From ddd5d99938147de7961c0be76871af6a619de041 Mon Sep 17 00:00:00 2001 From: Ryan Urbanowicz Date: Wed, 1 Jul 2026 22:52:00 -0700 Subject: [PATCH 1/4] skrebate updates for tests and demo notebook Initial fixes for updated skrebate package for feature selection. This is optionally used by HEROS for generating feature importance estimates as expert knowledge weights to probabilistically guide rule initialization. --- .gitignore | 2 + HEROS_Demo_Notebook.ipynb | 1545 +---------------- .../unit_EK/Multiplexer6_MultiSURF_Scores.csv | 7 - .../Multiplexer6_MultiSWRFDB_Scores.csv | 7 + .../Multiplexer6_NA_MultiSWRFDB_Scores.csv | 7 + ...xer6_feature_type_mix_MultiSURF_Scores.csv | 7 - ...r6_feature_type_mix_MultiSWRFDB_Scores.csv | 7 + ...feature_type_mix_NA_MultiSWRFDB_Scores.csv | 7 + ...ltiplexer6_multiclass_MultiSURF_Scores.csv | 7 - ...iplexer6_multiclass_MultiSWRFDB_Scores.csv | 7 + ...plexer6_quant_outcome_MultiSURF_Scores.csv | 7 - requirements.txt | Bin 248 -> 252 bytes tests/test_heros.py | 39 +- 13 files changed, 151 insertions(+), 1498 deletions(-) delete mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSURF_Scores.csv create mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSWRFDB_Scores.csv create mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_NA_MultiSWRFDB_Scores.csv delete mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSURF_Scores.csv create mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSWRFDB_Scores.csv create mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_NA_MultiSWRFDB_Scores.csv delete mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSURF_Scores.csv create mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSWRFDB_Scores.csv delete mode 100644 evaluation/datasets/unit_testing/unit_EK/Multiplexer6_quant_outcome_MultiSURF_Scores.csv diff --git a/.gitignore b/.gitignore index 25a4441..b191797 100644 --- a/.gitignore +++ b/.gitignore @@ -173,6 +173,8 @@ cython_debug/ # PyPI configuration file .pypirc +# VS Code +.vscode/ #Project specific output/ .DS_Store diff --git a/HEROS_Demo_Notebook.ipynb b/HEROS_Demo_Notebook.ipynb index 395edde..0c6717c 100644 --- a/HEROS_Demo_Notebook.ipynb +++ b/HEROS_Demo_Notebook.ipynb @@ -78,8 +78,8 @@ "example_dataset = 'MUX6' # Dataset Options: 'MUX6', 'MUX11', 'MUX20', 'GAM_A', 'GAM_C', 'GAM_E'\n", "\n", "# Expert Knowldge Generation Run Parameters ---------------------------------------------------------------------\n", - "max_instances = 2000 # Maximum number of available training instances to use in estimating feature importance scores with 'MultiSURF' algorithm.\n", - "use_turf = False # Idicate whether to use TuRF wrapper algorithm in combination with MultiSURF (recommended for large feature spaces, e.g. > 10000 features)\n", + "max_instances = 2000 # Maximum number of available training instances to use in estimating feature importance scores with 'MultiSWRFDB' algorithm.\n", + "use_turf = False # Idicate whether to use TuRF wrapper algorithm in combination with MultiSWRFDB (recommended for large feature spaces, e.g. > 10000 features)\n", "turf_pct = 0.2 # Controls the number of TuRF iterations as well as the number of features removed from calculations each iteration. (0.2 runs for 5 iterations with 20% of bottom scoring features removed each time)\n", "\n", "# HEROS Key Hyperparameters -------------------------------------------------------------------------------------\n", @@ -137,10 +137,14 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "c:\\Users\\ryanu\\Documents\\GitHub\\heros\n" + "ename": "ImportError", + "evalue": "cannot import name 'MultiSWRFDB' from 'skrebate' (c:\\Users\\ryanu\\anaconda3\\envs\\heros_dev_12\\Lib\\site-packages\\skrebate\\__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 23\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m 22\u001b[39m \u001b[38;5;66;03m#from skrebate import MultiSURF, TURF\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m skrebate \u001b[38;5;28;01mimport\u001b[39;00m MultiSWRFDB, TURF\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.metrics \u001b[38;5;28;01mimport\u001b[39;00m classification_report\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.inspection \u001b[38;5;28;01mimport\u001b[39;00m permutation_importance\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.metrics \u001b[38;5;28;01mimport\u001b[39;00m roc_curve, roc_auc_score\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'MultiSWRFDB' from 'skrebate' (c:\\Users\\ryanu\\anaconda3\\envs\\heros_dev_12\\Lib\\site-packages\\skrebate\\__init__.py)" ] } ], @@ -166,7 +170,8 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "from skrebate import MultiSURF, TURF # Install using: pip install skrebate==0.7\n", + "#from skrebate import MultiSURF, TURF \n", + "from skrebate import MultiSWRFDB, TURF \n", "from sklearn.metrics import classification_report\n", "from sklearn.inspection import permutation_importance\n", "from sklearn.metrics import roc_curve, roc_auc_score\n", @@ -196,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6ebd4719", "metadata": {}, "outputs": [], @@ -260,23 +265,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8ffac0c7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", - "0 1 0 1 0 1 1 1 2 1\n", - "1 1 0 0 0 0 1 0 2 2\n", - "2 0 1 1 0 0 1 0 1 3\n", - "3 1 1 1 1 1 1 1 3 4\n", - "4 0 0 0 1 1 0 0 0 5\n" - ] - } - ], + "outputs": [], "source": [ "# Load training dataset ------------------------\n", "train_df = pd.read_csv(train_data_path, sep=\"\\t\")\n", @@ -325,16 +317,16 @@ "metadata": {}, "source": [ "### Generate Expert Knowlege Scores for Features (Optional, but suggested in HEROS) \n", - "Previous LCS research has indicated that using Relief-based algorithms such as 'MultiSURF' to generate feature importance scores as a guide for rule initialization improves evolutionary algorithm performance. Here we show how these scores can be generated, saved, and later used by HEROS for rule initialization. \n", + "Previous LCS research has indicated that using Relief-based algorithms such as 'MultiSWRFDB' to generate feature importance scores as a guide for rule initialization improves evolutionary algorithm performance. Here we show how these scores can be generated, saved, and later used by HEROS for rule initialization. \n", "\n", - "These scores only need to be generated once for a given training dataset and then can be saved for future use. This code only runs MultiSURF if the scores have not yet been generated for a training dataset with a unique name.\n", + "These scores only need to be generated once for a given training dataset and then can be saved for future use. This code only runs MultiSWRFDB if the scores have not yet been generated for a training dataset with a unique name.\n", "\n", - "The MultiSURF (feature importance estimation) algorithm is found in our scikit-rebate respository at https://github.com/UrbsLab/scikit-rebate. " + "The MultiSWRFDB (feature importance estimation) algorithm is found in our scikit-rebate respository at https://github.com/UrbsLab/scikit-rebate. " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "3bd1b3c0", "metadata": {}, "outputs": [], @@ -361,20 +353,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "0f7d39ba", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading MultiSURF Scores\n", - "['A_0', 'A_1', 'R_0', 'R_1', 'R_2', 'R_3']\n", - "[0.0653628803320886, 0.0917736536553188, 0.0697677623350289, 0.0134458939320383, 0.0229747192062444, 0.0148895858857668]\n" - ] - } - ], + "outputs": [], "source": [ "# Further data preparation for expert knowldge generation (i.e. balanced subsampling of training instances for faster run times)\n", "fs_train_df = balanced_sampling(train_df, outcome_label, max_instances)\n", @@ -396,25 +378,25 @@ "# --------------------------------------\n", "score_path_name = None\n", "if use_turf:\n", - " score_path_name = output_path+'/MultiSURF_TuRF_Scores.csv' #No need to change\n", + " score_path_name = output_path+'/MultiSWRFDB_TuRF_Scores.csv' #No need to change\n", "else:\n", - " score_path_name = output_path+'/MultiSURF_Scores.csv' #No need to change\n", - "# Calculate or load feature importance estimates with MultiSURF or MultiSURF+TuRF ------------------------------------\n", + " score_path_name = output_path+'/MultiSWRFDB_Scores.csv' #No need to change\n", + "# Calculate or load feature importance estimates with MultiSWRFDB or MultiSWRFDB+TuRF ------------------------------------\n", "if not os.path.isfile(score_path_name):\n", - " if use_turf: # Run MultiSURF with TuRF wrapper\n", - " print(\"Generating MultiSURF/TuRF Scores:\")\n", - " clf = TURF(MultiSURF(n_jobs=None), pct=turf_pct, num_scores_to_return=num_scores_to_return).fit(fs_X, fs_y)\n", + " if use_turf: # Run MultiSWRFDB with TuRF wrapper\n", + " print(\"Generating MultiSWRFDB/TuRF Scores:\")\n", + " clf = TURF(relief_object=MultiSWRFDB(n_jobs=None,categorical_features=cat_feat_indexes), pct=turf_pct, num_scores_to_return=num_scores_to_return).fit(fs_X, fs_y)\n", " ek = clf.feature_importances_\n", " score_data = pd.DataFrame({'Feature':feature_names,'Score':ek})\n", " score_data.to_csv(score_path_name,index=False)\n", - " else: #Just run MultiSURF\n", - " print(\"Generating MultiSURF Scores:\")\n", - " clf = MultiSURF(n_jobs=None).fit(fs_X, fs_y)\n", + " else: #Just run MultiSWRFDB\n", + " print(\"Generating MultiSWRFDB Scores:\")\n", + " clf = MultiSWRFDB(n_jobs=None,categorical_features=cat_feat_indexes).fit(fs_X, fs_y)\n", " ek = clf.feature_importances_\n", " score_data = pd.DataFrame({'Feature':feature_names,'Score':ek})\n", " score_data.to_csv(score_path_name,index=False)\n", "else: #load previously trained scores\n", - " print(\"Loading MultiSURF Scores\")\n", + " print(\"Loading MultiSWRFDB Scores\")\n", " loaded_data = pd.read_csv(score_path_name)\n", " ek = loaded_data['Score'].tolist()\n", "print(list(feature_names))\n", @@ -437,435 +419,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "42047be2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Manage Summary: ------------------------------------------------\n", - "Number of quantitative features: 0\n", - "Number of categorical features: 6\n", - "Total Features: 6\n", - "Total Instances: 450\n", - "Feature Types: [1, 1, 1, 1, 1, 1]\n", - "Missing Values: 0\n", - "Quantiative Feature Range: [[inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf]]\n", - "Categorical Feature Values: [[np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(1), np.int64(0)]]\n", - "Average States: 2.0\n", - "Rule Specificity Limit: 6\n", - "Classes: [np.int64(1), np.int64(0)]\n", - "Class Counts: {np.int64(1): 225, np.int64(0): 225}\n", - "Class Weights: {np.int64(1): 0.5, np.int64(0): 0.5}\n", - "Majority Class: 1\n", - "Expert Knowledge Weights Used: True\n", - "--------------------------------------------------------------------\n", - "Beginning Decision Tree Rule Inititialization...\n", - "\n", - "One-hot encoding 6 categorical features...\n", - " Original features: 6, After one-hot encoding: 12\n", - " One-hot mapping created for 12 encoded features\n", - " Quantitative feature mapping: 0 features\n", - "Random Seed Check After RF: 0.6394267984578837\n", - "\n", - "Extracting rules from all decision tree branches in all forests...\n", - "Random Seed Check After Rule Extract: 0.025010755222666936\n", - "Deduplicating rules...\n", - "Random Seed Check After Deduplication: 0.27502931836911926\n", - "\n", - "Converting extracted rules to HEROS format and checking for redundancy...\n", - "Random Seed Check After Convert to HEROS rules: 0.8601666658261801\n", - "\n", - "Summary: Converted rules to HEROS format and added to population.\n", - "Total Population Numerosity: 667\n", - "Unique HEROS Rules: 290\n", - "PopSize After Tree Initialization 290\n", - "Micro PopSize After Tree Initialization 667\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 290\n", - "Post-Cleaning Population Size: 279\n", - "27 rules subsumed with a specificity of 2\n", - "80 rules subsumed with a specificity of 3\n", - "75 rules subsumed with a specificity of 4\n", - "20 rules subsumed with a specificity of 5\n", - "1 rules subsumed with a specificity of 6\n", - "Post-Subsumption Compaction Population Size: 76\n", - "HEROS Evolution Beginning!\n", - "Archiving: 500\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 1000 0.891 202 500 0.956\n", - "Archiving: 1000\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 2000 0.899 211 500 1.628\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 3000 0.902 217 500 2.301\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 4000 0.897 213 500 2.966\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 5000 0.908 228 500 3.627\n", - "Archiving: 5000\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 6000 0.92 227 500 4.326\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 7000 0.897 225 500 5.006\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 8000 0.902 234 500 5.672\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 9000 0.911 225 500 6.342\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 230\n", - "Post-Cleaning Population Size: 229\n", - "13 rules subsumed with a specificity of 2\n", - "57 rules subsumed with a specificity of 3\n", - "64 rules subsumed with a specificity of 4\n", - "1 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 94\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 10000 0.91 94 499 7.011\n", - "Archiving: 10000\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:10000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 1 0.904 0.904 11 0.525\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 2 0.904 0.904 11 0.958\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 3 0.904 0.904 11 1.413\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 4 0.927 1.0 38 1.802\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 5 0.927 1.0 38 2.241\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 6 0.927 1.0 38 2.681\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 7 0.927 1.0 38 3.074\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 8 0.936 1.0 23 3.501\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 9 0.936 1.0 23 3.863\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 10 0.944 1.0 24 4.246\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 11 0.944 1.0 24 4.58\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 12 0.953 1.0 19 4.958\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 13 0.953 1.0 18 5.294\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 14 0.953 1.0 18 5.574\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 15 0.953 1.0 18 5.861\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 16 0.953 1.0 18 6.26\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 17 0.956 1.0 17 6.534\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 18 0.969 1.0 14 6.891\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 19 0.969 1.0 14 7.236\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 20 0.969 1.0 14 7.563\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.1923035034118793\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 11000 0.886 204 500 15.709\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 12000 0.918 221 500 16.357\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 13000 0.905 224 500 17.004\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 14000 0.909 219 500 17.66\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 15000 0.898 219 500 18.319\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 16000 0.908 219 500 18.964\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 17000 0.9 225 500 19.617\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 18000 0.912 224 500 20.262\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 19000 0.909 221 500 20.905\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 