvvv7+p/csvv1TNmjXzpSgAAABncjggDR48WMOHD9fOnTtlsVh05swZrVy5UqNHj9bQoUMLokYAAIBC5fBp/uPGjVN2dra6dOmiy5cvq3379nJ3d9fo0aP1/PPPF0SNAAAAhcrhgGSxWDRx4kSNGTNG33//vdLT01W/fn2VLl26IOoDAAAodA4HpBvc3NxUv379/KwFAADgjpDngPTkk0/mqd+SJUtuuxgAAIA7QZ4D0rJly1S9enU1a9ZMxpiCrAkAAMCp8hyQhg4dqvfee08JCQkaOHCgHn/8cVWoUKEgawMAAHCKPJ/mv2DBAp09e1Zjx47Vhx9+qICAAD366KP6+OOP2aMEAACKFYeug+Tu7q6+ffsqJiZGR44cUYMGDfTMM8+oRo0aSk9PL6gaAQAACtVtn8Xm4uIii8UiY4yysrLysyYAAHAb5sQcK7CxR3atU2Bj34kc2oN05coVvffee+ratavq1KmjgwcPav78+UpMTOQ6SAAAoNjI8x6kZ555RqtXr1ZAQICefPJJvffee6pUqVJB1gYAAOAUeQ5ICxcuVLVq1VSzZk1t375d27dvz7Hfhg0b8q04AAAAZ8hzQOrXr58sFktB1gIAAHBHcOhCkQAAAHcDhw7SBgAAuBsQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOwQkAAAAOzk+V5sQFE3J+ZYgY09smudAhsbAFD42IMEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgh4AEAABgx+kBacGCBapRo4Y8PDwUHBysXbt23bL/unXrVLduXXl4eKhRo0batGmTzXJjjCZPniw/Pz95enoqNDRUx48ft+nz8ssvKyQkRF5eXipXrlx+TwkAABRxTg1Ia9asUUREhKZMmaL4+Hg1adJEYWFhOnfuXI79d+zYob59+2rQoEHau3evwsPDFR4erkOHDln7zJw5U/PmzdPChQu1c+dOlSpVSmFhYfrtt9+sfTIzM/W3v/1NQ4cOLfA5AgCAosepAWn27NkaPHiwBg4cqPr162vhwoXy8vLSkiVLcuw/d+5cdevWTWPGjFG9evX00ksvqXnz5po/f76k63uPoqOjFRkZqYceekiNGzfWihUrdObMGW3cuNE6zosvvqiRI0eqUaNGea71ypUrSktLs/kCAADFk9MCUmZmpvbs2aPQ0ND/FePiotDQUMXFxeW4TlxcnE1/SQoLC7P2T0hIUFJSkk0fb29vBQcH5zpmXkVFRcnb29v6FRAQ8KfGAwAAdy6nBaSUlBRlZWXJx8fHpt3Hx0dJSUk5rpOUlHTL/je+OzJmXo0fP16pqanWrx9//PFPjQcAAO5cJZxdQFHh7u4ud3d3Z5cBAAAKgdP2IFWqVEmurq5KTk62aU9OTpavr2+O6/j6+t6y/43vjowJAABgz2kByc3NTS1atFBsbKy1LTs7W7GxsWrTpk2O67Rp08amvyTFxMRY+wcGBsrX19emT1pamnbu3JnrmAAAAPac+hFbRESE+vfvr5YtW6p169aKjo5WRkaGBg4cKEnq16+fqlSpoqioKEnS8OHD1aFDB82aNUs9evTQ6tWrtXv3bi1evFiSZLFYNGLECE2fPl1BQUEKDAzUpEmT5O/vr/DwcOt2ExMTdfHiRSUmJiorK0v79u2TJNWuXVulS5cu1OcAAADceZwakHr37q3z589r8uTJSkpKUtOmTbVlyxbrQdaJiYlycfnfTq6QkBCtWrVKkZGRmjBhgoKCgrRx40Y1bNjQ2mfs2LHKyMjQkCFDdOnSJbVt21ZbtmyRh4eHtc/kyZO1fPly6+NmzZpJkrZt26aOHTsW8KwBAMCdzmKMMc4uoihKS0uTt7e3UlNTVbZsWWeXgzyYE3OswMYe2bVOgY19pyuo5/Vufk5RMO6G3wF3wxz/rLy+fzv9ViMAAAB3GgISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAHQISAACAnRLOLgAAABRNc2KOFdjYI7vWKbCx84I9SAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHYISAAAAHZKOLsASVqwYIFee+01JSUlqUmTJnrjjTfUunXrXPuvW7dOkyZN0smTJxUUFKQZM2aoe/fu1uXGGE2ZMkX/+te/dOnSJd1///365z//qaCgIGufixcv6vnnn9eHH34oFxcX9erVS3PnzlXp0qULdK4A/pw5MccKbOyRXesU2NgAihanB6Q1a9YoIiJCCxcuVHBwsKKjoxUWFqajR4/qnnvuuan/jh071LdvX0VFRekvf/mLVq1apfDwcMXHx6thw4aSpJkzZ2revHlavny5AgMDNWnSJIWFhenIkSPy8PCQJP3973/X2bNnFRMTo6tXr2rgwIEaMmSIVq1aVajzBwAUT4T5os3pH7HNnj1bgwcP1sCBA1W/fn0tXLhQXl5eWrJkSY79586dq27dumnMmDGqV6+eXnrpJTVv3lzz58+XdH3vUXR0tCIjI/XQQw+pcePGWrFihc6cOaONGzdKkr799ltt2bJFb731loKDg9W2bVu98cYbWr16tc6cOVNYUwcAAHcop+5ByszM1J49ezR+/Hhrm4uLi0JDQxUXF5fjOnFxcYqIiLBpCwsLs4afhIQEJSUlKTQ01Lrc29tbwcHBiouLU58+fRQXF6dy5cqpZcuW1j6hoaFycXHRzp071bNnz5u2e+XKFV25csX6ODU1VZKUlpbm+MThFL9lpBfY2Hfz66CgntfcnlN+jrhdhf3accZr9W6YY36Na4y5ZT+nBqSUlBRlZWXJx8fHpt3Hx0ffffddjuskJSXl2D8pKcm6/EbbrfrYf3xXokQJVahQwdrHXlRUlF588cWb2gMCAnKbHu4iE5xdQDHkjOeUnyNuV2G/du6G/x8Fvb1ffvlF3t7euS53+jFIRcX48eNt9lxlZ2fr4sWLqlixoiwWi6TrqTQgIEA//vijypYt66xSCxRzLB7uhjlKd8c8mWPxwBwLjzFGv/zyi/z9/W/Zz6kBqVKlSnJ1dVVycrJNe3Jysnx9fXNcx9fX95b9b3xPTk6Wn5+fTZ+mTZta+5w7d85mjGvXrunixYu5btfd3V3u7u42beXKlcuxb9myZYvtC/wG5lg83A1zlO6OeTLH4oE5Fo5b7Tm6wakHabu5ualFixaKjY21tmVnZys2NlZt2rTJcZ02bdrY9JekmJgYa//AwED5+vra9ElLS9POnTutfdq0aaNLly5pz5491j6ffvqpsrOzFRwcnG/zAwAARZPTP2KLiIhQ//791bJlS7Vu3VrR0dHKyMjQwIEDJUn9+vVTlSpVFBUVJUkaPny4OnTooFmzZqlHjx5avXq1du/ercWLF0uSLBaLRowYoenTpysoKMh6mr+/v7/Cw8MlSfXq1VO3bt00ePBgLVy4UFevXtVzzz2nPn36/OEuNwAAUPw5PSD17t1b58+f1+TJk5WUlKSmTZtqy5Yt1oOsExMT5eLyvx1dISEhWrVqlSIjIzVhwgQFBQVp48aN1msgSdLYsWOVkZGhIUOG6NKlS2rbtq22bNlivQaSJK1cuVLPPfecunTpYr1Q5Lx58/7UXNzd3TVlypSbPoorTphj8XA3zFG6O+bJHIsH5njnsZg/Os8NAADgLuP0C0UCAADcaQhIAAAAdghIAAAAdghIAAAAdghIAAAAdghIAAAAdghIeZCdna2srCxnl1EoivNVH86ePasjR444u4wCdfnyZWVmZjq7DAAo8ghIf+DIkSPq16+fwsLCNHToUO3YscPZJeW7jIwM/fLLL0pLS7PeeLe4+emnn9SoUSNFRkZq9+7dzi6nQBw6dEiPPvqovv76a125csXZ5RSI06dPa+3atdqwYYMOHjzo7HIKVXH+46W4unjxos6fP+/sMgrU999/r3//+9/F8g8zAtItHD16VCEhIcrKylKrVq0UFxen4cOH/+krbt9Jjhw5oocfflgdOnRQvXr1tHLlSknF75fx8ePHlZqaqtTUVL3xxhuKj4+3LisOcz18+LDatWunqlWrKjAwsMhcqdYRBw8eVNu2bfXaa6/pmWee0cSJE3XixAlnl5Xvjh07phdeeEEDBw7U3Llzdfz4cUnXb6NUHF6rkpSQkKA5c+Zo1KhRWrNmjbPLKRA//PCDWrVqpTfeeENnzpxxdjkF4sCBAwoJCdHmzZuVkpLi7HLyn0GOsrOzzYQJE8yjjz5qbUtLSzPTp083TZs2NTNmzHBidfnj8OHDpmLFimbkyJFm5cqVJiIiwpQsWdLs3bvX2aXluwsXLpgHH3zQLFq0yDRv3tz8/e9/N4cOHTLGGJOVleXk6v6c9PR088ADD5ihQ4da27799luzd+9ec+rUKSdWln9OnjxpqlSpYsaNG2fS09PNpk2bjK+vr9m5c6ezS8tXhw8fNt7e3qZbt26mV69extvb24SGhpp//etf1j7Z2dlOrPDPO3DggKlatarp0qWLCQkJMS4uLmbmzJnOLivfLVy40FgsFtOsWTPz8ssvm7Nnz1qXZWdnF/mf46lTp0y1atXMmDFjcu1T1OdIQLqFAQMGmPbt29u0paWlmddff920bNnSvPvuu06q7M+7cOGCeeCBB8ywYcNs2jt27Gief/55Y0zRf3HfcO3aNXPu3DlTp04dc/r0abNhwwbTqlUrM3jwYBMSEmJ69erl7BL/lN9++820bdvWxMfHm2vXrpmwsDDTqlUrU6ZMGXPfffeZt956y9kl/mmLFi0yHTt2tHlNdu/e3SxatMgsX77cfPrpp06sLn9cuXLFPP7442bw4MHWtuPHj5vevXub++67z8ydO9eJ1eWPkydPmtq1a5uxY8da/zB5++23jY+Pjzl27JiTq8tf+/fvN/379zfTp083/v7+5qWXXjI///yzs8vKNx9++KHp3r27McaYzMxMM3HiRBMeHm6eeuops3z5cmu