diff --git a/notebooks/main.ipynb b/notebooks/main.ipynb index b9db6ae..7c0ca89 100644 --- a/notebooks/main.ipynb +++ b/notebooks/main.ipynb @@ -1,1178 +1,1513 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "# Imports\n", - "import tensorflow as tf\n", - "import tie.recommender\n", - "from tie.recommender import Recommender, FactorizationRecommender, BPRRecommender, ImplicitBPRRecommender, WalsRecommender, ImplicitWalsRecommender, TopItemsRecommender\n", - "from tie.matrix_builder import ReportTechniqueMatrixBuilder\n", - "from tie.engine import TechniqueInferenceEngine\n", - "from tie.constants import PredictionMethod\n", - "import random\n", - "import math\n", - "import importlib\n", - "import pandas as pd\n", - "import numpy as np\n", - "import sklearn.manifold\n", - "import matplotlib.pyplot as plt\n", - "import json\n", - "\n", - "tf.config.run_functions_eagerly(True)\n", - "\n", - "assert tf.executing_eagerly()\n", - "\n", - "importlib.reload(tie.recommender)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num training interactions 33311.0\n", - "Num test interactions 9517.0\n", - "Num validation interactions 4758.0\n" - ] - } - ], - "source": [ - "validation_ratio = 0.1\n", - "test_ratio = 0.2\n", - "\n", - "# data locations\n", - "dataset_filepath = \"../data/combined_dataset_full_frequency.json\"\n", - "enterprise_attack_filepath = \"../data/stix/enterprise-attack.json\"\n", - "\n", - "# make data\n", - "data_builder = ReportTechniqueMatrixBuilder(\n", - " combined_dataset_filepath=dataset_filepath,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "training_data, test_data, validation_data = data_builder.build_train_test_validation(test_ratio, validation_ratio)\n", - "\n", - "print(\"Num training interactions\", training_data.to_numpy().sum())\n", - "print(\"Num test interactions\", test_data.to_numpy().sum())\n", - "print(\"Num validation interactions\", validation_data.to_numpy().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "oilrig_techniques = {\n", - " \"T1047\", \"T1059.005\", \"T1124\", \"T1082\",\n", - " \"T1497.001\", \"T1053.005\", \"T1027\", \"T1105\",\n", - " \"T1070.004\", \"T1059.003\", \"T1071.001\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def test_multiple_embeding_dimensions(model_class: Recommender, method: PredictionMethod, out_file: str, **kwargs):\n", - " \"\"\"Runs model_class at multiple embedding dimensions and saves results.\n", - "\n", - " Runs each model for embedding dimensions 4, 8, 10, 16, 32, and 64.\n", - "\n", - " Args:\n", - " model_class: A model on which to train at multiple embedding dimensions.\n", - " out_file: filename for saving the results file. Requires len(out_file) > 0\n", - " and out_file is a valid csv filename.\n", - " kwargs: Parameters mapped to values.\n", - "\n", - " Mutates:\n", - " Saves model results, including embedding_dimension, hyperparameters,\n", - " and precision, recall, and ndcg at 10, 20, 50, and 100 to out_file.\n", - " \"\"\"\n", - " assert len(out_file) > 0\n", - "\n", - " results = []\n", - "\n", - " embedding_dimensions = (4,8,10,16,32,64)\n", - " # for every embedding\n", - " for embedding_dimension in embedding_dimensions:\n", - "\n", - " # make model\n", - " model = model_class(\n", - " m=training_data.m,\n", - " n=training_data.n,\n", - " k=embedding_dimension,\n", - " )\n", - "\n", - " # make tie\n", - " tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=method,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - " )\n", - "\n", - " # fit hyperparameters\n", - " best_hyperparameters = tie.fit_with_validation(**kwargs)\n", - "\n", - " # calculate precision, recall, ndcg\n", - " run_stats = {\n", - " \"embedding_dimension\": embedding_dimension,\n", - " **best_hyperparameters\n", - " }\n", - " k_values = (10, 20, 50, 100)\n", - " for k in k_values:\n", - " run_stats[f\"precision_at_{k}\"] = tie.precision(k=k)\n", - " run_stats[f\"recall_at_{k}\"] = tie.recall(k=k)\n", - " run_stats[f\"ndcg_at_{k}\"] = tie.normalized_discounted_cumulative_gain(k=k)\n", - "\n", - " print(run_stats)\n", - " results.append(run_stats)\n", - "\n", - "\n", - " # save as csv\n", - " results_dataframe = pd.DataFrame(results)\n", - " results_dataframe.to_csv(out_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtest_multiple_embeding_dimensions\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mWalsRecommender\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPredictionMethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDOT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mout_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwals_model_results_training_data_correction_dot.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_iterations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.005\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mregularization_coefficient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.00001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[12], line 42\u001b[0m, in \u001b[0;36mtest_multiple_embeding_dimensions\u001b[0;34m(model_class, method, out_file, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m tie \u001b[38;5;241m=\u001b[39m TechniqueInferenceEngine(\n\u001b[1;32m 33\u001b[0m training_data\u001b[38;5;241m=\u001b[39mtraining_data,\n\u001b[1;32m 34\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mvalidation_data,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 38\u001b[0m enterprise_attack_filepath\u001b[38;5;241m=\u001b[39menterprise_attack_filepath,\n\u001b[1;32m 39\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# fit hyperparameters\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m best_hyperparameters \u001b[38;5;241m=\u001b[39m \u001b[43mtie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_with_cross_validation\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;66;03m# calculate precision, recall, ndcg\u001b[39;00m\n\u001b[1;32m 45\u001b[0m run_stats \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding_dimension\u001b[39m\u001b[38;5;124m\"\u001b[39m: embedding_dimension,\n\u001b[1;32m 47\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mbest_hyperparameters\n\u001b[1;32m 48\u001b[0m }\n", - "File \u001b[0;32m~/Desktop/CTID/technique-inference-engine/src/tie/engine.py:188\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.fit_with_cross_validation\u001b[0;34m(self, method, **kwargs)\u001b[0m\n\u001b[1;32m 183\u001b[0m variable_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(kwargs\u001b[38;5;241m.\u001b[39mget(key) \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m variable_names)\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hyperparameters \u001b[38;5;129;01min\u001b[39;00m parameter_cartesian_product(\n\u001b[1;32m 186\u001b[0m variable_names, variable_values\n\u001b[1;32m 187\u001b[0m ):\n\u001b[0;32m--> 188\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhyperparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m score \u001b[38;5;241m=\u001b[39m recall_at_k(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validation_data\u001b[38;5;241m.\u001b[39mto_pandas(), k\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m score \u001b[38;5;241m>\u001b[39m best_score:\n", - "File \u001b[0;32m~/Desktop/CTID/technique-inference-engine/src/tie/engine.py:124\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.fit\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model to the data.\u001b[39;00m\n\u001b[1;32m 107\u001b[0m \n\u001b[1;32m 108\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;124;03m The MSE of the prediction matrix, as determined by the test set.\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# train\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_training_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_sparse_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 126\u001b[0m mean_squared_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model\u001b[38;5;241m.\u001b[39mevaluate(\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_test_data\u001b[38;5;241m.\u001b[39mto_sparse_tensor(), method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prediction_method\n\u001b[1;32m 128\u001b[0m )\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkrep()\n", - "File \u001b[0;32m~/Desktop/CTID/technique-inference-engine/src/tie/recommender/wals_recommender.py:216\u001b[0m, in \u001b[0;36mWalsRecommender.fit\u001b[0;34m(self, data, num_iterations, c, regularization_coefficient)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_U \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_factor(\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V, P\u001b[38;5;241m.\u001b[39mT, alpha, regularization_coefficient\n\u001b[1;32m 213\u001b[0m )\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# step 2: update V\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_factor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_U\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregularization_coefficient\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkrep()\n", - "File \u001b[0;32m~/Desktop/CTID/technique-inference-engine/src/tie/recommender/wals_recommender.py:162\u001b[0m, in \u001b[0;36mWalsRecommender._update_factor\u001b[0;34m(self, opposing_factors, data, alpha, regularization_coefficient)\u001b[0m\n\u001b[1;32m 159\u001b[0m C_u \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(P_u \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, alpha \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m C_u\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m (p,)\n\u001b[0;32m--> 162\u001b[0m confidence_scaled_v_transpose_v \u001b[38;5;241m=\u001b[39m \u001b[43mV_T_C_I_V\u001b[49m\u001b[43m(\u001b[49m\u001b[43mV\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mC_u\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# X = (V^T CV + \\lambda I)^{-1} V^T CP\u001b[39;00m\n\u001b[1;32m 165\u001b[0m inv \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39minv(\n\u001b[1;32m 166\u001b[0m V_T_V\n\u001b[1;32m 167\u001b[0m \u001b[38;5;241m+\u001b[39m confidence_scaled_v_transpose_v\n\u001b[1;32m 168\u001b[0m \u001b[38;5;241m+\u001b[39m regularization_coefficient \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39midentity(k)\n\u001b[1;32m 169\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/CTID/technique-inference-engine/src/tie/recommender/wals_recommender.py:140\u001b[0m, in \u001b[0;36mWalsRecommender._update_factor..V_T_C_I_V\u001b[0;34m(V, c_array)\u001b[0m\n\u001b[1;32m 137\u001b[0m square_addition \u001b[38;5;241m=\u001b[39m v_i \u001b[38;5;241m@\u001b[39m v_i\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m square_addition\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m (k, k)\n\u001b[0;32m--> 140\u001b[0m product \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m square_addition\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m product\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "test_multiple_embeding_dimensions(\n", - " model_class=WalsRecommender,\n", - " method=PredictionMethod.DOT,\n", - " out_file=\"wals_model_results_training_data_correction_dot.csv\",\n", - " epochs=[25],\n", - " c=[0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", - " regularization_coefficient=[0.0, 0.00001, 0.0001, 0.001, 0.01]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'embedding_dimension': 4, 'regularization': 0.0001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.01560204407537528, 'recall_at_10': 0.06050501513663635, 'ndcg_at_10': 0.07683413537968624, 'precision_at_20': 0.011992973490897476, 'recall_at_20': 0.09067910772439934, 'ndcg_at_20': 0.09634989967193944, 'precision_at_50': 0.0079112104758863, 'recall_at_50': 0.14733022592714648, 'ndcg_at_50': 0.12541038111253533, 'precision_at_100': 0.00527946343021399, 'recall_at_100': 0.1978219117723097, 'ndcg_at_100': 0.1456379374612586}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid 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"/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'embedding_dimension': 8, 'regularization': 0.001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.012791440434366017, 'recall_at_10': 0.048452527172814557, 'ndcg_at_10': 0.06212604210714955, 'precision_at_20': 0.010012775471095497, 'recall_at_20': 0.07689506441974149, 'ndcg_at_20': 0.07903656541040587, 'precision_at_50': 0.006547428936442032, 'recall_at_50': 0.12561008986844754, 'ndcg_at_50': 0.10273975134585815, 'precision_at_100': 0.004500159693388694, 'recall_at_100': 0.17281461717597588, 'ndcg_at_100': 0.12142186359861892}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - 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value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'embedding_dimension': 10, 'regularization': 0.001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.011992973490897476, 'recall_at_10': 0.04644692238371199, 'ndcg_at_10': 0.05802910032592288, 'precision_at_20': 0.009374001916320665, 'recall_at_20': 0.0721340954731668, 'ndcg_at_20': 0.07385875110489287, 'precision_at_50': 0.006055573299265411, 'recall_at_50': 0.11468008558455581, 'ndcg_at_50': 0.09530259678856781, 'precision_at_100': 0.004227083998722453, 'recall_at_100': 0.16112090162064058, 'ndcg_at_100': 0.1135609038916972}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: 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"/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'embedding_dimension': 16, 'regularization': 0.0001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.010923027786649633, 'recall_at_10': 0.04140103153989063, 'ndcg_at_10': 0.05513784313726271, 'precision_at_20': 0.008312040881507506, 'recall_at_20': 0.0647656160113604, 'ndcg_at_20': 0.06839756124757733, 'precision_at_50': 0.005416799744490578, 'recall_at_50': 0.10646787811167806, 'ndcg_at_50': 0.08773154884648639, 'precision_at_100': 0.0039603960396039604, 'recall_at_100': 0.15611050618282554, 'ndcg_at_100': 0.10676608984315174}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n", - "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", - " data / np.expand_dims(num_items_per_user, axis=1)\n" - ] - } - ], - "source": [ - "test_multiple_embeding_dimensions(\n", - " BPRRecommender,\n", - " out_file=\"bpr_model_results.csv\",\n", - " epochs=[20*training_data.m*training_data.n],\n", - " learning_rate=[0.00001, 0.00005, 0.0001, 0.001],\n", - " regularization=[0., 0.0001, 0.001, 0.01],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error 0.04852325970978819\n", - "Precision 0.02400990099009901\n", - "Recall 0.1905249816378097\n", - "Normalized Discounted Cumulative Gain 0.1841149999164702\n" - ] - } - ], - "source": [ - "embedding_dimension = 10\n", - "k = 20\n", - "best_hyperparameters = {'gravity_coefficient': 0.001, 'regularization_coefficient': 0.5, 'epochs': 1000, 'learning_rate': 100.0}\n", - "\n", - "model = TopItemsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.DOT,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "mse = tie.fit()\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=k)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " predictions training_data test_data \\\n", - "T1592.004 0.000000 0.0 0.0 \n", - "T1557.001 0.586885 0.0 0.0 \n", - "T1600 0.291803 0.0 0.0 \n", - "T1647 0.293443 0.0 0.0 \n", - "T1068 0.916393 0.0 0.0 \n", - "... ... ... ... \n", - "T1656 0.149180 0.0 0.0 \n", - "T1557.003 0.147541 0.0 0.0 \n", - "T1499.001 0.145902 0.0 0.0 \n", - "T1027.005 0.708197 0.0 0.0 \n", - "T1059.007 0.896721 0.0 0.0 \n", - "\n", - " technique_name \n", - "T1592.004 Client Configurations \n", - "T1557.001 LLMNR/NBT-NS Poisoning and SMB Relay \n", - "T1600 Weaken Encryption \n", - "T1647 Plist File Modification \n", - "T1068 Exploitation for Privilege Escalation \n", - "... ... \n", - "T1656 Impersonation \n", - "T1557.003 DHCP Spoofing \n", - "T1499.001 OS Exhaustion Flood \n", - "T1027.005 Indicator Removal from Tools \n", - "T1059.007 JavaScript \n", - "\n", - "[611 rows x 4 columns]\n" - ] - } - ], - "source": [ - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", - "print(new_report_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error 8.200210708280245\n", - "Precision 0.0014993585631815267\n", - "Recall 0.018532725776008053\n", - "Normalized Discounted Cumulative Gain 0.00973503731752359\n" - ] - } - ], - "source": [ - "embedding_dimension = 10\n", - "k = 20\n", - "best_hyperparameters = {'gravity_coefficient': 0.001, 'regularization_coefficient': 0.001, 'epochs': 10, 'learning_rate': 1.0}\n", - "\n", - "model = FactorizationRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.DOT,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "mse = tie.fit(**best_hyperparameters)\n", - "# mse = tie.fit_with_validation(\n", - "# learning_rate=[0.001, 0.01, 0.1, 1.0, 10., 20., 50., 100.],\n", - "# epochs=[1000],\n", - "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5],\n", - "# gravity_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5],\n", - "# )\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=k)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " predictions training_data test_data \\\n", - "T1204.002 -44127.687500 0.0 0.0 \n", - "T1592.001 -18382.687500 0.0 0.0 \n", - "T1547.012 43962.042969 0.0 0.0 \n", - "T1561.002 46403.843750 0.0 0.0 \n", - "T1110.004 67053.593750 0.0 0.0 \n", - "... ... ... ... \n", - "T1612 34722.996094 0.0 0.0 \n", - "T1588.006 -39228.710938 0.0 0.0 \n", - "T1003 -42823.429688 0.0 0.0 \n", - "T1069.002 -36731.289062 0.0 0.0 \n", - "T1070.005 12406.809570 0.0 0.0 \n", - "\n", - " technique_name \n", - "T1204.002 Malicious File \n", - "T1592.001 Hardware \n", - "T1547.012 Print Processors \n", - "T1561.002 Disk Structure Wipe \n", - "T1110.004 Credential Stuffing \n", - "... ... \n", - "T1612 Build Image on Host \n", - "T1588.006 Vulnerabilities \n", - "T1003 OS Credential Dumping \n", - "T1069.002 Domain Groups \n", - "T1070.005 Network Share Connection Removal \n", - "\n", - "[611 rows x 4 columns]\n" - ] - } - ], - "source": [ - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", - "print(new_report_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 22\u001b[0m\n\u001b[1;32m 9\u001b[0m tie \u001b[38;5;241m=\u001b[39m TechniqueInferenceEngine(\n\u001b[1;32m 10\u001b[0m training_data\u001b[38;5;241m=\u001b[39mtraining_data,\n\u001b[1;32m 11\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mvalidation_data,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 15\u001b[0m enterprise_attack_filepath\u001b[38;5;241m=\u001b[39menterprise_attack_filepath,\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# mse = tie.fit_with_validation(\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# num_iterations=[500 * 512],\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# regularization_coefficient=[0, 0.0001, 0.001, 0.01],\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m mse \u001b[38;5;241m=\u001b[39m \u001b[43mtie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbest_hyperparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse)\n\u001b[1;32m 24\u001b[0m precision \u001b[38;5;241m=\u001b[39m tie\u001b[38;5;241m.\u001b[39mprecision(k\u001b[38;5;241m=\u001b[39mk)\n", - "File \u001b[0;32m~/code/technique-inference-engine/models/tie.py:122\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.fit\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model to the data.\u001b[39;00m\n\u001b[1;32m 105\u001b[0m \n\u001b[1;32m 106\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124;03m The MSE of the prediction matrix, as determined by the test set.\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# train\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_training_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_sparse_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 124\u001b[0m mean_squared_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model\u001b[38;5;241m.\u001b[39mevaluate(\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_test_data\u001b[38;5;241m.\u001b[39mto_sparse_tensor(), method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prediction_method\n\u001b[1;32m 126\u001b[0m )\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkrep()\n", - "File \u001b[0;32m~/code/technique-inference-engine/models/recommender/bpr_recommender.py:244\u001b[0m, in \u001b[0;36mBPRRecommender.fit\u001b[0;34m(self, data, learning_rate, epochs, regularization_coefficient)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_U[u, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 239\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_w \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_U[u, :])\n\u001b[1;32m 240\u001b[0m )\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[i, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 242\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_hi \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[i, :])\n\u001b[1;32m 243\u001b[0m )\n\u001b[0;32m--> 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[j, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 245\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_hj \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[j, :])\n\u001b[1;32m 246\u001b[0m )\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# hyperparameters\n", - "embedding_dimension = 4\n", - "k = 20\n", - "best_hyperparameters = {'regularization_coefficient': 0.0001, 'epochs': 2, 'learning_rate': 0.0001}\n", - "# best_hyperparameters[\"epochs\"] = 20*training_data.m*training_data.n\n", - "\n", - "model = BPRRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.COSINE,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "# mse = tie.fit_with_validation(\n", - "# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\n", - "# epochs=[500 * 512],\n", - "# regularization_coefficient=[0, 0.0001, 0.001, 0.01],\n", - "# )\n", - "mse = tie.fit(**best_hyperparameters)\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=20)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " predictions training_data test_data \\\n", - "T1558.002 0.555484 0.0 0.0 \n", - "T1132.002 0.494036 0.0 0.0 \n", - "T1211 -0.492932 0.0 0.0 \n", - "T1601.002 -0.185998 0.0 0.0 \n", - "T1596 -0.025210 0.0 0.0 \n", - "... ... ... ... \n", - "T1546.011 -0.217676 0.0 0.0 \n", - "T1535 0.464719 0.0 0.0 \n", - "T1071 0.199836 0.0 0.0 \n", - "T1587 0.658772 0.0 0.0 \n", - "T1499.002 0.464182 0.0 0.0 \n", - "\n", - " technique_name \n", - "T1558.002 Silver Ticket \n", - "T1132.002 Non-Standard Encoding \n", - "T1211 Exploitation for Defense Evasion \n", - "T1601.002 Downgrade System Image \n", - "T1596 Search Open Technical Databases \n", - "... ... \n", - "T1546.011 Application Shimming \n", - "T1535 Unused/Unsupported Cloud Regions \n", - "T1071 Application Layer Protocol \n", - "T1587 Develop Capabilities \n", - "T1499.002 Service Exhaustion Flood \n", - "\n", - "[611 rows x 4 columns]\n" - ] - } - ], - "source": [ - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", - "print(new_report_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 261.94it/s, train_auc=51.95%, skipped=9.34%]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error 0.8416396221072098\n", - "Precision 0.008555163566388711\n", - "Recall 0.1188407602595878\n", - "Normalized Discounted Cumulative Gain 0.05200668172568438\n" - ] - } - ], - "source": [ - "# hyperparameters\n", - "embedding_dimension = 10\n", - "k = 20\n", - "best_hyperparameters = {'regularization_coefficient': 0.0001, \"epochs\": 20, 'learning_rate': 0.005}\n", - "\n", - "model = ImplicitBPRRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.COSINE,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "# mse = tie.fit_with_validation(\n", - "# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\n", - "# epochs=[math.floor(500 * 512 / training_data.to_numpy().sum())],\n", - "# regularization=[0, 0.0001, 0.001, 0.01],\n", - "# )\n", - "mse = tie.fit(**best_hyperparameters)\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=k)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 51.99it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error 0.5028757316475128\n", - "Precision 0.008659397049390635\n", - "Recall 0.11683780275644119\n", - "Normalized Discounted Cumulative Gain 0.06099917717388372\n" - ] - } - ], - "source": [ - "# hyperparameters\n", - "embedding_dimension = 10\n", - "k = 20\n", - "\n", - "best_hyperparameters = {'regularization_coefficient': 0.05, 'c': 0.5, 'epochs': 20}\n", - "\n", - "model = ImplicitWalsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.COSINE,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "mse = tie.fit(**best_hyperparameters)\n", - "# mse = tie.fit_with_validation(\n", - "# epochs=[20],\n", - "# c=[0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", - "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05]\n", - "# )\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=k)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " predictions training_data test_data \\\n", - "T1204.002 0.190945 0.0 0.0 \n", - "T1592.001 -0.088588 0.0 0.0 \n", - "T1547.012 0.595495 0.0 0.0 \n", - "T1561.002 0.591824 0.0 0.0 \n", - "T1110.004 -0.004705 0.0 0.0 \n", - "... ... ... ... \n", - "T1612 0.140598 0.0 0.0 \n", - "T1588.006 0.665867 0.0 0.0 \n", - "T1003 -0.068861 0.0 0.0 \n", - "T1069.002 0.364830 0.0 0.0 \n", - "T1070.005 0.198465 0.0 0.0 \n", - "\n", - " technique_name \n", - "T1204.002 Malicious File \n", - "T1592.001 Hardware \n", - "T1547.012 Print Processors \n", - "T1561.002 Disk Structure Wipe \n", - "T1110.004 Credential Stuffing \n", - "... ... \n", - "T1612 Build Image on Host \n", - "T1588.006 Vulnerabilities \n", - "T1003 OS Credential Dumping \n", - "T1069.002 Domain Groups \n", - "T1070.005 Network Share Connection Removal \n", - "\n", - "[611 rows x 4 columns]\n" - ] - } - ], - "source": [ - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", - "print(new_report_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error 0.22009765631062408\n", - "Precision 0.0081783194355356\n", - "Recall 0.11611657887235578\n", - "Normalized Discounted Cumulative Gain 0.05730162211191529\n" - ] - } - ], - "source": [ - "# hyperparameters\n", - "embedding_dimension = 4\n", - "k = 20\n", - "\n", - "# best_hyperparameters = {'regularization_coefficient': 0.1, 'c': 0.5, 'epochs': 20}\n", - "# best_hyperparameters = {'regularization_coefficient': 0.0001, 'c': 0.3, 'epochs': 100}\n", - "best_hyperparameters = {'regularization_coefficient': 0.001, 'c': 0.1, \"epochs\": 20}\n", - "model = WalsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", - "\n", - "tie = TechniqueInferenceEngine(\n", - " training_data=training_data,\n", - " validation_data=validation_data,\n", - " test_data=test_data,\n", - " model=model,\n", - " prediction_method=PredictionMethod.COSINE,\n", - " enterprise_attack_filepath=enterprise_attack_filepath,\n", - ")\n", - "mse = tie.fit(**best_hyperparameters)\n", - "# mse = tie.fit_with_validation(\n", - "# epochs=[20],\n", - "# c=[0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", - "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05]\n", - "# )\n", - "print(\"Mean Squared Error\", mse)\n", - "precision = tie.precision(k=k)\n", - "print(\"Precision\", precision)\n", - "recall = tie.recall(k=k)\n", - "print(\"Recall\", recall)\n", - "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - "print(\"Normalized Discounted Cumulative Gain\", ndcg)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " predictions training_data test_data \\\n", - "T1204.002 0.746440 0.0 0.0 \n", - "T1592.001 -0.030477 0.0 0.0 \n", - "T1547.012 0.976308 0.0 0.0 \n", - "T1561.002 0.676964 0.0 0.0 \n", - "T1110.004 0.389903 0.0 0.0 \n", - "... ... ... ... \n", - "T1612 0.093252 0.0 0.0 \n", - "T1588.006 0.731710 0.0 0.0 \n", - "T1003 -0.059756 0.0 0.0 \n", - "T1069.002 0.502788 0.0 0.0 \n", - "T1070.005 0.731683 0.0 0.0 \n", - "\n", - " technique_name \n", - "T1204.002 Malicious File \n", - "T1592.001 Hardware \n", - "T1547.012 Print Processors \n", - "T1561.002 Disk Structure Wipe \n", - "T1110.004 Credential Stuffing \n", - "... ... \n", - "T1612 Build Image on Host \n", - "T1588.006 Vulnerabilities \n", - "T1003 OS Credential Dumping \n", - "T1069.002 Domain Groups \n", - "T1070.005 Network Share Connection Removal \n", - "\n", - "[611 rows x 4 columns]\n" - ] - } - ], - "source": [ - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", - "print(new_report_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0.00000000e+00 3.10272485e-01 -5.06079257e-01 ... -2.96728946e-02\n", - " 1.03696346e-01 -4.59698914e-03]\n", - " [ 1.00000000e+00 2.34661356e-01 -3.45782340e-01 ... -8.81108269e-02\n", - " 7.32592419e-02 2.15996355e-01]\n", - " [ 2.00000000e+00 4.81936446e-08 6.88791080e-10 ... 