233\n", - "Post-Cleaning Population Size: 232\n", - "15 rules subsumed with a specificity of 2\n", - "69 rules subsumed with a specificity of 3\n", - "59 rules subsumed with a specificity of 4\n", - "3 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 86\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 20000 0.905 86 499 21.551\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:10000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 21 0.969 1.0 14 7.851\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 22 0.984 1.0 14 8.186\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 23 0.984 1.0 14 8.455\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 24 1.0 1.0 9 8.827\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 25 1.0 1.0 9 9.139\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 26 1.0 1.0 9 9.374\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 27 1.0 1.0 9 9.674\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 28 1.0 1.0 9 10.005\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 29 1.0 1.0 9 10.406\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 30 1.0 1.0 9 10.754\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 31 1.0 1.0 9 11.076\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 32 1.0 1.0 9 11.4\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 33 1.0 1.0 9 11.779\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 34 1.0 1.0 8 12.117\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 35 1.0 1.0 8 12.518\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 36 1.0 1.0 8 12.798\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 37 1.0 1.0 8 13.133\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 38 1.0 1.0 8 13.477\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 39 1.0 1.0 8 13.836\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 40 1.0 1.0 8 14.212\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.12363543840117786\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 21000 0.909 202 500 28.852\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 22000 0.9 209 500 29.496\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 23000 0.911 216 500 30.151\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 24000 0.902 209 500 30.81\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 25000 0.891 218 500 31.462\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 26000 0.894 225 500 32.11\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 27000 0.893 219 500 32.759\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 28000 0.908 226 500 33.398\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 29000 0.897 222 500 34.086\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 225\n", - "Post-Cleaning Population Size: 225\n", - "12 rules subsumed with a specificity of 2\n", - "60 rules subsumed with a specificity of 3\n", - "62 rules subsumed with a specificity of 4\n", - "3 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 88\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 30000 0.917 88 500 34.761\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:10000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 41 1.0 1.0 8 14.504\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 42 1.0 1.0 8 14.894\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 43 1.0 1.0 8 15.228\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 44 1.0 1.0 8 15.569\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 45 1.0 1.0 8 15.926\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 46 1.0 1.0 8 16.257\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 47 1.0 1.0 8 16.649\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 48 1.0 1.0 8 17.052\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 49 1.0 1.0 8 17.481\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 50 1.0 1.0 8 17.817\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 51 1.0 1.0 8 18.136\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 52 1.0 1.0 8 18.514\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 53 1.0 1.0 8 18.885\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 54 1.0 1.0 8 19.195\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 55 1.0 1.0 8 19.543\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 56 1.0 1.0 8 19.982\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 57 1.0 1.0 8 20.37\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 58 1.0 1.0 8 20.769\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 59 1.0 1.0 8 21.148\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 60 1.0 1.0 8 21.559\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.051236907929415376\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 31000 0.895 195 500 42.761\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 32000 0.908 204 500 43.395\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 33000 0.905 206 500 44.041\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 34000 0.895 211 500 44.682\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 35000 0.896 218 500 45.335\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 36000 0.897 213 500 46.032\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 37000 0.911 221 500 46.704\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 38000 0.9 217 500 47.371\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 39000 0.91 224 500 48.032\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 222\n", - "Post-Cleaning Population Size: 222\n", - "15 rules subsumed with a specificity of 2\n", - "63 rules subsumed with a specificity of 3\n", - "53 rules subsumed with a specificity of 4\n", - "4 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 87\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 40000 0.897 87 500 48.699\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:10000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 61 1.0 1.0 8 22.014\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 62 1.0 1.0 8 22.34\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 63 1.0 1.0 8 22.769\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 64 1.0 1.0 8 23.17\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 65 1.0 1.0 8 23.552\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 66 1.0 1.0 8 23.98\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 67 1.0 1.0 8 24.381\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 68 1.0 1.0 8 24.766\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 69 1.0 1.0 8 25.156\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 70 1.0 1.0 8 25.561\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 71 1.0 1.0 8 25.982\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 72 1.0 1.0 8 26.383\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 73 1.0 1.0 8 26.826\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 74 1.0 1.0 8 27.224\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 75 1.0 1.0 8 27.604\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 76 1.0 1.0 8 27.957\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 77 1.0 1.0 8 28.36\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 78 1.0 1.0 8 28.648\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 79 1.0 1.0 8 29.022\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 80 1.0 1.0 8 29.375\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.6845892083633415\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 41000 0.897 199 500 57.184\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 42000 0.904 216 500 57.846\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 43000 0.896 217 500 58.521\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 44000 0.91 224 500 59.185\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 45000 0.893 236 500 59.898\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 46000 0.915 228 500 60.578\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 47000 0.897 232 500 61.246\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 48000 0.9 236 500 61.925\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 49000 0.899 221 500 62.596\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 230\n", - "Post-Cleaning Population Size: 230\n", - "13 rules subsumed with a specificity of 2\n", - "60 rules subsumed with a specificity of 3\n", - "65 rules subsumed with a specificity of 4\n", - "1 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 91\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 50000 0.902 91 500 63.282\n", - "Archiving: 50000\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:10000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 81 1.0 1.0 8 29.808\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 82 1.0 1.0 8 30.218\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 83 1.0 1.0 8 30.566\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 84 1.0 1.0 8 31.018\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 85 1.0 1.0 8 31.399\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 86 1.0 1.0 8 31.858\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 87 1.0 1.0 8 32.204\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 88 1.0 1.0 8 32.615\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 89 1.0 1.0 8 33.068\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 90 1.0 1.0 8 33.433\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 91 1.0 1.0 8 33.827\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 92 1.0 1.0 8 34.237\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 93 1.0 1.0 8 34.578\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 94 1.0 1.0 8 34.941\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 95 1.0 1.0 8 35.294\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 96 1.0 1.0 8 35.624\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 97 1.0 1.0 8 36.05\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 98 1.0 1.0 8 36.423\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 99 1.0 1.0 8 36.745\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 100 1.0 1.0 8 37.1\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.8362786118187231\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize HEROS algorithm with run parameters\n", "heros = HEROS(outcome_type=outcome_type,iterations=iterations,pop_size=pop_size,cross_prob=cross_prob,mut_prob=mut_prob,nu=nu,beta=beta,theta_sel=theta_sel,\n", @@ -889,23 +446,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d4a8ff12", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", - "0 0 0 0 1 0 0 0 0 8\n", - "1 1 0 0 1 1 0 1 2 14\n", - "2 0 0 1 1 1 1 1 0 29\n", - "3 1 1 0 0 1 0 0 3 44\n", - "4 0 0 1 1 0 0 1 0 58\n" - ] - } - ], + "outputs": [], "source": [ "# Load testing dataset ---------------------------\n", "test_df = pd.read_csv(test_data_path, sep=\"\\t\")\n", @@ -936,67 +480,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "47927583", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.96, 0.88, 0.8, 0.76, 0.76, 0.5]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.94), np.float64(0.86), np.float64(0.74), np.float64(1.0), np.float64(1.0), np.float64(0.52)]\n", - "Model rule counts: [8, 7, 6, 5, 4, 2, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 1, 2] [0, 0, 0] 0 5 1.000000 \n", - "1 [0, 1, 3] [0, 1, 1] 1 7 0.998143 \n", - "2 [0, 1, 5] [1, 1, 1] 1 7 0.997429 \n", - "3 [0, 1, 4] [1, 0, 0] 0 6 0.997857 \n", - "4 [1, 2, 0] [0, 1, 0] 1 6 0.998714 \n", - "5 [0, 1, 5] [1, 1, 0] 0 7 0.996857 \n", - "6 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", - "7 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 34.0 1.0 68 68 \n", - "1 1.0 28.5 1.0 57 57 \n", - "2 1.0 26.0 1.0 52 52 \n", - "3 1.0 27.5 1.0 55 55 \n", - "4 1.0 30.5 1.0 61 61 \n", - "5 1.0 24.0 1.0 48 48 \n", - "6 1.0 27.5 1.0 55 55 \n", - "7 1.0 27.0 1.0 54 54 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 0 \n", - "1 None None None 0 \n", - "2 None None None 0 \n", - "3 None None None 0 \n", - "4 None None None 104 \n", - "5 None None None 0 \n", - "6 None None None 0 \n", - "7 None None None 0 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 93.127103 0.004923 \n", - "1 3 88.732769 0.010573 \n", - "2 3 86.089364 0.013973 \n", - "3 3 84.024844 0.010015 \n", - "4 3 94.375813 0.011239 \n", - "5 3 90.676391 0.014726 \n", - "6 3 83.373389 0.009937 \n", - "7 3 94.056982 0.015261 \n" - ] - } - ], + "outputs": [], "source": [ "best_model_index = heros.auto_select_top_model(X_test,y_test,verbose=True)\n", "set_df = heros.get_model_rules(best_model_index)\n", @@ -1013,27 +500,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "0485d99a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HEROS Top Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Report performance results for the top model\n", "predictions = heros.predict(X_test,whole_rule_pop=False, target_model=best_model_index)\n", @@ -1051,72 +521,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "b9e0ce2c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction Probabilities for all Testing Instances:\n", - "[[1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]]\n", - "Coverage for all Testing Instances:\n", - "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1]\n", - "50 instances covered out of 50\n" - ] - } - ], + "outputs": [], "source": [ "predict_prob = heros.predict_proba(X_test,whole_rule_pop=False, target_model=best_model_index)\n", "print(\"Prediction Probabilities for all Testing Instances:\")\n", @@ -1139,21 +547,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "f79beb2d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get false positive rate, true positive rate, and thresholds\n", "fpr, tpr, thresholds = roc_curve(y_test, predict_prob[:, 1]) #based on class 1\n", @@ -1182,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "d18f5118", "metadata": {}, "outputs": [], @@ -1203,21 +600,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "1583cf95", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " heros.get_rule_set_heatmap(feature_names, best_model_index, weighting='useful_accuracy', specified_filter=1, display_micro=False, show=True, save=True, output_path=output_path)" @@ -1233,21 +619,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "f49f7597", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " node_size = 1000\n", @@ -1265,21 +640,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "f3950089", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells and feat_track != None:\n", " heros.run_model_feature_tracking(best_model_index)\n", @@ -1303,21 +667,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "07a336b2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Run permutation importance\n", "result = permutation_importance(heros, X_test, y_test, n_repeats=100, random_state=random_state, scoring='balanced_accuracy')\n", @@ -1348,29 +701,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "66567604", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Number of Available Testing Instances: 50\n", - "Making Prediction on Testing Instance Index: 0\n", - "PREDICTION REPORT ------------------------------------------------------------------\n", - "Outcome Prediction: 0\n", - "Model Prediction Probabilities: {np.int64(0): 1.0, np.int64(1): 0.0}\n", - "Instance Covered by Model: Yes\n", - "Number of Matching Rules: 1\n", - "PREDICTION EXPLANATION -------------------------------------------------------------\n", - "Supporting Rules: --------------------\n", - "5 rule copies assert that IF: (A_0 = 0) AND (A_1 = 0) AND (R_0 = 0) THEN: predict outcome '0' with 100.0% confidence based on 68 matching training instances (15.11% of training instances).\n", - "Contradictory Rules: -----------------\n", - "No contradictory rules matched.