/ovw+4vSb1d6JjDGyWCxq3ry5jh8/rqNHj+ree++VJJUpU0ZPPvmkjh49qjfffFM9e/aUl5eXkyt23NWrV3Xp0iU98sgjkq4fiO7i4qLAwEBdvHhRkorN8UguLi6qXLmyWrVqpUOHDqlnz55yd3dX//79deXKFQ0ePNjZJf4ply5d0tGjR5WSkqIxY8ZIkt566y2dOXNGn376qSIjI+Xt7W39WRdFxhglJiZq3759atasmV5++WVt3rxZmZmZSk1N1alTpzRjxgwNGDDA2aXeNjc3NyUnJyswMFDS9TnXrl1bM2fO1JQpU/T+++8rMDBQf/3rX51c6e3Jzs7W6tWrVbt2bU2YMMF6E/JWrVqpZMmSys7OdnKF+csYox07dmjp0qXKysrS4sWLVaZMGW3fvl316tXTyy+/7OwS/5T4+Hjre0X37t117do1NWnSREeOHNHu3bv13Xff6ZVXXinS7yMcg5SDGz/Q7t276+jRo5o5c6bS09MlXX/Rly9fXpMmTVJcXJw+//xzZ5Z623x8fPTuu++qXbt2kmQ9S69KlSrWX1w33Jh7UXXj5+nq6qrPPvtMkrRhwwZlZWUpICBAX3zxhXbt2uXECv+ce+65R126dNEHH3yg48ePa+TIkWrcuLG6deumYcOGKTQ0VLGxscrKyiqyx7A88MAD8vX11aOPPqpHHnlEkyZN0r///W998skn+uijj9SnTx8tX75cFy5cKJJzzMrK0tWrV1W1alVdvHjRepB9dna2qlWrpkmTJunatWvWYwSLIhcXF7Vp00ZNmzaVt7e3tb1BgwYqUaKEzp4968Tq8l+TJk0UGBioU6dOafLkyXr++ec1ceJEffrpp+rQoYOzy/vTQkJC5OXlpbffflsWi0XvvvuuoqOjtW7dOvXs2VPbtm0r8mcNE5BuoVatWlq7dq1WrlypcePGKSUlxfpmW7JkSTVu3NjmP3pRExQUJOn6L+GSJUtKuh4Az507Z+0TFRWlxYsX69q1a06pMT/ceMPs3Lmz3N3d9cwzz2jTpk3as2ePpk+fru3bt2vp0qX67bffnFzp7bFYLBo1apSWLl2q//73vzZnk1StWlU+Pj46cuSIXFxciuxfc4GBgXr33Xf18ssvq2HDhurVq5ceeughWSwW3XPPPfL399fPP/+sUqVKFak53vjDxNXVVSVLllT//v3173//W4sWLZLFYpGLi4uysrJUs2ZNRUVFad26dTp8+LCTq3bM7y+R0r59e0VFRUmyPTnCYrHo6tWr1sexsbFF6uyv3C4Dk5mZaf0j+ujRo3J1dZWnp6f2799f5A7ctp9j1apV9d1332n27NkyxqhKlSqSJG9vbw0cOFAHDhzQ/v37nVFqviEg/YFOnTpp3bp1euutt/T0009rzZo1+vbbbzV37lydO3dOAQEBzi7xT3NxcbH5ZXVjD9LkyZM1ceJEdenSRSVKFN1PY2+8YQYGBmratGn697//rQ8//FCBgYHq2bOnXn/9dY0dO1YeHh5OrvT2tWzZUps3b5YkLV682OZN9OrVq6pTp06RDrnS9Z/fo48+qqpVq+rXX3+1CYLJycmqUaNGkbpe2bFjxxQdHW2z56RDhw6aMWOGRo4cqbfeekvS9fAkXf94/95771WpUqWcUu/tyGmON37XWCwWXbt2Tb/++qtcXV1VtmxZSdKECRPUtWtXm8B0J8tpjjdqDw4OlouLi4YNG6bNmzdr3759GjZsmKZOnarVq1cXmddrTnOsW7euFi9erGPHjunAgQOKi4uzLvPx8dF9992nChUqOKPc/OOUI5+KoD179pgOHTqY6tWrm1q1apk6deqY+Ph4Z5eVb24cMDllyhQzZMgQ89prrxl3d3ezZ88eJ1eWfzIzM83bb79t9u/fb4wp2gcP5mb79u3G39/ftG7d2gwaNMg88cQTxtvb2xw8eNDZpeWbG2d6zZw506xYscKMHTvWlCtXzhw4cMDZpeXZ8ePHTYUKFYzFYjHjx48358+fty7LyMgwL774orFYLCYyMtLEx8ebCxcumHHjxpnatWubc+fOObHyvLvVHG/Iysoyv/76q6lVq5bZvXu3mTZtmilVqpTZtWuXEyp23B/NccmSJcZisRg/Pz/zzTffWNtnzJhRZA5K/6M5vvfee8bFxcWEhYWZ9957zxw/ftyMGzfO+Pv7m8TERCdVnT8ISA5ITU01CQkJ5sCBAzn+Zy8Opk+fbiwWi/H29rb5D11cFPVT+vPiu+++M5GRkSY0NNQMHTq0WIWjGz799FNTq1YtExQUZDp27GgNvUVBenq6efLJJ82AAQPMggULjMViMWPGjLEJPllZWWb58uXG19fXVKlSxdStW9f4+/sXmT9Ycptjbr83mzVrZlq1amXc3NyKzO+dvMzx6NGjJjIy0nrplKL2+yevP8etW7eaNm3aGB8fH1O3bt1iswOBgAQb33zzjbFYLObw4cPOLgV/UlZWVpH7heyICxcumKSkpCJ36vTly5fNggULzOrVq40xxqxZsybHkGSMMQkJCWb79u1m8+bN5vTp084o97bcao6/f3O9du2auXDhgvH29jaurq5Fai9gXueYkZFh/XdR22ud1zkaY0xKSoo5duyY2bt3b7HZgUBAwk3S09OdXQJQrNn/H1u9erWxWCxm9OjR1jeXq1evFukLfd5qjikpKcaY63M8f/682bJli/XCrUXJreZ4I+xmZWWZH374wRnl5Yu8/hwTEhKcUF3BKrpH3qLAFKWDQIGi6Mb/saysLLm4uKh3794yxuixxx6TxWLRiBEj9Prrr+vUqVNasWKFvLy8itTZeVLe53jy5Em9++67RfJ6co78HN95551iP8ei+lrNjcWYInjREAAoJsz1PflycXHRmjVr9MQTT6hmzZo6ceKEvvnmGzVt2tTZJf5puc3x+++/1+7du4v1HO+Gn2NxmuPvEZAAwMnM705979Kli/bt26fPPvtMjRo1cnJl+Yc5Fg93wxxv4CM2AHAyi8WirKwsjRkzRtu2bdO+ffuK3RsOcywe7oY53sCFIgHgDtGgQQPFx8ercePGzi6lwDDH4uFumCMfsQHAHcL8/xtlF2fMsXi4G+ZIQAIAALDDR2wAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAAAB2CEgAUABq1Kih6OhoZ5cB4DYRkADku/Pnz2vo0KGqVq2a3N3d5evrq7CwMH311VfOLo3gAiBPuBcbgHzXq1cvZWZmavny5apZs6aSk5MVGxurCxcuFNg2MzMz5ebmVmDjA7i7sAcJQL66dOmSvvjiC82YMUOdOnVS9erV1bp1a40fP14PPvigTb+nnnpKlStXVtmyZdW5c2ft37/fZqwPP/xQrVq1koeHhypVqqSePXtal9WoUUMvvfSS+vXrp7Jly2rIkCGSpC+//FLt2rWTp6enAgICNGzYMGVkZEiSOnbsqFOnTmnkyJGyWCy3vFXCpUuX9PTTT8vHx0ceHh5q2LChPvroI+vy9evXq0GDBnJ3d1eNGjU0a9asXMc6efKkLBaL9u3bZzO+xWLRZ599Jkn67LPPZLFY9PHHH6tZs2by9PRU586dde7cOW3evFn16tVT2bJl9dhjj+ny5cvWcTp27Khhw4Zp7NixqlChgnx9fTV16tTcf0AA8oSABCBflS5dWqVLl9bGjRt15cqVXPv97W9/s77579mzR82bN1eXLl108eJFSdJ///tf9ezZU927d9fevXsVGxur1q1b24zx+uuvq0mTJtq7d68mTZqkEydOqFu3burVq5cOHDigNWvW6Msvv9Rzzz0nSdqwYYOqVq2qadOm6ezZszp79myOtWVnZ+v//u//9NVXX+ndd9/VkSNH9Oqrr8rV1VWStGfPHj366KPq06ePDh48qKlTp2rSpElatmzZn37+pk6dqvnz52vHjh368ccf9eijjyo6OlqrVq3Sf//7X33yySd64403bNZZvny5SpUqpZ07d2rmzJmaNm2aYmJi/nQtwF3NAEA+e//990358uWNh4eHCQkJMePHjzf79++3Lv/iiy9M2bJlzW+//WazXq1atcyiRYuMMca0adPG/P3vf891G9WrVzfh4eE2bYMGDTJDhgyxafviiy+Mi4uL+fXXX63rzZkz55b1f/zxx8bFxcUcPXo0x+WPPfaY6dq1q03bmDFjTP369W3qu7GdhIQEI8ns3bvXuvznn382ksy2bduMMcZs27bNSDJbt2619omKijKSzIkTJ6xtTz/9tAkLC7M+7tChg2nbtq1NLa1atTIvvPDCLecI4NbYgwQg3/Xq1UtnzpzRBx98oG7duumzzz5T8+bNrXtY9u/fr/T0dFWsWNG6x6l06dJKSEjQiRMnJEn79u1Tly5dbrmdli1b2jzev3+/li1bZjNmWFiYsrOzlZCQkOf69+3bp6pVq6pOnTo5Lv/22291//3327Tdf//9On78uLKysvK8nZw0btzY+m8fHx95eXmpZs2aNm3nzp3LdR1J8vPzu6kPAMdwkDaAAuHh4aGuXbuqa9eumjRpkp566ilNmTJFAwYMUHp6uvz8/KzH3/xeuXLlJEmenp5/uI1SpUrZPE5PT9fTTz+tYcOG3dS3WrVqea49L9t2hIvL9b9FjTHWtqtXr+bYt2TJktZ/WywWm8c32rKzs3NdJ7c+ABxDQAJQKOrXr6+NGzdKkpo3b66kpCSVKFFCNWrUyLF/48aNFRsbq4EDB+Z5G82bN9eRI0dUu3btXPu4ubn94V6exo0b6/Tp0zp27FiOe5Hq1at30yULvvrqK9WpU8d6nNLvVa5cWZJ09uxZNWvWTJJsDtgGcOfhIzYA+erChQvq3Lmz3n33XR04cEAJCQlat26dZs6cqYceekiSFBoaqjZt2ig8PFyffPKJTp48qR07dmjixInavXu3JGnKlCl67733NGXKFH377bc6ePCgZsyYccttv/DCC9qxY4eee+457du3T8ePH9d//vMf60Ha0vWz3z7//HP99NNPSklJyXGcDh06qH379urVq5diYmKUkJCgzZs3a8uWLZKkUaNGKTY2Vi+99JKOHTum5cuXa/78+Ro9enSO43l6euq+++7Tq6++qm+//Vbbt29XZGSkw88tgMJDQAKQr0qXLq3g4GDNmTNH7du3V8OGDTVp0iQNHjxY8+fPl3T9I6BNmzapffv2GjhwoOrUqaM+ffro1KlT8vHxkXT99PV169bpgw8+UNOmTdW5c2ft2rXrlttu3Lixtm/frmPHjqldu3Zq1qyZJk+eLH9/f2ufadOm6eTJk6pVq5Z1z05O1q9fr1atWqlv376qX7++xo4da93z1Lx5c61du1arV69Ww4YNNXnyZE2bNk0DBgzIdbwlS5bo2rVratGihUaMGKHp06fn9SkF4AQW8/sPxQEAAMAeJAAAAHsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADsEJAAAADv/D2MrJqGBKIRlAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for