3.42836657e-08\n", - " -7.81015075e-09 -4.81467985e-08]\n", - " ...\n", - " [ 6.25900000e+03 3.53572398e-01 -4.88738894e-01 ... 2.14364976e-02\n", - " 1.39371127e-01 1.01979606e-01]\n", - " [ 6.26000000e+03 -5.61978075e-10 -3.23640350e-08 ... -7.43976116e-08\n", - " 8.64229861e-08 8.77083117e-09]\n", - " [ 6.26100000e+03 -6.00948269e-08 -1.48262300e-08 ... 3.57593208e-08\n", - " -9.07301079e-09 2.34565452e-08]]\n", - "(6262, 11)\n", - "(611, 11)\n" - ] - } - ], - "source": [ - "# TEMPORARY - GET EMBEDDINGS FOR FE\n", - "U = tie.get_U() # entity (report) ids\n", - "V = tie.get_V() # item (technique) embeddings\n", - "\n", - "U_with_index = np.hstack((np.expand_dims(training_data.report_ids, axis=1), U))\n", - "V_with_index = np.hstack((np.expand_dims(training_data.technique_ids, axis=1), V))\n", - "\n", - "print(U_with_index.shape)\n", - "print(V_with_index.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "recalls [0.3087573406401257, 0.3257859760812914, 0.3458226653670225, 0.3745375233589626, 0.3949941633991787, 0.4110964558541681, 0.4219309973671012, 0.4317147374599419, 0.44042389049516534, 0.44770298702149547, 0.45387773687027355, 0.45797033624322875, 0.46304119765720547, 0.4679221448741617, 0.47182791494477766, 0.47617333685846847, 0.4791363202775644, 0.48264413250862803, 0.48577394889957126, 0.4883141430997783, 0.4904385260449852]\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k_values = [1, 5] + list(range(10, 200, 10))\n", - "recalls = []\n", - "ndcgs = []\n", - "for k in k_values:\n", - " # print(\"Mean Squared Error\", mse)\n", - " precision = tie.precision(k=k)\n", - " # print(\"Precision\", precision)\n", - " recall = tie.recall(k=k)\n", - " recalls.append(recall)\n", - " # print(\"Recall\", recall)\n", - " ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", - " ndcgs.append(ndcg)\n", - " # print(\"Normalized Discounted Cumulative Gain\", ndcg)\n", - "\n", - "print(\"recalls\", recalls)\n", - "\n", - "plt.xlabel(\"k\")\n", - "plt.ylabel(\"Normalized discounted cumulative gain (NDCG)\")\n", - "plt.title(\"NDCG@k for various values of k\")\n", - "plt.plot(k_values, ndcgs)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "FactorizationRecommender.predict_new_entity() got an unexpected keyword argument 'c'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m oilrig_techniques \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1047\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1059.005\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1124\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1082\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1497.001\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1053.005\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1027\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1105\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1070.004\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1059.003\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1071.001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 5\u001b[0m }\n\u001b[0;32m----> 6\u001b[0m new_report_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mtie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_for_new_report\u001b[49m\u001b[43m(\u001b[49m\u001b[43moilrig_techniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregularization_coefficient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iterations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(new_report_predictions\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n", - "File \u001b[0;32m~/code/technique-inference-engine/models/tie.py:338\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.predict_for_new_report\u001b[0;34m(self, techniques, **kwargs)\u001b[0m\n\u001b[1;32m 332\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_training_data\u001b[38;5;241m.\u001b[39mn\n\u001b[1;32m 334\u001b[0m technique_tensor \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mSparseTensor(\n\u001b[1;32m 335\u001b[0m indices\u001b[38;5;241m=\u001b[39mtechnique_indices_2d, values\u001b[38;5;241m=\u001b[39mvalues, dense_shape\u001b[38;5;241m=\u001b[39m(n,)\n\u001b[1;32m 336\u001b[0m )\n\u001b[0;32m--> 338\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_new_entity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtechnique_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prediction_method\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 340\u001b[0m training_indices_dense \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mlen\u001b[39m(predictions))\n\u001b[1;32m 341\u001b[0m training_indices_dense[technique_indices] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "\u001b[0;31mTypeError\u001b[0m: FactorizationRecommender.predict_new_entity() got an unexpected keyword argument 'c'" - ] - } - ], - "source": [ - "oilrig_techniques = {\n", - " \"T1047\", \"T1059.005\", \"T1124\", \"T1082\",\n", - " \"T1497.001\", \"T1053.005\", \"T1027\", \"T1105\",\n", - " \"T1070.004\", \"T1059.003\", \"T1071.001\"\n", - "}\n", - "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, c=0.5, regularization_coefficient=0.05, learning_rate=0.01, epochs=100)\n", - "\n", - "print(new_report_predictions.sort_values(by=\"predictions\", ascending=False).head(10))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[t-SNE] Computing 46 nearest neighbors...\n", - "[t-SNE] Indexed 6262 samples in 0.003s...\n", - "[t-SNE] Computed neighbors for 6262 samples in 0.226s...\n", - "[t-SNE] Computed conditional probabilities for sample 1000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 2000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 3000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 4000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 5000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 6000 / 6262\n", - "[t-SNE] Computed conditional probabilities for sample 6262 / 6262\n", - "[t-SNE] Mean sigma: 0.000000\n", - "[t-SNE] KL divergence after 250 iterations with early exaggeration: 95.187531\n", - "[t-SNE] KL divergence after 10000 iterations: 0.847868\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def make_tsne_embeddings(embeddings: np.ndarray) -> tuple[np.array, np.array]:\n", - " \"\"\"Create 2D representation of embeddings using t-SNE.\n", - "\n", - " Args:\n", - " embeddings: an mxk array of m embeddings in k-dimensional space.\n", - "\n", - " Returns:\n", - " A tuple of the form (x_1, x_2) where x_1 and x_2 are length m\n", - " such that (x_1[i], x_2[i]) is the 2-dimensional point cotnaining the 2-dimensional\n", - " repsresentation for embeddings[i, :].\n", - " \"\"\"\n", - " tsne = sklearn.manifold.TSNE(\n", - " n_components=2,\n", - " perplexity=15,\n", - " learning_rate=\"auto\",\n", - " # metric='cosine',\n", - " # early_exaggeration=10.0,\n", - " init='pca',\n", - " verbose=True,\n", - " n_iter=10000,\n", - " )\n", - "\n", - " V_proj = tsne.fit_transform(embeddings)\n", - " x = V_proj[:, 0]\n", - " y = V_proj[:, 1]\n", - "\n", - " return x, y\n", - "\n", - "U = tie.get_U()\n", - "x_1, x_2 = make_tsne_embeddings(U)\n", - "\n", - "plt.scatter(x_1, x_2, s=0.5)\n", - "plt.show()" - ] - } + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-18 11:00:01.252369: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-07-18 11:00:01.252701: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-07-18 11:00:01.255211: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-07-18 11:00:01.287622: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-07-18 11:00:01.867428: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# Imports\n", + "import tensorflow as tf\n", + "import tie.recommender\n", + "from tie.recommender import Recommender, FactorizationRecommender, BPRRecommender, ImplicitBPRRecommender, WalsRecommender, ImplicitWalsRecommender, TopItemsRecommender\n", + "from tie.matrix_builder import ReportTechniqueMatrixBuilder\n", + "from tie.engine import TechniqueInferenceEngine\n", + "from tie.constants import PredictionMethod\n", + "import random\n", + "import math\n", + "import importlib\n", + "import pandas as pd\n", + "import numpy as np\n", + "import sklearn.manifold\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "\n", + "tf.config.run_functions_eagerly(True)\n", + "\n", + "assert tf.executing_eagerly()\n", + "\n", + "importlib.reload(tie.recommender)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num training interactions 33311.0\n", + "Num test interactions 9517.0\n", + "Num validation interactions 4758.0\n" + ] + } + ], + "source": [ + "validation_ratio = 0.1\n", + "test_ratio = 0.2\n", + "\n", + "# data locations\n", + "dataset_filepath = \"../data/combined_dataset_full_frequency.json\"\n", + "enterprise_attack_filepath = \"../data/stix/enterprise-attack.json\"\n", + "\n", + "# make data\n", + "data_builder = ReportTechniqueMatrixBuilder(\n", + " combined_dataset_filepath=dataset_filepath,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "training_data, test_data, validation_data = data_builder.build_train_test_validation(test_ratio, validation_ratio)\n", + "\n", + "print(\"Num training interactions\", training_data.to_numpy().sum())\n", + "print(\"Num test interactions\", test_data.to_numpy().sum())\n", + "print(\"Num validation interactions\", validation_data.to_numpy().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "oilrig_techniques = {\n", + " \"T1047\", \"T1059.005\", \"T1124\", \"T1082\",\n", + " \"T1497.001\", \"T1053.005\", \"T1027\", \"T1105\",\n", + " \"T1070.004\", \"T1059.003\", \"T1071.001\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def test_multiple_embeding_dimensions(model_class: Recommender, method: PredictionMethod, out_file: str, **kwargs):\n", + " \"\"\"Runs model_class at multiple embedding dimensions and saves results.\n", + "\n", + " Runs each model for embedding dimensions 4, 8, 10, 16, 32, and 64.\n", + "\n", + " Args:\n", + " model_class: A model on which to train at multiple embedding dimensions.\n", + " out_file: filename for saving the results file. Requires len(out_file) > 0\n", + " and out_file is a valid csv filename.\n", + " kwargs: Parameters mapped to values.\n", + "\n", + " Mutates:\n", + " Saves model results, including embedding_dimension, hyperparameters,\n", + " and precision, recall, and ndcg at 10, 20, 50, and 100 to out_file.\n", + " \"\"\"\n", + " assert len(out_file) > 0\n", + "\n", + " results = []\n", + "\n", + " embedding_dimensions = (4,8,10,16,32,64)\n", + " # for every embedding\n", + " for embedding_dimension in embedding_dimensions:\n", + "\n", + " # make model\n", + " model = model_class(\n", + " m=training_data.m,\n", + " n=training_data.n,\n", + " k=embedding_dimension,\n", + " )\n", + "\n", + " # make tie\n", + " tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=method,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + " )\n", + "\n", + " # fit hyperparameters\n", + " best_hyperparameters = tie.fit_with_validation(**kwargs)\n", + "\n", + " # calculate precision, recall, ndcg\n", + " run_stats = {\n", + " \"embedding_dimension\": embedding_dimension,\n", + " **best_hyperparameters\n", + " }\n", + " k_values = (10, 20, 50, 100)\n", + " for k in k_values:\n", + " run_stats[f\"precision_at_{k}\"] = tie.precision(k=k)\n", + " run_stats[f\"recall_at_{k}\"] = tie.recall(k=k)\n", + " run_stats[f\"ndcg_at_{k}\"] = tie.normalized_discounted_cumulative_gain(k=k)\n", + "\n", + " print(run_stats)\n", + " results.append(run_stats)\n", + "\n", + "\n", + " # save as csv\n", + " results_dataframe = pd.DataFrame(results)\n", + " results_dataframe.to_csv(out_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-18 11:00:56.954876: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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n3NxcWa3tf3ReXp4+++wzvfLKKxfd78KFC1VdXe1dDh06FIjyL4ozMQAAmMenEJOUlCSbzaaKioo26ysqKtS7d+92t0lOTtYbb7yhmpoaHTx4ULt27VJ0dLQGDhx4Xtt58+bpj3/8o9577z1ddtllF63F4XAoNja2zdLdGCcGAADz+BRi7Ha7Ro8ereLiYu86j8ej4uJijRs37qLbOp1OpaWlqampSa+99ppuvfVW73uGYWjevHl6/fXX9ec//1kDBgzw8TDMce4kkD7eHw0AALoozNcN8vPzNXPmTGVmZmrs2LEqKipSTU2NcnNzJUkzZsxQWlqaCgoKJElbt25VWVmZRo4cqbKyMj388MPyeDx64IEHvPvMy8vTmjVr9OabbyomJsZ7f01cXJwiIiL8cZwB0ToJZEOTRzUNbu80BAAAIPB8/qs7depUVVZWasmSJSovL9fIkSO1fv16782+paWlbe53qaur06JFi3TgwAFFR0dr8uTJeuGFFxQfH+9t8/TTT0uSbrjhhjaf9dxzz2nWrFm+H1U3ibSHyRluVV2jR8dPNxBiAADoRj6PExOszBgnRpIm/PzPKjt5Rq/fO16j+vXqts8FAKAn6LZxYnA+bu4FAMAchJguOvfmXgAA0H0IMV3EWDEAAJiDENNFXE4CAMAchJguSmjpZn3sNCEGAIDuRIjporOXk+pNrgQAgEsLIaaLmAQSAABzEGK6iN5JAACYgxDTRdzYCwCAOQgxXdQaYmob3KprdJtcDQAAlw5CTBfFOsMUbrNI4pISAADdiRDTRRaLRb0iWy4p0c0aAIBuQ4jxg7M399LNGgCA7kKI8YPEaG7uBQCguxFi/ICxYgAA6H6EGD9IZKwYAAC6HSHGD7xjxXBjLwAA3YYQ4weM2gsAQPcjxPgBk0ACAND9CDF+0Hom5kRto8mVAABw6SDE+EFrF+tjpzkTAwBAdyHE+EFrF2tXXZMa3R6TqwEA4NJAiPGD+IhwWZunT9IJbu4FAKBbEGL8wGo9O38SPZQAAOgehBg/8Y4VQ4gBAKBbEGL8hLFiAADoXoQYP/FOAkkPJQAAugUhxk+4nAQAQPcixPhJazdrLicBANA9CDF+ksiZGAAAuhUhxk+4sRcAgO5FiPETzsQAANC9CDF+khBNiAEAoDt1KsSsWLFCGRkZcjqdysrK0rZt2y7YtrGxUcuWLdOgQYPkdDo1YsQIrV+/vkv7DEZnZ7JukNtjmFwNAAA9n88hZu3atcrPz9fSpUu1fft2jRgxQjk5OTp69Gi77RctWqRVq1bpiSee0M6dOzV37lzdfvvt+uSTTzq9z2DUOu2AYUgnazkbAwBAoFkMw/DptEFWVpbGjBmjJ598UpLk8XiUnp6u++67TwsWLDivfd++ffXQQw8pLy/Pu+6OO+5QRESEXnzxxU7tsz0ul0txcXGqrq5WbGysL4fkN1c//I5cdU16d/5EDU6NMaUGAABCSVf+fvt0JqahoUElJSXKzs4+uwOrVdnZ2dqyZUu729TX18vpdLZZFxERoc2bN3d6n637dblcbRazJUYzVgwAAN3FpxBTVVUlt9ut1NTUNutTU1NVXl7e7jY5OTkqLCzU3r175fF49O6772rdunU6cuRIp/cpSQUFBYqLi/Mu6enpvhxKQDBqLwAA3SfgvZMef/xxDR48WEOHDpXdbte8efOUm5srq7VrH71w4UJVV1d7l0OHDvmp4s5jrBgAALqPT0kiKSlJNptNFRUVbdZXVFSod+/e7W6TnJysN954QzU1NTp48KB27dql6OhoDRw4sNP7lCSHw6HY2Ng2i9m8Y8WcJsQAABBoPoUYu92u0aNHq7i42LvO4/GouLhY48aNu+i2TqdTaWlpampq0muvvaZbb721y/sMNmcvJzGTNQAAgRbm6wb5+fmaOXOmMjMzNXbsWBUVFammpka5ubmSpBkzZigtLU0FBQWSpK1bt6qsrEwjR45UWVmZHn74YXk8Hj3wwAMd3meo4HISAADdx+cQM3XqVFVWVmrJkiUqLy/XyJEjtX79eu+NuaWlpW3ud6mrq9OiRYt04MABRUdHa/LkyXrhhRcUHx/f4X2GikRG7QUAoNv4PE5MsAqGcWI27qnUzNXbNLR3jNbfP9GUGgAACCXdNk4MLi6Ry0kAAHQbQowfeedPqmlQDznBBQBA0CLE+FFriGnyGHLVNZlcDQAAPRshxo+c4TZF2W2SuLkXAIBAI8T4WUI0Y8UAANAdCDF+lhDVMgkko/YCABBQhBg/S2QSSAAAugUhxs8YtRcAgO5BiPEzzsQAANA9CDF+lkCIAQCgWxBi/IzLSQAAdA9CjJ8l0sUaAIBuQYjxs9Yu1sfpYg0AQEARYvzs3EkgmT8JAIDAIcT4Wes9MfVNHtU2uE2uBgCAnosQ42eRdpscYc1fKz2UAAAIHEKMn1ksljaXlAAAQGAQYgKASSABAAg8QkwA9IpsORNDDyUAAAKGEBMATD0AAEDgEWICwDtWDCEGAICAIcQEQOuovdzYCwBA4BBiAoBJIAEACDxCTAAwCSQAAIFHiAmAszf20sUaAIBAIcQEgPdyEl2sAQAIGEJMACS29E6qaXCrrpH5kwAACARCTADERoQpzGqRxM29AAAECiEmACwWi3rRQwkAgIAixAQIo/YCABBYhJgAYawYAAACixATIIwVAwBAYBFiAoSxYgAACKxOhZgVK1YoIyNDTqdTWVlZ2rZt20XbFxUVaciQIYqIiFB6errmz5+vuro67/tut1uLFy/WgAEDFBERoUGDBumRRx6RYRidKS8oMAkkAACBFebrBmvXrlV+fr5WrlyprKwsFRUVKScnR7t371ZKSsp57desWaMFCxZo9erVGj9+vPbs2aNZs2bJYrGosLBQkvSLX/xCTz/9tJ5//nldddVV+stf/qLc3FzFxcXpxz/+cdeP0gQJrZNAMuAdAAAB4fOZmMLCQs2ePVu5ubm68sortXLlSkVGRmr16tXttv/www81YcIETZs2TRkZGZo0aZLuuuuuNmdvPvzwQ91666265ZZblJGRoX/+53/WpEmTvvYMTzCjdxIAAIHlU4hpaGhQSUmJsrOzz+7AalV2dra2bNnS7jbjx49XSUmJN5AcOHBAb7/9tiZPntymTXFxsfbs2SNJ+utf/6rNmzfr5ptv9vmAggW9kwAACCyfLidVVVXJ7XYrNTW1zfrU1FTt2rWr3W2mTZumqqoqXXfddTIMQ01NTZo7d64efPBBb5sFCxbI5XJp6NChstlscrvdevTRRzV9+vQL1lJfX6/6+rM3zbpcLl8OJeAS6Z0EAEBABbx30oYNG7R8+XI99dRT2r59u9atW6e33npLjzzyiLfNb3/7W7300ktas2aNtm/frueff17/8R//oeeff/6C+y0oKFBcXJx3SU9PD/Sh+KT1TEz1mUY1uj0mVwMAQM/j05mYpKQk2Ww2VVRUtFlfUVGh3r17t7vN4sWL9f3vf1933323JGn48OGqqanRnDlz9NBDD8lqteqnP/2pFixYoDvvvNPb5uDBgyooKNDMmTPb3e/ChQuVn5/vfe1yuYIqyMRH2mWxSIYhnahtUEqM0+ySAADoUXw6E2O32zV69GgVFxd713k8HhUXF2vcuHHtblNbWyurte3H2Gw2SfJ2ob5QG4/nwmcwHA6HYmNj2yzBxGa1qFck98UAABAoPnexzs/P18yZM5WZmamxY8eqqKhINTU1ys3NlSTNmDFDaWlpKigokCRNmTJFhYWFGjVqlLKysrRv3z4tXrxYU6ZM8YaZKVOm6NFHH1W/fv101VVX6ZNPPlFhYaF+8IMf+PFQu19ClF3Haxp0nG7WAAD4nc8hZurUqaqsrNSSJUtUXl6ukSNHav369d6bfUtLS9ucVVm0aJEsFosWLVqksrIyJScne0NLqyeeeEKLFy/Wvffeq6NHj6pv37760Y9+pCVLlvjhEM3D1AMAAASOxQjlYXHP4XK5FBcXp+rq6qC5tHTPiyX602fl+tn/vUozx2eYXQ4AAEGnK3+/mTspgDgTAwBA4BBiAiiBSSABAAgYQkwAMWovAACBQ4gJIO/lJHonAQDgd4SYAEqMckjiTAwAAIFAiAkgLicBABA4hJgASoxuDjEnahvk8fSInuwAAAQNQkwAtU474DGkk2caTa4GAICehRATQPYwq2KczYMi080aAAD/IsQEWCI9lAAACAhCTIC13tx7opYQAwCAPxFiAiyhpZs1Uw8AAOBfhJgAa72cdJzLSQAA+BUhJsASopkEEgCAQCDEBFgiA94BABAQhJgAY9ReAAACgxATYN5JIAkxAAD4FSEmwM5OAslgdwAA+BMhJsBab+w9XtMgw2D+JAAA/IUQE2CtN/Y2ug2dqm8yuRoAAHoOQkyAOcNtirTbJDFWDAAA/kSI6Qbc3AsAgP8RYroBY8UAAOB/hJhucHasGHooAQDgL4SYbsAkkAAA+B8hphskRjMJJAAA/kaI6QZMPQAAgP8RYroBvZMAAPA/Qkw3SIjkTAwAAP5GiOkG5049AAAA/IMQ0w0SvZeT6GINAIC/EGK6Qes9MXWNHtU2MH8SAAD+QIjpBtGOMNltzV/1MbpZAwDgF4SYbmCxWOhmDQCAn3UqxKxYsUIZGRlyOp3KysrStm3bLtq+qKhIQ4YMUUREhNLT0zV//nzV1dW1aVNWVqbvfe97SkxMVEREhIYPH66//OUvnSkvKBFiAADwrzBfN1i7dq3y8/O1cuVKZWVlqaioSDk5Odq9e7dSUlLOa79mzRotWLBAq1ev1vjx47Vnzx7NmjVLFotFhYWFkqQTJ05owoQJ+ta3vqU//elPSk5O1t69e9WrV6+uH2GQaB21l7FiAADwD59DTGFhoWbPnq3c3FxJ0sqVK/XWW29p9erVWrBgwXntP/zwQ02YMEHTpk2TJGVkZOiuu+7S1q1bvW1+8YtfKD09Xc8995x33YABA3w+mGDGJJAAAPiXT5eTGhoaVFJSouzs7LM7sFqVnZ2tLVu2tLvN+PHjVVJS4r3kdODAAb399tuaPHmyt83vf/97ZWZm6jvf+Y5SUlI0atQoPfvssxetpb6+Xi6Xq80SzM6GmEaTKwEAoGfwKcRUVVXJ7XYrNTW1zfrU1FSVl5e3u820adO0bNkyXXfddQoPD9egQYN0ww036MEHH/S2OXDggJ5++mkNHjxY77zzju655x79+Mc/1vPPP3/BWgoKChQXF+dd0tPTfTmUbpfImRgAAPwq4L2TNmzYoOXLl+upp57S9u3btW7dOr311lt65JFHvG08Ho+uueYaLV++XKNGjdKcOXM0e/ZsrVy58oL7Xbhwoaqrq73LoUOHAn0oXZIQ5ZDEjb0AAPiLT/fEJCUlyWazqaKios36iooK9e7du91tFi9erO9///u6++67JUnDhw9XTU2N5syZo4ceekhWq1V9+vTRlVde2Wa7b3zjG3rttdcuWIvD4ZDD4fClfFMxCSQAAP7l05kYu92u0aNHq7i42LvO4/GouLhY48aNa3eb2tpaWa1tP8Zms0mSDMOQJE2YMEG7d+9u02bPnj3q37+/L+UFtUTmTwIAwK987p2Un5+vmTNnKjMzU2PHjlVRUZFqamq8vZVmzJihtLQ0FRQUSJKmTJmiwsJCjRo1SllZWdq3b58WL16sKVOmeMPM/PnzNX78eC1fvlzf/e53tW3bNj3zzDN65pln/Hio5mo9E1N5ql6Nbo/CbYwzCABAV/gcYqZOnarKykotWbJE5eXlGjlypNavX++92be0tLTNmZdFixbJYrFo0aJFKisrU3JysqZMmaJHH33U22bMmDF6/fXXtXDhQi1btkwDBgxQUVGRpk+f7odDDA7pvSKVFG1X1ekGvf23I7p1ZJrZJQEAENIsRus1nRDncrkUFxen6upqxcbGml1Ou54o3qtfvbtHV/WN1R/vu04Wi8XskgAAMFVX/n5zTaMbfe/a/ooIt+nvh13asv+Y2eUAABDSCDHdqFeUXd/NvEyStGrTAZOrAQAgtBFiutkPrxsoq0XauKdSu8tPmV0OAAAhixDTzfolRurmYX0kSc9wNgYAgE4jxJhg9sSBkqTf/7VM5dV1JlcDAEBoIsSYYGR6vMYOSFCj29BzH35hdjkAAIQkQoxJ5lzffDZmzUelOlXHzNYAAPiKEGOSfxqaokHJUTpV36S1Hwf35JUAAAQjQoxJrFaLZrecjVm9+Qs1uj0mVwQAQGghxJjotlFpSop26HB1nd769IjZ5QAAEFIIMSZyhts0a3zzTN3PbDqgHjIDBAAA3YIQY7LWqQh2HnHpg31MRQAAQEcRYkwWH2nX1DHpkqRn3mfwOwAAOooQEwR+eN0AWS3Spj2V+vyIy+xyAAAICYSYIJCeEKmbhzdPRfAsZ2MAAOgQQkyQ+FHrVAQ7DutI9RmTqwEAIPgRYoLE1ZfFK2tAgpo8hp774B9mlwMAQNAjxASRH32zZSqCraVyMRUBAAAXRYgJIjdckaLLU6J1ur5Jr2wrNbscAACCGiEmiFitFu/EkKs3/0MNTUxFAADAhRBigsyto/oqOcahcled/vjpYbPLAQAgaBFigowjzKZZ4zMkMRUBAAAXQ4gJQt/L6q9Iu027yk/p/b1VZpcDAEBQIsQEobjIcO9UBAx+BwBA+wgxQeoHEwbIZrXo/b1V+vvharPLAQAg6BBiglR6QqQmt0xF8F/vf2FyNQAABB9CTBBr7W79h78e1uGTTEUAAMC5CDFBbPhlcRo3MLFlKgLOxgAAcC5CTJCb0zIx5MvbDjEVAQAA5yDEBLkbhiRrcMtUBC9vZSoCAABaEWKCnMVi0eyWszHPfcBUBAAAtCLEhIBbR/ZVSstUBH/4K1MRAAAgEWJCgiPMplkTMiQ1D37HVAQAAHQyxKxYsUIZGRlyOp3KysrStm3bLtq+qKhIQ4YMUUREhNLT0zV//nzV1dW12/bnP/+5LBaL7r///s6U1mNNz+qvqJapCDYxFQEAAL6HmLVr1yo/P19Lly7V9u3bNWLECOXk5Ojo0aPttl+zZo0WLFigpUuX6vPPP9d///d/a+3atXrwwQfPa/vxxx9r1apVuvrqq30/kh4uLiJcU8f0kyQ9s2m/ydUAAGA+n0NMYWGhZs+erdzcXF155ZVauXKlIiMjtXr16nbbf/jhh5owYYKmTZumjIwMTZo0SXfdddd5Z29Onz6t6dOn69lnn1WvXr06dzQ93A+uy5DNatEH+47pszKmIgAAXNp8CjENDQ0qKSlRdnb22R1YrcrOztaWLVva3Wb8+PEqKSnxhpYDBw7o7bff1uTJk9u0y8vL0y233NJm3xdTX18vl8vVZunpLusVqVtapiJgYkgAwKXOpxBTVVUlt9ut1NTUNutTU1NVXl7e7jbTpk3TsmXLdN111yk8PFyDBg3SDTfc0OZy0iuvvKLt27eroKCgw7UUFBQoLi7Ou6Snp/tyKCGrdfC7P356RGVMRQAAuIQFvHfShg0btHz5cj311FPavn271q1bp7feekuPPPKIJOnQoUP6yU9+opdeeklOp7PD+124cKGqq6u9y6FDhwJ1CEFlWFqcxg9KlNtjqOjdPfRUAgBcssJ8aZyUlCSbzaaKioo26ysqKtS7d+92t1m8eLG+//3v6+6775YkDR8+XDU1NZozZ44eeughlZSU6OjRo7rmmmu827jdbm3atElPPvmk6uvrZbPZztuvw+GQw+HwpfweI+9bl+vD/cf0asmXCrNZ9e+3DZPNajG7LAAAupVPZ2LsdrtGjx6t4uJi7zqPx6Pi4mKNGzeu3W1qa2tltbb9mNZQYhiGbrzxRv3tb3/Tjh07vEtmZqamT5+uHTt2tBtgLnUTLk9SwbeHy2qRXt5Wqvte3q76JrfZZQEA0K18OhMjSfn5+Zo5c6YyMzM1duxYFRUVqaamRrm5uZKkGTNmKC0tzXt/y5QpU1RYWKhRo0YpKytL+/bt0+LFizVlyhTZbDbFxMRo2LBhbT4jKipKiYmJ563HWXeN7af4iHD95JUdevtv5ao+87FWfT9T0Q6f/0kBAAhJPv/Fmzp1qiorK7VkyRKVl5dr5MiRWr9+vfdm39LS0jZnXhYtWiSLxaJFixaprKxMycnJmjJlih599FH/HcUl6ubhfRTjDNecF/6iD/Yd0/RnP9JzuWOVEGU3uzQAAALOYvSQO0NdLpfi4uJUXV2t2NhYs8vpVjsOnVTuc9t0orZRg5Kj9MIPs9Q3PsLssgAA+Fpd+fvN3Ek9wMj0eL06d5z6xDm1v7JG//z0h9p39LTZZQEAEFCEmB7i8pQY/e6e8RqYHKXD1XX67qot+vTLk2aXBQBAwBBiepC0+Ai9+qNxuvqyOB2vadBdz3ykD/cxWSQAoGcixPQwidEOrZl9rcYPSlRNg1uznvtY6z87YnZZAAD4HSGmB4p2hOm53DG66areanB7dO9L2/XKtlKzywIAwK8IMT2UI8ymFdOv0V1j0+UxpAXr/qanN+xnmgIAQI9BiOnBbFaLlt8+XPfeMEiS9Iv1u1Twp10EGQBAj0CI6eEsFoseuGmoHpr8DUnSM5sO6IHffaomt8fkygAA6BpCzCVi9sSBeuyfr5bNatGrJV/qnpe2q66R+ZYAAKGLEHMJ+U5mup6efo3sYVa9u7NCM1dvk6uu0eyyAADoFELMJWbSVb31Pz8Yq2hHmLZ+cVx3PfORqk7Xm10WAAA+I8Rcgq4dmKhX5lyrpGi7/n7Ype+s3KLPj7jMLgsAAJ8QYi5Rw9Li9Orc8UqLj9AXVTWa/Ov3NX/tDh06Xmt2aQAAdAgh5hI2IClK6+4dr1uG95FhSK9/UqZ/+tUGPfz7v6vyFJeYAADBzWL0kEFDujKVN6RPvzypx97Zrff3Ns+1FGm36e7rBujuiQMV6ww3uToAQE/Vlb/fhBi08cG+Kv1y/S799ctqSVKvyHDlfetyfe/a/nKG20yuDgDQ0xBiRIjxJ8Mw9M7fy/XLd3brQGWNJKlvnFP3Z1+hb1+TpjAbVyEBAP5BiBEhJhCa3B6t216m//x/e3Skuk6SNCg5Sj/NGaKcq3rLYrGYXCEAINQRYkSICaS6Rrde/Oignnxvn07WNg+ONyI9Xv+WM0TjL08yuToAQCgjxIgQ0x1cdY16dtMB/df7X+hMy5QF1w9O0gM5QzX8sjiTqwMAhCJCjAgx3anyVL2e/PNerdlWqkZ388/nlqv76F/+zxUamBxtcnUAgFBCiBEhxgylx2r1n/9vj97YUSbDkGxWi76bma4f33i5+sRFmF0eACAEEGJEiDHT50dc+o93dqt411FJkj3Mqu9f21/33DBISdEOk6sDAAQzQowIMcHg438c12Pv7Na2L45Lah4w7wcTBmj2xIGKi2DAPADA+QgxIsQEC8Mw9P7eKv3H/+7Wpy0D5sU6w/Sjbw7SrPEZinKEmVwhACCYEGJEiAk2hmHof3dWqPB/92h3xSlJUmKUXfd+63JNz+rH6L8AAEmEGEmEmGDl9hj646eHVfjuHh081jxDdp84p+77p8H6TuZlCmf0XwC4pBFiRIgJdo1uj35X8qV+XbzXO/pv/8RI3Z89WP93RJpsVkb/BYBLESFGhJhQUdfo1pqtpXpqwz5VnW6QJF2RGq38/3MFUxkAwCWIECNCTKipqW/Sbz78h1Zt3C9XXZMkaXhanP5l0hX65hXJhBkAuEQQYkSICVXVZxr1X+8f0H9v/kK1Dc1TGYzJ6KV/nTREWQMTTa4OABBohBgRYkLdsdP1enrDfv3PRwfV0OSRJE24PFGzxg/QPw1N4Z4ZAOihCDEixPQU5dV1euLPe7X240Nq8jT/NNPiIzQtq5++m5mu5BhGAAaAnqQrf7871b91xYoVysjIkNPpVFZWlrZt23bR9kVFRRoyZIgiIiKUnp6u+fPnq66uzvt+QUGBxowZo5iYGKWkpOi2227T7t27O1MaQlzvOKcevX243vvXG/SjiQMVHxmuspNn9Ng7uzX+58W67+VPtO2L4+oh2RsA0AU+n4lZu3atZsyYoZUrVyorK0tFRUV69dVXtXv3bqWkpJzXfs2aNfrBD36g1atXa/z48dqzZ49mzZqlO++8U4WFhZKkm266SXfeeafGjBmjpqYmPfjgg/rss8+0c+dORUVFdaguzsT0THWNbr39tyN64aOD+qT0pHf9kNQYfW9cf90+Kk3RjAIMACGrWy8nZWVlacyYMXryySclSR6PR+np6brvvvu0YMGC89rPmzdPn3/+uYqLi73r/uVf/kVbt27V5s2b2/2MyspKpaSkaOPGjZo4cWKH6iLE9HyflVXrxY8O6o0dZaprbL5vJspu0+3XpOl71/bX0N78uwNAqOm2y0kNDQ0qKSlRdnb22R1YrcrOztaWLVva3Wb8+PEqKSnxXnI6cOCA3n77bU2ePPmCn1Nd3TznTkJCgi/loYcblhann99xtbY+mK2lU67UwOQo1TS49eJHpbqp6H19d+UW/f6vh703BgMAejafzsNXVVXJ7XYrNTW1zfrU1FTt2rWr3W2mTZumqqoqXXfddTIMQ01NTZo7d64efPDBdtt7PB7df//9mjBhgoYNG3bBWurr61VfX+997XK5fDkUhLC4iHDlThigWeMztGX/Mb249aDe+XuFtv3juLb947iSou26c0w/3ZXVT2nxEWaXCwAIkIBPXLNhwwYtX75cTz31lLZv365169bprbfe0iOPPNJu+7y8PH322Wd65ZVXLrrfgoICxcXFeZf09PRAlI8gZrFYNP7yJD01fbQ++Ld/0v3Zg5Ua61DV6QY9+d4+Xf+LP+vu5/+ijXsq5fFwIzAA9DQ+3RPT0NCgyMhI/e53v9Ntt93mXT9z5kydPHlSb7755nnbXH/99br22mv12GOPede9+OKLmjNnjk6fPi2r9WyOmjdvnt58801t2rRJAwYMuGgt7Z2JSU9P556YS1yj26P/t7NCL249qA/2HfOuT45x6PrLkzTximRdNzhJSdF01QaAYNCVe2J8upxkt9s1evRoFRcXe0OMx+NRcXGx5s2b1+42tbW1bYKKJNlsNknydpM1DEP33XefXn/9dW3YsOFrA4wkORwOORz8IUJb4Tarbh7eRzcP76N9R0/rpa0H9buSL1V5ql7rPinTuk/KJElX9Y3V9YOTNfGKJGX2T5A9jNm0ASDU+Nw3NT8/XzNnzlRmZqbGjh2roqIi1dTUKDc3V5I0Y8YMpaWlqaCgQJI0ZcoUFRYWatSoUcrKytK+ffu0ePFiTZkyxRtm8vLytGbNGr355puKiYlReXm5JCkuLk4REdzTgM65PCVaS6dcpQU3D1XJP05o094qbdpTqZ1HXPr74eZl5cb9irTbdO3ARE0cnKTrr0jWwKQo5m4CgBDQqRF7n3zyST322GMqLy/XyJEj9etf/1pZWVmSpBtuuEEZGRn6zW9+I0lqamrSo48+qhdeeEFlZWVKTk7WlClT9Oijjyo+Pr65iAv8wXjuuec0a9asDtVEF2t0VOWpem3eV6n391Rp094qVZ2ub/N+WnyEJl6RpOsHJ2vCoCTFRYabVCkA9HxMOyBCDDrH4zG0q/yUNu2t1Pt7K/XxFyfU4D7bRdtqkUakx2tiy6WnEZfFK8zGpScA8BdCjAgx8I8zDW599MWxlrM0ldp39HSb92OcYRpxWby+0SdGV/aN1Tf6xGpQcrTCCTYA0CmEGBFiEBiHT57R+3srtWlvlTbvrVL1mcbz2thtVl2eEu0NNd/oE6Mr+8QqPtJuQsUAEFoIMSLEIPDcHkM7D7v098PV+vyIS58fOaXPj7h0qr6p3fZ945wtoSbWG3D6J0TKauWmYQBoRYgRIQbmMAxDX544o51HXNp52NUcbspdOnT8TLvtI+02DendfKZmaJ9YDUiMUnpChPrGR3BJCsAliRAjQgyCi6uuUbtaztR8fsSlnUdc2l1+SvUXmNfJapH6xkeoX0Kk0ntFql9ipNITIlteRyghyk63bwA9EiFGhBgEvya3R/84VqO/H26+FLWn4pRKj9fq0PHaC4abVlF2mzfU9Es4J+AkROqyXhFyhtu66SgAwL8IMSLEIHR5PIaqTter9Hitdzl0/IwOtTwvd9V97T5SYhxKiXUoNcaplFiHkmOcSo11KOWcx6RoO93DAQSdbpt2AID/Wa0WpcQ6lRLrVGZGwnnv1zW69eWJMzp0ovmsTemxlqBz4oxKj9WopsGto6fqdfRUvT7ThWdzt1ikxCjHeYEnJdbZvC7GodRYp5KiHUzDACAkEGKAIOcMt+nylGhdnhJ93nuGYehEbaPKTpzR0VN1OnqqXhWu5sej3sd6VZ6ul7vljE/V6XrtPHLxz4x2hCk+MlwJUXb1irQrIcre/DrSrl4t63pFNb+fEGlXfKSd4AOg2xFigBBmsViag0SUXVLcBdu5PYaO1zSowlWnynODzqk6VbjqvaGn8lS9mjyGTtc36XR9k7480X4vq/ZEO8LUKyq8OeC0BJ+4iHDFOsMU4wxXbETLY5vnzY8EIACdQYgBLgE2q0XJMQ4lx1x85nePx5CrrlHHaxp0orZRJ2oadLy2QSdrG3S8pvn1idrm5XhNg07WNupEbYM8hrzB50Ldyy/GGW5VrDNcMc4wxUaEewNO8/MwxTrDFe0IU5QjTFF2W/OjI0xRDpui7GGKdoQp0mGTI4wbnIFLCSEGgJfValF8y+WhjmoNPidqW8LPOUGn+kyjTtU1yXWmUa66Jp2qa5TrTMtjXXPokaS6Ro/qGpvPCHVFuM3SEnRaAk67z8MUEW5TpN0mp92myHCbIuwtS7jt7HstjxF2m5xhNgYpBIIQIQZAl5wbfAYkRfm0rdtj6HRdk1x1jWcDT12jXOc8bw1BNQ1NOl3vVm3LGZ+ahibV1rt1ur7J20W90W3oZG2jTtaePz1EVznDrYq0Nwegc587wq1yhDWv++qjM9wmR1jz41ffd3zlfUeYVfYwqxw2m+wtz20EJ+CiCDEATGOzWhQXGa64yHCld2E/jW6Pahvcqqlval5anp+ub1JtS/ipqW9qCUBunWl0q67RrdqGJp1p9OhMQ5PONLp1pqFlaXSrtsHdZvye5rNFDV0/aB+EWS3eQGO3tYScMKvsYbaWwHPuurbtwr2PFoXbWl7bWl63vO9oeQxvWW+3Wb3vtb4Os1kVZm3eR5jNonBr82Prc85QwUyEGAAhL9xmVVyEVXER4X7dr8djNIebxrPh5kxDc8CpazwbhuqbPG0e6xo9qm9qeWzv/Sa36hs9Zx9b3mtwe3TuyF1NHkNNLZ8XrKwWKcxmVbjV0vxosyisJeiEtwSg1vU2a3PwsVmbQ5DN2tLWapHNZmlu295rb9uzr62W5tdWa9tHm6W57XmLpXmftna2a93GarHIapVslq+sb3ne+p6tve0szaGckbW7FyEGAC7AarV4byLuDoZhqNFtqMHtUUPT2aW+6WzIaX59zvvu5iB07nuN7tbFUMNXX7s9amz6yuvWpclQo9vjXdfQ5FGT21Cjp/mxyXP+2KgeQ811SJKCN2x1F4tF3lDT/GhpCTfyPj/3Paul+Xd2XjvL2edWa/Ojpc1+9ZXXlvM+2+Jtd7aNWt+TvO3armv+PKmdbSXlT7pCsU7//mehKwgxABAkLBaL7GHNl5B08Y5kpjCM5iDTJti4PWr0GGps8qjJ0xyM2nu/ye1Rk8eQ22O0PJ4NRk0eQ+6vvN/kbmnTsq7xnNdNbkNuw5CndVvDkLtlndvTztJSd2v75keP9z2PR97nxjn78Bg6p03r+1/3Ham5juZX3fCv0r3u/dYgQgwAIPRYLJaWe2ykCF2a3dm9Iac1/LS89ngMeYzm4GMYLe8barPe7THOvudRy/q27331eWsbw/tcLa/PfV8tr8/ut7V9a/AyWmr3eAwZknebc/drqOW1t03zep3zWVH24IoNwVUNAABBzGJpvp+HP57BgWEyAQBASCLEAACAkESIAQAAIYkQAwAAQhIhBgAAhCRCDAAACEmEGAAAEJIIMQAAICQRYgAAQEgixAAAgJBEiAEAACGJEAMAAEISIQYAAISkHjMRp2EYkiSXy2VyJQAAoKNa/263/h33RY8JMadOnZIkpaenm1wJAADw1alTpxQXF+fTNhajM9EnCHk8Hh0+fFgxMTGyWCx+26/L5VJ6eroOHTqk2NhYv+0XF8f3bg6+d3PwvZuD790cX/3eDcPQqVOn1LdvX1mtvt3l0mPOxFitVl122WUB239sbCw/chPwvZuD790cfO/m4Hs3x7nfu69nYFpxYy8AAAhJhBgAABCSCDFfw+FwaOnSpXI4HGaXcknhezcH37s5+N7NwfduDn9+7z3mxl4AAHBp4UwMAAAISYQYAAAQkggxAAAgJBFiAABASCLEfI0VK1YoIyNDTqdTWVlZ2rZtm9kl9WgPP/ywLBZLm2Xo0KFml9XjbNq0SVOmTFHfvn1lsVj0xhtvtHnfMAwtWbJEffr0UUREhLKzs7V3715ziu1Bvu57nzVr1nm//5tuusmcYnuIgoICjRkzRjExMUpJSdFtt92m3bt3t2lTV1envLw8JSYmKjo6WnfccYcqKipMqrhn6Mj3fsMNN5z3e587d65Pn0OIuYi1a9cqPz9fS5cu1fbt2zVixAjl5OTo6NGjZpfWo1111VU6cuSId9m8ebPZJfU4NTU1GjFihFasWNHu+7/85S/161//WitXrtTWrVsVFRWlnJwc1dXVdXOlPcvXfe+SdNNNN7X5/b/88svdWGHPs3HjRuXl5emjjz7Su+++q8bGRk2aNEk1NTXeNvPnz9cf/vAHvfrqq9q4caMOHz6sb3/72yZWHfo68r1L0uzZs9v83n/5y1/69kEGLmjs2LFGXl6e97Xb7Tb69u1rFBQUmFhVz7Z06VJjxIgRZpdxSZFkvP76697XHo/H6N27t/HYY4951508edJwOBzGyy+/bEKFPdNXv3fDMIyZM2cat956qyn1XCqOHj1qSDI2btxoGEbzbzs8PNx49dVXvW0+//xzQ5KxZcsWs8rscb76vRuGYXzzm980fvKTn3Rpv5yJuYCGhgaVlJQoOzvbu85qtSo7O1tbtmwxsbKeb+/everbt68GDhyo6dOnq7S01OySLilffPGFysvL2/z24+LilJWVxW+/G2zYsEEpKSkaMmSI7rnnHh07dszsknqU6upqSVJCQoIkqaSkRI2NjW1+70OHDlW/fv34vfvRV7/3Vi+99JKSkpI0bNgwLVy4ULW1tT7tt8dMAOlvVVVVcrvdSk1NbbM+NTVVu3btMqmqni8rK0u/+c1vNGTIEB05ckQ/+9nPdP311+uzzz5TTEyM2eVdEsrLyyWp3d9+63sIjJtuuknf/va3NWDAAO3fv18PPvigbr75Zm3ZskU2m83s8kKex+PR/fffrwkTJmjYsGGSmn/vdrtd8fHxbdrye/ef9r53SZo2bZr69++vvn376tNPP9W//du/affu3Vq3bl2H902IQVC5+eabvc+vvvpqZWVlqX///vrtb3+rH/7whyZWBgTenXfe6X0+fPhwXX311Ro0aJA2bNigG2+80cTKeoa8vDx99tln3GfXzS70vc+ZM8f7fPjw4erTp49uvPFG7d+/X4MGDerQvrmcdAFJSUmy2Wzn3aFeUVGh3r17m1TVpSc+Pl5XXHGF9u3bZ3Ypl4zW3ze/ffMNHDhQSUlJ/P79YN68efrjH/+o9957T5dddpl3fe/evdXQ0KCTJ0+2ac/v3T8u9L23JysrS5J8+r0TYi7Abrdr9OjRKi4u9q7zeDwqLi7WuHHjTKzs0nL69Gnt379fffr0MbuUS8aAAQPUu3fvNr99l8ulrVu38tvvZl9++aWOHTvG778LDMPQvHnz9Prrr+vPf/6zBgwY0Ob90aNHKzw8vM3vfffu3SotLeX33gVf9723Z8eOHZLk0++dy0kXkZ+fr5kzZyozM1Njx45VUVGRampqlJuba3ZpPda//uu/asqUKerfv78OHz6spUuXymaz6a677jK7tB7l9OnTbf6388UXX2jHjh1KSEhQv379dP/99+vf//3fNXjwYA0YMECLFy9W3759ddttt5lXdA9wse89ISFBP/vZz3THHXeod+/e2r9/vx544AFdfvnlysnJMbHq0JaXl6c1a9bozTffVExMjPc+l7i4OEVERCguLk4//OEPlZ+fr4SEBMXGxuq+++7TuHHjdO2115pcfej6uu99//79WrNmjSZPnqzExER9+umnmj9/viZOnKirr7664x/Upb5Nl4AnnnjC6Nevn2G3242xY8caH330kdkl9WhTp041+vTpY9jtdiMtLc2YOnWqsW/fPrPL6nHee+89Q9J5y8yZMw3DaO5mvXjxYiM1NdVwOBzGjTfeaOzevdvconuAi33vtbW1xqRJk4zk5GQjPDzc6N+/vzF79myjvLzc7LJDWnvftyTjueee87Y5c+aMce+99xq9evUyIiMjjdtvv904cuSIeUX3AF/3vZeWlhoTJ040EhISDIfDYVx++