\n" - ] - } - ], + "outputs": [], "source": [ "# Get example testing instance (with no label) --------------------------------\n", "print(\"Total Number of Available Testing Instances: \"+str(len(X_test)))\n", @@ -1393,21 +727,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "fb557984", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " resolution = 500\n", @@ -1426,21 +749,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "eb49bc82", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " resolution = 500\n", @@ -1459,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "2cf56653", "metadata": {}, "outputs": [], @@ -1490,21 +802,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "1fb01410", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Save Phase 1 Rule Training Performance Estimates to .csv\n", "rule_tracking_df = heros.get_performance_tracking()\n", @@ -1523,21 +824,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "ce8af5c4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_tracking_df = heros.get_model_performance_tracking()\n", "model_tracking_df.to_csv(output_path+'/model_tracking.csv', index=False)\n", @@ -1557,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "3f39c1c1", "metadata": {}, "outputs": [], @@ -1580,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "75c2551d", "metadata": {}, "outputs": [], @@ -1598,28 +888,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "9a6f9391", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Global Phase 1 Phase 2 Rule Initialization Rule Covering \\\n", - "0 71.058657 175.39728 37.123023 0.312301 0.008042 \n", - "\n", - " Rule Equality Rule Matching Rule Evaluation Feature Tracking \\\n", - "0 1.853574 3.108993 30.487597 0.0 \n", - "\n", - " Rule Subsumption Rule Selection Rule Mating Rule Deletion \\\n", - "0 0.486336 1.141657 4.591866 4.873166 \n", - "\n", - " Rule Compaction Rule Prediction \n", - "0 0.027999 0.853225 \n" - ] - } - ], + "outputs": [], "source": [ "time_df = heros.get_runtimes()\n", "time_df.to_csv(output_path+'/runtimes.csv', index=False)\n", @@ -1647,21 +919,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "708cf494", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " heros.get_rule_pop_heatmap(feature_names, weighting='useful_accuracy', specified_filter=1, display_micro=True, show=True, save=True, output_path=output_path)" @@ -1677,21 +938,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "892ea1a5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " node_size = 1000\n", @@ -1711,21 +961,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "4454a331", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Save Feature Tracking Scores to .csv\n", "if heros.feat_track != None:\n", @@ -1747,27 +986,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "48a269b0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HEROS Whole Rule Population Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "predictions = heros.predict(X_test,whole_rule_pop=True)\n", "print(\"HEROS Whole Rule Population Testing Data Performance Report:\")\n", @@ -1787,66 +1009,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "d4390561", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 1, 2] [0, 0, 0] 0 5 1.000000 \n", - "1 [0, 1, 3] [0, 1, 1] 1 7 0.998143 \n", - "2 [0, 1, 5] [1, 1, 1] 1 7 0.997429 \n", - "3 [0, 1, 4] [1, 0, 0] 0 6 0.997857 \n", - "4 [0, 1, 2] [0, 0, 1] 1 6 0.998714 \n", - "5 [0, 1, 5] [1, 1, 0] 0 7 0.996857 \n", - "6 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", - "7 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 34.0 1.0 68 68 \n", - "1 1.0 28.5 1.0 57 57 \n", - "2 1.0 26.0 1.0 52 52 \n", - "3 1.0 27.5 1.0 55 55 \n", - "4 1.0 30.5 1.0 61 61 \n", - "5 1.0 24.0 1.0 48 48 \n", - "6 1.0 27.5 1.0 55 55 \n", - "7 1.0 27.0 1.0 54 54 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 0 \n", - "1 None None None 0 \n", - "2 None None None 0 \n", - "3 None None None 0 \n", - "4 None None None 104 \n", - "5 None None None 0 \n", - "6 None None None 0 \n", - "7 None None None 0 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 93.127103 0.004923 \n", - "1 3 88.732769 0.010573 \n", - "2 3 86.089364 0.013973 \n", - "3 3 84.024844 0.010015 \n", - "4 3 94.375813 0.011239 \n", - "5 3 90.676391 0.014726 \n", - "6 3 83.373389 0.009937 \n", - "7 3 94.056982 0.015261 \n", - "HEROS Top 'Default' Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "set_df = heros.get_model_rules() #returns top training model by default based on balanced accuracy, then covering, then rule-set size.\n", @@ -1869,70 +1035,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "ddeab87c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 1, 2] [0, 0, 0] 0 5 1.000000 \n", - "1 [0, 1, 3] [0, 1, 1] 1 7 0.998143 \n", - "2 [0, 1, 5] [1, 1, 1] 1 7 0.997429 \n", - "3 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "4 [0, 1, 2] [0, 0, 1] 1 6 0.998714 \n", - "5 [1, 3, 5] [1, 0, 0] 0 5 0.996714 \n", - "6 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", - "7 [0, 1, 5] [1, 1, 0] 0 7 0.996857 \n", - "8 [0, 1, 4] [1, 0, 0] 0 6 0.997857 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 34.0 1.0 68 68 \n", - "1 1.0 28.5 1.0 57 57 \n", - "2 1.0 26.0 1.0 52 52 \n", - "3 1.0 27.0 1.0 54 54 \n", - "4 1.0 30.5 1.0 61 61 \n", - "5 1.0 23.5 1.0 47 47 \n", - "6 1.0 27.5 1.0 55 55 \n", - "7 1.0 24.0 1.0 48 48 \n", - "8 1.0 27.5 1.0 55 55 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 0 \n", - "1 None None None 0 \n", - "2 None None None 0 \n", - "3 None None None 0 \n", - "4 None None None 104 \n", - "5 None None None 0 \n", - "6 None None None 0 \n", - "7 None None None 0 \n", - "8 None None None 0 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 93.127103 0.004923 \n", - "1 3 88.732769 0.010573 \n", - "2 3 86.089364 0.013973 \n", - "3 3 94.056982 0.015261 \n", - "4 3 94.375813 0.011239 \n", - "5 3 96.028050 0.002865 \n", - "6 3 83.373389 0.009937 \n", - "7 3 90.676391 0.014726 \n", - "8 3 84.024844 0.010015 \n", - "HEROS Top 'Default' Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "desired_model_index = 1\n", @@ -1956,72 +1062,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "ceac3c53", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rule population evaluation at iteration 500\n", - "Run Time: 0.6236279010772705\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n", - "Rule population evaluation at iteration 1000\n", - "Run Time: 0.9600560665130615\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.85185185 0.88461538 27\n", - " 1 0.84000000 0.91304348 0.87500000 23\n", - "\n", - " accuracy 0.88000000 50\n", - " macro avg 0.88000000 0.88244767 0.87980769 50\n", - "weighted avg 0.88320000 0.88000000 0.88019231 50\n", - "\n", - "Rule population evaluation at iteration 5000\n", - "Run Time: 3.630852460861206\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.85185185 0.88461538 27\n", - " 1 0.84000000 0.91304348 0.87500000 23\n", - "\n", - " accuracy 0.88000000 50\n", - " macro avg 0.88000000 0.88244767 0.87980769 50\n", - "weighted avg 0.88320000 0.88000000 0.88019231 50\n", - "\n", - "Rule population evaluation at iteration 10000\n", - "Run Time: 7.013664960861206\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n", - "Rule population evaluation at iteration 50000\n", - "Run Time: 63.28484010696411\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_rule_iterations != None:\n", " rule_iteration_list = [int(x) for x in stored_rule_iterations.split(',')]\n", @@ -2045,50 +1089,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "4ddb1cb2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top default model evaluation at iteration 10\n", - "Run Time: 4.246081113815308\n", - " precision recall f1-score support\n", - "\n", - " 0 0.96000000 1.00000000 0.97959184 24\n", - " 1 1.00000000 0.96153846 0.98039216 26\n", - "\n", - " accuracy 0.98000000 50\n", - " macro avg 0.98000000 0.98076923 0.97999200 50\n", - "weighted avg 0.98080000 0.98000000 0.98000800 50\n", - "\n", - "Top default model evaluation at iteration 50\n", - "Run Time: 17.817209720611572\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "Top default model evaluation at iteration 100\n", - "Run Time: 37.10042476654053\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -2109,86 +1113,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "cf9e32c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 10\n", - "Run Time: 4.246081113815308\n", - "7 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [0.98, 0.8999999999999999, 0.8200000000000001, 0.8200000000000001, 0.6200000000000001, 0.78, 0.6200000000000001]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.92), np.float64(0.64), np.float64(0.76), np.float64(0.62), np.float64(0.68), np.float64(0.44)]\n", - "Model rule counts: [8, 7, 6, 5, 4, 2, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 0.98\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 50\n", - "Run Time: 17.817209720611572\n", - "7 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.88, 0.88, 0.8, 0.76, 0.78, 0.64]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.76), np.float64(0.78), np.float64(0.74), np.float64(0.9), np.float64(1.0), np.float64(0.58)]\n", - "Model rule counts: [8, 7, 6, 5, 4, 2, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 100\n", - "Run Time: 37.10042476654053\n", - "7 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.98, 0.92, 0.78, 0.76, 0.76, 0.44000000000000006]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.94), np.float64(0.86), np.float64(0.74), np.float64(1.0), np.float64(1.0), np.float64(0.52)]\n", - "Model rule counts: [8, 7, 6, 5, 4, 2, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -2212,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "b0a873b6", "metadata": {}, "outputs": [], @@ -2228,211 +1156,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "eac4849a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Manage Summary: ------------------------------------------------\n", - "Number of quantitative features: 0\n", - "Number of categorical features: 6\n", - "Total Features: 6\n", - "Total Instances: 450\n", - "Feature Types: [1, 1, 1, 1, 1, 1]\n", - "Missing Values: 0\n", - "Quantiative Feature Range: [[inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf]]\n", - "Categorical Feature Values: [[np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(1), np.int64(0)]]\n", - "Average States: 2.0\n", - "Rule Specificity Limit: 6\n", - "Classes: [np.int64(1), np.int64(0)]\n", - "Class Counts: {np.int64(1): 225, np.int64(0): 225}\n", - "Class Weights: {np.int64(1): 0.5, np.int64(0): 0.5}\n", - "Majority Class: 1\n", - "Expert Knowledge Weights Used: True\n", - "--------------------------------------------------------------------\n", - "Initializing Rule Population via Loaded File!\n", - "Max Rule ID in Loaded Population: 22241\n", - "Loading Rule Population Complete: 91 unique rules and 500 total rules loaded.\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 91\n", - "Post-Cleaning Population Size: 91\n", - "Post-Subsumption Compaction Population Size: 91\n", - "HEROS Evolution Beginning!\n", - "Archiving: 500\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 1000 0.9 208 500 0.677\n", - "Archiving: 1000\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 221\n", - "Post-Cleaning Population Size: 221\n", - "17 rules subsumed with a specificity of 2\n", - "60 rules subsumed with a specificity of 3\n", - "54 rules subsumed with a specificity of 4\n", - "2 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 88\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 2000 0.889 88 500 1.365\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:2000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 1 0.893 1.0 38 0.502\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 2 0.907 1.0 27 1.0\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 3 0.909 1.0 34 1.475\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 4 0.916 1.0 23 1.862\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 5 0.922 1.0 31 2.243\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 6 0.922 1.0 31 2.618\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 7 0.927 1.0 22 2.987\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 8 0.931 0.971 15 3.341\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.7877394216722057\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 3000 0.906 192 500 5.804\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 202\n", - "Post-Cleaning Population Size: 201\n", - "18 rules subsumed with a specificity of 2\n", - "53 rules subsumed with a specificity of 3\n", - "40 rules subsumed with a specificity of 4\n", - "1 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 89\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 4000 0.906 89 499 6.461\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:2000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 9 0.936 1.0 17 3.741\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 10 0.964 1.0 17 4.121\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 11 0.964 1.0 17 4.444\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 12 0.964 1.0 17 4.828\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 13 0.964 1.0 17 5.169\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 14 0.964 1.0 17 5.452\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 15 0.964 1.0 17 5.715\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 16 0.964 1.0 17 6.071\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.18578045670174903\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 5000 0.901 188 500 9.837\n", - "Archiving: 5000\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 213\n", - "Post-Cleaning Population Size: 213\n", - "17 rules subsumed with a specificity of 2\n", - "63 rules subsumed with a specificity of 3\n", - "43 rules subsumed with a specificity of 4\n", - "2 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 88\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 6000 0.911 88 500 10.507\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:2000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 17 0.964 1.0 17 6.397\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 18 0.964 1.0 17 6.772\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 19 0.964 1.0 17 7.112\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 20 0.964 1.0 17 7.407\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 21 0.964 1.0 17 7.76\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 22 0.964 1.0 17 8.156\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 23 0.964 1.0 17 8.436\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 24 0.964 1.0 17 8.705\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.08748387057121221\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 7000 0.894 209 500 13.787\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 223\n", - "Post-Cleaning Population Size: 223\n", - "17 rules subsumed with a specificity of 2\n", - "63 rules subsumed with a specificity of 3\n", - "52 rules subsumed with a specificity of 4\n", - "1 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 90\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 8000 0.897 90 500 14.492\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:2000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 25 0.964 1.0 17 8.981\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 26 0.964 1.0 17 9.352\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 27 0.964 1.0 17 9.702\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 28 0.973 1.0 18 10.048\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 29 0.973 1.0 18 10.343\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 30 0.973 1.0 18 10.695\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 31 0.973 1.0 17 11.107\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 32 0.973 1.0 17 