secret in columns:\n", - " curr_res = results_for_groups[secret]\n", - "\n", - " fig, ax = plt.subplots()\n", - "\n", - " risks = [res[\"risk\"].value for _, res in curr_res.items()]\n", - " columns = [key for key,_ in curr_res.items()]\n", - "\n", - " ax.bar(x=columns, height=risks, alpha=0.5, ecolor='black', capsize=10)\n", - "\n", - " plt.xticks(rotation=45, ha='right')\n", - " plt.title(secret)\n", - " ax.set_ylabel(\"Measured inference risk\")\n", - " _ = ax.set_xlabel(\"Secret column\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c790d894-e3d9-4cee-bd08-f4ef5f364d4e", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-15T11:53:47.147242Z", - "start_time": "2025-05-15T11:53:46.776658Z" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "\n", - "risks = [res[1].risk().value for res in results]\n", - "columns = [res[0] for res in results]\n", - "\n", - "ax.bar(x=columns, height=risks, alpha=0.5, ecolor='black', capsize=10)\n", - "\n", - "plt.xticks(rotation=45, ha='right')\n", - "plt.title(\"Risk scores per secret\")\n", - "ax.set_ylabel(\"Measured inference risk\")\n", - "_ = ax.set_xlabel(\"Secret column\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79503a44-6fc3-4ba4-a56d-97cb8994d176", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.22" - }, - "vscode": { - "interpreter": { - "hash": "237cf5f6b3dcd73bf2688629baee50bd53e43ee0aa8f2bde7060bbd4d3c193da" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/anonymeter/evaluators/__init__.py b/src/anonymeter/evaluators/__init__.py index d31cb0a..26b2848 100644 --- a/src/anonymeter/evaluators/__init__.py +++ b/src/anonymeter/evaluators/__init__.py @@ -5,6 +5,5 @@ from anonymeter.evaluators.inference_evaluator import InferenceEvaluator from anonymeter.evaluators.linkability_evaluator import LinkabilityEvaluator from anonymeter.evaluators.singling_out_evaluator import SinglingOutEvaluator -from anonymeter.evaluators.inference_predictor import KNNInference, MLModelInference -__all__ = ["InferenceEvaluator", "LinkabilityEvaluator", "SinglingOutEvaluator", "KNNInference", "MLModelInference"] +__all__ = ["InferenceEvaluator", "LinkabilityEvaluator", "SinglingOutEvaluator"] diff --git a/src/anonymeter/evaluators/inference_evaluator.py b/src/anonymeter/evaluators/inference_evaluator.py index 341a40c..fd20e7a 100644 --- a/src/anonymeter/evaluators/inference_evaluator.py +++ b/src/anonymeter/evaluators/inference_evaluator.py @@ -2,17 +2,16 @@ # Copyright (c) 2022 Anonos IP LLC. # See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. """Privacy evaluator that measures the inference risk.""" -from typing import Optional, Union +from typing import Optional import numpy as np import numpy.typing as npt import pandas as pd -from pandas import DataFrame +from anonymeter.evaluators.inference_predictor import InferencePredictor +from anonymeter.neighbors.mixed_types_kneighbors import KNNInferencePredictor from anonymeter.stats.confidence import EvaluationResults, PrivacyRisk -from anonymeter.evaluators.inference_predictor import InferencePredictor, KNNInference - def _run_attack( target: pd.DataFrame, @@ -24,7 +23,7 @@ def _run_attack( naive: bool, regression: Optional[bool], inference_model: Optional[InferencePredictor], -) -> tuple[int, Union[tuple[npt.NDArray, npt.NDArray], npt.NDArray, None], DataFrame]: +) -> int: if regression is None: regression = pd.api.types.is_numeric_dtype(target[secret]) @@ -34,22 +33,12 @@ def _run_attack( else: # Instantiate the default KNN model if no other model is passed through `inference_model`. if inference_model is None: - inference_model = KNNInference(data=syn, columns=aux_cols, target_col=secret, n_jobs=n_jobs) - - # Depending on the `inference_model.sample_targets` we might not need to sample, - # but rather predict for all targets as we assume it can scale to all target samples. - if inference_model.sample_targets: - targets = target.sample(n_attacks, replace=False) - else: - targets = target + inference_model = KNNInferencePredictor(data=syn, columns=aux_cols, target_col=secret, n_jobs=n_jobs) + targets = target.sample(n_attacks, replace=False) guesses = inference_model.predict(targets) - assert len(guesses) == len(targets), "Predictions and targets have different lengths" - guesses.index = targets.index - - return evaluate_inference_guesses(guesses=guesses, secrets=targets[secret], - regression=regression).sum(), guesses, targets + return evaluate_inference_guesses(guesses=guesses, secrets=targets[secret], regression=regression).sum() def