eXGT3/6U6O6utqnz7G0fBgAAEBI4Z4YAAAQkggxAAAgJBFiAABASCLEAACAkESIAQAAIYkQAwAAQhIhBgAAhCRCDAAACEmEGAAAEJIIMQAAICQRYgAAQEgixAAAgJD0/wGDIQTMxNWCuwAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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atGjRpZTvNdGdd13ZDelsU5vJ1QAAcOVxOehs3LhRBQUFWrVqlXbv3q20tDTl5OToxIkTfbYvLCzUiy++qGeffVZffvmlFi1apFmzZumzzz5ztNm2bZvy8/O1Y8cObdmyRa2trbr55pvV0NDgtK0FCxbo+PHjjtdTTz3lavleFRxg06AgmyQm9gQAwAwWwzBcuqaSmZmpSZMm6bnnnpMk2e12JScna8mSJVq2bFmv9omJiXrkkUeUn5/vWJabm6vQ0FC99tprfe7j5MmTGjp0qLZt26Zp06ZJ6ujRSU9PV3FxsSvlOtTX1ysqKkp1dXWKjIy8pG1cihv+74f6+ptz2vTzqfrBiMFe2y8AAP7gcr+/XerRaWlpUXl5ubKzs7s3YLUqOztbZWVlfa7T3NyskJAQp2WhoaHavn37BfdTV1cnSYqJiXFa/vrrrys2Nlbjxo3T8uXL1djYeMFtNDc3q76+3ullBqaBAADAPAGuND516pTa29sVHx/vtDw+Pl779u3rc52cnBytWbNG06ZNU2pqqkpLS7Vp0ya1t7f32d5ut+uBBx7Q9ddfr3HjxjmWz507VyNHjlRiYqL27Nmjhx56SBUVFdq0aVOf2ykqKtKjjz7qyuF5RPdDA7nzCgAAb3Mp6FyKZ555RgsWLNDYsWNlsViUmpqqvLw8vfzyy322z8/P1xdffNGrx2fhwoWO38ePH69hw4bppptuUmVlpVJTU3ttZ/ny5SooKHD8XV9fr+TkZDcdVf8xDQQAAOZx6dJVbGysbDabampqnJbX1NQoISGhz3Xi4uK0efNmNTQ06PDhw9q3b5/Cw8M1evToXm0XL16s9957Tx999JGGDx/+nbVkZmZKkg4cONDn+8HBwYqMjHR6maHr0hWDkQEA8D6Xgk5QUJAyMjJUWlrqWGa321VaWqqsrKzvXDckJERJSUlqa2vT22+/rVtvvdXxnmEYWrx4sd555x19+OGHGjVq1EVr+fzzzyVJw4YNc+UQvK77oYEEHQAAvM3lS1cFBQWaP3++Jk6cqMmTJ6u4uFgNDQ3Ky8uTJM2bN09JSUkqKiqSJO3cuVNHjx5Venq6jh49qtWrV8tut+vBBx90bDM/P18bNmzQH//4R0VERKi6ulqSFBUVpdDQUFVWVmrDhg2aMWOGhgwZoj179mjp0qWaNm2aJkyY4I7z4DExTAMBAIBpXA46c+bM0cmTJ7Vy5UpVV1crPT1dJSUljgHKVVVVslq7O4qamppUWFiogwcPKjw8XDNmzNCrr76q6OhoR5vnn39eUsct5D2tX79ed999t4KCgrR161ZHqEpOTlZubq4KCwsv4ZC9KzqMwcgAAJjF5efoDFRmPUfnTwdO6c7f7tTVQ8O1peBGr+0XAAB/4NXn6MB1g+nRAQDANAQdDxvcOUantrFFV0jnGQAAPoOg42FdPTptdkNnm5nYEwAAbyLoeFhIoE2hgR0Te/LQQAAAvIug4wUxTAMBAIApCDpeEM00EAAAmIKg4wVdPTo8NBAAAO8i6HhB90MDCToAAHgTQccLYrouXRF0AADwKoKOFzANBAAA5iDoeIHjrivG6AAA4FUEHS8YPIgxOgAAmIGg4wWDHbeXc+kKAABvIuh4wWDuugIAwBQEHS/oeemKiT0BAPAego4XxHT26LS2G2poaTe5GgAArhwEHS8IDbIpOKDjVHPnFQAA3kPQ8RKmgQAAwPsIOl7CNBAAAHgfQcdLYgYxDQQAAN5G0PESxy3mPEsHAACvIeh4Cc/SAQDA+wg6XsI0EAAAeB9Bx0uYBgIAAO8j6HhJDD06AAB4HUHHS7puL+c5OgAAeA9Bx0tiGIwMAIDXEXS8JLprjE5jKxN7AgDgJQQdL+kao9PSZlcjE3sCAOAVBB0vCQuyKcjWObEnl68AAPAKgo6XWCwWDR7ELeYAAHgTQceLeDoyAADeRdDxIoIOAADeRdDxIsdDA3mWDgAAXkHQ8aKuW8zPNDJGBwAAbyDoeFFXj04tl64AAPAKgo4XMQ0EAADeRdDxopiu28vp0QEAwCsIOl7U1aPDc3QAAPCOSwo6a9euVUpKikJCQpSZmaldu3ZdsG1ra6see+wxpaamKiQkRGlpaSopKXF5m01NTcrPz9eQIUMUHh6u3Nxc1dTUXEr5pmFiTwAAvMvloLNx40YVFBRo1apV2r17t9LS0pSTk6MTJ0702b6wsFAvvviinn32WX355ZdatGiRZs2apc8++8ylbS5dulTvvvuu3nrrLW3btk3Hjh3T7NmzL+GQzcNzdAAA8C6L4eJU2pmZmZo0aZKee+45SZLdbldycrKWLFmiZcuW9WqfmJioRx55RPn5+Y5lubm5Cg0N1WuvvdavbdbV1SkuLk4bNmzQz372M0nSvn37dO2116qsrExTpky5aN319fWKiopSXV2dIiMjXTlktznb1Krxq/9TkrT3sZ8oNMhmSh0AAAwUl/v97VKPTktLi8rLy5Wdnd29AatV2dnZKisr63Od5uZmhYSEOC0LDQ3V9u3b+73N8vJytba2OrUZO3asRowY8Z37ra+vd3qZLTw4QIE2iyR6dQAA8AaXgs6pU6fU3t6u+Ph4p+Xx8fGqrq7uc52cnBytWbNG+/fvl91u15YtW7Rp0yYdP36839usrq5WUFCQoqOj+73foqIiRUVFOV7JycmuHKpHWCwWbjEHAMCLPH7X1TPPPKOrr75aY8eOVVBQkBYvXqy8vDxZrZ7d9fLly1VXV+d4HTlyxKP766+uAcm1PB0ZAACPcyltxMbGymaz9brbqaamRgkJCX2uExcXp82bN6uhoUGHDx/Wvn37FB4ertGjR/d7mwkJCWppaVFtbW2/9xscHKzIyEinly/ongaCHh0AADzNpaATFBSkjIwMlZaWOpbZ7XaVlpYqKyvrO9cNCQlRUlKS2tra9Pbbb+vWW2/t9zYzMjIUGBjo1KaiokJVVVUX3a+vYWJPAAC8J8DVFQoKCjR//nxNnDhRkydPVnFxsRoaGpSXlydJmjdvnpKSklRUVCRJ2rlzp44ePar09HQdPXpUq1evlt1u14MPPtjvbUZFRenee+9VQUGBYmJiFBkZqSVLligrK6tfd1z5kmhuMQcAwGtcDjpz5szRyZMntXLlSlVXVys9PV0lJSWOwcRVVVVO42+amppUWFiogwcPKjw8XDNmzNCrr77qNLD4YtuUpKefflpWq1W5ublqbm5WTk6O1q1bdxmHbg7HNBD06AAA4HEuP0dnoPKF5+hI0m//+6D++f29+j9pifq3O75vWh0AAAwEXn2ODi4fT0cGAMB7CDpe5hiMTNABAMDjCDpe1nV7OTOYAwDgeQQdL6NHBwAA7yHoeFnX7eWNLe1qam03uRoAAPwbQcfLIkMCZLN2TOzJNBAAAHgWQcfLLBaLBndNA8GzdAAA8CiCjgm4xRwAAO8g6JiAoAMAgHcQdEwwmGkgAADwCoKOCbp7dBiMDACAJxF0TDC481k6DEYGAMCzCDomiOns0alljA4AAB5F0DFB1zQQZ7h0BQCARxF0TNA1DQQ9OgAAeBZBxwRd00AwRgcAAM8i6JjAMbEnQQcAAI8i6JigawqIhpZ2NbcxsScAAJ5C0DFBZEigOuf1ZGJPAAA8iKBjAqvV4hinwzQQAAB4DkHHJMxgDgCA5xF0TDLY8dBALl0BAOApBB2TMA0EAACeR9AxCdNAAADgeQQdk0QP6hqjw6UrAAA8haBjEnp0AADwPIKOSboGI58h6AAA4DEEHZMMZhoIAAA8jqBjkq7n6HzD7eUAAHgMQcck9OgAAOB5BB2TdI3ROdvcptZ2u8nVAADgnwg6JokKDZSlc2JP5rsCAMAzCDomsVktigrtGKfDNBAAAHgGQcdEXc/SYRoIAAA8g6Bjoq4ByTw0EAAAzyDomKjrFnOmgQAAwDMIOibquvOKwcgAAHgGQcdEPEsHAADPIuiYiPmuAADwLIKOibrG6HB7OQAAnnFJQWft2rVKSUlRSEiIMjMztWvXru9sX1xcrDFjxig0NFTJyclaunSpmpqaHO+npKTIYrH0euXn5zvaTJ8+vdf7ixYtupTyfUbXpStuLwcAwDMCXF1h48aNKigo0AsvvKDMzEwVFxcrJydHFRUVGjp0aK/2GzZs0LJly/Tyyy9r6tSp+tvf/qa7775bFotFa9askSR9+umnam9vd6zzxRdf6Mc//rFuv/12p20tWLBAjz32mOPvsLAwV8v3KV2Xrri9HAAAz3A56KxZs0YLFixQXl6eJOmFF17Q+++/r5dfflnLli3r1f6TTz7R9ddfr7lz50rq6L254447tHPnTkebuLg4p3X+5V/+RampqbrxxhudloeFhSkhIcHVkn1WzKCu28sJOgAAeIJLl65aWlpUXl6u7Ozs7g1YrcrOzlZZWVmf60ydOlXl5eWOy1sHDx7UBx98oBkzZlxwH6+99pruueceWbomg+r0+uuvKzY2VuPGjdPy5cvV2Nh4wVqbm5tVX1/v9PI1XT069U1tamNiTwAA3M6lHp1Tp06pvb1d8fHxTsvj4+O1b9++PteZO3euTp06pRtuuEGGYaitrU2LFi3Sww8/3Gf7zZs3q7a2VnfffXev7YwcOVKJiYnas2ePHnroIVVUVGjTpk19bqeoqEiPPvqoK4fndV1zXUlS7blWxYYHm1gNAAD+x+N3XX388cd68skntW7dOu3evVubNm3S+++/r8cff7zP9i+99JJuueUWJSYmOi1fuHChcnJyNH78eN155536/e9/r3feeUeVlZV9bmf58uWqq6tzvI4cOeL2Y7tcATZrj4k9uXwFAIC7udSjExsbK5vNppqaGqflNTU1Fxw7s2LFCt1111267777JEnjx49XQ0ODFi5cqEceeURWa3fWOnz4sLZu3XrBXpqeMjMzJUkHDhxQampqr/eDg4MVHOz7PSSDwwJVd66VaSAAAPAAl3p0goKClJGRodLSUscyu92u0tJSZWVl9blOY2OjU5iRJJvNJkkyDMNp+fr16zV06FD99Kc/vWgtn3/+uSRp2LBhrhyCz+EWcwAAPMflu64KCgo0f/58TZw4UZMnT1ZxcbEaGhocd2HNmzdPSUlJKioqkiTNnDlTa9as0fe//31lZmbqwIEDWrFihWbOnOkIPFJHYFq/fr3mz5+vgADnsiorK7VhwwbNmDFDQ4YM0Z49e7R06VJNmzZNEyZMuJzjNx23mAMA4DkuB505c+bo5MmTWrlypaqrq5Wenq6SkhLHAOWqqiqnHpzCwkJZLBYVFhbq6NGjiouL08yZM/XEE084bXfr1q2qqqrSPffc02ufQUFB2rp1qyNUJScnKzc3V4WFha6W73OYBgIAAM+xGOdfP/JT9fX1ioqKUl1dnSIjI80ux+Gf3/tSv91+SAunjdbDM641uxwAAHzK5X5/M9eVyRijAwCA5xB0TMYYHQAAPIegYzKmgQAAwHMIOibr7tHhOToAALgbQcdkjjE6XLoCAMDtCDom6+rRqTvXqnb7FXEDHAAAXkPQMVl0WMcYHcPoCDsAAMB9CDomC7RZFRHS8dxGBiQDAOBeBB0fwC3mAAB4BkHHB/DQQAAAPIOg4wMGd47T4RZzAADci6DjA2KY2BMAAI8g6PiA6M6g8w1BBwAAtyLo+ICuaSC+YYwOAABuRdDxAV2Dkb9hjA4AAG5F0PEBXbeX06MDAIB7EXR8wGAGIwMA4BEEHR8weBC3lwMA4AkEHR8Q0+PJyHYm9gQAwG0IOj6g6/ZyuyHVN9GrAwCAuxB0fEBQgFXhwUzsCQCAuxF0fER05zQQ3GIOAID7EHR8RMwgbjEHAMDdCDo+YjDTQAAA4HYEHR8x2HHpiqADAIC7EHR8BNNAAADgfgQdH8E0EAAAuB9Bx0d09ehwezkAAO5D0PERXWN0mAYCAAD3Iej4iBgm9gQAwO0IOj4iusd8VwAAwD0IOj4ipsddV4bBxJ4AALgDQcdHdE0B0W43VN/UZnI1AAD4B4KOjwgJtCksyCaJW8wBAHAXgo4PYRoIAADci6DjQwYPYhoIAADciaDjQ7p6dM408CwdAADcgaDjQwZzizkAAG5F0PEhMUwDAQCAW11S0Fm7dq1SUlIUEhKizMxM7dq16zvbFxcXa8yYMQoNDVVycrKWLl2qpqYmx/urV6+WxWJxeo0dO9ZpG01NTcrPz9eQIUMUHh6u3Nxc1dTUXEr5Pis2vCPo/K3mW5MrAQDAP7gcdDZu3KiCggKtWrVKu3fvVlpamnJycnTixIk+22/YsEHLli3TqlWrtHfvXr300kvauHGjHn74Yad23/ve93T8+HHHa/v27U7vL126VO+++67eeustbdu2TceOHdPs2bNdLd+n/fi6BEnSh/tqdKz2nMnVAAAw8LkcdNasWaMFCxYoLy9P1113nV544QWFhYXp5Zdf7rP9J598ouuvv15z585VSkqKbr75Zt1xxx29eoECAgKUkJDgeMXGxjreq6ur00svvaQ1a9boRz/6kTIyMrR+/Xp98skn2rFjh6uH4LPGJEQoc1SM7Ia0YWeV2eUAADDguRR0WlpaVF5eruzs7O4NWK3Kzs5WWVlZn+tMnTpV5eXljmBz8OBBffDBB5oxY4ZTu/379ysxMVGjR4/WnXfeqaqq7i/68vJytba2Ou137NixGjFixAX329zcrPr6eqfXQDB/aook6Q+7qtTc1m5uMQAADHAuBZ1Tp06pvb1d8fHxTsvj4+NVXV3d5zpz587VY489phtuuEGBgYFKTU3V9OnTnS5dZWZm6pVXXlFJSYmef/55HTp0SD/84Q919uxZSVJ1dbWCgoIUHR3d7/0WFRUpKirK8UpOTnblUE3z4+viFR8ZrNMNLfr//tr3sQEAgP7x+F1XH3/8sZ588kmtW7dOu3fv1qZNm/T+++/r8ccfd7S55ZZbdPvtt2vChAnKycnRBx98oNraWr355puXvN/ly5errq7O8Tpy5Ig7DsfjAm1W3Zk5UpL0u7KvzC0GAIABLsCVxrGxsbLZbL3udqqpqVFCQkKf66xYsUJ33XWX7rvvPknS+PHj1dDQoIULF+qRRx6R1do7a0VHR+uaa67RgQMHJEkJCQlqaWlRbW2tU6/Od+03ODhYwcHBrhyez/j7ycl69sP9+qyqVn/9uk7jh0eZXRIAAAOSSz06QUFBysjIUGlpqWOZ3W5XaWmpsrKy+lynsbGxV5ix2TomrzQMo891vv32W1VWVmrYsGGSpIyMDAUGBjrtt6KiQlVVVRfc70A2NCJEt4zrOPbf06sDAMAlc/nSVUFBgX7zm9/od7/7nfbu3av7779fDQ0NysvLkyTNmzdPy5cvd7SfOXOmnn/+eb3xxhs6dOiQtmzZohUrVmjmzJmOwPOrX/1K27Zt01dffaVPPvlEs2bNks1m0x133CFJioqK0r333quCggJ99NFHKi8vV15enrKysjRlyhR3nAefM39qx+Wrf//LMWYzBwDgErl06UqS5syZo5MnT2rlypWqrq5Wenq6SkpKHAOUq6qqnHpwCgsLZbFYVFhYqKNHjyouLk4zZ87UE0884Wjz9ddf64477tDp06cVFxenG264QTt27FBcXJyjzdNPPy2r1arc3Fw1NzcrJydH69atu5xj92k/GDFY1w2L1JfH6/Xmn4/o/7kx1eySAAAYcCzGha4f+Zn6+npFRUWprq5OkZGRZpfTLxs/rdJDb/9VyTGh+vhX/0s2q8XskgAA8KrL/f5mrisf9n/SkhQVGqgjZ87p44q+nzwNAAAujKDjw0KDbPq7icMlSb8vO2xyNQAADDwEHR/3D1NGymKRtv3tpA6dajC7HAAABhSCjo8bOWSQpl/TMSj7tR306gAA4AqCzgAwLytFkvTmn4+osaXN3GIAABhACDoDwI3XxGlETJjONrXpj58fM7scAAAGDILOAGC1WnTXlM75rz756oJPlAYAAM4IOgPE7ROHKyTQqn3VZ/Xnw9+YXQ4AAAMCQWeAiA4L0q1pSZI6enUAAMDFEXQGkLuyOi5flXxRrRP1TSZXAwCA7yPoDCDjkqKUMXKw2uyGNuyqMrscAAB8HkFngJnX2auzYWeVWtvtJlcDAIBvI+gMMLeMG6bY8GCdONus//ifarPLAQDApxF0BpigAKvmTk6WxPxXAABcDEFnAJqbOVI2q0W7Dp3Rvup6s8sBAMBnEXQGoISoEOV8L14SvToAAHwXgs4AddeUFEnSO7uPqu5cq7nFAADgowg6A9SU0TG6Jj5c51rb9Xb512aXAwCATyLoDFAWi0V3dc5q/uqOw7Lbmf8KAIDzEXQGsNnfT1JEcIAOnWrQ9gOnzC4HAACfQ9AZwAYFByg3Y7gk6fdlX5lbDAAAPoigM8D9w5SOJyWX7juhI2caTa4GAADfQtAZ4K4aGq4broqVYUiv7eRWcwAAeiLo+IGu+a/e/PSImlrbTa4GAADfQdDxAzddG6+k6FB909iqd/9yzOxyAADwGQQdP2CzWnTnlBGSOp6UbBjcag4AgETQ8RtzJiYryGbVX4/W6fMjtWaXAwCATyDo+Ikh4cH632nDJEmvMv8VAACSCDp+ZV7nk5Lf23Ncp75tNrcYAAB8AEHHj6QnRytteJRa2u3a+OkRs8sBAMB0BB0/0zX/1es7Dqut3W5uMQAAmIyg42f+94RhGhwWqGN1TSrdd8LscgAAMBVBx8+EBNo0Z1LXreZfmVsMAAAmI+j4oTszR8hqkf504LT+wq3mAIArGEHHDyXHhCnnewmSpH/47U799/6TJlcEAIA5CDp+qmj2eE0eFaOzzW3KW/+pNn5aZXZJAAB4HUHHT0WHBenVeyfrtvREtdkNPfT2X/VUyT7Z7UwPAQC4chB0/FhwgE1Pz0nXP/7oKknSuo8r9Y9vfMYM5wCAKwZBx89ZLBYV3DxG//qzCQqwWvTenuP6h9/u1JmGFrNLAwDA4wg6V4jbJybrd/dMVkRIgP58+BvNXvcnHTrVYHZZAAB41CUFnbVr1yolJUUhISHKzMzUrl27vrN9cXGxxowZo9DQUCUnJ2vp0qVqampyvF9UVKRJkyYpIiJCQ4cO1W233aaKigqnbUyfPl0Wi8XptWjRoksp/4p1/VWx2nT/VCVFh+qr042ave5P+vSrM2aXBQCAx7gcdDZu3KiCggKtWrVKu3fvVlpamnJycnTiRN9P4d2wYYOWLVumVatWae/evXrppZe0ceNGPfzww44227ZtU35+vnbs2KEtW7aotbVVN998sxoanHscFixYoOPHjzteTz31lKvlX/Gujo/QO/lTlTY8St80turO3+zUv//lmNllAQDgERbDMFy6DSczM1OTJk3Sc889J0my2+1KTk7WkiVLtGzZsl7tFy9erL1796q0tNSx7Je//KV27typ7du397mPkydPaujQodq2bZumTZsmqaNHJz09XcXFxa6U61BfX6+oqCjV1dUpMjLykrbhT861tOsXb3ym//yyRpL0Tzlj9PPpqbJYLCZXBgBAt8v9/napR6elpUXl5eXKzs7u3oDVquzsbJWVlfW5ztSpU1VeXu64vHXw4EF98MEHmjFjxgX3U1dXJ0mKiYlxWv76668rNjZW48aN0/Lly9XY2HjBbTQ3N6u+vt7phW6hQTY9/w8Zuuf6UZKkf/2PCi17+69qZSJQAIAfCXCl8alTp9Te3q74+Hin5fHx8dq3b1+f68ydO1enTp3SDTfcIMMw1NbWpkWLFjlduurJbrfrgQce0PXXX69x48Y5bWfkyJFKTEzUnj179NBDD6miokKbNm3qcztFRUV69NFHXTm8K47NatHKmddp5JAwPfru/2jjn4/oWN05rb3zB4oMCTS7PAAALpvH77r6+OOP9eSTT2rdunXavXu3Nm3apPfff1+PP/54n+3z8/P1xRdf6I033nBavnDhQuXk5Gj8+PG688479fvf/17vvPOOKisr+9zO8uXLVVdX53gdOXLE7cfmL+ZPTdFv5k1UaKBN/73/lG5/vkxHa8+ZXRYAAJfNpaATGxsrm82mmpoap+U1NTVKSEjoc50VK1borrvu0n333afx48dr1qxZevLJJ1VUVCS73fkyyeLFi/Xee+/po48+0vDhw7+zlszMTEnSgQMH+nw/ODhYkZGRTi9c2E3XxuutRVkaGhGsipqzum3tn/TXr+vMLgsAgMviUtAJCgpSRkaG08Biu92u0tJSZWVl9blOY2OjrFbn3dhsNklS1zhowzC0ePFivfPOO/rwww81atSoi9by+eefS5KGDRvmyiHgO4xLitI7+ddrTHyETp5t1t+9WKatX9ZcfEUAAHyUy5euCgoK9Jvf/Ea/+93vtHfvXt1///1qaGhQXl6eJGnevHlavny5o/3MmTP1/PPP64033tChQ4e0ZcsWrVixQjNnznQEnvz8fL322mvasGGDIiIiVF1drerqap0713H5pLKyUo8//rjKy8v11Vdf6d///d81b948TZs2TRMmTHDHeUCnpOhQvXV/ln54dazOtbZr4at/1it/OmR2WQAAXBKXBiNL0pw5c3Ty5EmtXLlS1dXVSk9PV0lJiWOAclVVlVMPTmFhoSwWiwoLC3X06FHFxcVp5syZeuKJJxxtnn/+eUkdt5D3tH79et19990KCgrS1q1bVVxcrIaGBiUnJys3N1eFhYWXcsy4iMiQQL189ySt2PyF3vj0iFa/+6W+Ot2oZbeMVUigzezyAADoN5efozNQ8Rwd1xmGoee3Veqpko6nVKcMCdNjt47TtGviTK4MAHCl8OpzdHBlsVgs+vn0q/TiXRkaGhGsr043at7Lu5S/Ybdq6psuvgEAAExG0MFF5XwvQaW/vFF516fIapHe33NcN/16m17efkhtPGAQAODDuHQFl3xxtE6PbP5CfzlSK0m6bliknpg1Tt8fMdjcwgAAfolLV/CqcUlR2nT/VD0xa5wiQwL05fF6zX7+Ez38zl9V19hqdnkAADgh6MBlNqtFd2aO1Ie/mq7ZP0iSYUgbdlbpR7/+WG+Xf60rpJMQADAAEHRwyWLDg7Xm79L1xsIpumpouE43tOiXb/1Ff///7tD+mrNmlwcAAEEHl2/K6CH64B9/qAd/MkYhgVbtPHRGtzzz3/q/Jft0rqXd7PIAAFcwgg7cIijAqp9Pv0pblt6o7GuHqs1u6PmPK5W9ZhvTSAAATEPQgVslx4Tpt/Mn6TfzJiopOlRHa8/pvt//WQt+/2dmRAcAeB1BBx7x4+vitaVgmhbdmKoAq0VbvqxR9q+36YVtlWrl2TsAAC/hOTrwuL/VnFXh5i+069AZSdKImDDNyxqp2ycmKyo00OTqAAC+7HK/vwk68ArDMPT27qMq+mCvTje0SJLCgmya/YMkzc9K0dXxESZXCADwRQSdfiLo+IbGljZt/uyYXvnkkP5W861j+Q1XxWr+1BT9aOxQ2awWEysEAPgSgk4/EXR8i2EYKqs8rVc++Upb99bI3vkpTI4J1bwpKfq7icmKCuOyFgBc6Qg6/UTQ8V1HzjTqtR2H9canR1R3rmMaidBAm2b9IEl3T03RNVzWAoArFkGnnwg6vu9cS7s2f35Uv/vkK+2r7n6y8tTUIZo/NUXZ18ZzWQsArjAEnX4i6AwchmFo56EzeuVPX+k/v6x2XNYaPjhUd00ZqTmTkhUdFmRukQAAryDo9BNBZ2D6+ptGvbajSm98WqXaztnRQwKtmvX9JM2fmqKxCfy3BAB/RtDpJ4LOwNbU2q4/fn5Ur3xyWHuP1zuWXxMfrimjhyhr9BBljh6imEH09ACAPyHo9BNBxz8YhqFPv/pGr3xySP/xPzVqtzt/fMcmRGjK6CGaMjpGmaOGaDDBBwAGNIJOPxF0/M+ZhhbtOnRaZZWntePgGVXUnO3VZmxChLJSh2jK6CHKHBXD2B4AGGAIOv1E0PF/p79t1s5DZ7TjYEf42X/iW6f3LRbp2oTIjktdqUM0OSWGZ/UAgI8j6PQTQefKc/Jss3YdOqOyg6e04+AZHegj+Fw3LFJZo4do8qgYXR0foaToUAUFMNctAPgKgk4/EXRw4myTdh48o7KDp7Xj4GkdPNnQq43VIiVGh2pETJhGDgnTiJhB3b8PCVNkCD1AAOBNBJ1+IujgfCfqmzpDzxntPvyNDp9pUFOr/TvXiQ4L1MiYMI0YMkgjYkI1MmaQkjuDUEJkiKw80BAA3Iqg008EHVyMYRg6ebZZh880qup0Y+fPBlWdaVTVmUad+rblO9cPslk1PCZUwweHaXBYoKJDAxUVGqiosCBFhXb8HR3WtazjZ3CAzUtHBwAD0+V+fwd4oCZgQLJYLBoaGaKhkSGalBLT6/1vm9t05EyjDp9uVNWZjgDU8Xujjn5zTi3tdh082dDnJbELCQ20dYefzld0WKCiO8NReHCAQgKtCg6wKTjAquBAq0ICbAruuSzA1t0m0Kogm5WepSuIYRgyDMmQZHf83vnT6FzW872LtDdkyN6zXY/3u7bl2I56rGPvbic5r9dzX+qxrNd2Olc2+li/q5161eu8jZ7b76uOnv+2733sPc7pefs6v67u9R07/o7j6N5uz313Vuu8rfPeO78rwrHtC7R3/tu5wcW2f6Ht6fztnVdHz/dS48I1N3OEfAlBB+in8OAAXTssUtcO6/0virZ2u47XNXWEntpzqj/XqtrGVtWda1XtuY6fdY0tTn8bhnSutV3n6tp1vK7JrbUG2azOYSjQqkBrRwCyWSWbxSKbteNlvcDvXW2sVotsFnX+tCjAZpHUEaQsnXmqK1Z1/90dtHq36R3CHF8s6uuLydGq1xdvn+s4vth7fql3fxHbje4vxK429p5fmD3ad3+xd3/Rn9/Wsaznup3LdV7QsHdeGbWft+75tfS5riGnZYZjXcB33HhNHEEH8EcBNquSY8KUHBPWr/Z2u6GzzW2qc4ShzhDU+XfH7y1qaG5Xc1u7mtvsam61q6mtXc2tdseyptbunz2/9Fra7Wppt+us2jx0xPAnFotktXTEU4ulI4w6fpdF1h7L1NXWos42He9LPZf13F53KD5/edf2em7Hcn6Idqqr4/2u/cipzt51d6Xrnst6budC++lep+eyHsfR47z13I5T0HdqZzlvne7lji33WNdyXt29ljv+reDcVue1d/7beZ/q0f67/2HSR63du++1r1Gxg+RrCDqACaxWi+NSlbu0tdudwk/Hq11NrR3L2u1Gx8swZLcbarN3/Gw3OpbbDUPtdjne62rX/V5n2/YLd4P3/Ltnm+/qOj//i0vq44upc72uL2Gd937XOo4v4M4vlq4vaGvnBq091un5u+NL1un9Hj/Vs02PthaL4/3uffe9Ttdy5/32vZ+ef/fa7kXayiKnYNJz/Z6hpesn4O8IOoCfCLBZFWCzalAw/1sDQBeejAYAAPwWQQcAAPgtgg4AAPBbBB0AAOC3CDoAAMBvEXQAAIDfIugAAAC/RdABAAB+65KCztq1a5WSkqKQkBBlZmZq165d39m+uLhYY8aMUWhoqJKTk7V06VI1NTnP7XOxbTY1NSk/P19DhgxReHi4cnNzVVNTcynlAwCAK4TLQWfjxo0qKCjQqlWrtHv3bqWlpSknJ0cnTpzos/2GDRu0bNkyrVq1Snv37tVLL72kjRs36uGHH3Zpm0uXLtW7776rt956S9u2bdOxY8c0e/bsSzhkAABwpbAYPeeN74fMzExNmjRJzz33nCTJbrcrOTlZS5Ys0bJly3q1X7x4sfbu3avS0lLHsl/+8pfauXOntm/f3q9t1tXVKS4uThs2bNDPfvYzSdK+fft07bXXqqysTFOmTLlo3fX19YqKilJdXZ0iI3vPPg0AAHzP5X5/u9Sj09LSovLycmVnZ3dvwGpVdna2ysrK+lxn6tSpKi8vd1yKOnjwoD744APNmDGj39ssLy9Xa2urU5uxY8dqxIgRF9xvc3Oz6uvrnV4AAODK4tLsf6dOnVJ7e7vi4+OdlsfHx2vfvn19rjN37lydOnVKN9xwgwzDUFtbmxYtWuS4dNWfbVZXVysoKEjR0dG92lRXV/e536KiIj366KOuHB4AAPAzHp/m+OOPP9aTTz6pdevWKTMzUwcOHNAvfvELPf7441qxYoXH9rt8+XIVFBQ4/q6rq9OIESPo2QEAYADp+t52caSNg0tBJzY2VjabrdfdTjU1NUpISOhznRUrVuiuu+7SfffdJ0kaP368GhoatHDhQj3yyCP92mZCQoJaWlpUW1vr1KvzXfsNDg5WcHCw4++uE5WcnOzKIQMAAB9w9uxZRUVFubyeS0EnKChIGRkZKi0t1W233SapY+BwaWmpFi9e3Oc6jY2NslqdhwLZbDZJHemsP9vMyMhQYGCgSktLlZubK0mqqKhQVVWVsrKy+lV7YmKijhw5ooiICFksFlcO+6Lq6+uVnJysI0eOMNDZizjv5uC8m4Pzbg7Ou/edf84Nw9DZs2eVmJh4Sdtz+dJVQUGB5s+fr4kTJ2ry5MkqLi5WQ0OD8vLyJEnz5s1TUlKSioqKJEkzZ87UmjVr9P3vf99x6WrFihWaOXOmI/BcbJtRUVG69957VVBQoJiYGEVGRmrJkiXKysrq1x1XUscA5+HDh7t6uC6JjIzkfwQTcN7NwXk3B+fdHJx37+t5zi+lJ6eLy0Fnzpw5OnnypFauXKnq6mqlp6erpKTEMZi4qqrKqQensLBQFotFhYWFOnr0qOLi4jRz5kw98cQT/d6mJD399NOyWq3Kzc1Vc3OzcnJytG7duks+cAAA4P9cfo4OeuMZPebgvJuD824Ozrs5OO/e5+5zzlxXbhAcHKxVq1Y5DX6G53HezcF5Nwfn3Rycd+9z9zmnRwcAAPgtenQAAIDfIugAAAC/RdABAAB+i6ADAAD8FkHnMq1du1YpKSkKCQlRZmamY5Z2eMbq1atlsVicXmPHjjW7LL/zX//1X5o5c6YSExNlsVi0efNmp/cNw9DKlSs1bNgwhYaGKjs7W/v37zenWD9ysfN+99139/r8/+QnPzGnWD9SVFSkSZMmKSIiQkOHDtVtt92miooKpzZNTU3Kz8/XkCFDFB4ertzc3F5TF8E1/Tnv06dP7/WZX7RokUv7Iehcho0bN6qgoECrVq3S7t27lZaWppycHJ04ccLs0vza9773PR0/ftzx2r59u9kl+Z2GhgalpaVp7dq1fb7/1FNP6d/+7d/0wgsvaOfOnRo0aJBycnLU1NTk5Ur9y8XOuyT95Cc/cfr8/+EPf/Bihf5p27Ztys/P144dO7Rlyxa1trbq5ptvVkNDg6PN0qVL9e677+qtt97Stm3bdOzYMc2ePdvEqge+/px3SVqwYIHTZ/6pp55ybUcGLtnkyZON/Px8x9/t7e1GYmKiUVRUZGJV/m3VqlVGWlqa2WVcUSQZ77zzjuNvu91uJCQkGP/6r//qWFZbW2sEBwcbf/jDH0yo0D+df94NwzDmz59v3HrrrabUcyU5ceKEIcnYtm2bYRgdn+/AwEDjrbfecrTZu3evIckoKyszq0y/c/55NwzDuPHGG41f/OIXl7VdenQuUUtLi8rLy5Wdne1YZrValZ2drbKyMhMr83/79+9XYmKiRo8erTvvvFNVVVVml3RFOXTokKqrq50++1FRUcrMzOSz7wUff/yxhg4dqjFjxuj+++/X6dOnzS7J79TV1UmSYmJiJEnl5eVqbW11+syPHTtWI0aM4DPvRuef9y6vv/66YmNjNW7cOC1fvlyNjY0ubdflua7Q4dSpU2pvb3eaj0uS4uPjtW/fPpOq8n+ZmZl65ZVXNGbMGB0/flyPPvqofvjDH+qLL75QRESE2eVdEaqrqyWpz89+13vwjJ/85CeaPXu2Ro0apcrKSj388MO65ZZbVFZW5pgkGZfHbrfrgQce0PXXX69x48ZJ6vjMBwUFKTo62qktn3n36eu8S9LcuXM1cuRIJSYmas+ePXrooYdUUVGhTZs29XvbBB0MKLfccovj9wkTJigzM1MjR47Um2++qXvvvdfEygDP+/u//3vH7+PHj9eECROUmpqqjz/+WDfddJOJlfmP/Px8ffHFF4z987ILnfeFCxc6fh8/fryGDRumm266SZWVlUpNTe3Xtrl0dYliY2Nls9l6jbqvqalRQkKCSVVdeaKjo3XNNdfowIEDZpdyxej6fPPZN9/o0aMVGxvL599NFi9erPfee08fffSRhg8f7liekJCglpYW1dbWOrXnM+8eFzrvfcnMzJQklz7zBJ1LFBQUpIyMDJWWljqW2e12lZaWKisry8TKrizffvutKisrNWzYMLNLuWKMGjVKCQkJTp/9+vp67dy5k8++l3399dc6ffo0n//LZBiGFi9erHfeeUcffvihRo0a5fR+RkaGAgMDnT7zFRUVqqqq4jN/GS523vvy+eefS5JLn3kuXV2GgoICzZ8/XxMnTtTkyZNVXFyshoYG5eXlmV2a3/rVr36lmTNnauTIkTp27JhWrVolm82mO+64w+zS/Mq3337r9C+mQ4cO6fPPP1dMTIxGjBihBx54QP/8z/+sq6++WqNGjdKKFSuUmJio2267zbyi/cB3nfeYmBg9+uijys3NVUJCgiorK/Xggw/qqquuUk5OjolVD3z5+fnasGGD/vjHPyoiIsIx7iYqKkqhoaGKiorSvffeq4KCAsXExCgyMlJLlixRVlaWpkyZYnL1A9fFzntlZaU2bNigGTNmaMiQIdqzZ4+WLl2qadOmacKECf3f0WXdswXj2WefNUaMGGEEBQUZkydPNnbs2GF2SX5tzpw5xrBhw4ygoCAjKSnJmDNnjnHgwAGzy/I7H330kSGp12v+/PmGYXTcYr5ixQojPj7eCA4ONm666SajoqLC3KL9wHed98bGRuPmm2824uLijMDAQGPkyJHGggULjOrqarPLHvD6OueSjPXr1zvanDt3zvj5z39uDB482AgLCzNmzZplHD9+3Lyi/cDFzntVVZUxbdo0IyYmxggODjauuuoq45/+6Z+Muro6l/Zj6dwZAACA32GMDgAA8FsEHQAA4LcIOgAAwG8RdAAAgN8i6AAAAL9F0AEAAH6LoAMAAPwWQQcAAPgtgg4AAPBbBB0AAOC3CDoAAMBvEXQAAIDf+v8B8ysNC2YxNt8AAAAASUVORK5CYII=", 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Vl5f7l3m9XpWXl2vatGkXXdfhcGjIkCFqbW3Vyy+/rFtuuaXH2zRTYrxNcVaLJGayBgDADHHBrlBYWKj58+dr8uTJmjp1qsrKylRfX6/8/HxJ0rx58zRkyBCVlJRIknbs2KHq6mpNnDhR1dXVeuihh+T1evXAAw90eZvRyGKxyJUYr5P1zao706IrXIlmlwQAwGUl6BAze/ZsHT9+XCtWrFBNTY0mTpyoTZs2+QfmVlVVBYx3aWxsVFFRkQ4ePKjk5GTNmjVLzz33nPr27dvlbUYrf4hpoCcGAIBIC/o5MdEq0s+JkaRb1/yPdh3+Qr++c5JmXnXh8TsAAKBzEXtODAIx9QAAAOYhxPQAUw8AAGAeQkwPuHjgHQAApiHE9IDT0X45iRADAEDEEWJ6wHc5iRADAEDkEWJ6gDExAACYhxDTA4QYAADMQ4jpgbO3WBNiAACINEJMD9ATAwCAeQgxPUCIAQDAPISYHvBdTmps8aqp1WNyNQAAXF4IMT2QkhAni6XtvfsMUw8AABBJhJgesFotSkngqb0AAJiBENNDriTGxQAAYAZCTA/5px7gNmsAACKKENNDTD0AAIA5CDE9xG3WAACYgxDTQ/4Q00CIAQAgkggxPcTUAwAAmIMQ00NcTgIAwByEmB5yEmIAADAFIaaHzt6dxBN7AQCIJEJMDzkdPLEXAAAzEGJ6iDExAACYgxDTQzzsDgAAcxBiesg3sPdUU6s8XsPkagAAuHwQYnrI1xMjSad4VgwAABFDiOmheJtVSXabJMbFAAAQSYSYEOA2awAAIo8QEwJOB3coAQAQaYSYEOA2awAAIo8QEwJMPQAAQOQRYkLAmdj21F5msgYAIHIIMSHA5SQAACKPEBMChBgAACKPEBMChBgAACKPEBMCvlusmT8JAIDIIcSEAJNAAgAQeYSYEHAlcTkJAIBI61aIWbNmjTIzM+VwOJSdna2dO3detH1ZWZnGjBmjxMREZWRkaMmSJWpsbPR/7vF4VFxcrBEjRigxMVGjRo3Sww8/LMOIjVmh/T0xjUw7AABApMQFu8KGDRtUWFiotWvXKjs7W2VlZcrNzdXevXs1aNCg89qvX79eS5cu1bp16zR9+nR98sknuuuuu2SxWFRaWipJ+ulPf6onn3xSzz77rK666iq9//77ys/Pl8vl0v3339/zowyzjtMOGIYhi8VickUAAPR+QffElJaWasGCBcrPz9eVV16ptWvXKikpSevWreu0/TvvvKMZM2Zozpw5yszM1MyZM3XHHXcE9N688847uuWWW3TzzTcrMzNT3/zmNzVz5sxL9vBEC19PjMdrqL7ZY3I1AABcHoIKMc3NzaqoqFBOTs7ZDVitysnJ0fbt2ztdZ/r06aqoqPAHkoMHD+qNN97QrFmzAtqUl5frk08+kST95S9/0bZt23TTTTcFfUBmcMRbZbe1nUrGxQAAEBlBXU46ceKEPB6P0tLSApanpaVpz549na4zZ84cnThxQtdee60Mw1Bra6vuueceLV++3N9m6dKlcrvdGjt2rGw2mzwejx555BHNnTv3grU0NTWpqanJ/7vb7Q7mUELKYrHImRinE6eb5T7ToiF9E02rBQCAy0XY707asmWLVq9erSeeeEKVlZXauHGjXn/9dT388MP+Ni+99JJeeOEFrV+/XpWVlXr22Wf1i1/8Qs8+++wFt1tSUiKXy+V/ZWRkhPtQLopJIAEAiKygemJSU1Nls9lUW1sbsLy2tlbp6emdrlNcXKw777xTd999tyRp/Pjxqq+v18KFC/Xggw/KarXqhz/8oZYuXarvfOc7/jaHDh1SSUmJ5s+f3+l2ly1bpsLCQv/vbrfb1CDDU3sBAIisoHpi7Ha7Jk2apPLycv8yr9er8vJyTZs2rdN1GhoaZLUG7sZms0mS/xbqC7Xxer0XrCUhIUFOpzPgZSZCDAAAkRX0LdaFhYWaP3++Jk+erKlTp6qsrEz19fXKz8+XJM2bN09DhgxRSUmJJCkvL0+lpaW6+uqrlZ2drf3796u4uFh5eXn+MJOXl6dHHnlEw4YN01VXXaUPPvhApaWl+pd/+ZcQHmp4MfUAAACRFXSImT17to4fP64VK1aopqZGEydO1KZNm/yDfauqqgJ6VYqKimSxWFRUVKTq6moNHDjQH1p8HnvsMRUXF+v73/++jh07psGDB+t73/ueVqxYEYJDjAymHgAAILIsRqw8FvcS3G63XC6X6urqTLm09Is/79Xjb+3X/GnD9aNbxkV8/wAAxKKe/P1m7qQQcSa2dWox9QAAAJFBiAkRBvYCABBZhJgQIcQAABBZhJgQ4WF3AABEFiEmRLjFGgCAyCLEhAiXkwAAiCxCTIi4ktpCTFOrV40tHpOrAQCg9yPEhEiyPU5WS9t7dyO9MQAAhBshJkSsVotSGBcDAEDEEGJCiHExAABEDiEmhAgxAABEDiEmhPxTD5xh6gEAAMKNEBNC9MQAABA5hJgQIsQAABA5hJgQYuoBAAAihxATQkw9AABA5BBiQojLSQAARA4hJoQIMQAARA4hJoR8IcbdyC3WAACEGyEmhHwDexkTAwBA+BFiQojLSQAARA4hJoR8IeZ0U6taPV6TqwEAoHcjxIRQiiPO//4U42IAAAgrQkwIxdus6mO3SeKSEgAA4UaICTHGxQAAEBmEmBDz36HUSIgBACCcCDEhxvxJAABEBiEmxLicBABAZBBiQowQAwBAZBBiQuzsTNbcYg0AQDgRYkKMnhgAACKDEBNirsS2B94xfxIAAOFFiAkxVxI9MQAARAIhJsT8Y2J4TgwAAGFFiAkxxsQAABAZhJgQI8QAABAZhJgQ84UY95kWGYZhcjUAAPRehJgQ80074DWk0008KwYAgHAhxISYI94me1zbaeWSEgAA4UOICQPGxQAAEH7dCjFr1qxRZmamHA6HsrOztXPnzou2Lysr05gxY5SYmKiMjAwtWbJEjY2NAW2qq6v1z//8zxowYIASExM1fvx4vf/++90pz3ROh++Bd1xOAgAgXOKCXWHDhg0qLCzU2rVrlZ2drbKyMuXm5mrv3r0aNGjQee3Xr1+vpUuXat26dZo+fbo++eQT3XXXXbJYLCotLZUkff7555oxY4a+/vWv67/+6780cOBA7du3T/369ev5EZqAnhgAAMIv6BBTWlqqBQsWKD8/X5K0du1avf7661q3bp2WLl16Xvt33nlHM2bM0Jw5cyRJmZmZuuOOO7Rjxw5/m5/+9KfKyMjQM8884182YsSIoA8mWnS8QwkAAIRHUJeTmpubVVFRoZycnLMbsFqVk5Oj7du3d7rO9OnTVVFR4b/kdPDgQb3xxhuaNWuWv81//ud/avLkyfrWt76lQYMG6eqrr9bTTz990VqamprkdrsDXtHCH2J4ai8AAGETVIg5ceKEPB6P0tLSApanpaWppqam03XmzJmjVatW6dprr1V8fLxGjRql66+/XsuXL/e3OXjwoJ588kmNHj1af/7zn3Xvvffq/vvv17PPPnvBWkpKSuRyufyvjIyMYA4lrJxcTgIAIOzCfnfSli1btHr1aj3xxBOqrKzUxo0b9frrr+vhhx/2t/F6vbrmmmu0evVqXX311Vq4cKEWLFigtWvXXnC7y5YtU11dnf91+PDhcB9KlzEmBgCA8AtqTExqaqpsNptqa2sDltfW1io9Pb3TdYqLi3XnnXfq7rvvliSNHz9e9fX1WrhwoR588EFZrVZdccUVuvLKKwPW+8pXvqKXX375grUkJCQoISEhmPIjhhADAED4BdUTY7fbNWnSJJWXl/uXeb1elZeXa9q0aZ2u09DQIKs1cDc2m02S/I/lnzFjhvbu3RvQ5pNPPtHw4cODKS9q+GeyJsQAABA2Qd+dVFhYqPnz52vy5MmaOnWqysrKVF9f779bad68eRoyZIhKSkokSXl5eSotLdXVV1+t7Oxs7d+/X8XFxcrLy/OHmSVLlmj69OlavXq1vv3tb2vnzp369a9/rV//+tchPNTIYUwMAADhF3SImT17to4fP64VK1aopqZGEydO1KZNm/yDfauqqgJ6XoqKimSxWFRUVKTq6moNHDhQeXl5euSRR/xtpkyZoldeeUXLli3TqlWrNGLECJWVlWnu3LkhOMTI43ISAADhZzF6yVTLbrdbLpdLdXV1cjqdptby8RG3Zv3qv5WanKD3i3IuvQIAAJepnvz9Zu6kMHAmtk87wHNiAAAIG0JMGPguJzW3etXY4jG5GgAAeidCTBgkJ8TJZrVIYlwMAADhQogJA4vF0mEma0IMAADhQIgJE26zBgAgvAgxYcJt1gAAhBchJkwIMQAAhBchJkyYegAAgPAixITJ2TExrSZXAgBA70SICRMuJwEAEF6EmDDxhRie2gsAQHgQYsLEN/UAPTEAAIQHISZMuJwEAEB4EWLCxH85iRADAEBYEGLChBADAEB4EWLCxPecGC4nAQAQHoSYMPH1xNQ3e9Ti8ZpcDQAAvQ8hJkxS2mexlrikBABAOBBiwiTOZlVyQluQcTfy1F4AAEKNEBNG3GYNAED4EGLCyEmIAQAgbAgxYeRqf2ovY2IAAAg9QkwYcZs1AADhQ4gJI8bEAAAQPoSYMOKpvQAAhA8hJox8A3vdjYQYAABCjRATRlxOAgAgfAgxYUSIAQAgfAgxYUSIAQAgfAgxYeT0PyeGaQcAAAg1QkwY0RMDAED4EGLCqOPdSV6vYXI1AAD0LoSYMPI9sdcwpNPNXFICACCUCDFh5Ii3KSGu7RTXNXBJCQCAUCLEhBnjYgAACA9CTJgx9QAAAOFBiAkzph4AACA8CDFhxuUkAADCgxATZoQYAADCo1shZs2aNcrMzJTD4VB2drZ27tx50fZlZWUaM2aMEhMTlZGRoSVLlqixsbHTtj/5yU9ksVi0ePHi7pQWdc6OieEWawAAQinoELNhwwYVFhZq5cqVqqysVFZWlnJzc3Xs2LFO269fv15Lly7VypUrtXv3bv3mN7/Rhg0btHz58vPavvfee3rqqac0YcKE4I8kSjkdbVMP0BMDAEBoBR1iSktLtWDBAuXn5+vKK6/U2rVrlZSUpHXr1nXa/p133tGMGTM0Z84cZWZmaubMmbrjjjvO6705ffq05s6dq6efflr9+vXr3tFEISeXkwAACIugQkxzc7MqKiqUk5NzdgNWq3JycrR9+/ZO15k+fboqKir8oeXgwYN64403NGvWrIB2BQUFuvnmmwO23RswJgYAgPCIC6bxiRMn5PF4lJaWFrA8LS1Ne/bs6XSdOXPm6MSJE7r22mtlGIZaW1t1zz33BFxOevHFF1VZWan33nuvy7U0NTWpqanJ/7vb7Q7mUCKGW6wBAAiPsN+dtGXLFq1evVpPPPGEKisrtXHjRr3++ut6+OGHJUmHDx/WD37wA73wwgtyOBxd3m5JSYlcLpf/lZGREa5D6BF6YgAACI+gemJSU1Nls9lUW1sbsLy2tlbp6emdrlNcXKw777xTd999tyRp/Pjxqq+v18KFC/Xggw+qoqJCx44d0zXXXONfx+Px6O2339bjjz+upqYm2Wy287a7bNkyFRYW+n93u91RGWR4Yi8AAOERVE+M3W7XpEmTVF5e7l/m9XpVXl6uadOmdbpOQ0ODrNbA3fhCiWEYuuGGG/TXv/5Vu3bt8r8mT56suXPnateuXZ0GGElKSEiQ0+kMeEWjjj0xhmGYXA0AAL1HUD0xklRYWKj58+dr8uTJmjp1qsrKylRfX6/8/HxJ0rx58zRkyBCVlJRIkvLy8lRaWqqrr75a2dnZ2r9/v4qLi5WXlyebzaaUlBSNGzcuYB99+vTRgAEDzlsei3xjYlo8hhpbvEq0dx7KAABAcIIOMbNnz9bx48e1YsUK1dTUaOLEidq0aZN/sG9VVVVAz0tRUZEsFouKiopUXV2tgQMHKi8vT4888kjojiKK9bHbZLNa5PEaqjvTQogBACBELEYvucbhdrvlcrlUV1cXdZeWrnl4sz6rb9afF1+nMekpZpcDAEDU6Mnfb+ZOigAXt1kDABByhJgI8E890ECIAQAgVAgxEcDUAwAAhB4hJgJ44B0AAKFHiIkAph4AACD0CDERQE8MAAChR4iJAEIMAAChR4iJgLPzJ7WaXAkAAL0HISYCnA4mgQQAINQIMRHA5SQAAEKPEBMBhBgAAEKPEBMBTDsAAEDoEWIiwJnYNu1AQ7NHLR6vydUAANA7EGIiIKV9YK/EJSUAAEKFEBMBNqtFKb5JIAkxAACEBCEmQrjNGgCA0CLERAh3KAEAEFqEmAghxAAAEFqEmAg5e5s1Uw8AABAKhJgI8d1mzZgYAABCgxATIVxOAgAgtAgxEeIPMQ2EGAAAQoEQEyFOph4AACCkCDERwuUkAABCixATIU5CDAAAIUWIiRBmsgYAILQIMRHim3bg8/oWtTKTNQAAPUaIiZCh/RLlSozX6aZWPbHlgNnlAAAQ8wgxEeKIt2nVLVdJkh4t36e/HP7C3IIAAIhxhJgI+n+yBut/TbhCHq+hJS/t0plmj9klAQAQswgxEWSxWPTjW8cpzZmgg8fr9ZP/2m12SQAAxCxCTIT1TbLr59/MkiQ9u/2Q3v7kuMkVAQAQmwgxJrjuywM1f9pwSdIP//AXfdHQbHJFAADEHkKMSZbe9BWNHNhHte4mFb36kdnlAAAQcwgxJkm02/TLb0+UzWrRax8e1R93VZtdEgAAMYUQY6KsjL66/x9HS5KKX/1IR744Y3JFAADEDkKMyQq+PkpZGX3lbmzVD//wF3m9htklAQAQEwgxJouzWfXLb2cpMd6m/9l/Ur99529mlwQAQEwgxESBkQOTtfzmr0iSfrppj/bVnjK5IgAAoh8hJkr8c/Ywfe3LA9XU6tWSl3apuZVJIgEAuJhuhZg1a9YoMzNTDodD2dnZ2rlz50Xbl5WVacyYMUpMTFRGRoaWLFmixsZG/+clJSWaMmWKUlJSNGjQIN16663au3dvd0qLWRaLRT//5gT1TYrXR9Vu/ap8n9klAQAQ1YIOMRs2bFBhYaFWrlypyspKZWVlKTc3V8eOHeu0/fr167V06VKtXLlSu3fv1m9+8xtt2LBBy5cv97fZunWrCgoK9O6772rz5s1qaWnRzJkzVV9f3/0ji0GDnA6tvm28JOmJLftVcehzkysCACB6WQzDCOp2mOzsbE2ZMkWPP/64JMnr9SojI0P33Xefli5del77RYsWaffu3SovL/cv+9d//Vft2LFD27Zt63Qfx48f16BBg7R161Zdd911XarL7XbL5XKprq5OTqczmEOKOoUbdmnjB9UaPiBJb9z/VfVJiDO7JAAAwqInf7+D6olpbm5WRUWFcnJyzm7AalVOTo62b9/e6TrTp09XRUWF/5LTwYMH9cYbb2jWrFkX3E9dXZ0kqX///hds09TUJLfbHfDqLR665SoN6ZuoQycb9OPXmSQSAIDOBBViTpw4IY/Ho7S0tIDlaWlpqqmp6XSdOXPmaNWqVbr22msVHx+vUaNG6frrrw+4nNSR1+vV4sWLNWPGDI0bN+6CtZSUlMjlcvlfGRkZwRxKVHM64vWLb2XJYpF+t7NK5btrzS4JAICoE/a7k7Zs2aLVq1friSeeUGVlpTZu3KjXX39dDz/8cKftCwoK9NFHH+nFF1+86HaXLVumuro6/+vw4cPhKN8000YN0HdnjJAk/fvLf9XJ000mVwQAQHQJarBFamqqbDabamsDewZqa2uVnp7e6TrFxcW68847dffdd0uSxo8fr/r6ei1cuFAPPvigrNazOWrRokV67bXX9Pbbb2vo0KEXrSUhIUEJCQnBlB9z/i13jN7ed1yf1J7Wso1/1VN3TpLFYjG7LAAAokJQPTF2u12TJk0KGKTr9XpVXl6uadOmdbpOQ0NDQFCRJJvNJknyjSk2DEOLFi3SK6+8ojfffFMjRowI6iB6K0e8Tb+cPVHxNov+z8e1+kPF380uCQCAqBH05aTCwkI9/fTTevbZZ7V7927de++9qq+vV35+viRp3rx5WrZsmb99Xl6ennzySb344ov69NNPtXnzZhUXFysvL88fZgoKCvT8889r/fr1SklJUU1NjWpqanTmDBMiXjXYpSX/9GVJ0o/+9LEOf9ZgckUAAESHoO/dnT17to4fP64VK1aopqZGEydO1KZNm/yDfauqqgJ6XoqKimSxWFRUVKTq6moNHDhQeXl5euSRR/xtnnzySUnS9ddfH7CvZ555RnfddVc3Dqt3+d51o/Tm7mN6/9Dn+teX/qLfLfwH2axcVgIAXN6Cfk5MtOpNz4npTNXJBt306Nuqb/Zo2U1j9b2vjTK7JAAAeixiz4mBeYYNSNLKvKskSf/xfz7R7qO957k4AAB0ByEmhnxr8lDlfCVNzR6vlmzYpaZWj9klAQBgGkJMDLFYLPrJ7eM1oI9de2pOqeCFSu0/dsrssgAAMAUhJsakJifoZ9+cIKtF+v92H9M//fJtFbxQqY+PcHkJAHB5YWBvjPqouk6PvblPf/6/Zx88mPOVNC36xy9pYkZf8woDACAIPfn7TYiJcXtq3Frz1gG99uER+f4lvzo6VfffMFpTMi88gSYAANGAEKPLN8T4HDh+Wk+8dUCv7qqWx9v2T5o9or/uv2G0po8awHQFAICoRIgRIcan6mSDntx6QH+oOKwWT9s/7dXD+ur+fxyt68cMJMwAAKIKIUaEmHMd+eKMfv32Qf1uZ5WaWr2SpHFDnFr09dGaeWWarDzxFwAQBQgxIsRcyLFTjfrf//2pnn/3kBqa254r8+W0ZBV8/Uv6XxMGM30BAMBUhBgRYi7ls/pmrdv2qZ5952861dQqSRqR2kffv36Ubr16iOJt3G0PAIg8QowIMV1Vd6ZFz77zN637n0/1RUOLJGlov0T905VpmprZX5Mz+2tgSoLJVQIALheEGBFignW6qVXPv3tI//u/D+rE6eaAz0ak9tHk4f00ZUR/Tcnsr8wBSQwIBgCEBSFGhJjuOtPs0ebdtXrv08/03t8+097aUzr3G5GanKApmf00JbMt1HzlihTFcfkJABAChBgRYkKlrqFFFVWf6b2/fa73Pv1MH/69Ts0eb0CbPnabrhneFmomZ/bT1Rn9lGi3mVQxACCWEWJEiAmXxhaPPvx7nd77W1tPTcXfPvcPDPaJs1o0bohLU0f01zXD+ioztY8y+iWpT0KcSVUDAGIFIUaEmEjxeA19UnuqPdS09dbUuBs7bZuanKBh/RM1rH+ShvVPUkb/JA0f0EfD+idpUEoCz6oBABBiJEKMWQzD0N8/P+MPNR9V16nqswbVnWm56Hr2OKsy+rUFnOED+iijPei0hZ1EJdnpxQGAywEhRoSYaFPX0KLDnzeo6rMGHTrZ9vPwZ20/q78445/f6UIGpiRocN9EDehjb3slJ7T/tKt/H7tSkxPUv0/be0c843EAIFb15O83/91FWLiS4uVKcmncENd5n7V6vDryRaOq2kNN26veH3hONbbq+KkmHT/V1KV9JSfEaUByW9jp3ydBqe1Bp2PwcSXGK8URr+SEOKU44gg+ANALEGIQcXE2q4YNSNKwAUmdfl7X0KJDn9Wrpq5RJ+ub9Vl9s06cbtJn9c06ebpZJ+ubdbL991avodNNrTrd1KpDJxu6XIPdZlWyoy3Q+IJNckK8nI64DsvjldL+vu3VFoL62OPksFuVGG9TYryN280BwCSEGEQdV1K8JiT11YShF29nGIbcZ1p1sr6pPdg062R9kz7zBZ32sHPydLPcjS061dgWdiSp2ePVZ+0BqafsNqsc8VYl2eOUaLfJEW9Tkr0t4HR8n2hvf7WHH4fdpoQ4a4eXTfb29/Zzfu+4LN5m4eGDACBCDGKYxWJpv2wVr5EDu7aO12vodHOrTje2toeaFrl97xtbdaqxRaeb2n4/1f67L/z43p9p8ehMi8f/UMBmj1fNHq/cja0X33kIdQw1vpATZ7Mo3mZVnM0q+znv46xWxcdZFW+zKN5qVXxc2zJ7nFVx1ra2vvdxNt9Pi+KsFtms1vaflrM/bWeXx53ze8d2NqtFVksn7y0WWa3qZBnhDEDXEWJwWbFaLXI64uV0xPdoO4ZhqKnVqzPNHn+o8b9v9qih2aPG9uX+9+3Lz7S0/d7Q3KrmVq+aWr3n/PScv/ycBw42tS8/pcgFp0gJCDntwcYXdqyWtvBq6/DealX7ZxZZLPK3a/v97HurtX0dyd/WYrGc83vbe3VYZm1vc6H2FrVttK1N2+fyte+4zNc24LO2Zb5tnXW2XeCSs8vO7qnjMt/vkQuDnd0b0tmwfV8zo8OnZ5cF/t5xqb+N0fET44Lr+rd/3ufGee07fubfvHF2G4YRuN3AdQJrNM7Zx7nLzrYzAo7p3O2qk2W++gLbdVwWWO95x3yRbapDnYHnxgg8/vY3f7rvWg1Ijp759QgxQDdYLBY52i8X9YvA/rxeQ82eiwed1vYeoVaPoRaPVy1eQy2tXrV6vWr2nH3f4vvcE/i+1WP41/d4DbV6ve0/238/Z3nLhdq1/2zxeOX1GvIYhrxeyWMYl7wrTWp7FpFHhuSJwIkFEBRPlN3QTIgBYoDVapHDausVd1X5go3Ha8jr+9kh5PiWBXxuGPIaavvpbftp+H4P+KztvdFxWXvbjtuRfO3at6X2/4l2+B+obzvnLWtfN+B/wuf8T/lC//s2zvldHfbrb99hvbPLzumV6HhCO+lhCBVDRkCPj8+5HT2d9vt00htk6eTjznqUOrYNWHbONs/dhq+H60KfnbsdiwJ7sfzLO2xH5/WuWc5Zp8N+ztlWwHqW82s5t47Oeup03rKOPYOB+z73c99n/uPptH5L1/cni/ol2RVNCDEAIspqtcgqi3pBHgNgMu4NBQAAMYkQAwAAYhIhBgAAxCRCDAAAiEmEGAAAEJMIMQAAICYRYgAAQEwixAAAgJhEiAEAADGJEAMAAGISIQYAAMQkQgwAAIhJhBgAABCTes0s1kb7/PNut9vkSgAAQFf5/m77/o4Ho9eEmFOnTkmSMjIyTK4EAAAE69SpU3K5XEGtYzG6E32ikNfr1ZEjR5SSkiKLxRKy7brdbmVkZOjw4cNyOp0h2y4ujvNuDs67OTjv5uC8m+Pc824Yhk6dOqXBgwfLag1ulEuv6YmxWq0aOnRo2LbvdDr5kpuA824Ozrs5OO