11.47\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.8276400675077251\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 9000 0.893 207 500 17.952\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 223\n", - "Post-Cleaning Population Size: 223\n", - "19 rules subsumed with a specificity of 2\n", - "60 rules subsumed with a specificity of 3\n", - "51 rules subsumed with a specificity of 4\n", - "1 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 92\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 10000 0.899 92 500 18.677\n", - "Archiving: 10000\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - "Number of Iterations Used:2000\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 33 0.973 1.0 17 11.848\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 34 0.973 1.0 17 12.218\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 35 0.973 1.0 17 12.526\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 36 0.978 1.0 12 12.93\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 37 0.978 1.0 12 13.31\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 38 0.978 1.0 12 13.627\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 39 1.0 1.0 11 14.035\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 40 1.0 1.0 11 14.336\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.43306151147896965\n" - ] - } - ], + "outputs": [], "source": [ "rule_pop_init = 'load' # Change the rule population initialization run parameter to load (all other parameters kept the same)\n", "\n", @@ -2448,79 +1175,10 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "a71e7654", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "14 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 1.0, 0.96, 0.9199999999999999, 0.88, 0.88, 0.78, 0.76, 0.76, 0.76, 0.76, 0.76, 0.74, 0.64]\n", - "Model testing coverages: [np.float64(1.0), np.float64(1.0), np.float64(0.96), np.float64(0.9), np.float64(0.98), np.float64(0.9), np.float64(0.92), np.float64(0.8), np.float64(0.74), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(0.72), np.float64(0.42)]\n", - "Model rule counts: [11, 11, 10, 9, 8, 6, 5, 5, 4, 3, 3, 3, 2, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 11\n", - "Best model index: 0\n", - "----------------------------------------\n", - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [1, 2, 4] [0, 1, 1] 1 13 0.998714 \n", - "1 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "2 [0, 4, 5] [1, 0, 0] 0 9 0.998000 \n", - "3 [0, 1, 5] [1, 1, 1] 1 5 0.997429 \n", - "4 [0, 1, 3] [0, 1, 1] 1 1 0.998143 \n", - "5 [0, 1, 2] [0, 0, 0] 0 6 1.000000 \n", - "6 [0, 1, 5] [1, 1, 0] 0 3 0.996857 \n", - "7 [0, 1, 4] [1, 0, 0] 0 6 0.997857 \n", - "8 [0, 1, 2] [0, 0, 1] 1 4 0.998714 \n", - "9 [0, 2, 3] [0, 1, 1] 1 7 0.997143 \n", - "10 [0, 1, 4] [1, 0, 1] 1 10 0.997857 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 30.5 1.0 61 61 \n", - "1 1.0 27.0 1.0 54 54 \n", - "2 1.0 28.0 1.0 56 56 \n", - "3 1.0 26.0 1.0 52 52 \n", - "4 1.0 28.5 1.0 57 57 \n", - "5 1.0 34.0 1.0 68 68 \n", - "6 1.0 24.0 1.0 48 48 \n", - "7 1.0 27.5 1.0 55 55 \n", - "8 1.0 30.5 1.0 61 61 \n", - "9 1.0 25.0 1.0 50 50 \n", - "10 1.0 27.5 1.0 55 55 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 0 \n", - "1 None None None 0 \n", - "2 None None None 0 \n", - "3 None None None 0 \n", - "4 None None None 0 \n", - "5 None None None 0 \n", - "6 None None None 0 \n", - "7 None None None 0 \n", - "8 None None None 0 \n", - "9 None None None 0 \n", - "10 None None None 0 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 109.048706 0.007553 \n", - "1 3 96.425541 0.013104 \n", - "2 3 98.333002 0.004362 \n", - "3 3 89.790339 0.006227 \n", - "4 3 87.333473 0.000272 \n", - "5 3 99.903499 0.009952 \n", - "6 3 88.093832 0.002201 \n", - "7 3 98.315752 0.009815 \n", - "8 3 93.700073 0.004154 \n", - "9 3 89.696024 0.003983 \n", - "10 3 101.273674 0.028083 \n" - ] - } - ], + "outputs": [], "source": [ "# Get the best model (as before)\n", "best_model_index = heros_reboot.auto_select_top_model(X_test,y_test,verbose=True)\n", @@ -2530,27 +1188,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "b08f7f25", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HEROS Top Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Report performance results for the top model\n", "predictions = heros_reboot.predict(X_test,whole_rule_pop=False, target_model=best_model_index)\n", @@ -2560,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "81dbd9e3", "metadata": {}, "outputs": [], diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSURF_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSURF_Scores.csv deleted file mode 100644 index 87415f4..0000000 --- a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSURF_Scores.csv +++ /dev/null @@ -1,7 +0,0 @@ -Feature,Score -A_0,0.0527170745920746 -A_1,0.05271707459207461 -R_0,0.009829319985570005 -R_1,0.009829319985569995 -R_2,0.009829319985569981 -R_3,0.009829319985570014 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSWRFDB_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSWRFDB_Scores.csv new file mode 100644 index 0000000..b5f21bd --- /dev/null +++ b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_MultiSWRFDB_Scores.csv @@ -0,0 +1,7 @@ +Feature,Score +A_0,0.03698526666089879 +A_1,0.03698526666089878 +R_0,-0.0067603240148978025 +R_1,-0.006760324014897812 +R_2,-0.006760324014897798 +R_3,-0.006760324014897799 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_NA_MultiSWRFDB_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_NA_MultiSWRFDB_Scores.csv new file mode 100644 index 0000000..f740462 --- /dev/null +++ b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_NA_MultiSWRFDB_Scores.csv @@ -0,0 +1,7 @@ +Feature,Score +A_0,0.04750169656556999 +A_1,0.04136667893466966 +R_0,-0.012803708788285396 +R_1,-0.000140838501968226 +R_2,-0.03255689170033571 +R_3,-0.020210843651172736 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSURF_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSURF_Scores.csv deleted file mode 100644 index 46798ec..0000000 --- a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSURF_Scores.csv +++ /dev/null @@ -1,7 +0,0 @@ -Feature,Score -A_0,0.028900179681429695 -A_1,0.006967901889776873 -R_0,0.02878068112443113 -R_1,0.02526574987512488 -R_2,0.10847770182874525 -R_3,0.1110565439537103 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSWRFDB_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSWRFDB_Scores.csv new file mode 100644 index 0000000..86a65ba --- /dev/null +++ b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_MultiSWRFDB_Scores.csv @@ -0,0 +1,7 @@ +Feature,Score +A_0,0.023352064355566495 +A_1,-0.006675774085889205 +R_0,-0.005446260273540202 +R_1,-0.001021047845508582 +R_2,0.02249503731040649 +R_3,0.026399857724587535 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_NA_MultiSWRFDB_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_NA_MultiSWRFDB_Scores.csv new file mode 100644 index 0000000..efc9f8f --- /dev/null +++ b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_feature_type_mix_NA_MultiSWRFDB_Scores.csv @@ -0,0 +1,7 @@ +Feature,Score +A_0,0.003406041029592358 +A_1,-0.00609654412659257 +R_0,-0.011544289442236113 +R_1,-0.007067428280550776 +R_2,0.007229959454005406 +R_3,0.036719539100810356 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSURF_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSURF_Scores.csv deleted file mode 100644 index d45fed3..0000000 --- a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSURF_Scores.csv +++ /dev/null @@ -1,7 +0,0 @@ -Feature,Score -A_0,0.5599999999999998 -A_1,0.5599999999999998 -R_0,0.044999999999999804 -R_1,0.04500000000000004 -R_2,0.04500000000000004 -R_3,0.045 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSWRFDB_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSWRFDB_Scores.csv new file mode 100644 index 0000000..f877f93 --- /dev/null +++ b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_multiclass_MultiSWRFDB_Scores.csv @@ -0,0 +1,7 @@ +Feature,Score +A_0,0.40813339250514147 +A_1,0.40813339250514147 +R_0,-0.0015949013505510805 +R_1,-0.0015949013505511637 +R_2,-0.0015949013505511499 +R_3,-0.0015949013505510527 diff --git a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_quant_outcome_MultiSURF_Scores.csv b/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_quant_outcome_MultiSURF_Scores.csv deleted file mode 100644 index b743479..0000000 --- a/evaluation/datasets/unit_testing/unit_EK/Multiplexer6_quant_outcome_MultiSURF_Scores.csv +++ /dev/null @@ -1,7 +0,0 @@ -Feature,Score -A_0,-0.0012410281711752284 -A_1,0.03376405559309969 -R_0,0.020097860676904777 -R_1,0.04022945625657026 -R_2,-0.02584510694575032 -R_3,0.006584637788681912 diff --git a/requirements.txt b/requirements.txt index bd452741a3e1d64829b93c494dc4f4ea72455304..270cbe29082b260d1357b4fa4b81777da17aed79 100644 GIT binary patch delta 21 bcmeyt_=j=AJ3c!GTLuFLJq8OPHevt(OoasW delta 17 Ycmeyv_=9o6J1$!WTLuFLJqB|I066IcjsO4v diff --git a/tests/test_heros.py b/tests/test_heros.py index d058951..103adce 100644 --- a/tests/test_heros.py +++ b/tests/test_heros.py @@ -1,25 +1,26 @@ -#import pytest +# To test using pytest run `pytest --log-cli-level=DEBUG` from the root folder` + import os import pandas as pd from src.skheros.heros import HEROS from sklearn.metrics import classification_report -from skrebate import MultiSURF # Install using: pip install skrebate==0.7 - -# To test using pytest run `pytest --log-cli-level=DEBUG` from the root folder` +#from skrebate import MultiSURF +from skrebate import MultiSWRFDB -def get_EK(X,y,data_name,feature_names): +def get_EK(X,y,data_name,feature_names,cat_feat_indexes): """ Calculates or loads expert knowledge (EK) scores for the given unit testing dataset """ # Further data preparation for expert knowldge generation (i.e. balanced subsampling of training instances for faster run times) - score_path_name = 'evaluation/datasets/unit_testing/unit_EK/'+str(data_name)+'_MultiSURF_Scores.csv' #No need to change - # Calculate or load feature importance estimates with MultiSURF ------------------------------------ + score_path_name = 'evaluation/datasets/unit_testing/unit_EK/'+str(data_name)+'_MultiSWRFDB_Scores.csv' #No need to change + # Calculate or load feature importance estimates with MultiSWRFDB ------------------------------------ if not os.path.isfile(score_path_name): - print("Generating MultiSURF Scores:") - clf = MultiSURF(n_jobs=1).fit(X, y) + print("Generating MultiSWRFDB Scores:") + #clf = MultiSURF(n_jobs=1).fit(X, y) + clf = MultiSWRFDB(n_jobs=1,categorical_features=cat_feat_indexes).fit(X, y) ek = clf.feature_importances_ score_data = pd.DataFrame({'Feature':feature_names,'Score':ek}) score_data.to_csv(score_path_name,index=False) else: #load previously trained scores - print("Loading MultiSURF Scores") + print("Loading MultiSWRFDB Scores") loaded_data = pd.read_csv(score_path_name) ek = loaded_data['Score'].tolist() return ek @@ -36,7 +37,7 @@ def test_6mux(): cat_feat_indexes = list(range(X.shape[1])) #all feature are categorical so provide indexes 0-5 in this list for 6-bit multiplexer dataset X = X.values y = df[outcome_label].values #outcome values - ek = get_EK(X,y,data_name,feature_names) + ek = get_EK(X,y,data_name,feature_names,cat_feat_indexes) print(ek) heros = HEROS(outcome_type='class',iterations=10000,pop_size=500,cross_prob=0.8,mut_prob=0.04,nu=1,beta=0.2,theta_sel=0.5, fitness_function='pareto',subsumption='both',rsl=0,feat_track=None, model_iterations=40, @@ -65,7 +66,8 @@ def test_na(): cat_feat_indexes = list(range(X.shape[1])) #all feature are categorical so provide indexes 0-5 in this list for 6-bit multiplexer dataset X = X.values y = df[outcome_label].values #outcome values - ek = [0.0527170745920746, 0.0527170745920746, 0.00982931998557, 0.0098293199855699, 0.0098293199855699, 0.00982931998557] #temporary overide until Rebate fixed + ek = get_EK(X,y,data_name,feature_names,cat_feat_indexes) + #ek = [0.0527170745920746, 0.0527170745920746, 0.00982931998557, 0.0098293199855699, 0.0098293199855699, 0.00982931998557] #temporary overide until Rebate fixed #ek = get_EK(X,y,data_name,feature_names) print(ek) heros = HEROS(outcome_type='class',iterations=10000,pop_size=500,cross_prob=0.8,mut_prob=0.04,nu=1,beta=0.2,theta_sel=0.5, @@ -92,10 +94,10 @@ def test_mixed_feature_types(): outcome_label = 'outcome' X = df.drop(outcome_label, axis=1) feature_names = X.columns - cat_feat_indexes = [0,1,2,3] #all feature are categorical so provide indexes 0-5 in this list for 6-bit multiplexer dataset + cat_feat_indexes = [0,1,2,3] # only first four features in this dataset can be treated as categorical X = X.values y = df[outcome_label].values #outcome values - ek = get_EK(X,y,data_name,feature_names) + ek = get_EK(X,y,data_name,feature_names,cat_feat_indexes) print(ek) heros = HEROS(outcome_type='class',iterations=20000,pop_size=500,cross_prob=0.8,mut_prob=0.04,nu=1,beta=0.2,theta_sel=0.5, fitness_function='pareto',subsumption='both',rsl=0,feat_track=None, model_iterations=40, @@ -116,15 +118,16 @@ def test_mixed_feature_types_na(): print("------------------------------------------------------") print("Test: 6-bit MUX with mixed feature types and NAs - binary and quantitative features and binary outcome") # Load and prepare data - data_name = 'Multiplexer6_NA' + data_name = 'Multiplexer6_feature_type_mix_NA' df = pd.read_csv('evaluation/datasets/unit_testing/'+str(data_name)+'.csv') outcome_label = 'outcome' X = df.drop(outcome_label, axis=1) feature_names = X.columns - cat_feat_indexes = [0,1,2,3] #all feature are categorical so provide indexes 0-5 in this list for 6-bit multiplexer dataset + cat_feat_indexes = [0,1,2,3] # only first four features in this dataset can be treated as categorical X = X.values y = df[outcome_label].values #outcome values - ek = [0.0527170745920746, 0.0527170745920746, 0.00982931998557, 0.0098293199855699, 0.0098293199855699, 0.00982931998557] #temporary overide until Rebate fixed + ek = get_EK(X,y,data_name,feature_names,cat_feat_indexes) + #ek = [0.0527170745920746, 0.0527170745920746, 0.00982931998557, 0.0098293199855699, 0.0098293199855699, 0.00982931998557] #temporary overide until Rebate fixed #ek = get_EK(X,y,data_name,feature_names) print(ek) heros = HEROS(outcome_type='class',iterations=20000,pop_size=500,cross_prob=0.8,mut_prob=0.04,nu=1,beta=0.2,theta_sel=0.5, @@ -154,7 +157,7 @@ def test_multiclass(): cat_feat_indexes = list(range(X.shape[1])) #all feature are categorical so provide indexes 0-5 in this list for 6-bit multiplexer dataset X = X.values y = df[outcome_label].values #outcome values - ek = get_EK(X,y,data_name,feature_names) + ek = get_EK(X,y,data_name,feature_names,cat_feat_indexes) print(ek) heros = HEROS(outcome_type='class',iterations=10000,pop_size=500,cross_prob=0.8,mut_prob=0.04,nu=1,beta=0.2,theta_sel=0.5, fitness_function='pareto',subsumption='both',rsl=0,feat_track=None, model_iterations=40, From 5086c3e71eb2bdcdbb89c9f9533650d88f8a2b19 Mon Sep 17 00:00:00 2001 From: Ryan Urbanowicz Date: Wed, 1 Jul 2026 23:08:31 -0700 Subject: [PATCH 2/4] Update HEROS_Demo_Notebook.ipynb Fixed use of updated skrebate package for feature importance estimation (optionally used by HEROS). --- HEROS_Demo_Notebook.ipynb | 1696 +++++++++++++++++++++++++++++++++++-- 1 file changed, 1618 insertions(+), 78 deletions(-) diff --git a/HEROS_Demo_Notebook.ipynb b/HEROS_Demo_Notebook.ipynb index 0c6717c..6c61fc9 100644 --- a/HEROS_Demo_Notebook.ipynb +++ b/HEROS_Demo_Notebook.ipynb @@ -47,7 +47,7 @@ "source": [ "## BEFORE RUNNING THIS NOTEBOOK\n", "In addition to the packages within requirments.txt, to run this demonstration notebook you will also need to install the following packages:\n", - "* pip install skrebate==0.7\n", + "* pip install skrebate\n", "* pip install pickle" ] }, @@ -136,15 +136,204 @@ "id": "f931305f", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "A module that was compiled using NumPy 1.x cannot be run in\n", + "NumPy 2.4.4 as it may crash. To support both 1.x and 2.x\n", + "versions of NumPy, modules must be compiled with NumPy 2.0.