evaluate_inference_guesses( @@ -173,14 +162,13 @@ def __init__( self._control = control self._n_attacks = n_attacks self._inference_model = inference_model - if self._inference_model is not None and not self._inference_model.sample_targets: - self._n_attacks_ori = self._ori.shape[0] - self._n_attacks_baseline = min(self._syn.shape[0], self._n_attacks_ori) - self._n_attacks_control = self._control.shape[0] + + self._n_attacks_ori = min(n_attacks, self._ori.shape[0]) + self._n_attacks_baseline = min(self._syn.shape[0], self._n_attacks_ori) + if self._control is None: + self._n_attacks_control = -1 else: - self._n_attacks_ori = self._n_attacks - self._n_attacks_baseline = self._n_attacks - self._n_attacks_control = self._n_attacks + self._n_attacks_control = min(n_attacks, self._control.shape[0]) # check if secret is a string column if not isinstance(secret, str): @@ -194,11 +182,8 @@ def __init__( self._regression = regression self._aux_cols = aux_cols self._evaluated = False - self._data_groups = self._ori[self._secret].unique().tolist() - def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int, n_attacks: int) -> tuple[ - int, Union[tuple[npt.NDArray, npt.NDArray], - pd.Series, None], DataFrame]: + def _attack(self, target: pd.DataFrame, naive: bool, n_jobs: int, n_attacks: int) -> int: return _run_attack( target=target, syn=self._syn, @@ -226,17 +211,17 @@ def evaluate(self, n_jobs: int = -2) -> "InferenceEvaluator": """ # n_attacks is effective here - self._n_baseline, _, _ = self._attack(target=self._ori, naive=True, n_jobs=n_jobs, - n_attacks=self._n_attacks_baseline) + self._n_baseline = self._attack(target=self._ori, naive=True, n_jobs=n_jobs, + n_attacks=self._n_attacks_baseline) # n_attacks is not effective here, just needed for the baseline - self._n_success, self.guesses_success, self.target = self._attack(target=self._ori, naive=False, n_jobs=n_jobs, - n_attacks=self._n_attacks_ori) + self._n_success = self._attack(target=self._ori, naive=False, n_jobs=n_jobs, + n_attacks=self._n_attacks_ori) # n_attacks is not effective here, just needed for the baseline - self._n_control, self.guesses_control, self.target_control = ( + self._n_control = ( None if self._control is None else self._attack(target=self._control, naive=False, n_jobs=n_jobs, n_attacks=self._n_attacks_control) - ) + ) self._evaluated = True return self @@ -259,10 +244,7 @@ def results(self, confidence_level: float = 0.95) -> EvaluationResults: raise RuntimeError("The inference evaluator wasn't evaluated yet. Please, run `evaluate()` first.") return EvaluationResults( - n_attacks=self._n_attacks, - n_attacks_ori=self._n_attacks_ori, - n_attacks_baseline=self._n_attacks_baseline, - n_attacks_control=self._n_attacks_control, + n_attacks=(self._n_attacks_ori, self._n_attacks_baseline, self._n_attacks_control), n_success=self._n_success, n_baseline=self._n_baseline, n_control=self._n_control, @@ -294,73 +276,3 @@ def risk(self, confidence_level: float = 0.95, baseline: bool = False) -> Privac """ results = self.results(confidence_level=confidence_level) return results.risk(baseline=baseline) - - def risk_for_groups(self, confidence_level: float = 0.95) -> dict[ - str, dict[str, Union[EvaluationResults, PrivacyRisk]]]: - """Compute the attack risks on a group level, for every unique value of `self._data_groups`. - - Parameters - ---------- - confidence_level : float, default is 0.95 - Confidence level for the error bound calculation. - - Returns - ------- - dict[str, dict[str, EvaluationResults | PrivacyRisk]] - The group as a key, and then for every group the results (EvaluationResults), - and the risks (PrivacyRisk). - - """ - if not self._evaluated: - self.evaluate(n_jobs=-2) - - all_results = {} - - # For every unique group in `self._data_groups` - for group in self._data_groups: - # Get the targets for the current group - target = self.target[self.target[self._secret] == group] - - # Get the guesses for the current group - guess = self.guesses_success.loc[target.index] - - # Count the number of success attacks - n_success = evaluate_inference_guesses(guesses=guess, - secrets=target[self._secret], - regression=self._regression).sum() - - if self._control is not None: - # Get the targets for the current control group - target_control = self.target_control[self.target_control[self._secret] == group] - - # Get the guesses for the current control group - guesses_control = self.guesses_control.loc[target_control.index] - - # Count the number of success control attacks - n_control = evaluate_inference_guesses(guesses=guesses_control, - secrets=target_control[self._secret], - regression=self._regression).sum() - else: - n_control = None - - # Recreate the EvaluationResults for the current group - results = EvaluationResults( - n_attacks=self._n_attacks, # passing for - n_attacks_ori=self._n_attacks_ori, - n_attacks_baseline=self._n_attacks_baseline, - # We leave the overall n_attacks_baseline here, it doesn't change the