/m4Lybo+N5D7YHxoeBvQAAICYRYgAAQEwixFxCQkKCVq5cqYSEBLNLuaxw3s3BeTcH590cnHdzhPK895qBvQAA4PJCTwwAAIhJhBgAABCTCDEAACAmEWIAAEBMIsRcwpo1a5SZmSmHw6Hs7Gzt3LnT7JJ6tYceekgWiyXgNXbsWLPL6nXefvtt5eXlafDgwbJYLHr11VcDPjcMQytWrNAVV1yhxMRE5eTkaN++feYU24tc6rzfdddd533/b7zxRnOK7SVKSko0ZcoUpaSkaNCgQbr11lu1d+/egDaNjY0qKCjQgAEDlJycrNtvv121tbUmVdw7dOW8X3/99ed93++5556g9kOIuYgNGzaosLBQK1euVGVlpbKyspSbm6tjx46ZXVqvdtVVV+no0aP+17Zt28wuqdepr69XVlaW1qxZ0+nnP/vZz/SrX/1Ka9eu1Y4dO9SnTx/l5uaqsbExwpX2Lpc675J04403Bnz/f/e730Wwwt5n69atKigo0LvvvqvNmzerpaVFM2fOVH19vb/NkiVL9Kc//Um///3vtXXrVh05ckTf+MY3TKw69nXlvEvSggULAr7vP/vZz4LbkYELmjp1qlFQUOD/3ePxGIMHDzZKSkpMrKp3W7lypZGVlWV2GZcVScYrr7zi/93r9Rrp6enGz3/+c/+yL774wkhISDB+97vfmVBh73TueTcMw5g/f75xyy23mFLP5eLYsWOGJGPr1q2GYbR9t+Pj443f//73/ja7d+82JBnbt283q8xe59zzbhiG8bWvfc34wQ9+0KPt0hNzAc3NzaqoqFBOTo5/mdVqVU5OjrZv325iZb3fvn37NHjwYI0cOVJz585VVVWV2SVdVj799FPV1NQEfPddLpeys7P57kfAli1bNGjQII0ZM0b33nuvTp48aXZJvUpdXZ0kqX///pKkiooKtbS0BHzfx44dq2HDhvF9D6Fzz7vPCy+8oNTUVI0bN07Lli1TQ0NDUNvtNRNAhtqJEyfk8XiUlpYWsDwtLU179uwxqareLzs7W7/97W81ZswYHT16VD/60Y/01a9+VR999JFSUlLMLu+yUFNTI0mdfvd9nyE8brzxRn3jG9/QiBEjdODAAS1fvlw33XSTtm/fLpvNZnZ5Mc/r9Wrx4sWaMWOGxo0bJ6nt+26329W3b9+AtnzfQ6ez8y5Jc+bM0fDhwzV48GB9+OGH+vd//3ft3btXGzdu7PK2CTGIKjfddJP//YQJE5Sdna3hw4frpZde0ne/+10TKwPC7zvf+Y7//fjx4zVhwgSNGjVKW7Zs0Q033GBiZb1DQUGBPvroI8bZRdiFzvvChQv978ePH68rrrhCN9xwgw4cOKBRo0Z1adtcTrqA1NRU2Wy280ao19bWKj093aSqLj99+/bVl7/8Ze3fv9/sUi4bvu83333zjRw5UqmpqXz/Q2DRokV67bXX9NZbb2no0KH+5enp6WpubtYXX3wR0J7ve2hc6Lx3Jjs7W5KC+r4TYi7Abrdr0qRJKi8v9y/zer0qLy/XtGnTTKzs8nL69GkdOHBAV1xxhdmlXDZGjBih9PT0gO++2+3Wjh07+O5H2N///nedPHmS738PGIahRYsW6ZVXXtGbb76pESNGBHw+adIkxcfHB3zf9+7dq6qqKr7vPXCp896ZXbt2SVJQ33cuJ11EYWGh5s+fr8mTJ2vq1KkqKytTfX298vPzzS6t1/q3f/s35eXlafjw4Tpy5IhWrlwpm82mO+64w+zSepXTp08H/G/n008/1a5du9S/f38NGzZMixcv1o9//GONHj1aI0aMUHFxsQYPHqxbb73VvKJ7gYud9/79++tHP/qRbr/9dqWnp+vAgQN64IEH9KUvfUm5ubkmVh3bCgoKtH79ev3xj39USkqKf5yLy+VSYmKiXC6Xvvvd76qwsFD9+/eX0+nUfffdp2nTpukf/uEfTK4+dl3qvB84cEDr16/XrFmzNGDAAH344YdasmSJrrvuOk2YMKHrO+rRvU2Xgccee8wYNmyYYbfbjalTpxrvvvuu2SX1arNnzzauuOIKw263G0OGDDFmz55t7N+/3+yyep233nrLkHTea/78+YZhtN1mXVxcbKSlpRkJCQnGDTfcYOzdu9fconuBi533hoYGY+bMmcbAgQON+Ph4Y/jw4caCBQuMmpoas8uOaZ2db0nGM888429z5swZ4/vf/77Rr18/IykpybjtttuMo0ePmld0L3Cp815VVWVcd911Rv/+/Y2EhATjS1/6kvHDH/7QqKurC2o/lvadAQAAxBTGxAAAgJhEiAEAADGJEAMAAGISIQYAAMQkQgwAAIhJhBgAABCTCDEAACAmEWIAAEBMIsQAAICYRIgBAAAxiRADAABiEiEGAADEpP8fwOTK0HbW9RQAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_multiple_embeding_dimensions(\n", + " model_class=WalsRecommender,\n", + " method=PredictionMethod.DOT,\n", + " out_file=\"wals_model_results_final_dot.csv\",\n", + " epochs=[25],\n", + " c=[0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", + " regularization_coefficient=[0.0, 0.00001, 0.0001, 0.001, 0.01]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid 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"/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'embedding_dimension': 4, 'regularization': 0.0001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.01560204407537528, 'recall_at_10': 0.06050501513663635, 'ndcg_at_10': 0.07683413537968624, 'precision_at_20': 0.011992973490897476, 'recall_at_20': 0.09067910772439934, 'ndcg_at_20': 0.09634989967193944, 'precision_at_50': 0.0079112104758863, 'recall_at_50': 0.14733022592714648, 'ndcg_at_50': 0.12541038111253533, 'precision_at_100': 0.00527946343021399, 'recall_at_100': 0.1978219117723097, 'ndcg_at_100': 0.1456379374612586}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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'ndcg_at_20': 0.07903656541040587, 'precision_at_50': 0.006547428936442032, 'recall_at_50': 0.12561008986844754, 'ndcg_at_50': 0.10273975134585815, 'precision_at_100': 0.004500159693388694, 'recall_at_100': 0.17281461717597588, 'ndcg_at_100': 0.12142186359861892}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + 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"{'embedding_dimension': 10, 'regularization': 0.001, 'learning_rate': 0.0001, 'num_iterations': 76521640, 'precision_at_10': 0.011992973490897476, 'recall_at_10': 0.04644692238371199, 'ndcg_at_10': 0.05802910032592288, 'precision_at_20': 0.009374001916320665, 'recall_at_20': 0.0721340954731668, 'ndcg_at_20': 0.07385875110489287, 'precision_at_50': 0.006055573299265411, 'recall_at_50': 0.11468008558455581, 'ndcg_at_50': 0.09530259678856781, 'precision_at_100': 0.004227083998722453, 'recall_at_100': 0.16112090162064058, 'ndcg_at_100': 0.1135609038916972}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " 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np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n", + "/Users/mjturner/code/technique-inference-engine/models/recommender/bpr_recommender.py:107: RuntimeWarning: invalid value encountered in divide\n", + " data / np.expand_dims(num_items_per_user, axis=1)\n" + ] + } + ], + "source": [ + "test_multiple_embeding_dimensions(\n", + " BPRRecommender,\n", + " out_file=\"bpr_model_results.csv\",\n", + " epochs=[20*training_data.m*training_data.n],\n", + " learning_rate=[0.00001, 0.00005, 0.0001, 0.001],\n", + " regularization=[0., 0.0001, 0.001, 0.01],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error 0.04852325970978819\n", + "Precision 0.02400990099009901\n", + "Recall 0.1905249816378097\n", + "Normalized Discounted Cumulative Gain 0.1841149999164702\n" + ] + } ], + "source": [ + "embedding_dimension = 10\n", + "k = 20\n", + "best_hyperparameters = {'gravity_coefficient': 0.001, 'regularization_coefficient': 0.5, 'epochs': 1000, 'learning_rate': 100.0}\n", + "\n", + "model = TopItemsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.DOT,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "mse = tie.fit()\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=k)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " predictions training_data test_data \\\n", + "T1592.004 0.000000 0.0 0.0 \n", + "T1557.001 0.586885 0.0 0.0 \n", + "T1600 0.291803 0.0 0.0 \n", + "T1647 0.293443 0.0 0.0 \n", + "T1068 0.916393 0.0 0.0 \n", + "... ... ... ... \n", + "T1656 0.149180 0.0 0.0 \n", + "T1557.003 0.147541 0.0 0.0 \n", + "T1499.001 0.145902 0.0 0.0 \n", + "T1027.005 0.708197 0.0 0.0 \n", + "T1059.007 0.896721 0.0 0.0 \n", + "\n", + " technique_name \n", + "T1592.004 Client Configurations \n", + "T1557.001 LLMNR/NBT-NS Poisoning and SMB Relay \n", + "T1600 Weaken Encryption \n", + "T1647 Plist File Modification \n", + "T1068 Exploitation for Privilege Escalation \n", + "... ... \n", + "T1656 Impersonation \n", + "T1557.003 DHCP Spoofing \n", + "T1499.001 OS Exhaustion Flood \n", + "T1027.005 Indicator Removal from Tools \n", + "T1059.007 JavaScript \n", + "\n", + "[611 rows x 4 columns]\n" + ] + } + ], + "source": [ + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", + "print(new_report_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error 8.200210708280245\n", + "Precision 0.0014993585631815267\n", + "Recall 0.018532725776008053\n", + "Normalized Discounted Cumulative Gain 0.00973503731752359\n" + ] + } + ], + "source": [ + "embedding_dimension = 10\n", + "k = 20\n", + "best_hyperparameters = {'gravity_coefficient': 0.001, 'regularization_coefficient': 0.001, 'epochs': 10, 'learning_rate': 1.0}\n", + "\n", + "model = FactorizationRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.DOT,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "mse = tie.fit(**best_hyperparameters)\n", + "# mse = tie.fit_with_validation(\n", + "# learning_rate=[0.001, 0.01, 0.1, 1.0, 10., 20., 50., 100.],\n", + "# epochs=[1000],\n", + "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5],\n", + "# gravity_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5],\n", + "# )\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=k)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " predictions training_data test_data \\\n", + "T1204.002 -44127.687500 0.0 0.0 \n", + "T1592.001 -18382.687500 0.0 0.0 \n", + "T1547.012 43962.042969 0.0 0.0 \n", + "T1561.002 46403.843750 0.0 0.0 \n", + "T1110.004 67053.593750 0.0 0.0 \n", + "... ... ... ... \n", + "T1612 34722.996094 0.0 0.0 \n", + "T1588.006 -39228.710938 0.0 0.0 \n", + "T1003 -42823.429688 0.0 0.0 \n", + "T1069.002 -36731.289062 0.0 0.0 \n", + "T1070.005 12406.809570 0.0 0.0 \n", + "\n", + " technique_name \n", + "T1204.002 Malicious File \n", + "T1592.001 Hardware \n", + "T1547.012 Print Processors \n", + "T1561.002 Disk Structure Wipe \n", + "T1110.004 Credential Stuffing \n", + "... ... \n", + "T1612 Build Image on Host \n", + "T1588.006 Vulnerabilities \n", + "T1003 OS Credential Dumping \n", + "T1069.002 Domain Groups \n", + "T1070.005 Network Share Connection Removal \n", + "\n", + "[611 rows x 4 columns]\n" + ] + } + ], + "source": [ + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", + "print(new_report_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "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": "3.11.8" - } + "metadata": {} + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 22\u001b[0m\n\u001b[1;32m 9\u001b[0m tie \u001b[38;5;241m=\u001b[39m TechniqueInferenceEngine(\n\u001b[1;32m 10\u001b[0m training_data\u001b[38;5;241m=\u001b[39mtraining_data,\n\u001b[1;32m 11\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mvalidation_data,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 15\u001b[0m enterprise_attack_filepath\u001b[38;5;241m=\u001b[39menterprise_attack_filepath,\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# mse = tie.fit_with_validation(\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# num_iterations=[500 * 512],\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# regularization_coefficient=[0, 0.0001, 0.001, 0.01],\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m mse \u001b[38;5;241m=\u001b[39m \u001b[43mtie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbest_hyperparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse)\n\u001b[1;32m 24\u001b[0m precision \u001b[38;5;241m=\u001b[39m tie\u001b[38;5;241m.\u001b[39mprecision(k\u001b[38;5;241m=\u001b[39mk)\n", + "File \u001b[0;32m~/code/technique-inference-engine/models/tie.py:122\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.fit\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model to the data.\u001b[39;00m\n\u001b[1;32m 105\u001b[0m \n\u001b[1;32m 106\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124;03m The MSE of the prediction matrix, as determined by the test set.\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# train\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_training_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_sparse_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 124\u001b[0m mean_squared_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model\u001b[38;5;241m.\u001b[39mevaluate(\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_test_data\u001b[38;5;241m.\u001b[39mto_sparse_tensor(), method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prediction_method\n\u001b[1;32m 126\u001b[0m )\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkrep()\n", + "File \u001b[0;32m~/code/technique-inference-engine/models/recommender/bpr_recommender.py:244\u001b[0m, in \u001b[0;36mBPRRecommender.fit\u001b[0;34m(self, data, learning_rate, epochs, regularization_coefficient)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_U[u, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 239\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_w \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_U[u, :])\n\u001b[1;32m 240\u001b[0m )\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[i, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 242\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_hi \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[i, :])\n\u001b[1;32m 243\u001b[0m )\n\u001b[0;32m--> 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[j, :] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (\n\u001b[1;32m 245\u001b[0m sigmoid_derivative \u001b[38;5;241m*\u001b[39m d_hj \u001b[38;5;241m-\u001b[39m (regularization_coefficient \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V[j, :])\n\u001b[1;32m 246\u001b[0m )\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# hyperparameters\n", + "embedding_dimension = 4\n", + "k = 20\n", + "best_hyperparameters = {'regularization_coefficient': 0.0001, 'epochs': 2, 'learning_rate': 0.0001}\n", + "# best_hyperparameters[\"epochs\"] = 20*training_data.m*training_data.n\n", + "\n", + "model = BPRRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.COSINE,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "# mse = tie.fit_with_validation(\n", + "# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\n", + "# epochs=[500 * 512],\n", + "# regularization_coefficient=[0, 0.0001, 0.001, 0.01],\n", + "# )\n", + "mse = tie.fit(**best_hyperparameters)\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=20)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " predictions training_data test_data \\\n", + "T1558.002 0.555484 0.0 0.0 \n", + "T1132.002 0.494036 0.0 0.0 \n", + "T1211 -0.492932 0.0 0.0 \n", + "T1601.002 -0.185998 0.0 0.0 \n", + "T1596 -0.025210 0.0 0.0 \n", + "... ... ... ... \n", + "T1546.011 -0.217676 0.0 0.0 \n", + "T1535 0.464719 0.0 0.0 \n", + "T1071 0.199836 0.0 0.0 \n", + "T1587 0.658772 0.0 0.0 \n", + "T1499.002 0.464182 0.0 0.0 \n", + "\n", + " technique_name \n", + "T1558.002 Silver Ticket \n", + "T1132.002 Non-Standard Encoding \n", + "T1211 Exploitation for Defense Evasion \n", + "T1601.002 Downgrade System Image \n", + "T1596 Search Open Technical Databases \n", + "... ... \n", + "T1546.011 Application Shimming \n", + "T1535 Unused/Unsupported Cloud Regions \n", + "T1071 Application Layer Protocol \n", + "T1587 Develop Capabilities \n", + "T1499.002 Service Exhaustion Flood \n", + "\n", + "[611 rows x 4 columns]\n" + ] + } + ], + "source": [ + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", + "print(new_report_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 261.94it/s, train_auc=51.95%, skipped=9.34%]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error 0.8416396221072098\n", + "Precision 0.008555163566388711\n", + "Recall 0.1188407602595878\n", + "Normalized Discounted Cumulative Gain 0.05200668172568438\n" + ] + } + ], + "source": [ + "# hyperparameters\n", + "embedding_dimension = 10\n", + "k = 20\n", + "best_hyperparameters = {'regularization_coefficient': 0.0001, \"epochs\": 20, 'learning_rate': 0.005}\n", + "\n", + "model = ImplicitBPRRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.COSINE,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "# mse = tie.fit_with_validation(\n", + "# learning_rate=[0.001, 0.005, 0.01, 0.02, 0.05],\n", + "# epochs=[math.floor(500 * 512 / training_data.to_numpy().sum())],\n", + "# regularization=[0, 0.0001, 0.001, 0.01],\n", + "# )\n", + "mse = tie.fit(**best_hyperparameters)\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=k)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 51.