\n", + "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", + "\n", + "If you are a user of the module, the easiest solution will be to\n", + "downgrade to 'numpy<2' or try to upgrade the affected module.\n", + "We expect that some modules will need time to support NumPy 2.\n", + "\n", + "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_92620\\1537052189.py\", line 12, in \n", + " from src.skheros.heros import HEROS\n", + " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\__init__.py\", line 7, in \n", + " from .heros import HEROS\n", + " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\heros.py\", line 3, in \n", + " import pandas as pd\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 58, in \n", + " from pandas.core.api import (\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\api.py\", line 27, in \n", + " from pandas.core.arrays import Categorical\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\__init__.py\", line 1, in \n", + " from pandas.core.arrays.arrow import ArrowExtensionArray\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\__init__.py\", line 5, in \n", + " from pandas.core.arrays.arrow.array import ArrowExtensionArray\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\array.py\", line 65, in \n", + " from pandas.core import (\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\__init__.py\", line 9, in \n", + " from pandas.core.ops.array_ops import (\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py\", line 55, in \n", + " from pandas.core.computation import expressions\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py\", line 22, in \n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\computation\\check.py\", line 5, in \n", + " ne = import_optional_dependency(\"numexpr\", errors=\"warn\")\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py\", line 158, in import_optional_dependency\n", + " module = importlib.import_module(name)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\importlib\\__init__.py\", line 90, in import_module\n", + " return _bootstrap._gcd_import(name[level:], package, level)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\numexpr\\__init__.py\", line 24, in \n", + " from numexpr.interpreter import MAX_THREADS, use_vml, __BLOCK_SIZE1__\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "_ARRAY_API not found", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;31mAttributeError\u001b[0m: _ARRAY_API not found" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "A module that was compiled using NumPy 1.x cannot be run in\n", + "NumPy 2.4.4 as it may crash. To support both 1.x and 2.x\n", + "versions of NumPy, modules must be compiled with NumPy 2.0.\n", + "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", + "\n", + "If you are a user of the module, the easiest solution will be to\n", + "downgrade to 'numpy<2' or try to upgrade the affected module.\n", + "We expect that some modules will need time to support NumPy 2.\n", + "\n", + "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_92620\\1537052189.py\", line 12, in \n", + " from src.skheros.heros import HEROS\n", + " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\__init__.py\", line 7, in \n", + " from .heros import HEROS\n", + " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\heros.py\", line 3, in \n", + " import pandas as pd\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 58, in \n", + " from pandas.core.api import (\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\api.py\", line 27, in \n", + " from pandas.core.arrays import Categorical\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\__init__.py\", line 1, in \n", + " from pandas.core.arrays.arrow import ArrowExtensionArray\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\__init__.py\", line 5, in \n", + " from pandas.core.arrays.arrow.array import ArrowExtensionArray\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\array.py\", line 79, in \n", + " from pandas.core.arrays.masked import BaseMaskedArray\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py\", line 56, in \n", + " from pandas.core import (\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py\", line 54, in \n", + " bn = import_optional_dependency(\"bottleneck\", errors=\"warn\")\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py\", line 158, in import_optional_dependency\n", + " module = importlib.import_module(name)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\importlib\\__init__.py\", line 90, in import_module\n", + " return _bootstrap._gcd_import(name[level:], package, level)\n", + " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\bottleneck\\__init__.py\", line 7, in \n", + " from .move import (move_argmax, move_argmin, move_max, move_mean, move_median,\n" + ] + }, { "ename": "ImportError", - "evalue": "cannot import name 'MultiSWRFDB' from 'skrebate' (c:\\Users\\ryanu\\anaconda3\\envs\\heros_dev_12\\Lib\\site-packages\\skrebate\\__init__.py)", + "evalue": "\nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.4.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n", "output_type": "error", "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 23\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m 22\u001b[39m \u001b[38;5;66;03m#from skrebate import MultiSURF, TURF\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m skrebate \u001b[38;5;28;01mimport\u001b[39;00m MultiSWRFDB, TURF\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.metrics \u001b[38;5;28;01mimport\u001b[39;00m classification_report\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.inspection \u001b[38;5;28;01mimport\u001b[39;00m permutation_importance\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.metrics \u001b[38;5;28;01mimport\u001b[39;00m roc_curve, roc_auc_score\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'MultiSWRFDB' from 'skrebate' (c:\\Users\\ryanu\\anaconda3\\envs\\heros_dev_12\\Lib\\site-packages\\skrebate\\__init__.py)" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\numpy\\core\\_multiarray_umath.py:46\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr_name)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;66;03m# Also print the message (with traceback). This is because old versions\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# of NumPy unfortunately set up the import to replace (and hide) the\u001b[39;00m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# error. The traceback shouldn't be needed, but e.g. pytest plugins\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# seem to swallow it and we should be failing anyway...\u001b[39;00m\n\u001b[0;32m 45\u001b[0m sys\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mwrite(msg \u001b[38;5;241m+\u001b[39m tb_msg)\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m 48\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_multiarray_umath, attr_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[1;31mImportError\u001b[0m: \nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.4.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\ryanu\\Documents\\GitHub\\heros\n" ] } ], @@ -167,11 +356,12 @@ "# Load all other packages used in this notebook --------------------------------\n", "import os\n", "import pickle\n", - "import numpy as np\n", + "#import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "#from skrebate import MultiSURF, TURF \n", - "from skrebate import MultiSWRFDB, TURF \n", + "from skrebate import MultiSWRFDB,TURF\n", + "from skrebate import TURF \n", "from sklearn.metrics import classification_report\n", "from sklearn.inspection import permutation_importance\n", "from sklearn.metrics import roc_curve, roc_auc_score\n", @@ -201,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6ebd4719", "metadata": {}, "outputs": [], @@ -265,10 +455,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8ffac0c7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", + "0 1 0 1 0 1 1 1 2 1\n", + "1 1 0 0 0 0 1 0 2 2\n", + "2 0 1 1 0 0 1 0 1 3\n", + "3 1 1 1 1 1 1 1 3 4\n", + "4 0 0 0 1 1 0 0 0 5\n" + ] + } + ], "source": [ "# Load training dataset ------------------------\n", "train_df = pd.read_csv(train_data_path, sep=\"\\t\")\n", @@ -326,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "3bd1b3c0", "metadata": {}, "outputs": [], @@ -353,10 +556,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "0f7d39ba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MultiSWRFDB Scores:\n", + "['A_0', 'A_1', 'R_0', 'R_1', 'R_2', 'R_3']\n", + "[ 0.0421836 0.05607324 0.01940984 -0.00595891 -0.00275663 -0.00373355]\n" + ] + } + ], "source": [ "# Further data preparation for expert knowldge generation (i.e. balanced subsampling of training instances for faster run times)\n", "fs_train_df = balanced_sampling(train_df, outcome_label, max_instances)\n", @@ -419,10 +632,435 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "42047be2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Manage Summary: ------------------------------------------------\n", + "Number of quantitative features: 0\n", + "Number of categorical features: 6\n", + "Total Features: 6\n", + "Total Instances: 450\n", + "Feature Types: [1, 1, 1, 1, 1, 1]\n", + "Missing Values: 0\n", + "Quantiative Feature Range: [[inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf]]\n", + "Categorical Feature Values: [[np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(1), np.int64(0)]]\n", + "Average States: 2.0\n", + "Rule Specificity Limit: 6\n", + "Classes: [np.int64(1), np.int64(0)]\n", + "Class Counts: {np.int64(1): 225, np.int64(0): 225}\n", + "Class Weights: {np.int64(1): 0.5, np.int64(0): 0.5}\n", + "Majority Class: 1\n", + "Expert Knowledge Weights Used: True\n", + "--------------------------------------------------------------------\n", + "Beginning Decision Tree Rule Inititialization...\n", + "\n", + "One-hot encoding 6 categorical features...\n", + " Original features: 6, After one-hot encoding: 12\n", + " One-hot mapping created for 12 encoded features\n", + " Quantitative feature mapping: 0 features\n", + "Random Seed Check After RF: 0.6394267984578837\n", + "\n", + "Extracting rules from all decision tree branches in all forests...\n", + "Random Seed Check After Rule Extract: 0.025010755222666936\n", + "Deduplicating rules...\n", + "Random Seed Check After Deduplication: 0.27502931836911926\n", + "\n", + "Converting extracted rules to HEROS format and checking for redundancy...\n", + "Random Seed Check After Convert to HEROS rules: 0.8601666658261801\n", + "\n", + "Summary: Converted rules to HEROS format and added to population.\n", + "Total Population Numerosity: 667\n", + "Unique HEROS Rules: 290\n", + "PopSize After Tree Initialization 290\n", + "Micro PopSize After Tree Initialization 667\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 290\n", + "Post-Cleaning Population Size: 279\n", + "27 rules subsumed with a specificity of 2\n", + "80 rules subsumed with a specificity of 3\n", + "75 rules subsumed with a specificity of 4\n", + "20 rules subsumed with a specificity of 5\n", + "1 rules subsumed with a specificity of 6\n", + "Post-Subsumption Compaction Population Size: 76\n", + "HEROS Evolution Beginning!\n", + "Archiving: 500\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 1000 0.885 199 500 1.008\n", + "Archiving: 1000\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 2000 0.902 216 500 1.718\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 3000 0.903 216 500 2.415\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 4000 0.905 210 500 3.108\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 5000 0.907 226 500 3.798\n", + "Archiving: 5000\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 6000 0.909 219 500 4.52\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 7000 0.898 211 500 5.21\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 8000 0.902 209 500 5.893\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 9000 0.907 219 500 6.586\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 221\n", + "Post-Cleaning Population Size: 221\n", + "14 rules subsumed with a specificity of 2\n", + "65 rules subsumed with a specificity of 3\n", + "51 rules subsumed with a specificity of 4\n", + "2 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 89\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 10000 0.914 89 500 7.284\n", + "Archiving: 10000\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:10000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 1 0.891 1.0 37 0.48\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 2 0.891 1.0 37 1.01\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 3 0.92 1.0 23 1.476\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 4 0.922 1.0 27 1.92\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 5 0.922 1.0 27 2.395\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 6 0.956 1.0 18 2.785\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 7 0.956 1.0 18 3.124\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 8 0.956 1.0 18 3.49\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 9 0.956 1.0 18 3.872\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 10 0.962 0.984 25 4.243\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 11 0.962 0.984 25 4.62\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 12 0.962 0.984 25 4.99\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 13 0.962 0.984 25 5.398\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 14 0.962 0.984 25 5.758\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 15 0.962 0.984 25 6.192\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 16 0.969 1.0 25 6.567\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 17 0.969 1.0 25 6.927\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 18 0.969 1.0 25 7.261\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 19 0.978 1.0 27 7.569\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 20 0.978 1.0 27 7.952\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.11457692579230672\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 11000 0.895 198 500 16.397\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 12000 0.906 207 500 17.077\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 13000 0.904 203 500 17.757\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 14000 0.912 224 500 18.439\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 15000 0.906 212 500 19.12\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 16000 0.908 225 500 19.792\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 17000 0.91 216 500 20.474\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 18000 0.909 221 500 21.149\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 19000 0.914 231 500 21.827\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 212\n", + "Post-Cleaning Population Size: 212\n", + "12 rules subsumed with a specificity of 2\n", + "52 rules subsumed with a specificity of 3\n", + "55 rules subsumed with a specificity of 4\n", + "3 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 90\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 20000 0.9 90 500 22.526\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:10000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 21 0.978 1.0 27 8.285\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 22 0.978 1.0 26 8.659\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 23 0.978 1.0 26 9.034\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 24 0.978 1.0 26 9.359\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 25 0.978 1.0 26 9.78\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 26 0.978 