risk - n_attacks_control=self._n_attacks_control, - n_success=n_success, - n_baseline=self._n_baseline, # We leave the overall _n_baseline here, it doesn't change the risk - n_control=n_control, - confidence_level=confidence_level, - ) - # Compute the risk - risk = results.risk() - - all_results[group] = { - "results": results, - "risk": risk - } - - return all_results diff --git a/src/anonymeter/evaluators/inference_predictor.py b/src/anonymeter/evaluators/inference_predictor.py index 8b5e62c..3e9351d 100644 --- a/src/anonymeter/evaluators/inference_predictor.py +++ b/src/anonymeter/evaluators/inference_predictor.py @@ -2,44 +2,7 @@ import pandas as pd -from anonymeter.neighbors.mixed_types_kneighbors import MixedTypeKNeighbors - class InferencePredictor(Protocol): def predict(self, X: pd.DataFrame) -> pd.Series: ... - - @property - def sample_targets(self) -> bool: - ... - - -class MLModelInference: - def __init__(self, model, sample_targets: bool = False): - self._model = model - self._sample_targets = sample_targets - - @property - def sample_targets(self) -> bool: - return self._sample_targets - - def predict(self, x: pd.DataFrame) -> pd.Series: - return self._model.predict(x) - - -class KNNInference: - def __init__(self, data: pd.DataFrame, columns: list[str], target_col: str, n_jobs: int): - self._nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=data[columns]) - self._data = data - self._target_col = target_col - self._columns = columns - - @property - def sample_targets(self) -> bool: - return True - - def predict(self, x: pd.DataFrame) -> pd.Series: - guesses_idx = self._nn.kneighbors(queries=x[self._columns]) - if isinstance(guesses_idx, tuple): - raise RuntimeError("guesses_idx cannot be a tuple") - return self._data.iloc[guesses_idx.flatten()][self._target_col] \ No newline at end of file diff --git a/src/anonymeter/evaluators/sklearn_inference_predictor.py b/src/anonymeter/evaluators/sklearn_inference_predictor.py new file mode 100644 index 0000000..19aaff2 --- /dev/null +++ b/src/anonymeter/evaluators/sklearn_inference_predictor.py @@ -0,0 +1,41 @@ +import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, is_classifier, is_regressor + +from anonymeter.evaluators.inference_predictor import InferencePredictor + + +class SklearnInferencePredictor(InferencePredictor): + """Wrapper class to use sklearn methods in the inference evaluator. + + Parameters + ---------- + model : sklearn.base.BaseEstimator + A classifier or regressor which implements ::predict(). + + """ + def __init__(self, model: BaseEstimator): + if not (is_classifier(estimator=model) or is_regressor(estimator=model)): + raise ValueError("Model must be classifier or regressor %s", model) + if not hasattr(model, "predict"): + raise ValueError("Model must have a predict method, %s", model) + self._model = model + + def predict(self, x: pd.DataFrame) -> pd.Series: + """Predict the targets for input `x`. + + Parameters + ---------- + x : pd.DataFrame + The input data to predict. + + Returns + ------- + pd.Series + The predictions as pd.Series. + + """ + prediction = self._model.predict(x) + if isinstance(prediction, np.ndarray): + return pd.Series(prediction) + return prediction diff --git a/src/anonymeter/neighbors/mixed_types_kneighbors.py b/src/anonymeter/neighbors/mixed_types_kneighbors.py index 36c94b8..443bba6 100644 --- a/src/anonymeter/neighbors/mixed_types_kneighbors.py +++ b/src/anonymeter/neighbors/mixed_types_kneighbors.py @@ -12,6 +12,7 @@ from joblib import Parallel, delayed from numba import jit +from anonymeter.evaluators.inference_predictor import InferencePredictor from anonymeter.preprocessing.transformations import mixed_types_transform from anonymeter.preprocessing.type_detection import detect_consistent_col_types @@ -75,7 +76,7 @@ def gower_distance(r0: npt.NDArray, r1: npt.NDArray, cat_cols_index: int) -> flo @jit(nopython=True, nogil=True) def _nearest_neighbors( - queries: npt.NDArray, candidates: npt.NDArray, cat_cols_index: int, n_neighbors: int + queries: npt.NDArray, candidates: npt.NDArray, cat_cols_index: int, n_neighbors: int ) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.float64]]: r"""For every element of ``queries``, find its nearest neighbors in ``candidates``. @@ -166,7 +167,7 @@ def fit(self, candidates: pd.DataFrame, ctypes: Optional[dict[str, list[str]]] = return self def kneighbors( - self, queries: pd.DataFrame, n_neighbors: Optional[int] = None, return_distance: bool = False + self, queries: pd.DataFrame, n_neighbors: Optional[int] = None, return_distance: bool = False ) -> Union[tuple[npt.NDArray, npt.NDArray], npt.NDArray]: """Find the nearest neighbors for a set of query points. @@ -220,7 +221,7 @@ def kneighbors( with Parallel(n_jobs=self._n_jobs, backend="threading") as executor: res = executor( delayed(_nearest_neighbors)( - queries=queries[ii : ii + 1], + queries=queries[ii: ii + 1], candidates=candidates, cat_cols_index=len(self._ctypes["num"]), n_neighbors=n_neighbors, @@ -235,3 +236,46 @@ def