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error 0.5028757316475128\n", + "Precision 0.008659397049390635\n", + "Recall 0.11683780275644119\n", + "Normalized Discounted Cumulative Gain 0.06099917717388372\n" + ] + } + ], + "source": [ + "# hyperparameters\n", + "embedding_dimension = 10\n", + "k = 20\n", + "\n", + "best_hyperparameters = {'regularization_coefficient': 0.05, 'c': 0.5, 'epochs': 20}\n", + "\n", + "model = ImplicitWalsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.COSINE,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "mse = tie.fit(**best_hyperparameters)\n", + "# mse = tie.fit_with_validation(\n", + "# epochs=[20],\n", + "# c=[0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", + "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05]\n", + "# )\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=k)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " predictions training_data test_data \\\n", + "T1204.002 0.190945 0.0 0.0 \n", + "T1592.001 -0.088588 0.0 0.0 \n", + "T1547.012 0.595495 0.0 0.0 \n", + "T1561.002 0.591824 0.0 0.0 \n", + "T1110.004 -0.004705 0.0 0.0 \n", + "... ... ... ... \n", + "T1612 0.140598 0.0 0.0 \n", + "T1588.006 0.665867 0.0 0.0 \n", + "T1003 -0.068861 0.0 0.0 \n", + "T1069.002 0.364830 0.0 0.0 \n", + "T1070.005 0.198465 0.0 0.0 \n", + "\n", + " technique_name \n", + "T1204.002 Malicious File \n", + "T1592.001 Hardware \n", + "T1547.012 Print Processors \n", + "T1561.002 Disk Structure Wipe \n", + "T1110.004 Credential Stuffing \n", + "... ... \n", + "T1612 Build Image on Host \n", + "T1588.006 Vulnerabilities \n", + "T1003 OS Credential Dumping \n", + "T1069.002 Domain Groups \n", + "T1070.005 Network Share Connection Removal \n", + "\n", + "[611 rows x 4 columns]\n" + ] + } + ], + "source": [ + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", + "print(new_report_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error 0.22009765631062408\n", + "Precision 0.0081783194355356\n", + "Recall 0.11611657887235578\n", + "Normalized Discounted Cumulative Gain 0.05730162211191529\n" + ] + } + ], + "source": [ + "# hyperparameters\n", + "embedding_dimension = 4\n", + "k = 20\n", + "\n", + "# best_hyperparameters = {'regularization_coefficient': 0.1, 'c': 0.5, 'epochs': 20}\n", + "# best_hyperparameters = {'regularization_coefficient': 0.0001, 'c': 0.3, 'epochs': 100}\n", + "best_hyperparameters = {'regularization_coefficient': 0.001, 'c': 0.1, \"epochs\": 20}\n", + "model = WalsRecommender(m=training_data.m, n=training_data.n, k=embedding_dimension)\n", + "\n", + "tie = TechniqueInferenceEngine(\n", + " training_data=training_data,\n", + " validation_data=validation_data,\n", + " test_data=test_data,\n", + " model=model,\n", + " prediction_method=PredictionMethod.COSINE,\n", + " enterprise_attack_filepath=enterprise_attack_filepath,\n", + ")\n", + "mse = tie.fit(**best_hyperparameters)\n", + "# mse = tie.fit_with_validation(\n", + "# epochs=[20],\n", + "# c=[0.001, 0.005, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7],\n", + "# regularization_coefficient=[0.001, 0.005, 0.01, 0.02, 0.05]\n", + "# )\n", + "print(\"Mean Squared Error\", mse)\n", + "precision = tie.precision(k=k)\n", + "print(\"Precision\", precision)\n", + "recall = tie.recall(k=k)\n", + "print(\"Recall\", recall)\n", + "ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + "print(\"Normalized Discounted Cumulative Gain\", ndcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " predictions training_data test_data \\\n", + "T1204.002 0.746440 0.0 0.0 \n", + "T1592.001 -0.030477 0.0 0.0 \n", + "T1547.012 0.976308 0.0 0.0 \n", + "T1561.002 0.676964 0.0 0.0 \n", + "T1110.004 0.389903 0.0 0.0 \n", + "... ... ... ... \n", + "T1612 0.093252 0.0 0.0 \n", + "T1588.006 0.731710 0.0 0.0 \n", + "T1003 -0.059756 0.0 0.0 \n", + "T1069.002 0.502788 0.0 0.0 \n", + "T1070.005 0.731683 0.0 0.0 \n", + "\n", + " technique_name \n", + "T1204.002 Malicious File \n", + "T1592.001 Hardware \n", + "T1547.012 Print Processors \n", + "T1561.002 Disk Structure Wipe \n", + "T1110.004 Credential Stuffing \n", + "... ... \n", + "T1612 Build Image on Host \n", + "T1588.006 Vulnerabilities \n", + "T1003 OS Credential Dumping \n", + "T1069.002 Domain Groups \n", + "T1070.005 Network Share Connection Removal \n", + "\n", + "[611 rows x 4 columns]\n" + ] + } + ], + "source": [ + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, **best_hyperparameters)\n", + "print(new_report_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.00000000e+00 3.10272485e-01 -5.06079257e-01 ... -2.96728946e-02\n", + " 1.03696346e-01 -4.59698914e-03]\n", + " [ 1.00000000e+00 2.34661356e-01 -3.45782340e-01 ... -8.81108269e-02\n", + " 7.32592419e-02 2.15996355e-01]\n", + " [ 2.00000000e+00 4.81936446e-08 6.88791080e-10 ... 3.42836657e-08\n", + " -7.81015075e-09 -4.81467985e-08]\n", + " ...\n", + " [ 6.25900000e+03 3.53572398e-01 -4.88738894e-01 ... 2.14364976e-02\n", + " 1.39371127e-01 1.01979606e-01]\n", + " [ 6.26000000e+03 -5.61978075e-10 -3.23640350e-08 ... -7.43976116e-08\n", + " 8.64229861e-08 8.77083117e-09]\n", + " [ 6.26100000e+03 -6.00948269e-08 -1.48262300e-08 ... 3.57593208e-08\n", + " -9.07301079e-09 2.34565452e-08]]\n", + "(6262, 11)\n", + "(611, 11)\n" + ] + } + ], + "source": [ + "# TEMPORARY - GET EMBEDDINGS FOR FE\n", + "U = tie.get_U() # entity (report) ids\n", + "V = tie.get_V() # item (technique) embeddings\n", + "\n", + "U_with_index = np.hstack((np.expand_dims(training_data.report_ids, axis=1), U))\n", + "V_with_index = np.hstack((np.expand_dims(training_data.technique_ids, axis=1), V))\n", + "\n", + "print(U_with_index.shape)\n", + "print(V_with_index.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recalls [0.3087573406401257, 0.3257859760812914, 0.3458226653670225, 0.3745375233589626, 0.3949941633991787, 0.4110964558541681, 0.4219309973671012, 0.4317147374599419, 0.44042389049516534, 0.44770298702149547, 0.45387773687027355, 0.45797033624322875, 0.46304119765720547, 0.4679221448741617, 0.47182791494477766, 0.47617333685846847, 0.4791363202775644, 0.48264413250862803, 0.48577394889957126, 0.4883141430997783, 0.4904385260449852]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k_values = [1, 5] + list(range(10, 200, 10))\n", + "recalls = []\n", + "ndcgs = []\n", + "for k in k_values:\n", + " # print(\"Mean Squared Error\", mse)\n", + " precision = tie.precision(k=k)\n", + " # print(\"Precision\", precision)\n", + " recall = tie.recall(k=k)\n", + " recalls.append(recall)\n", + " # print(\"Recall\", recall)\n", + " ndcg = tie.normalized_discounted_cumulative_gain(k=k)\n", + " ndcgs.append(ndcg)\n", + " # print(\"Normalized Discounted Cumulative Gain\", ndcg)\n", + "\n", + "print(\"recalls\", recalls)\n", + "\n", + "plt.xlabel(\"k\")\n", + "plt.ylabel(\"Normalized discounted cumulative gain (NDCG)\")\n", + "plt.title(\"NDCG@k for various values of k\")\n", + "plt.plot(k_values, ndcgs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "FactorizationRecommender.predict_new_entity() got an unexpected keyword argument 'c'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m oilrig_techniques \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1047\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1059.005\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1124\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1082\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1497.001\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1053.005\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1027\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1105\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1070.004\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1059.003\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT1071.001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 5\u001b[0m }\n\u001b[0;32m----> 6\u001b[0m new_report_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mtie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_for_new_report\u001b[49m\u001b[43m(\u001b[49m\u001b[43moilrig_techniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregularization_coefficient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iterations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(new_report_predictions\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n", + "File \u001b[0;32m~/code/technique-inference-engine/models/tie.py:338\u001b[0m, in \u001b[0;36mTechniqueInferenceEngine.predict_for_new_report\u001b[0;34m(self, techniques, **kwargs)\u001b[0m\n\u001b[1;32m 332\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_training_data\u001b[38;5;241m.\u001b[39mn\n\u001b[1;32m 334\u001b[0m technique_tensor \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mSparseTensor(\n\u001b[1;32m 335\u001b[0m indices\u001b[38;5;241m=\u001b[39mtechnique_indices_2d, values\u001b[38;5;241m=\u001b[39mvalues, dense_shape\u001b[38;5;241m=\u001b[39m(n,)\n\u001b[1;32m 336\u001b[0m )\n\u001b[0;32m--> 338\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_new_entity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtechnique_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prediction_method\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 340\u001b[0m training_indices_dense \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mlen\u001b[39m(predictions))\n\u001b[1;32m 341\u001b[0m training_indices_dense[technique_indices] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: FactorizationRecommender.predict_new_entity() got an unexpected keyword argument 'c'" + ] + } + ], + "source": [ + "oilrig_techniques = {\n", + " \"T1047\", \"T1059.005\", \"T1124\", \"T1082\",\n", + " \"T1497.001\", \"T1053.005\", \"T1027\", \"T1105\",\n", + " \"T1070.004\", \"T1059.003\", \"T1071.001\"\n", + "}\n", + "new_report_predictions = tie.predict_for_new_report(oilrig_techniques, c=0.5, regularization_coefficient=0.05, learning_rate=0.01, epochs=100)\n", + "\n", + "print(new_report_predictions.sort_values(by=\"predictions\", ascending=False).head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[t-SNE] Computing 46 nearest neighbors...\n", + "[t-SNE] Indexed 6262 samples in 0.003s...\n", + "[t-SNE] Computed neighbors for 6262 samples in 0.226s...\n", + "[t-SNE] Computed conditional probabilities for sample 1000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 2000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 3000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 4000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 5000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 6000 / 6262\n", + "[t-SNE] Computed conditional probabilities for sample 6262 / 6262\n", + "[t-SNE] Mean sigma: 0.000000\n", + "[t-SNE] KL divergence after 250 iterations with early exaggeration: 95.187531\n", + "[t-SNE] KL divergence after 10000 iterations: 0.847868\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_tsne_embeddings(embeddings: np.ndarray) -> tuple[np.array, np.array]:\n", + " \"\"\"Create 2D representation of embeddings using t-SNE.\n", + "\n", + " Args:\n", + " embeddings: an mxk array of m embeddings in k-dimensional space.\n", + "\n", + " Returns:\n", + " A tuple of the form (x_1, x_2) where x_1 and x_2 are length m\n", + " such that (x_1[i], x_2[i]) is the 2-dimensional point cotnaining the 2-dimensional\n", + " repsresentation for embeddings[i, :].\n", + " \"\"\"\n", + " tsne = sklearn.manifold.TSNE(\n", + " n_components=2,\n", + " perplexity=15,\n", + " learning_rate=\"auto\",\n", + " # metric='cosine',\n", + " # early_exaggeration=10.0,\n", + " init='pca',\n", + " verbose=True,\n", + " n_iter=10000,\n", + " )\n", + "\n", + " V_proj = tsne.fit_transform(embeddings)\n", + " x = V_proj[:, 0]\n", + " y = V_proj[:, 1]\n", + "\n", + " return x, y\n", + "\n", + "U = tie.get_U()\n", + "x_1, x_2 = make_tsne_embeddings(U)\n", + "\n", + "plt.scatter(x_1, x_2, s=0.5)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 }, - "nbformat": 4, - "nbformat_minor": 2 + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/src/tie/engine.py b/src/tie/engine.py index ed1042b..0a22355 100644 --- a/src/tie/engine.py +++ b/src/tie/engine.py @@ -111,7 +111,11 @@ def fit(self, **kwargs) -> float: The MSE of the prediction matrix, as determined by the test set. """ # train - self._model.fit(self._training_data.to_sparse_tensor(), **kwargs) + self._model.fit( + self._training_data.to_sparse_tensor(), + self._validation_data.to_sparse_tensor(), + **kwargs, + ) mean_squared_error = self._model.evaluate( self._test_data.to_sparse_tensor(), method=self._prediction_method diff --git a/src/tie/recommender/wals_recommender.py b/src/tie/recommender/wals_recommender.py index 8e5d3a5..953579b 100644 --- a/src/tie/recommender/wals_recommender.py +++ b/src/tie/recommender/wals_recommender.py @@ -4,7 +4,7 @@ from tie.constants import PredictionMethod from tie.utils import calculate_predicted_matrix - +import matplotlib.pyplot as plt from .recommender import Recommender @@ -179,6 +179,7 @@ def V_T_C_I_V(V, c_array): def fit( self, data: tf.SparseTensor, + test_data: tf.SparseTensor, epochs: int, c: float = 0.024, regularization_coefficient: float = 0.01, @@ -209,6 +210,8 @@ def fit( alpha = (1 / c) - 1 + losses = [] + for _ in range(epochs): # step 1: update U @@ -219,6 +222,10 @@ def fit( # step 2: update V self._V = self._update_factor(self._U, P, alpha, regularization_coefficient) + losses.append(self.evaluate(test_data)) + + plt.plot(list(range(len(losses))), losses) + plt.show() self._checkrep() def evaluate(