kneighbors( return distances, indexes return indexes + + +class KNNInferencePredictor(InferencePredictor): + """Wrapper class to use MixedTypeKNeighbors in the inference evaluator. + + Parameters + ---------- + data : pd.DataFrame + The train data to fit the model on (usually the synthetic data). + columns : list[str] + The auxiliary columns of `data`, used as input to the model. + target_col : str + The target column of `data`. + n_jobs : int, default is -2 + Number of jobs to use. It follows joblib convention, so that ``n_jobs = -1`` + means all available cores + + """ + + def __init__(self, data: pd.DataFrame, columns: list[str], target_col: str, n_jobs: int): + self._nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=data[columns]) + self._data = data + self._target_col = target_col + self._columns = columns + + def predict(self, x: pd.DataFrame) -> pd.Series: + """Predict the targets for input `x`. + + Parameters + ---------- + x : pd.DataFrame + The input data to predict. + + Returns + ------- + pd.Series + The predictions as pd.Series. + + """ + guesses_idx = self._nn.kneighbors(queries=x[self._columns]) + if isinstance(guesses_idx, tuple): + raise RuntimeError("guesses_idx cannot be a tuple") + return self._data.iloc[guesses_idx.flatten()][self._target_col] diff --git a/src/anonymeter/stats/confidence.py b/src/anonymeter/stats/confidence.py index 7f3469c..9829de7 100644 --- a/src/anonymeter/stats/confidence.py +++ b/src/anonymeter/stats/confidence.py @@ -5,7 +5,7 @@ import warnings from math import sqrt -from typing import NamedTuple, Optional +from typing import NamedTuple, Optional, Union from scipy.stats import norm @@ -194,35 +194,31 @@ class EvaluationResults: def __init__( self, - n_attacks: int, + n_attacks: Union[int, tuple[int, int, int]], n_success: int, n_baseline: int, n_control: Optional[int] = None, confidence_level: float = 0.95, - n_attacks_ori: Optional[int] = None, - n_attacks_baseline: Optional[int] = None, - n_attacks_control: Optional[int] = None, ): - self.n_attacks = n_attacks - if None in [n_attacks_ori, n_attacks_baseline, n_attacks_control]: - # Setting all to self.n_attacks because of at least one missing value.. - n_attacks_ori = self.n_attacks - n_attacks_baseline = self.n_attacks - n_attacks_control = self.n_attacks + if isinstance(n_attacks, int): + self.n_attacks_ori = n_attacks + self.n_attacks_baseline = n_attacks + self.n_attacks_control = n_attacks + elif isinstance(n_attacks, tuple): + self.n_attacks_ori, self.n_attacks_baseline, self.n_attacks_control = n_attacks + else: + raise ValueError(f"n_attacks must be an integer or a tuple of three integers, got {n_attacks}") - self.attack_rate = success_rate(n_total=n_attacks_ori, n_success=n_success, confidence_level=confidence_level) + self.attack_rate = success_rate(n_total=self.n_attacks_ori, n_success=n_success, confidence_level=confidence_level) - self.baseline_rate = success_rate(n_total=n_attacks_baseline, n_success=n_baseline, confidence_level=confidence_level) + self.baseline_rate = success_rate(n_total=self.n_attacks_baseline, n_success=n_baseline, confidence_level=confidence_level) self.control_rate = ( None if n_control is None - else success_rate(n_total=n_attacks_control, n_success=n_control, confidence_level=confidence_level) + else success_rate(n_total=self.n_attacks_control, n_success=n_control, confidence_level=confidence_level) ) - self.n_attacks_ori = n_attacks_ori - self.n_attacks_baseline = n_attacks_baseline - self.n_attacks_control = n_attacks_control self.n_success = n_success self.n_baseline = n_baseline self.n_control = n_control diff --git a/tests/test_inference_evaluator.py b/tests/test_inference_evaluator.py index c8cf9ab..eeaa934 100644 --- a/tests/test_inference_evaluator.py +++ b/tests/test_inference_evaluator.py @@ -72,9 +72,9 @@ def test_evaluate_inference_guesses_regression_tolerance(guesses, secrets, toler ], ) def test_inference_evaluator_rates( - ori: Iterable, - syn: Iterable, - expected: float, + ori: Iterable, + syn: Iterable, + expected: float, ): # created a dataframe from ori and name columns c0 and c1 ori = pd.DataFrame(ori, columns=pd.Index(["c0", "c1"])) diff --git a/tests/test_sklearn_inference_model.py b/tests/test_sklearn_inference_model.py new file mode 100644 index 0000000..ac45d2a --- /dev/null +++ b/tests/test_sklearn_inference_model.py @@ -0,0 +1,56 @@ +# This file is part of Anonymeter and is released under BSD 3-Clause Clear License. +# Copyright (c) 2022 Anonos IP LLC. +# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. + +import numpy as np +import pytest +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder +from sklearn.tree import DecisionTreeRegressor + +from anonymeter.evaluators.inference_evaluator import InferenceEvaluator +from anonymeter.evaluators.sklearn_inference_predictor import SklearnInferencePredictor + +from tests.fixtures import get_adult + + +@pytest.mark.parametrize( + "aux_cols", + [ + ["type_employer", "capital_loss", "hr_per_week", "age"], + ["education_num", "type_employer", "capital_loss"], + ["age", "type_employer", "race"], + ], +) +@pytest.mark.parametrize("secret", ["capital_gain", "capital_loss"]) +def test_inference_evaluator_custom_model_regressor(aux_cols, secret): + ori = get_adult("ori", n_samples=10) + + # Inference model prep + categorical_cols = ori[aux_cols].select_dtypes(include=["object"]).columns + numeric_cols = ori[aux_cols].select_dtypes(include=["number"]).columns + + preprocess = ColumnTransformer( + transformers=[ + ("cat", OneHotEncoder(handle_unknown="ignore"), categorical_cols), + ("num", "passthrough", numeric_cols) + ] + ) + tree = DecisionTreeRegressor(random_state=42) + + model = Pipeline(steps=[ + ("preprocess", preprocess), + ("tree", tree) + ]) + model.fit(ori[aux_cols], ori[secret]) + inference_model = SklearnInferencePredictor(model) + + # Evaluator + evaluator = InferenceEvaluator(ori=ori, syn=ori, control=ori, aux_cols=aux_cols, secret=secret, n_attacks=10, + inference_model=inference_model, regression=True) + evaluator.evaluate(n_jobs=1) + results = evaluator.results(confidence_level=0) + + np.testing.assert_equal(results.attack_rate, (1, 0)) + np.testing.assert_equal(results.control_rate, (1, 0)) From fd4b88dbab601242c521fad00456ac65ab85c20f Mon Sep 17 00:00:00 2001 From: ivana Date: Thu, 4 Dec 2025 15:47:18 +0100 Subject: [PATCH 5/7] Lint --- .gitignore | 2 +- README.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 0477015..75ec26b 100644 --- a/.gitignore +++ b/.gitignore @@ -132,4 +132,4 @@ dmypy.json .vscode/ .idea/ -**/*DS_Store* \ No newline at end of file +**/*DS_Store* diff --git a/README.md b/README.md index ec08757..9bbfd40 100644 --- a/README.md +++ b/README.md @@ -159,4 +159,3 @@ This `bibtex` entry can be used to refer to the paper: ### License Licensed under Clear BSD License, see `LICENSE.md` to see the full license text. Patent-pending code (application US-20230401336-A1). - From 2c40a2ef798b5dfd0b3e538b9be20990a0439596 Mon Sep 17 00:00:00 2001 From: ivana Date: Tue, 9 Dec 2025 10:58:27 +0100 Subject: [PATCH 6/7] Add docstrings. --- .../evaluators/inference_evaluator.py | 15 +++-------- .../evaluators/inference_predictor.py | 25 ++++++++++++++++++- .../evaluators/sklearn_inference_predictor.py | 11 +++++--- src/anonymeter/stats/confidence.py | 6 ++++- 4 files changed, 40 insertions(+), 17 deletions(-) diff --git a/src/anonymeter/evaluators/inference_evaluator.py b/src/anonymeter/evaluators/inference_evaluator.py index fd20e7a..a9ad545 100644 --- a/src/anonymeter/evaluators/inference_evaluator.py +++ b/src/anonymeter/evaluators/inference_evaluator.py @@ -27,15 +27,14 @@ def _run_attack( if regression is None: regression = pd.api.types.is_numeric_dtype(target[secret]) + targets = target.sample(n_attacks, replace=False) + if naive: guesses = syn.sample(n_attacks)[secret] - targets = target.sample(n_attacks, replace=False) else: # Instantiate the default KNN model if no other model is passed through `inference_model`. if inference_model is None: inference_model = KNNInferencePredictor(data=syn, columns=aux_cols, target_col=secret, n_jobs=n_jobs) - - targets = target.sample(n_attacks, replace=False) guesses = inference_model.predict(targets) return evaluate_inference_guesses(guesses=guesses, secrets=targets[secret], regression=regression).sum() @@ -140,6 +139,7 @@ class InferenceEvaluator: the variable. n_attacks : int, default is 500 Number of attack attempts. + In case the whole dataset size should be used, set this to np.inf. inference_model: InferencePredictor An ml model fitted on `syn` as training data, and `secret` as target, that supports ::predict(x). If not None, it will be used over the MixedTypeKNeighbors in the attack. @@ -165,10 +165,7 @@ def __init__( self._n_attacks_ori = min(n_attacks, self._ori.shape[0]) self._n_attacks_baseline = min(self._syn.shape[0], self._n_attacks_ori) - if self._control is None: - self._n_attacks_control = -1 - else: - self._n_attacks_control = min(n_attacks, self._control.shape[0]) + self._n_attacks_control = -1 if self._control is None else min(n_attacks, self._control.shape[0]) # check if secret is a string column if not isinstance(secret, str): @@ -210,14 +207,10 @@ def evaluate(self, n_jobs: int = -2) -> "InferenceEvaluator": The evaluated ``InferenceEvaluator`` object. """ - # n_attacks is effective here self._n_baseline = self._attack(target=self._ori, naive=True, n_jobs=n_jobs, n_attacks=self._n_attacks_baseline) - - # n_attacks is not effective here, just needed for the baseline self._n_success = self._attack(target=self._ori, naive=False, n_jobs=n_jobs, n_attacks=self._n_attacks_ori) - # n_attacks is not effective here, just needed for the baseline self._n_control = ( None if self._control is None else self._attack(target=self._control, naive=False, n_jobs=n_jobs, n_attacks=self._n_attacks_control) diff --git a/src/anonymeter/evaluators/inference_predictor.py b/src/anonymeter/evaluators/inference_predictor.py index 3e9351d..b8b96d0 100644 --- a/src/anonymeter/evaluators/inference_predictor.py +++ b/src/anonymeter/evaluators/inference_predictor.py @@ -1,8 +1,31 @@ +# This file is part of Anonymeter and is released under BSD 3-Clause Clear License. +# Copyright (c) 2022 Anonos IP LLC. +# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. +"""A protocol for a custom inference predictor.""" from typing import Protocol import pandas as pd class InferencePredictor(Protocol): - def predict(self, X: pd.DataFrame) -> pd.Series: + """Interface for custom inference models. + + It is used as `inference_model` in the InferenceEvaluator in inference_evaluator.py. + + For an example usage refer to the SklearnInferencePredictor in sklearn_inference_predictor.py. + """ + def predict(self, x: pd.DataFrame) -> pd.Series: + """Predict the targets for input `x`. + + Parameters + ---------- + x : pd.DataFrame + The input data to predict. + + Returns + ------- + pd.Series + The predictions as pd.Series. + + """ ... diff --git a/src/anonymeter/evaluators/sklearn_inference_predictor.py b/src/anonymeter/evaluators/sklearn_inference_predictor.py index 19aaff2..4418aa2 100644 --- a/src/anonymeter/evaluators/sklearn_inference_predictor.py +++ b/src/anonymeter/evaluators/sklearn_inference_predictor.py @@ -1,4 +1,7 @@ -import numpy as np +# This file is part of Anonymeter and is released under BSD 3-Clause Clear License. +# Copyright (c) 2022 Anonos IP LLC. +# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details. +"""A wrapper class around a sklearn model implementing the InferencePredictor.""" import pandas as pd from sklearn.base import BaseEstimator, is_classifier, is_regressor @@ -12,6 +15,8 @@ class SklearnInferencePredictor(InferencePredictor): ---------- model : sklearn.base.BaseEstimator A classifier or regressor which implements ::predict(). + The model needs to be fitted, it must contain its own preprocessing pipeline, + and it needs to respect the index of the input data. """ def __init__(self, model: BaseEstimator): @@ -36,6 +41,4 @@ def predict(self, x: pd.DataFrame) -> pd.Series: """ prediction = self._model.predict(x) - if isinstance(prediction, np.ndarray): - return pd.Series(prediction) - return prediction + return pd.Series(prediction, index=x.index) diff --git a/src/anonymeter/stats/confidence.py b/src/anonymeter/stats/confidence.py index 9829de7..ae9eb65 100644 --- a/src/anonymeter/stats/confidence.py +++ b/src/anonymeter/stats/confidence.py @@ -174,8 +174,12 @@ class EvaluationResults: Parameters ---------- - n_attacks : int + n_attacks : Union[int, tuple[int, int, int]] Total number of attacks performed. + It can be a single number (int) which will apply to all three: main (ori), baseline, and control attack, + or a tuple (n_attacks_ori, n_attacks_baseline, n_attacks_control) - (int, int, int) which will contain + different numbers of attacks in case the user wants to perform different number of attacks for each + main (ori), baseline and control. n_success : int Number of successful attacks. n_baseline : int From 3c53dfd6ad3c267619ab9d248f881032ae2e4a78 Mon Sep 17 00:00:00 2001 From: ivana Date: Tue, 9 Dec 2025 11:23:55 +0100 Subject: [PATCH 7/7] Keep target series only; extend docstring. --- src/anonymeter/neighbors/mixed_types_kneighbors.py | 5 ++--- src/anonymeter/stats/confidence.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/anonymeter/neighbors/mixed_types_kneighbors.py b/src/anonymeter/neighbors/mixed_types_kneighbors.py index 443bba6..318628a 100644 --- a/src/anonymeter/neighbors/mixed_types_kneighbors.py +++ b/src/anonymeter/neighbors/mixed_types_kneighbors.py @@ -257,8 +257,7 @@ class KNNInferencePredictor(InferencePredictor): def __init__(self, data: pd.DataFrame, columns: list[str], target_col: str, n_jobs: int): self._nn = MixedTypeKNeighbors(n_jobs=n_jobs, n_neighbors=1).fit(candidates=data[columns]) - self._data = data - self._target_col = target_col + self._target_series = data[target_col] self._columns = columns def predict(self, x: pd.DataFrame) -> pd.Series: @@ -278,4 +277,4 @@ def predict(self, x: pd.DataFrame) -> pd.Series: guesses_idx = self._nn.kneighbors(queries=x[self._columns]) if isinstance(guesses_idx, tuple): raise RuntimeError("guesses_idx cannot be a tuple") - return self._data.iloc[guesses_idx.flatten()][self._target_col] + return self._target_series.iloc[guesses_idx.flatten()] diff --git a/src/anonymeter/stats/confidence.py b/src/anonymeter/stats/confidence.py index ae9eb65..8ddae4b 100644 --- a/src/anonymeter/stats/confidence.py +++ b/src/anonymeter/stats/confidence.py @@ -179,7 +179,7 @@ class EvaluationResults: It can be a single number (int) which will apply to all three: main (ori), baseline, and control attack, or a tuple (n_attacks_ori, n_attacks_baseline, n_attacks_control) - (int, int, int) which will contain different numbers of attacks in case the user wants to perform different number of attacks for each - main (ori), baseline and control. + main (ori), baseline and control target dataset. n_success : int Number of successful attacks. n_baseline : int