1.0 19 10.17\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 27 0.978 1.0 19 10.533\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 28 0.978 1.0 19 10.851\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 29 0.991 1.0 23 11.22\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 30 0.991 1.0 19 11.545\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 31 0.991 1.0 19 11.96\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 32 0.991 1.0 19 12.387\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 33 0.991 1.0 19 12.794\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 34 1.0 1.0 25 13.166\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 35 1.0 1.0 19 13.515\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 36 1.0 1.0 8 13.807\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 37 1.0 1.0 8 14.17\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 38 1.0 1.0 8 14.546\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 39 1.0 1.0 8 14.936\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 40 1.0 1.0 8 15.296\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.007092506344841265\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 21000 0.906 195 500 30.553\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 22000 0.899 207 500 31.247\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 23000 0.906 219 500 31.962\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 24000 0.907 212 500 32.632\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 25000 0.899 225 500 33.32\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 26000 0.908 221 500 34.017\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 27000 0.897 224 500 34.691\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 28000 0.905 218 500 35.364\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 29000 0.909 222 500 36.056\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 215\n", + "Post-Cleaning Population Size: 215\n", + "14 rules subsumed with a specificity of 2\n", + "56 rules subsumed with a specificity of 3\n", + "54 rules subsumed with a specificity of 4\n", + "2 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 89\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 30000 0.913 89 500 36.73\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:10000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 41 1.0 1.0 8 15.664\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 42 1.0 1.0 8 16.074\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 43 1.0 1.0 8 16.398\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 44 1.0 1.0 8 16.826\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 45 1.0 1.0 8 17.234\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 46 1.0 1.0 8 17.606\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 47 1.0 1.0 8 17.941\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 48 1.0 1.0 8 18.297\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 49 1.0 1.0 8 18.768\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 50 1.0 1.0 8 19.174\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 51 1.0 1.0 8 19.533\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 52 1.0 1.0 8 19.833\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 53 1.0 1.0 8 20.19\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 54 1.0 1.0 8 20.593\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 55 1.0 1.0 8 21.088\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 56 1.0 1.0 8 21.458\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 57 1.0 1.0 8 21.889\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 58 1.0 1.0 8 22.299\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 59 1.0 1.0 8 22.653\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 60 1.0 1.0 8 23.009\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.5444387138775688\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 31000 0.905 190 500 45.151\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 32000 0.904 220 500 45.868\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 33000 0.904 225 500 46.582\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 34000 0.905 217 500 47.256\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 35000 0.902 223 500 47.926\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 36000 0.903 218 500 48.603\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 37000 0.914 220 500 49.28\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 38000 0.902 220 500 49.962\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 39000 0.91 211 500 50.633\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 218\n", + "Post-Cleaning Population Size: 218\n", + "14 rules subsumed with a specificity of 2\n", + "59 rules subsumed with a specificity of 3\n", + "52 rules subsumed with a specificity of 4\n", + "2 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 91\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 40000 0.906 91 500 51.331\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:10000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 61 1.0 1.0 8 23.403\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 62 1.0 1.0 8 23.799\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 63 1.0 1.0 8 24.149\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 64 1.0 1.0 8 24.47\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 65 1.0 1.0 8 24.865\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 66 1.0 1.0 8 25.174\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 67 1.0 1.0 8 25.613\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 68 1.0 1.0 8 26.022\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 69 1.0 1.0 8 26.352\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 70 1.0 1.0 8 26.722\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 71 1.0 1.0 8 27.133\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 72 1.0 1.0 8 27.502\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 73 1.0 1.0 8 27.805\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 74 1.0 1.0 8 28.162\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 75 1.0 1.0 8 28.651\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 76 1.0 1.0 8 29.084\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 77 1.0 1.0 8 29.416\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 78 1.0 1.0 8 29.838\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 79 1.0 1.0 8 30.237\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 80 1.0 1.0 8 30.569\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.5459455361616103\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 41000 0.9 212 500 59.572\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 42000 0.904 217 500 60.25\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 43000 0.911 210 500 60.93\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 44000 0.902 213 500 61.606\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 45000 0.902 209 500 62.276\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 46000 0.909 212 500 62.935\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 47000 0.891 217 500 63.597\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 48000 0.9 218 500 64.269\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 49000 0.907 217 500 64.963\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 215\n", + "Post-Cleaning Population Size: 215\n", + "18 rules subsumed with a specificity of 2\n", + "59 rules subsumed with a specificity of 3\n", + "49 rules subsumed with a specificity of 4\n", + "2 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 87\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 50000 0.905 87 500 65.662\n", + "Archiving: 50000\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:10000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 81 1.0 1.0 8 31.055\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 82 1.0 1.0 8 31.514\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 83 1.0 1.0 8 31.818\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 84 1.0 1.0 8 32.236\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 85 1.0 1.0 8 32.488\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 86 1.0 1.0 8 32.91\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 87 1.0 1.0 8 33.225\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 88 1.0 1.0 8 33.614\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 89 1.0 1.0 8 34.032\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 90 1.0 1.0 8 34.403\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 91 1.0 1.0 8 34.77\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 92 1.0 1.0 8 35.128\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 93 1.0 1.0 8 35.51\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 94 1.0 1.0 8 35.883\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 95 1.0 1.0 8 36.179\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 96 1.0 1.0 8 36.562\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 97 1.0 1.0 8 36.909\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 98 1.0 1.0 8 37.239\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 99 1.0 1.0 8 37.603\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 100 1.0 1.0 8 37.976\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.5410935159323432\n" + ] + } + ], "source": [ "# Initialize HEROS algorithm with run parameters\n", "heros = HEROS(outcome_type=outcome_type,iterations=iterations,pop_size=pop_size,cross_prob=cross_prob,mut_prob=mut_prob,nu=nu,beta=beta,theta_sel=theta_sel,\n", @@ -446,10 +1084,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "d4a8ff12", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", + "0 0 0 0 1 0 0 0 0 8\n", + "1 1 0 0 1 1 0 1 2 14\n", + "2 0 0 1 1 1 1 1 0 29\n", + "3 1 1 0 0 1 0 0 3 44\n", + "4 0 0 1 1 0 0 1 0 58\n" + ] + } + ], "source": [ "# Load testing dataset ---------------------------\n", "test_df = pd.read_csv(test_data_path, sep=\"\\t\")\n", @@ -480,10 +1131,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "47927583", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8 non-dominated models on Pareto-front.\n", + "----------------------------------------\n", + "Model testing accuracies: [1.0, 0.9199999999999999, 0.9199999999999999, 0.84, 0.8, 0.6200000000000001, 0.54, 0.6000000000000001]\n", + "Model testing coverages: [np.float64(1.0), np.float64(0.9), np.float64(0.9), np.float64(0.84), np.float64(1.0), np.float64(0.82), np.float64(0.7), np.float64(0.48)]\n", + "Model rule counts: [8, 7, 6, 5, 4, 3, 2, 1]\n", + "----------------------------------------\n", + "Best model testing accuracy: 1.0\n", + "Best model testing coverage: 1.0\n", + "Best rule count: 8\n", + "Best model index: 0\n", + "----------------------------------------\n", + " Condition Indexes Condition Values Action Numerosity Fitness \\\n", + "0 [0, 1, 2] [0, 0, 0] 0 7 1.000000 \n", + "1 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", + "2 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", + "3 [0, 1, 4] [1, 0, 0] 0 3 0.997857 \n", + "4 [0, 1, 3] [0, 1, 1] 1 9 0.998143 \n", + "5 [0, 1, 3] [0, 1, 0] 0 6 0.997714 \n", + "6 [0, 1, 5] [1, 1, 1] 1 6 0.997429 \n", + "7 [2, 1, 0] [1, 0, 0] 1 3 0.998714 \n", + "\n", + " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", + "0 1.0 34.0 1.0 68 68 \n", + "1 1.0 27.5 1.0 55 55 \n", + "2 1.0 24.0 1.0 48 48 \n", + "3 1.0 27.5 1.0 55 55 \n", + "4 1.0 28.5 1.0 57 57 \n", + "5 1.0 27.0 1.0 54 54 \n", + "6 1.0 26.0 1.0 52 52 \n", + "7 1.0 30.5 1.0 61 61 \n", + "\n", + " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", + "0 None None None 0 \n", + "1 None None None 0 \n", + "2 None None None 0 \n", + "3 None None None 0 \n", + "4 None None None 0 \n", + "5 None None None 0 \n", + "6 None None None 0 \n", + "7 None None None 190 \n", + "\n", + " Specified Count Average Match Set Size Deletion Probabiilty \n", + "0 3 105.858535 0.015066 \n", + "1 3 94.666956 0.009920 \n", + "2 3 95.967470 0.006990 \n", + "3 3 85.782919 0.002247 \n", + "4 3 94.068161 0.022172 \n", + "5 3 89.252098 0.009354 \n", + "6 3 86.476752 0.009065 \n", + "7 3 93.214078 0.002440 \n" + ] + } + ], "source": [ "best_model_index = heros.auto_select_top_model(X_test,y_test,verbose=True)\n", "set_df = heros.get_model_rules(best_model_index)\n", @@ -500,10 +1208,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "0485d99a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HEROS Top Model Testing Data Performance Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "# Report performance results for the top model\n", "predictions = heros.predict(X_test,whole_rule_pop=False, target_model=best_model_index)\n", @@ -521,10 +1246,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "b9e0ce2c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction Probabilities for all Testing Instances:\n", + "[[1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [0. 1.]\n", + " [0. 1.]]\n", + "Coverage for all Testing Instances:\n", + "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1]\n", + "50 instances covered out of 50\n" + ] + } + ], "source": [ "predict_prob = heros.predict_proba(X_test,whole_rule_pop=False, target_model=best_model_index)\n", "print(\"Prediction Probabilities for all Testing Instances:\")\n", @@ -547,10 +1334,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f79beb2d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get false positive rate, true positive rate, and thresholds\n", "fpr, tpr, thresholds = roc_curve(y_test, predict_prob[:, 1]) #based on class 1\n", @@ -579,7 +1377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "d18f5118", "metadata": {}, "outputs": [], @@ -600,10 +1398,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "1583cf95", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if run_all_cells:\n", " heros.get_rule_set_heatmap(feature_names, best_model_index, weighting='useful_accuracy', specified_filter=1, display_micro=False, show=True, save=True, output_path=output_path)" @@ -619,10 +1428,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "f49f7597", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if run_all_cells:\n", " node_size = 1000\n", @@ -640,10 +1460,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "f3950089", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if run_all_cells and feat_track != None:\n", " heros.run_model_feature_tracking(best_model_index)\n", @@ -667,10 +1498,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "07a336b2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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U5Ba2du3ala/ab7nlFlWqVElr1qxx3YAuq+2mpaVp9+7dbjef++OPP3Tq1ClFRES42kl/jQRWqVLF1e7YsWMZRkpzu88FJb2eYsWK5bjNxYsXq3Xr1oqLi3ObfurUKbfHqGXXz0qVKpXp3eMPHDjgdmyykn4KdFBQ0D92jHIrffT9cr/88ovrRmjp/SCzfvnzzz+rTJkyuXrsV1bH9/3335efn59WrFghp9Ppmj5nzpzclJ9B2bJlFRQUlOmd1i/3T/dZACiquIYbAIqw48ePu7338vJSvXr1JMl1amn6l/m8Pi4rP9JHzy4fLTt9+nSmX/4DAgIyralbt27asGGDVqxYkWHeqVOnXNeLd+jQQSkpKW6PHEtNTdWMGTPyVbvD4dBLL72k2NhY9enTJ8t2HTp0kKQMd2H+73//K0muu0K3a9dOxYoV04wZM9yOR2Z3b87tPheU0NBQtWrVSq+++qqOHDmSYf6xY8dc//b29s4w+rlo0SLXNd7psutnVatW1caNG93uzr5s2TL99ttvuaq3UaNGqlq1qqZNm6akpKRs6/2nLVmyxO1YbN68WZs2bVJMTIwkqXz58mrQoIHmzZvndmy2b9+uzz//3NWfcpLV8fX29pbD4XA71X///v1asmRJvvbHy8tLnTt31scff6zvvvsuw/z0vvBP91kAKKoY4QaAQmz58uX6+eefM0xv3ry5qlSpokGDBunEiRNq06aNrrvuOh04cEAzZsxQgwYNXKOvDRo0kLe3t6ZMmaLTp0/L6XS6npNd0Nq3b+8acb/vvvuUlJSk119/XaGhoRmCXaNGjTRr1iw988wzqlatmkJDQ9WmTRs9/vjjWrp0qW6//Xb1799fjRo10tmzZ/Xjjz9q8eLF2r9/v8qUKaM77rhDN998s5588knt379ftWvX1gcffJCrG7NlpVOnTurUqVO2berXr69+/frptdde06lTp9SyZUtt3rxZ8+bNU+fOndW6dWtJf40UjhgxQpMmTdLtt9+uDh06aMuWLVq+fLnbyLCkXO9zQZo5c6ZuueUW1a1bV/fee6+qVKmiP/74Qxs2bNDvv//ues727bffrvHjx2vAgAFq3ry5fvzxR73zzjsZRqarVq2qkiVL6n//+59KlCihgIAANW3aVJUrV9agQYO0ePFiRUdHq1u3btq7d6/efvttt5t3ZcfLy0tvvPGGYmJiVKdOHQ0YMEAVK1bUoUOHtHr1agUFBenjjz8u0OOTW9WqVdMtt9yioUOHKjk5WS+88IJKly7tdqr1c889p5iYGDVr1kwDBw50PRYsODhYY8eOzdV2GjVqJEl66KGHFBUVJW9vb919993q2LGj/vvf/yo6Olo9e/bU0aNHNXPmTFWrVi3TR9rlxsSJE/X555+rZcuWGjx4sGrVqqUjR45o0aJFWrt2rUqWLOmRPgsARZKH7o4OAMhGdo8F02WPXlq8eLFp3769CQ0NNb6+vqZSpUrmvvvuM0eOHHFb3+uvv26qVKlivL293R4NldVjmRYtWpRpPX9/TFD6Y4yOHTvmmrZ06VJTr1494+fnZyIjI82UKVPM7NmzjSSzb98+V7uEhATTsWNHU6JECSPJrY4zZ86YUaNGmWrVqhlfX19TpkwZ07x5czNt2jS3x5UdP37c9OnTxwQFBZng4GDTp08fs2XLljw/Fiw7f38smDHGXLp0yYwbN85UrlzZFCtWzISHh5tRo0a5PWLLGGNSU1PNuHHjTPny5Y2/v79p1aqV2b59e6aPycrtPisPjwW7/DFYmdm7d6/p27evCQsLM8WKFTMVK1Y0t99+u1m8eLGrzYULF8zw4cNd+3DzzTebDRs2ZOg7xhjz0Ucfmdq1axsfH58MP4Pnn3/eVKxY0TidTnPzzTeb7777Ltf9L92WLVvM//3f/5nSpUsbp9NpIiIiTLdu3cyqVavyfDzy0qeNMaZfv34mICAg03U+//zzJjw83DidTtOiRQuzdevWDDV88cUX5uabbzb+/v4mKCjI3HHHHWbHjh252rYxfz2a7MEHHzRly5Y1DofD7RFhcXFxpnr16sbpdJrrr7/ezJkzx7Wuy0kyDzzwQIZ1Z9YfDxw4YPr27WvKli1rnE6nqVKlinnggQdMcnKyq01u+ywAXMscxvxDdwABAAC4Suzfv1+VK1fWc889pxEjRni6HABAIcU13AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWcA03AAAAAAAWMMINAAAAAIAFBG4AAAAAACzw8XQBeZWWlqbDhw+rRIkScjgcni4HAAAAAHANMcbozJkzqlChgry8sh/DLnKB+/DhwwoPD/d0GQAAAACAa9hvv/2m6667Lts2RS5wlyhRQtJfOxcUFOThagAAAAAA15LExESFh4e7sml2ilzgTj+NPCgoiMANAAAAAPCI3FzizE3TAAAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABggY+nC8ivG2JXyMtZ3NNlAAAAAFe1/ZM7eroEoMhihBsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWJCvwL1hwwZ5e3urY8eOeVru4MGD6tixo4oXL67Q0FA9/vjjSklJyU8JAAAAAAAUavkK3HFxcXrwwQf19ddf6/Dhw7laJjU1VR07dtTFixe1fv16zZs3T3PnztXTTz+dnxIAAAAAACjU8hy4k5KStGDBAg0dOlQdO3bU3Llzc7Xc559/rh07dujtt99WgwYNFBMTowkTJmjmzJm6ePFiXssAAAAAAKBQ88nrAgsXLtT111+vmjVrqnfv3nrkkUc0atQoORyObJfbsGGD6tatq3LlyrmmRUVFaejQofrpp5/UsGHDvFcPAABwDUu7eMHTJeAacPbsWU+XgGtAQECAp0uwIs+BOy4uTr1795YkRUdH6/Tp0/rqq6/UqlWrbJdLSEhwC9uSXO8TEhKyXC45OVnJycmu94mJiXktGQAA4Kr02/S7PF0CrgGB0z1dAa4FxhhPl2BFnk4p37VrlzZv3qwePXpIknx8fNS9e3fFxcVZKU6SJk2apODgYNcrPDzc2rYAAAAAACgoeRrhjouLU0pKiipUqOCaZoyR0+nUyy+/rODg4CyXDQsL0+bNm92m/fHHH655WRk1apQee+wx1/vExERCNwAAgKTwRxd7ugRcA3ZOiPZ0CUCRlevAnZKSojfffFPPP/+82rdv7zavc+fOeu+99zRkyJAsl2/WrJmeffZZHT16VKGhoZKklStXKigoSLVr185yOafTKafTmdsyAQAArhlevn6eLgHXgKv12lrgn5DrwL1s2TKdPHlSAwcOzDCS3aVLF8XFxWUbuNu3b6/atWurT58+mjp1qhISEjR69Gg98MADBGoAAAAAwFUn19dwx8XFqV27dpmeNt6lSxd999132rZtW5bLe3t7a9myZfL29lazZs3Uu3dv9e3bV+PHj89f5QAAAAAAFGK5HuH++OOPs5zXpEmTXN1VLiIiQp9++mluNwkAAAAAQJGVp7uUAwAAAACA3CmwwD1kyBAFBgZm+sru2m4AAAAAAK5GeXosWHbGjx+vESNGZDovKCiooDYDAAAAAECRUGCBOzQ01PW4LwAAAAAArnVcww0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAh9PF5Bf28dFKSgoyNNlAAAAAACQKUa4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWODj6QLy64bYFfJyFvd0GQAAAMjG/skdPV0CAHgMI9wAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFuQrcG/YsEHe3t7q2LFjnpZ76KGH1KhRIzmdTjVo0CA/mwYAAAAAoEjIV+COi4vTgw8+qK+//lqHDx/O07L33HOPunfvnp/NAgAAAABQZPjkdYGkpCQtWLBA3333nRISEjR37lw99dRTuVr2pZdekiQdO3ZM27Zty+umAQCAB6VdvODpElAEnT171tMloAgKCAjwdAlAgchz4F64cKGuv/561axZU71799YjjzyiUaNGyeFw2KhPycnJSk5Odr1PTEy0sh0AAJC936bf5ekSUAQFTvd0BSiKjDGeLgEoEHk+pTwuLk69e/eWJEVHR+v06dP66quvCrywdJMmTVJwcLDrFR4ebm1bAAAAAAAUlDyNcO/atUubN2/Whx9++NfCPj7q3r274uLi1KpVKxv1adSoUXrsscdc7xMTEwndAAB4QPijiz1dAoqgnROiPV0CAHhMngJ3XFycUlJSVKFCBdc0Y4ycTqdefvllBQcHF3iBTqdTTqezwNcLAADyxsvXz9MloAjiWlwA17Jcn1KekpKiN998U88//7zi4+Ndr61bt6pChQp67733bNYJAAAAAECRkusR7mXLlunkyZMaOHBghpHsLl26KC4uTkOGDMl2HXv27FFSUpISEhJ0/vx5xcfHS5Jq164tX1/fvFcPAAAAAEAhlevAHRcXp3bt2mV62niXLl00depUbdu2TfXq1ctyHYMGDXK7wVrDhg0lSfv27VNkZGQeygYAAAAAoHDLdeD++OOPs5zXpEmTXN26f82aNbndHAAAAAAARVqeHwsGAAAAAAByVmCBe8iQIQoMDMz0ldO13QAAAAAAXG3y9Fiw7IwfP14jRozIdF5QUFBBbQYAAAAAgCKhwAJ3aGioQkNDC2p1AAAAAAAUaVzDDQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABY4OPpAvJr+7goBQUFeboMAAAAAAAyxQg3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAAC3w8XUB+3RC7Ql7O4p4uAwAAj9k/uaOnSwAAANlghBsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAgjwH7v79+8vhcMjhcKhYsWKqXLmyRo4cqQsXLuS47P79+zVw4EBVrlxZ/v7+qlq1qmJjY3Xx4sV8FQ8AAAAAQGHlk5+FoqOjNWfOHF26dEnff/+9+vXrJ4fDoSlTpmS73M8//6y0tDS9+uqrqlatmrZv3657771XZ8+e1bRp0/K1AwAAAAAAFEb5CtxOp1NhYWGSpPDwcLVr104rV67MMXBHR0crOjra9b5KlSratWuXZs2aReAGAMvSLuZ8JhKKlrNnz3q6BBSwgIAAT5cAAChA+Qrcl9u+fbvWr1+viIiIfC1/+vRphYSEZDk/OTlZycnJrveJiYn52g4AXOt+m36Xp0tAAQuc7ukKUNCMMZ4uAQBQgPIVuJctW6bAwEClpKQoOTlZXl5eevnll/O8nj179mjGjBnZjm5PmjRJ48aNy0+ZAAAAAAB4jMPk8U+p/fv316FDhzRr1iydPXtW06dPl4+Pj9544408bfjQoUNq2bKlWrVqle2ymY1wh4eHK/yRhfJyFs/TNgHgWsYp5VefnROic26EIoVTygGg8EtMTFRwcLBOnz6toKCgbNvma4Q7ICBA1apVkyTNnj1b9evXV1xcnAYOHJir5Q8fPqzWrVurefPmeu2117Jt63Q65XQ681MmAOAyXr5+ni4BBYxwBgBA4XbFz+H28vLSU089pdGjR+v8+fM5tj906JBatWqlRo0aac6cOfLy4lHgAAAAAICrT4Gk3a5du8rb21szZ87Mtl162K5UqZKmTZumY8eOKSEhQQkJCQVRBgAAAAAAhUaBBG4fHx8NGzZMU6dOzfYRJStXrtSePXu0atUqXXfddSpfvrzrBQAAAADA1STPN03ztPQL1LlpGgDgWrd/ckdPlwAAwDUnLzdN4wJqAAAAAAAsKNDAPXHiRAUGBmb6iomJKchNAQAAAABQqOXrsWBZGTJkiLp165bpPH9//4LcFAAAAAAAhVqBBu6QkBCFhIQU5CoBAAAAACiSuIYbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALDAx9MF5Nf2cVEKCgrydBkAAAAAAGSKEW4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAW+Hi6gPy6IXaFvJzFPV0GAFzz9k/u6OkSAAAACiVGuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsyHPg7t+/vxwOhxwOh4oVK6bKlStr5MiRunDhQq6Wv/POO1WpUiX5+fmpfPny6tOnjw4fPpznwgEAAAAAKMzyNcIdHR2tI0eO6Ndff9X06dP16quvKjY2NlfLtm7dWgsXLtSuXbv0/vvva+/evbrrrrvyUwYAAAAAAIWWT34WcjqdCgsLkySFh4erXbt2WrlypaZMmZLjso8++qjr3xEREXryySfVuXNnXbp0ScWKFctPOQCucmkXc3cGDTzj7Nmzni4BWQgICPB0CQAAXNPyFbgvt337dq1fv14RERF5XvbEiRN655131Lx58yzDdnJyspKTk13vExMT810rgKLpt+mcBVOYBU73dAXIijHG0yUAAHBNy9cp5cuWLVNgYKD8/PxUt25dHT16VI8//niul3/iiScUEBCg0qVL6+DBg/roo4+ybDtp0iQFBwe7XuHh4fkpGQAAAACAf5TD5PHP3/3799ehQ4c0a9YsnT17VtOnT5ePj4/eeOONXK/jzz//1IkTJ3TgwAGNGzdOwcHBWrZsmRwOR4a2mY1wh4eHK/yRhfJyFs9L6QCKKE4pL9x2Toj2dAnIAqeUAwBQ8BITExUcHKzTp08rKCgo27b5OqU8ICBA1apVkyTNnj1b9evXV1xcnAYOHJir5cuUKaMyZcqoRo0aqlWrlsLDw7Vx40Y1a9YsQ1un0ymn05mfMgFcJbx8/TxdArJBqAMAAMjcFT+H28vLS0899ZRGjx6t8+fP53n5tLQ0SXIbxQYAAAAAoKi74sAtSV27dpW3t7dmzpyZbbtNmzbp5ZdfVnx8vA4cOKAvv/xSPXr0UNWqVTMd3QYAAAAAoKgqkMDt4+OjYcOGaerUqdk+HqZ48eL64IMP1LZtW9WsWVMDBw5UvXr19NVXX3HaOAAAAADgqpLnm6Z5WvoF6tw0DQAKh/2TO3q6BAAAgH9MXm6aViAj3AAAAAAAwF2BBu6JEycqMDAw01dMTExBbgoAAAAAgEItX48Fy8qQIUPUrVu3TOf5+/sX5KYAAAAAACjUCjRwh4SEKCQkpCBXCQAAAABAkcQ13AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABT6eLiC/to+LUlBQkKfLAAAAAAAgU4xwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsMDH0wXk1w2xK+TlLO7pMgDgmrR/ckdPlwAAAFDoMcINAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYEGeA3f//v3lcDjkcDhUrFgxVa5cWSNHjtSFCxdytfyJEyfUq1cvBQUFqWTJkho4cKCSkpLyXDgAAAAAAIVZvka4o6OjdeTIEf3666+aPn26Xn31VcXGxuZq2V69eumnn37SypUrtWzZMn399dcaPHhwfsoAAAAAAKDQ8snPQk6nU2FhYZKk8PBwtWvXTitXrtSUKVOyXW7nzp367LPP9O2336px48aSpBkzZqhDhw6aNm2aKlSokJ9yABRBaRdzd1YMCqezZ896ugRcoYCAAE+XAADAVS9fgfty27dv1/r16xUREZFj2w0bNqhkyZKusC1J7dq1k5eXlzZt2qR///vfGZZJTk5WcnKy631iYuKVlgygEPht+l2eLgFXIHC6pyvAlTLGeLoEAACuevkK3MuWLVNgYKBSUlKUnJwsLy8vvfzyyzkul5CQoNDQUPcCfHwUEhKihISETJeZNGmSxo0bl58yAQAAAADwmHwF7tatW2vWrFk6e/aspk+fLh8fH3Xp0qWga5MkjRo1So899pjrfWJiosLDw61sC8A/J/zRxZ4uAVdg54RoT5cAAABQ6OUrcAcEBKhatWqSpNmzZ6t+/fqKi4vTwIEDs10uLCxMR48edZuWkpKiEydOuK4J/zun0ymn05mfMgEUYl6+fp4uAVeA638BAABydsXP4fby8tJTTz2l0aNH6/z589m2bdasmU6dOqXvv//eNe3LL79UWlqamjZteqWlAAAAAABQaFxx4Jakrl27ytvbWzNnzsy2Xa1atRQdHa17771Xmzdv1rp16zRs2DDdfffd3KEcAAAAAHBVKZDA7ePjo2HDhmnq1Kk5PirmnXfe0fXXX6+2bduqQ4cOuuWWW/Taa68VRBkAAAAAABQaDlPEnguSmJio4OBghT+yUF7O4p4uBwCuSfsnd/R0CQAAAB6RnklPnz6toKCgbNsWyAg3AAAAAABwV6CBe+LEiQoMDMz0FRMTU5CbAgAAAACgUMvXY8GyMmTIEHXr1i3Tef7+/gW5KQAAAAAACrUCDdwhISEKCQkpyFUCAAAAAFAkcQ03AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGCBj6cLyK/t46IUFBTk6TIAAAAAAMgUI9wAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGCBj6cLyK8bYlfIy1nc02UAwFVl/+SOni4BAADgqsEINwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwIE+Bu3///nI4HHI4HCpWrJgqV66skSNH6sKFC7la/tlnn1Xz5s1VvHhxlSxZMj/1AgAAAABQJOR5hDs6OlpHjhzRr7/+qunTp+vVV19VbGxsrpa9ePGiunbtqqFDh+a5UAAAAAAAihKfvC7gdDoVFhYmSQoPD1e7du20cuVKTZkyJcdlx40bJ0maO3duXjcL4CqRdjF3Z8TAM86ePevpEpCFgIAAT5cAAADyKM+B+3Lbt2/X+vXrFRERUVD1ZJCcnKzk5GTX+8TERGvbAmDfb9Pv8nQJyEbgdE9XgKwYYzxdAgAAyKM8B+5ly5YpMDBQKSkpSk5OlpeXl15++WUbtUmSJk2a5BoZBwAAAACgqMhz4G7durVmzZqls2fPavr06fLx8VGXLl1s1CZJGjVqlB577DHX+8TERIWHh1vbHgC7wh9d7OkSkI2dE6I9XQIAAMBVI8+BOyAgQNWqVZMkzZ49W/Xr11dcXJwGDhxY4MVJf10z7nQ6rawbwD/Py9fP0yUgG1wnDAAAUHCu6DncXl5eeuqppzR69GidP3++oGoCAAAAAKDIu6LALUldu3aVt7e3Zs6cmWPbgwcPKj4+XgcPHlRqaqri4+MVHx+vpKSkKy0DAAAAAIBC5YoDt4+Pj4YNG6apU6fm+DiZp59+Wg0bNlRsbKySkpLUsGFDNWzYUN99992VlgEAAAAAQKHiMEXsOSOJiYkKDg5W+CML5eUs7ulyAOCqsn9yR0+XAAAAUKilZ9LTp08rKCgo27ZXPMINAAAAAAAyKrDAPXHiRAUGBmb6iomJKajNAAAAAABQJOT5sWBZGTJkiLp165bpPH9//4LaDAAAAAAARUKBBe6QkBCFhIQU1OoAAAAAACjSuIYbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAICNwAAAAAAFhC4AQAAAACwgMANAAAAAIAFBG4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALDAx9MF5Nf2cVEKCgrydBkAAAAAAGSKEW4AAAAAACwgcAMAAAAAYAGBGwAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgAAAAAALCBwAwAAAABgAYEbAAAAAAALCNwAAAAAAFhA4AYAAAAAwAIfTxeQV8YYSVJiYqKHKwEAAAAAXGvSs2h6Ns1OkQvcx48flySFh4d7uBIAAAAAwLXqzJkzCg4OzrZNkQvcISEhkqSDBw/muHNAfiQmJio8PFy//fabgoKCPF0OrkL0MdhGH4NN9C/YRh+DbVfax4wxOnPmjCpUqJBj2yIXuL28/rrsPDg4mF9AWBUUFEQfg1X0MdhGH4NN9C/YRh+DbVfSx3I7+MtN0wAAAAAAsIDADQAAAACABUUucDudTsXGxsrpdHq6FFyl6GOwjT4G2+hjsIn+BdvoY7Dtn+xjDpObe5kDAAAAAIA8KXIj3AAAAAAAFAUEbgAAAAAALCBwAwAAAABgQaEI3DNnzlRkZKT8/PzUtGlTbd68Odv2ixYt0vXXXy8/Pz/VrVtXn376qdt8Y4yefvpplS9fXv7+/mrXrp12795tcxdQyBV0H+vfv78cDofbKzo62uYuoBDLS//66aef1KVLF0VGRsrhcOiFF1644nXi6lfQfWzs2LEZPsOuv/56i3uAwi4vfez1119XixYtVKpUKZUqVUrt2rXL0J7vYvi7gu5jfBfD3+Wlj33wwQdq3LixSpYsqYCAADVo0EBvvfWWW5uC+hzzeOBesGCBHnvsMcXGxuqHH35Q/fr1FRUVpaNHj2bafv369erRo4cGDhyoLVu2qHPnzurcubO2b9/uajN16lS99NJL+t///qdNmzYpICBAUVFRunDhwj+1WyhEbPQxSYqOjtaRI0dcr/fee++f2B0UMnntX+fOnVOVKlU0efJkhYWFFcg6cXWz0cckqU6dOm6fYWvXrrW1Cyjk8trH1qxZox49emj16tXasGGDwsPD1b59ex06dMjVhu9iuJyNPibxXQz/T177WEhIiP7zn/9ow4YN2rZtmwYMGKABAwZoxYoVrjYF9jlmPKxJkybmgQcecL1PTU01FSpUMJMmTcq0fbdu3UzHjh3dpjVt2tTcd999xhhj0tLSTFhYmHnuuedc80+dOmWcTqd57733LOwBCruC7mPGGNOvXz/TqVMnK/WiaMlr/7pcRESEmT59eoGuE1cfG30sNjbW1K9fvwCrRFF2pZ85KSkppkSJEmbevHnGGL6LIaOC7mPG8F0M7griu1PDhg3N6NGjjTEF+znm0RHuixcv6vvvv1e7du1c07y8vNSuXTtt2LAh02U2bNjg1l6SoqKiXO337dunhIQEtzbBwcFq2rRpluvE1ctGH0u3Zs0ahYaGqmbNmho6dKiOHz9e8DuAQi0//csT60TRZbM/7N69WxUqVFCVKlXUq1cvHTx48ErLRRFUEH3s3LlzunTpkkJCQiTxXQzubPSxdHwXg3TlfcwYo1WrVmnXrl269dZbJRXs55hHA/eff/6p1NRUlStXzm16uXLllJCQkOkyCQkJ2bZP/29e1omrl40+Jv11CtObb76pVatWacqUKfrqq68UExOj1NTUgt8JFFr56V+eWCeKLlv9oWnTppo7d64+++wzzZo1S/v27VOLFi105syZKy0ZRUxB9LEnnnhCFSpUcH0x5bsYLmejj0l8F8P/k98+dvr0aQUGBsrX11cdO3bUjBkzdNttt0kq2M8xnzy1BiBJuvvuu13/rlu3rurVq6eqVatqzZo1atu2rQcrA4CcxcTEuP5dr149NW3aVBEREVq4cKEGDhzowcpQ1EyePFnz58/XmjVr5Ofn5+lycBXKqo/xXQxXqkSJEoqPj1dSUpJWrVqlxx57TFWqVFGrVq0KdDseHeEuU6aMvL299ccff7hN/+OPP7K80UtYWFi27dP/m5d14uplo49lpkqVKipTpoz27Nlz5UWjyMhP//LEOlF0/VP9oWTJkqpRowafYdegK+lj06ZN0+TJk/X555+rXr16rul8F8PlbPSxzPBd7NqV3z7m5eWlatWqqUGDBho+fLjuuusuTZo0SVLBfo55NHD7+vqqUaNGWrVqlWtaWlqaVq1apWbNmmW6TLNmzdzaS9LKlStd7StXrqywsDC3NomJidq0aVOW68TVy0Yfy8zvv/+u48ePq3z58gVTOIqE/PQvT6wTRdc/1R+SkpK0d+9ePsOuQfntY1OnTtWECRP02WefqXHjxm7z+C6Gy9noY5nhu9i1q6D+X5mWlqbk5GRJBfw5lqdbrFkwf/5843Q6zdy5c82OHTvM4MGDTcmSJU1CQoIxxpg+ffqYJ5980tV+3bp1xsfHx0ybNs3s3LnTxMbGmmLFipkff/zR1Wby5MmmZMmS5qOPPjLbtm0znTp1MpUrVzbnz5//x/cPnlfQfezMmTNmxIgRZsOGDWbfvn3miy++MDfeeKOpXr26uXDhgkf2EZ6T1/6VnJxstmzZYrZs2WLKly9vRowYYbZs2WJ2796d63Xi2mKjjw0fPtysWbPG7Nu3z6xbt860a9fOlClTxhw9evQf3z94Xl772OTJk42vr69ZvHixOXLkiOt15swZtzZ8F0O6gu5jfBfD3+W1j02cONF8/vnnZu/evWbHjh1m2rRpxsfHx7z++uuuNgX1OebxwG2MMTNmzDCVKlUyvr6+pkmTJmbjxo2ueS1btjT9+vVza79w4UJTo0YN4+vra+rUqWM++eQTt/lpaWlmzJgxply5csbpdJq2bduaXbt2/RO7gkKqIPvYuXPnTPv27U3ZsmVNsWLFTEREhLn33nsJQ9ewvPSvffv2GUkZXi1btsz1OnHtKeg+1r17d1O+fHnj6+trKlasaLp372727NnzD+4RCpu89LGIiIhM+1hsbKyrDd/F8HcF2cf4LobM5KWP/ec//zHVqlUzfn5+plSpUqZZs2Zm/vz5busrqM8xhzHG5G1MHAAAAAAA5MSj13ADAAAAAHC1InADAAAAAGABgRsAAAAAAAsI3AAAAAAAWEDgBgAAAADAAgI3AAAAAAAWELgBAAAAALCAwA0AAAAAgAUEbgDwMIfDoSVLlni6DOTDrbfeqnfffdfTZaAArFmzRg6HQ6dOnfJ0KZKKzudC//791blzZ0+XUWj961//0vvvv+/pMgB4EIEbgMf1799fDodDDodDvr6+qlatmsaPH6+UlBRPl5ap/H7BHDt2rBo0aJBh+pEjRxQTE3PlhWVj7ty5KlmypNVtXKnIyEi98MILni4j15YuXao//vhDd999t2taZGSkHA6H5s+fn6F9nTp15HA4NHfu3H+wysxd/jtXrFgxlStXTrfddptmz56ttLQ0T5fnEc2bN9eRI0cUHByc73Xs37/fdVwv/zx75plnZIwpwGqLpuuvv15Op1MJCQmeLuUfM3r0aD355JPX7O8VAAI3gEIiOjpaR44c0e7duzV8+HCNHTtWzz33XL7WlZqaWqS+3ISFhcnpdHq6DI+5ePGip0vIl5deekkDBgyQl5f7/0rDw8M1Z84ct2kbN25UQkKCAgIC/skSs5X+O7d//34tX75crVu31sMPP6zbb7/d6h+7CuvP29fXV2FhYXI4HFe8ri+++ML1eTZu3Dg9++yzmj17dgFUWXStXbtW58+f11133aV58+Z5uhxdunTpH9lOTEyMzpw5o+XLl/8j2wNQ+BC4ARQKTqdTYWFhioiI0NChQ9WuXTstXbpUkpScnKwRI0aoYsWKCggIUNOmTbVmzRrXsumjt0uXLlXt2rXldDp18OBBRUZG6plnnlHfvn0VGBioiIgILV26VMeOHVOnTp0UGBioevXq6bvvvnOtK7NR6BdeeEGRkZGu+fPmzdNHH33kGsVKr+WJJ55QjRo1VLx4cVWpUkVjxoxxfambO3euxo0bp61bt7qWSx/p/Pupoz/++KPatGkjf39/lS5dWoMHD1ZSUpJrfvoI+7Rp01S+fHmVLl1aDzzwQJ6+QKbv5+zZs1WpUiUFBgbq/vvvV2pqqqZOnaqwsDCFhobq2WefdVvO4XBo1qxZiomJkb+/v6pUqaLFixe7tclt/c8++6wqVKigmjVrqlWrVjpw4IAeffRR1/GRpOPHj6tHjx6qWLGiihcvrrp16+q9995z216rVq300EMPaeTIkQoJCVFYWJjGjh3r1ubUqVO67777VK5cOfn5+emGG27QsmXLXPPXrl2rFi1ayN/fX+Hh4XrooYd09uzZLI/fsWPH9OWXX+qOO+7IMK9Xr1766quv9Ntvv7mmzZ49W7169ZKPj0+GugYNGqSyZcsqKChIbdq00datW13z9+7dq06dOqlcuXIKDAzUTTfdpC+++MJtHZGRkZo4caLuuecelShRQpUqVdJrr72WZe3p0n/nKlasqBtvvFFPPfWUPvroIy1fvtxtFD6nGiXp448/1k033SQ/Pz+VKVNG//73v93qmzBhgvr27augoCANHjxYUs7H/K233lLjxo1VokQJhYWFqWfPnjp69Khr/smTJ9WrVy+VLVtW/v7+ql69utsfOn777Td169ZNJUuWVEhIiDp16qT9+/dneTz+fkp5+ufKihUrVKtWLQUGBrr+SJGT0qVLuz7PevXqpZtvvlk//PCDa/63336r2267TWXKlFFwcLBatmzpNj8z2X2+SP/vd/qtt95SZGSkgoODdffdd+vMmTOuNmlpaZo6daqqVasmp9OpSpUquf2O53TMUlNT9dhjj6lkyZIqXbq0Ro4cmeuR+7i4OPXs2VN9+vTJ9I8Pv//+u3r06KGQkBAFBASocePG2rRpk2t+dn0ss9PvS5Ys6erH6WceLFiwQC1btpSfn5/eeeedXH2+ZHfM2rRpo2HDhrm1P3bsmHx9fbVq1SpJkre3tzp06JDpWS8Arg0EbgCFkr+/v2skbNiwYdqwYYPmz5+vbdu2qWvXroqOjtbu3btd7c+dO6cpU6bojTfe0E8//aTQ0FBJ0vTp03XzzTdry5Yt6tixo/r06aO+ffuqd+/e+uGHH1S1alX17ds3118aR4wYoW7durm+eB85ckTNmzeXJJUoUUJz587Vjh079OKLL+r111/X9OnTJUndu3fX8OHDVadOHddy3bt3z7D+s2fPKioqSqVKldK3336rRYsW6YsvvsjwpW716tXau3evVq9erXnz5mnu3Ll5PlV57969Wr58uT777DO99957iouLU8eOHfX777/rq6++0pQpUzR69Gi3L72SNGbMGHXp0kVbt25Vr169dPfdd2vnzp15qn/VqlXatWuXVq5cqWXLlumDDz7Qddddp/Hjx7uOjyRduHBBjRo10ieffKLt27dr8ODB6tOnjzZv3uy2vnnz5ikgIECbNm3S1KlTNX78eK1cuVLSX1+YY2JitG7dOr399tvasWOHJk+eLG9vb9dxiI6OVpcuXbRt2zYtWLBAa9euzVDz5dauXavixYurVq1aGeaVK1dOUVFRrlG8c+fOacGCBbrnnnsytO3atauOHj2q5cuX6/vvv9eNN96otm3b6sSJE5KkpKQkdejQQatWrdKWLVsUHR2tO+64QwcPHnRbz/PPP6/GjRtry5Ytuv/++zV06FDt2rUry/qz0qZNG9WvX18ffPBBrmv85JNP9O9//1sdOnTQli1btGrVKjVp0sRtvdOmTVP9+vW1ZcsWjRkzJlfH/NKlS5owYYK2bt2qJUuWaP/+/erfv79r/pgxY7Rjxw4tX75cO3fu1KxZs1SmTBnXslFRUSpRooS++eYbrVu3zhWY8zLCfu7cOU2bNk1vvfWWvv76ax08eFAjRozI0zH97rvv9P3336tp06auaWfOnFG/fv20du1abdy4UdWrV1eHDh3cwvHfZff5km7v3r1asmSJli1bpmXLlumrr77S5MmTXfNHjRqlyZMnu47du+++q3LlyknK3TF7/vnnNXfuXM2ePVtr167ViRMn9OGHH+Z4DM6cOaNFixapd+/euu2223T69Gl98803rvlJSUlq2bKlDh06pKVLl2rr1q0aOXKk60yl3PSx3HjyySf18MMPa+fOnYqKisrV50t2x2zQoEF69913lZyc7Gr/9ttvq2LFimrTpo1rWpMmTdz2F8A1xgCAh/Xr18906tTJGGNMWlqaWblypXE6nWbEiBHmwIEDxtvb2xw6dMhtmbZt25pRo0YZY4yZM2eOkWTi4+Pd2kRERJjevXu73h85csRIMmPGjHFN27Bhg5Fkjhw5YowxJjY21tSvX99tPdOnTzcRERGZ1pud5557zjRq1Mj1PrN1G2OMJPPhhx8aY4x57bXXTKlSpUxSUpJr/ieffGK8vLxMQkKCa/sREREmJSXF1aZr166me/fuWdYyZ84cExwc7FZL8eLFTWJiomtaVFSUiYyMNKmpqa5pNWvWNJMmTXKrdciQIW7rbtq0qRk6dGie6i9XrpxJTk52W09ERISZPn16lvuQrmPHjmb48OGu9y1btjS33HKLW5ubbrrJPPHEE8YYY1asWGG8vLzMrl27Ml3fwIEDzeDBg92mffPNN8bLy8ucP38+02WmT59uqlSpkmF6+j4sWbLEVK1a1aSlpZl58+aZhg0bGmOMCQ4ONnPmzHFtIygoyFy4cMFtHVWrVjWvvvpqlvtfp04dM2PGDLdtXt7P09LSTGhoqJk1a1aW68iuD3fv3t3UqlUr1zU2a9bM9OrVK8ttRUREmM6dO7tNy88x//bbb40kc+bMGWOMMXfccYcZMGBApm3feustU7NmTZOWluaalpycbPz9/c2KFSsyXWb16tVGkjl58qQx5v99ruzZs8fVZubMmaZcuXJZ7uu+ffuMJOPv728CAgJMsWLFjKQM+/p3qamppkSJEubjjz92Tbv8cyEzmX2+/P13+vHHHzdNmzY1xhiTmJhonE6nef311zNdX26OWfny5c3UqVNd8y9dumSuu+66HD8PX3vtNdOgQQPX+4cfftj069fP9f7VV181JUqUMMePH890+Zz6WGbH6vLftfSfywsvvJBtnca4f77kdMzOnz9vSpUqZRYsWOCaVq9ePTN27Fi3dh999JHx8vJy+2wFcO3wyTSFA8A/bNmyZQoMDNSlS5eUlpamnj17auzYsVqzZo1SU1NVo0YNt/bJyckqXbq0672vr6/q1auXYb2XT0sflahbt26GaUePHlVYWNgV7cOCBQv00ksvae/evUpKSlJKSoqCgoLytI6dO3eqfv36btf63nzzzUpLS9OuXbtc9dapU8c1QitJ5cuX148//pinbUVGRqpEiRKu9+XKlZO3t7fbNcnlypVzO41Xkpo1a5bhfXx8fJ7qr1u3rnx9fXOsMTU1VRMnTtTChQt16NAhXbx4UcnJySpevLhbu7//7MuXL++qOz4+Xtddd12GPpRu69at2rZtm9555x3XNGOM0tLStG/fvkxHsc+fPy8/P78s6+7YsaPuu+8+ff3115o9e3amo9tbt25VUlKSWz9OX/fevXsl/TXyN3bsWH3yySc6cuSIUlJSdP78+Qwj3Jfvv8PhUFhYWIafW24ZY1yn9Oemxvj4eN17773ZrrNx48Zu73NzzL///nuNHTtWW7du1cmTJ12jnQcPHlTt2rU1dOhQdenSRT/88IPat2+vzp07u8422bp1q/bs2ePWv6W/zphIrzs3ihcvrqpVq7reX96vsrNgwQLVqlVLly5d0vbt2/Xggw+qVKlSrtHmP/74Q6NHj9aaNWt09OhRpaam6ty5cxl+rn9fZ06fL3//nb683p07dyo5OVlt27bNdP05HbPTp0/ryJEjbiP1Pj4+aty4cY5nCM2ePVu9e/d2ve/du7datmypGTNmqESJEoqPj1fDhg0VEhKS6fK56WO58fd+mNPnS07HzM/Pz3WKfLdu3fTDDz9o+/btrsuh0vn7+ystLU3Jycny9/e/4v0AULQQuAEUCq1bt9asWbPk6+urChUquK51TUpKkre3t77//nu3gClJgYGBrn/7+/tnerOjYsWKuf6dPj+zaelf5r28vDJ8eczNtdEbNmxQr169NG7cOEVFRSk4OFjz58/X888/n+Oy+XH5Pkh/7UdebxSX2ToKYr25kdubhz333HN68cUX9cILL6hu3boKCAjQI488kuG04OzqzukLblJSku677z499NBDGeZVqlQp02XKlCmjkydPZrlOHx8f9enTR7Gxsdq0aVOmp90mJSWpfPnybvcjSJd+R/kRI0Zo5cqVmjZtmqpVqyZ/f3/dddddedr/vNq5c6cqV66c6xpzEyD+/vPO6ZinX5oQFRWld955R2XLltXBgwcVFRXl2veYmBgdOHBAn376qVauXKm2bdvqgQce0LRp05SUlKRGjRq5Bfp0ZcuWzbHedJkd15zCpfTXjfOqVasmSapVq5b27t2rMWPGaOzYsfLz81O/fv10/Phxvfjii4qIiJDT6VSzZs2yPN09t58vV/p7UBDH7O927NihjRs3avPmzXriiSdc01NTUzV//nzde++9OdaW0/zMfi6ZfW7/vR/m9PmSm749aNAgNWjQQL///rvmzJmjNm3aKCIiwq3NiRMnFBAQQNgGrlEEbgCFQkBAgOsL6uUaNmyo1NRUHT16VC1atLBeR9myZZWQkOA2ypc+epvO19dXqampbtPWr1+viIgI/ec//3FNO3DgQI7L/V2tWrU0d+5cnT171vXlcN26dfLy8lLNmjXzu1sFauPGjerbt6/b+4YNG0q6svozOz7r1q1Tp06dXKNjaWlp+uWXX1S7du1c11uvXj39/vvv+uWXXzId5b7xxhu1Y8eOTPtfVho2bKiEhASdPHlSpUqVyrTNPffco2nTpql79+6ZtrnxxhuVkJAgHx8f1035/m7dunXq37+/6wZRSUlJ2d7460p9+eWX+vHHH/Xoo4/musZ69epp1apVGjBgQK63k9Mx//HHH3X8+HFNnjxZ4eHhkuR2c8N0ZcuWVb9+/dSvXz+1aNFCjz/+uKZNm6Ybb7xRCxYsUGhoaJ7PMrHB29tbKSkpunjxovz8/LRu3Tq98sor6tChg6S/blb2559/Zrl8bj5fclK9enX5+/tr1apVGjRoUIb5uTlm5cuX16ZNm3TrrbdKklJSUlzX9WclLi5Ot956q2bOnOk2fc6cOYqLi9O9996revXq6Y033tCJEycyHeXOqY+VLVvW7WZ2u3fv1rlz57KsKV1Ony85HTPpr7N1GjdurNdff13vvvuuXn755Qxttm/f7vqMBHDt4aZpAAq1GjVqqFevXurbt68++OAD7du3T5s3b9akSZP0ySefFPj2WrVqpWPHjmnq1Knau3evZs6cmeFxLpGRkdq2bZt27dqlP//8U5cuXVL16tV18OBBzZ8/X3v37tVLL72UYVQzMjJS+/btU3x8vP7880+3G+2k69Wrl2sEbPv27Vq9erUefPBB9enTx3U6tqctWrRIs2fP1i+//KLY2Fht3rzZdbOrK6k/MjJSX3/9tQ4dOuQKH9WrV9fKlSu1fv167dy5U/fdd5/++OOPPNXbsmVL3XrrrerSpYtWrlypffv2uW4WJ/119+f169dr2LBhio+P1+7du/XRRx9le9O0hg0bqkyZMlq3bl2WbWrVqqU///wzwyPC0rVr107NmjVT586d9fnnn2v//v1av369/vOf/7jCZfXq1fXBBx8oPj5eW7duVc+ePQvsjIPk5GQlJCTo0KFD+uGHHzRx4kR16tRJt99+u+sPKrmpMTY2Vu+9955iY2O1c+dO/fjjj5oyZUq2287pmFeqVEm+vr6aMWOGfv31Vy1dulQTJkxwW8fTTz+tjz76SHv27NFPP/2kZcuWuU7/79Wrl8qUKaNOnTrpm2++0b59+7RmzRo99NBD+v333wvk+GXn+PHjSkhI0O+//67ly5frxRdfVOvWrV1Btnr16nrrrbe0c+dObdq0Sb169cp29DM3ny858fPz0xNPPKGRI0fqzTff1N69e7Vx40bFxcVJyt0xe/jhhzV58mQtWbJEP//8s+6//37XXd0zc+nSJb311lvq0aOHbrjhBrfXoEGDtGnTJv3000/q0aOHwsLC1LlzZ61bt06//vqr3n//fW3YsEFSzn2sTZs2evnll7VlyxZ99913GjJkSIbR/qyOa3afLzkds3SDBg3S5MmTZYxxu3t6um+++Ubt27fPsR4AVycCN4BCb86cOerbt6+GDx+umjVrqnPnzvr222+zPN33StSqVUuvvPKKZs6cqfr162vz5s0Z7kp87733qmbNmmrcuLHKli2rdevW6c4779Sjjz6qYcOGqUGDBlq/fr3GjBnjtlyXLl0UHR2t1q1bq2zZshkePyP9dc3oihUrdOLECd10002666671LZt20xHTTxl3Lhxmj9/vurVq6c333xT7733nmtE6ErqHz9+vPbv36+qVau6TmEdPXq0brzxRkVFRalVq1auL+V59f777+umm25Sjx49VLt2bY0cOdI1ml6vXj199dVX+uWXX9SiRQs1bNhQTz/9tCpUqJDl+ry9vTVgwIBMT7+9XOnSpbMMUg6HQ59++qluvfVWDRgwQDVq1NDdd9+tAwcOuP448d///lelSpVS8+bNdccddygqKirb0cS8+Oyzz1S+fHlFRkYqOjpaq1ev1ksvvaSPPvrIdflGbmps1aqVFi1apKVLl6pBgwZq06ZNhrvI/11Ox7xs2bKaO3euFi1apNq1a2vy5MmaNm2a2zp8fX01atQo1atXT7feequ8vb1dj14qXry4vv76a1WqVEn/93//p1q1amngwIG6cOHCPzLi3a5dO9exHTx4sDp06KAFCxa45sfFxenkyZO68cYb1adPHz300EOuJytkJjefL7kxZswYDR8+XE8//bRq1aql7t27u67xzs0xGz58uPr06aN+/fqpWbNmKlGiRKYBM93SpUt1/PjxTNvUqlVLtWrVUlxcnHx9ffX5558rNDRUHTp0UN26dd2eJJBTH3v++ecVHh6uFi1aqGfPnhoxYkSG+zxkJjefL9kds3Q9evSQj4+PevTokeHeDocOHdL69evzdAYIgKuLw+TmYiQAAPRXAPvwww/zFXqvNgkJCapTp45++OGHDNdsArh2pP+h8Ntvv83wB7EnnnhCJ0+e1Guvveah6gB4GiPcAADkQ1hYmOLi4rK9szSAq9elS5eUkJCg0aNH61//+lemZ5+EhoZmuBwCwLWFm6YBAJBPjPQD165169apdevWqlGjhhYvXpxpm+HDh//DVQEobDilHAAAAAAACzilHAAAAAAACwjcAAAAAABYQOAGAAAAAMACAjcAAAAAABYQuAEAAAAAsIDADQAAAACABQRuAAAAAAAsIHADAAAAAGABgRsAAAAAAAv+PzmGhy/KzDnwAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Run permutation importance\n", "result = permutation_importance(heros, X_test, y_test, n_repeats=100, random_state=random_state, scoring='balanced_accuracy')\n", @@ -701,10 +1543,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "66567604", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Number of Available Testing Instances: 50\n", + "Making Prediction on Testing Instance Index: 0\n", + "PREDICTION REPORT ------------------------------------------------------------------\n", + "Outcome Prediction: 0\n", + "Model Prediction Probabilities: {np.int64(0): 1.0, np.int64(1): 0.0}\n", + "Instance Covered by Model: Yes\n", + "Number of Matching Rules: 1\n", + "PREDICTION EXPLANATION -------------------------------------------------------------\n", + "Supporting Rules: --------------------\n", + "7 rule copies assert that IF: (A_0 = 0) AND (A_1 = 0) AND (R_0 = 0) THEN: predict outcome '0' with 100.0% confidence based on 68 matching training instances (15.11% of training instances).\n", + "Contradictory Rules: -----------------\n", + "No contradictory rules matched.\n" + ] + } + ], "source": [ "# Get example testing instance (with no label) --------------------------------\n", "print(\"Total Number of Available Testing Instances: \"+str(len(X_test)))\n", @@ -727,10 +1588,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "fb557984", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if run_all_cells:\n", " resolution = 500\n", @@ -771,7 +1654,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "2cf56653", "metadata": {}, "outputs": [], @@ -802,10 +1685,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "1fb01410", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Save Phase 1 Rule Training Performance Estimates to .csv\n", "rule_tracking_df = heros.get_performance_tracking()\n", @@ -824,10 +1718,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ce8af5c4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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IkUBqKvD117I1wRyKt9u3b2P9+vW4fZvvpAs5yTHeDbDMrOVCWfNFefNDWYujc89beno6pk2bBicnJ0O0x/K4ubGHjMyheGvdurXFbXAsx0xTwDKzlgtlzRflzQ9lLY7OPW+RkZH4999/DdEWIhPVUh1cx7zl09ZYYsnV80YIIUReOhdv/fv3x4wZMzB//nxs374dv/32m8aD6OjSJSAqCvjf/2Rrgiw9b4XS9rwlJiaiTZs2SFR1R5m5igr2rQPw73mztKzlRFnzRXnzQ1mLo/Nt0/HjxwMAFi5cWO01hUKBigq+q/SbvLQ0YNkyoE0bYMYMWZpgDrdNXV1d0bt3b7i6ukpyPmMn10xTwPKylhNlzRflzQ9lLY7OPW9KpbLWBxVuejCC2aZ+ruzWZV5JHorKirhcU33b1FWa26aBgYH44osvLGZ/XdUfq6GhgLU132tbWtZyoqz5orz5MZqs16xhnSeq8edduwJ//ln5eu/egEKh+Zg4UbbmqohaYKBYxrXJzIYRFG+udq5wtHEEwKf3TSkokVXIFumVquetuLgYV65csZjvSTnHu1la1nKirPmivPkxmqwbNgQ+/RSIiwP+/Rd48klg0KDKH7IAMH48kJFR+fjsM/na+4DOxVtFRQU++ugjBAQEwMXFBdeuXQMAzJkzB1/LuNyFyTKCRXoVCgXXW6d3799FubIcANtbVQqJiYlo1qyZxYyfkGumKbu2ZWUtJ8qaL8qbH6PJesAAoF8/oFkz4LHHgEWLABcX4PjxymOcnABf38qHzCtEAHoUb4sWLcK3336Lzz77DHZ2durnW7VqhfXr10vaOIvg4MDeytjzBvAd96a6RgPHBrCztnvE0dpp1qwZDh48iGbNmklyPmMnZ8+bpWUtJ8qaL8qbH0NnnZ+fj7y8PPWjRJvfsRUVwJYtQGEhu32qsnkz4OkJtGoFzJoFFPEZXlQXnScsfP/99/jqq6/Qt29fTKxy3zc8PByXVNPfiPaM4LYpUDn2TDUWzZCkHu8GVA5+tQRyzjQFLCtruVHWfFHe/Bg667CHfjjOmzcP8+fPr/ngc+dYsVZczHrdfvml8ofrK68AjRsD/v7A2bPA+++zTaV37DBY27Whc89beno6mjZtWu15pVKJsrIySRplUYykePN15t/zJtV4NwDIysrC//73P2RlZUl2TmOVksJ+xjg4AMHB/K9vSVnLjbLmi/Lmx9BZJyYmIjc3V/2YNWtW7Qc3bw6cOQOcOAFMmgSMGVM5NmXCBCAyEmjdGhgxAvj+e1bcXb1qkHZrS+fiLSwsDH///Xe153/++We0a9dOkkZZFG9vdg8sKQkQBNmaIcdtUymLt8zMTERHRyMzk99yJ3KRc6YpYFlZy42y5ovy5sfQWbu6usLNzU39sFd1lNTEzg5o2hTo0AGIjgbCw4EVK2o+tksX9vbKFekbrQOdb5vOnTsXY8aMQXp6OpRKJXbs2IGkpCR8//332L17tyHaaN5sbOS59/UQ9W1TDrssqK4h5e4K4eHhRr+37h9/AOvWAUqluPOkpLC3cu2sYApZmwvKmi/Kmx+jzlqprP1u2Jkz7K2fvLsD6Vy8DRo0CLt27cLChQvh7OyMuXPnon379ti1axeeeuopQ7SRcGDqPW+m4L33NGefi9W5s3TnIoQQizRrFvDss0CjRkB+PvDDD8ChQ8DevezW6A8/sNmoDRqwMW9vvw307MnWhpORzsUbADzxxBPYt2+f1G2xXAsWsNkrH3wAuLvL0gRTL96SkpLw6quv4ttvv0Xz5s0lO69UBAG4fp29Hx3NJi6J4ebGliKSg7FnbU4oa74ob36MJuvsbGD0aLZ+m7s7K8r27gWeeortgLR/P7B8OZuBGhgIvPgiMHu2fO19QK/iLScnBz///DOuXbuGd999F/Xr18fp06fh4+ODgIAAqdto/j79lI1AnzRJ9uItqzALSkEJK4Wo9ZvrZIjizcHBAS1btoSDaukVI5Oby/7vA8C0aWzZIFNl7FmbE8qaL8qbH6PJuq71aQMDgcOH+bVFBwpB0G2U/NmzZxEREQF3d3dcv34dSUlJaNKkCWbPno3U1FR8//33hmorNzdv3kRgYCDS0tLQsGFDw1+wXj0gJwe4eJGNQpdBWUUZ7D5ma65lv5sNL2cvg12r3uJ6yCnOQeKbiWjh1cJg1zEm58+zyUr16wN37sjdGkIIMU/cf3/LROfulaioKLz66qtITk7WqJj79euHI0eOSNo4i2EEy4XYWtvC04ndyzPkrdPi8mLkFOcAkLbnraysDBkZGUa7XM3Nm+ytOfwsMfaszQllzRflzQ9lLY7OxdupU6fwxhtvVHs+ICCAplfrywiKN4DPuLesAramj721PTwcPCQ777lz5+Dv749z585Jdk4ppaWxt3LvwSwFY8/anFDWfFHe/FDW4uhcvNnb2yMvL6/a85cvX4aXl+FutZk1IyneVEt3GHK5ENW5fV18oVAoJDtvSEgIdu/ejZCQEMnOKSVz6nkz9qzNCWXNF+XND2Utjs7F28CBA7Fw4UJ1V6dCoUBqairef/99vPjii5I30CJY0P6mhlomxN3dHf3794e7TBM+HsWcijdjz9qcUNZ8Ud78UNbi6Fy8LVmyBAUFBfD29sb9+/fRq1cvNG3aFK6urli0aJEh2mj+jKTnzZSLt1u3bmH16tW4deuWpOeVijndNjX2rM0JZc0X5c0PZS2OzsWbu7s79u3bh927d+OLL77AlClT8Mcff+Dw4cNwdnY2RBvN3/ffAwkJbOE/GXG5bZov/e4KAJthFBUVhZuqLi4jY049b8aetTmhrPmivPmhrMXRaZ23srIyODo64syZM+jevTu6d+9uqHZZlhbGsVyGKfe8tWvXDiUy91zWRfXzyRx63ow9a3NCWfNFefNDWYujU8+bra0tGjVqhIqKCkO1h8iIS/FWaHlbY+Xmsl1XAIDWsCaEECKWzrdNP/zwQ3zwwQfGu6GsKdq5E/joI+DkSVmbYco9b8nJyYiIiEBycrKk55WCqtetXj3AHEYWGHPW5oay5ovy5oeyFkfn7bFWrVqFK1euwN/fH40bN642zu306dOSNc5ibN0KbNnCNqyUcbdxP1c2Di2nOAf3y+7D0dZR8muox7y5SjvmzcbGBl5eXrCx0WvHN4Myp1umgHFnbW4oa74ob34oa3F0Tu355583QDMsnJHMNnW3d4e9tT1KKkqQVZiFII8gSc8vCILBet6Cg4Px448/SnpOqahmmprDZAXAuLM2N5Q1X5Q3P5S1ODoXb/PmzTNEOyybkRRvCoUCvi6+uJF7A5kFmZIXb/eK76FMydYH9HH2kfTcFRUVKCwshLOzM6ytrSU9t1jmNNMUMO6szQ1lzRflzQ9lLY7OY95U8vPzkZeXp34UFBRI2S7LoireiovlbQcqb2eqbm9KSXXO+o71YW9jL+m5ExIS4O7ujoSEBEnPKwVzu21qzFmbG8qaL8qbH8paHK2LtzNnzqBfv37qj/39/VGvXj31w8PDA6dOnTJII82ekfS8AYadtGCoW6YA64Lftm0bgoODJT+3WOZ429RYszY3lDVflDc/lLU4Wt82XblyJXr06KHx3MaNGxEQEABBELBhwwZ88cUX2Lhxo+SNNHtGsj0WAPg6m2bxVq9ePQwZMkTy80rB3G6bGnPW5oay5ovy5oeyFkfr4u2ff/7BlClTNJ57/PHH0aRJEwCAo6MjXn75ZWlbZymMsOftyr0ruJFzQ9JzX7p9SeMaUrpz5w527dqFAQMGoEGDBpKfXwxzu21qzFmbG8qaL8qbH8paHK2Ltxs3bsDLy0v98cKFC+Hp6an+2M/PD1lZWdK2zlKMHg08+STgJ+3yGfpQjXn74dwP+OHcD4a5hsRbYwHs+3Ps2LGIi4szqh8EeXnsAZjPAr3GmrU5oqz5orz5oazF0bp4c3BwwI0bN9Dwwb2ft99+W+P1tLQ0ODk5Sds6S9GoEXsYgaeaPIXG7o2RVWiYQtzN3g2Dmg+S/Lzt2rVDWVmZ0c1aUvW6eXgALi6yNkUyxpq1OaKs+aK8+aGsxdG6eGvXrh127txZ636mO3bsQLt27SRrGJFHcL1gXJ9+Xe5m6EyhUBjlYo/mdssUMN6szRFlzRflzQ9lLY7Ws03ffPNNLF++HKtXr4ZSqVQ/X1FRgZUrV2LlypWYNGmSQRpp9pKSgGXLgG3b5G6Jybp69SoGDhyIq1evyt0UDeY20xQw3qzNEWXNF+XND2UtjtZl74svvoioqChMnToVH3zwgXqiwrVr11BQUICoqCi89NJLBmuoWYuPB6KigD59AJr0YVbMbaYpIYQQ+SkEQRB0+YTjx4/jxx9/VG8m26xZMwwfPhyPP/64QRooh5s3byIwMBBpaWnqMX4G9csvwODBQLduwNGjhr8e4Wb8eGD9emDhQmDOHLlbQwgh5o3772+Z6HzD+fHHHzerQs0oGNFSIaZKEARUVFTA2toaCoVC7uaomeNtU2PN2hxR1nxR3vxQ1uJoNeYtNTVVp5Omp6fr1RiLpVqk1wi2xzJV8fHxsLW1RXx8vNxN0WCOt02NNWtzRFnzRXnzQ1mLo1Xx1qlTJ7zxxht1bn+Vm5uLdevWoVWrVti+fbtkDbQI1PMmWuPGjfHNN9+gcePGcjdFgznONjXWrM0RZc0X5c0PZS2OVmPe7ty5g0WLFmHDhg1wcHBAhw4d4O/vDwcHB9y7dw+JiYm4cOEC2rdvjzlz5mjsgWqKuN8z//dfoFMn9htex15OYrzy8wE3N/Z+Xh7g6ipvewghxNxZypg3rXreGjRogKVLlyIjIwOrVq1Cs2bNcPv2bfWkhREjRiAuLg7Hjh0z+cJNFqqeN7ptqrd79+7hp59+wr179+Ruipqq183d3bwKN2PM2lxR1nxR3vxQ1uJovc4bwPYvfemll7B8+XL88ssv2LNnDzZt2oR33nkHrVq10qsBq1evRlBQEBwcHNClSxecPHmy1mPLysqwcOFChISEwMHBAeHh4dizZ4/GMRUVFZgzZw6Cg4Ph6OiIkJAQfPTRR9BxUi1fwcHAX38BO3fK3RKTlZKSgpdffhkpKSlyN0XNHG+ZAsaZtbmirPmivPmhrMWRdXnjrVu3IioqCmvXrkWXLl2wfPlyREZGIikpCd7e3tWOnz17NjZt2oR169YhNDQUe/fuxQsvvIB//vlHvbvD4sWLsWbNGnz33Xdo2bIl/v33X4wdOxbu7u6YNm0a7y9ROy4uwFNPyd0KkxYeHo7c3Fw4OzvL3RQ1c5ysABhn1uaKsuaL8uaHshZHp543qS1duhTjx4/H2LFjERYWhrVr18LJyQkbNmyo8fiNGzfigw8+QL9+/dCkSRNMmjQJ/fr1w5IlS9TH/PPPPxg0aBD69++PoKAgvPTSS3j66afr7NEjps/a2hpubm5GtU+eOS4TAhhn1uaKsuaL8uaHshZHtuKttLQUcXFxiIiIqGyMlRUiIiJw7NixGj+npKQEDqplNR5wdHREbGys+uNu3bohJiYGly9fBgAkJCQgNjYWzz77bK1tKSkpQV5envqRn58v5kvTXWkp8NVXwMqVQHk532ubiZSUFAwfPtyouuDN+bapsWVtrihrvihvfihrcWS7bXr79m1UVFTAx8dH43kfHx9cunSpxs+JjIzE0qVL0bNnT4SEhCAmJgY7duxARUWF+piZM2ciLy8PoaGhsLa2RkVFBRYtWoQRI0bU2pbo6GgsWLBAmi9MHxUVwBtvsPfHjKmcoki0Vl5ejlu3bqHciIpfc71taoxZmyvKmi/Kmx/KWhxZx7zpasWKFRg/fjxCQ0OhUCgQEhKCsWPHatxm3bZtGzZv3owffvgBLVu2xJkzZzB9+nT4+/tjzJgxNZ531qxZiIqKUn+cnp6OsLAwg389aqrZpgCt9aanZs2aYf/+/XI3Q4Pqtqm59bwZY9bmirLmi/Lmh7IWR6/bphs3bkT37t3h7++PGzduAACWL1+OX3/9VetzeHp6wtraGllZWRrPZ2VlwdfXt8bP8fLyws6dO1FYWIgbN27g0qVLcHFxQZMmTdTHzJgxAzNnzsSwYcPQunVrjBo1Cm+//Taio6NrbYu9vT3c3NzUD1fe6zpYWQE2D+poKt7Mhrn2vBFCCJGXzsXbmjVrEBUVhX79+iEnJ0d9y9LDwwPLly/X+jx2dnbo0KEDYmJi1M8plUrExMSga9eudX6ug4MDAgICUF5eju3bt2PQoEHq14qKimBlpfllWVtbQ6lUat02WdAuC6LEx8fD3t7eaLZaKSgAcnLY++ZWvBlb1uaMsuaL8uaHshZH5+Jt5cqVWLduHT788EONWSIdO3bEuXPndDpXVFQU1q1bh++++w4XL17EpEmTUFhYiLFjxwIARo8ejVmzZqmPP3HiBHbs2IFr167h77//xjPPPAOlUon33ntPfcyAAQOwaNEi/P7777h+/Tp++eUXLF26FC+88IKuXypfqokYVLzppWHDhli6dKnRrKhtrgv0AsaXtTmjrPmivPmhrMXRecxbSkqKek21quzt7VFYWKjTuYYOHYpbt25h7ty5yMzMRNu2bbFnzx71JIbU1FSNXrTi4mLMnj0b165dg4uLC/r164eNGzfCw8NDfczKlSsxZ84cvPnmm8jOzoa/vz/eeOMNzJ07V9cvlS/qedNaSQmwfDlw61bVZ70ATMbixfK06WHmfMvUy8sLkydPlrsZFoGy5ovy5oeyFkfn4i04OBhnzpyptpnsnj170KJFC50bMGXKFEyZMqXG1w4dOqTxca9evZCYmFjn+VxdXbF8+XKdbuEaBSretPbTT8DMmQ8/mwsgFkAPAO7c21SbkBC5WyC93NxcxMbGokePHnB3N56szRFlzRflzQ9lLY7OxVtUVBQmT56M4uJiCIKAkydP4scff0R0dDTWr19viDZahi+/BMrKgObN5W6J0Ttzhr3t0gXo1Yu9n5l5Fd9//xxGj46Dr2972dpWla0tW/nF3Fy9ehXPPfcc4uLi0L69cWRtrihrvihvfihrcRSCHpt+bt68GfPnz8fVq1cBAP7+/liwYAHGjRsneQPlcPPmTQQGBiItLY3uxxuhfv2AP/8E1qwBJk5kz5WVleH27dvw9PSEra2tvA00c5Q1P5Q1X5Q3P4bK2lJ+f+tVvKkUFRWhoKCgxn1ITZml/OObqsaNgdRU4MgR4Ikn5G4NIYQQY2Epv79FbY/l5ORkdoWbbGJigG++AR70ZpKa5eezwg0Aqq6jfOPGDbz++uvqdQeJ4VDW/FDWfFHe/FDW4ug85q1du3ZQKBTVnlcoFHBwcEDTpk3x6quvok+fPpI00GIsXgzs2wds3Gieo9wlcvEie+vjAzRoUPl8cXExLly4gOLiYnkaZkEoa34oa74ob34oa3F07nl75plncO3aNTg7O6NPnz7o06cPXFxccPXqVXTq1AkZGRmIiIjQabcFApptqiXVZOOHdy9r3rw5jh07huY04cPgKGt+KGu+KG9+KGtxdO55u337Nt555x3MmTNH4/mPP/4YN27cwF9//YV58+bho48+0tj5gDyCqnijv0LqdOECe9uypbztIIQQQuSic8/btm3bMHz48GrPDxs2DNu2bQMADB8+HElJSeJbZ0mo500rtfW8JSQkoH79+khISODfKAtDWfNDWfNFefNDWYujc/Hm4OCAf/75p9rz//zzDxwebPGkVCrV7xMt0fZYWqmt583X1xezZs2Cr68v/0ZZGMqaH8qaL8qbH6PJes0aoE0bwM2NPbp2ZWtRqRQXA5Mns0HWLi7Aiy8CWVnytfcBnW+bTp06FRMnTkRcXBw6deoEADh16hTWr1+PDz74AACwd+9etG3bVtKGmj3qeXukggJANTHp4Z43Hx8fzJgxg3+jLBBlzQ9lzRflzY/RZN2wIfDpp0CzZoAgAN99BwwaBMTHs16Ct98Gfv+dbe3j7g5MmQIMHgwcPSprs3Uu3mbPno3g4GCsWrUKGzduBMAGHq5btw6vvPIKAGDixImYNGmStC01d1S8PZJqpqm3N+Dpqflafn4+4uLi0KFDB7ia207wRoay5oey5ovy5sdosh4wQPPjRYtYb9zx46yw+/pr4IcfgCefZK9/8w3QogV7/fHH+bf3Ab3WeRsxYgSOHTuGu3fv4u7duzh27Ji6cAMAR0dHum2qq9Gjga1bgREj5G6J0aptvBsAJCcno0+fPkhOTubbKAtEWfNDWfNFefNj6Kzz8/ORl5enfpRo0zFSUQFs2QIUFrLbp3FxbNvKiIjKY0JDgUaNgGPHDNJubenc80YMpF079iC1qmumaVhYGJKTk816RW1jQVnzQ1nzRXnzY+iswx76K3/evHmYP39+zQefO8eKteJiNq7tl19YL8GZM4CdHeDhoXm8jw+QmWmIZmtN5+KtoqICy5Ytw7Zt25CamorS0lKN1+/evStZ4wipqq6eN9UC0cTwKGt+KGu+KG9+DJ11YmIiAgIC1B/bq4Ym1aR5c1ao5eYCP/8MjBkDHD5ssLZJQefbpgsWLMDSpUsxdOhQ5ObmIioqCoMHD4aVlVXtVS15tKtX2YBImQdBGrO6et7S0tIwbdo0pKWl8W2UBaKs+aGs+aK8+TF01q6urnBzc1M/6ize7OyApk2BDh2A6GggPBxYsQLw9QVKS4GcHM3js7LYazLSuXjbvHkz1q1bh3feeQc2NjYYPnw41q9fj7lz5+L48eOGaKNl2LMHePllYPlyuVtilAoLgevX2fs19bzl5+fj0KFDyM/P59ouS0RZ80NZ80V582PUWSuVbPJghw6ArS3be1wlKYltsN21q3ztgx63TTMzM9G6dWsAgIuLC3JzcwEAzz33XLVdF4gOaLZpnVQzTb282ONhYWFhOHv2LN9GWSjKmh/Kmi/Kmx+jyXrWLODZZ9kkhPx8NrP00CFg7162NMi4cUBUFFC/PlsHbupUVrjJONMU0KN4a9iwITIyMtCoUSOEhITgr7/+Qvv27XHq1Km6uyVJ3VSzc2l7rBqpbpnW1OtGCCGE6CU7m632kJHBirU2bVjh9tRT7PVlywArK7Y4b0kJEBkJ/N//ydtm6HHb9IUXXkDMgy7EqVOnYs6cOWjWrBlGjx6N1157TfIGWgzqeauTarJCbXuanjt3Dg0bNsS5c+f4NcpCUdb8UNZ8Ud78GE3WX3/NxuSUlLBCbv/+ysINYB0rq1cDd++y8Ts7dsg+3g3Qo+ft008/Vb8/dOhQNG7cGP/88w+aNWuGAQ8vdke0R8VbnR7V8+bp6YnXX38dng+v3kskR1nzQ1nzRXnzQ1mLoxAEQdD24LKyMrzxxhuYM2cOgoODDdkuWd28eROBgYFIS0vjt97P3r3AM88AbduybTmIhiZNgJQU4OBBoHdvuVtDCCHEGMny+1sGOt02tbW1xfbt2w3VFstGY95qVVjICjeg9p63wsJCnDp1CoWFhfwaZqEoa34oa74ob34oa3F0HvP2/PPPY+fOnQZoioULDWX33qvclibMpUvsracn29e0JklJSejcuTOSkpL4NcxCUdb8UNZ8Ud78UNbi6DzmrVmzZli4cCGOHj2KDh06wNnZWeP1adOmSdY4i+LjA9CEjxppM9O0RYsWSEhIQLNmzfg0yoJR1vxQ1nxR3vxQ1uLoNOYNQJ1j3RQKBa5duya6UXKzlHvmpmLmTGDxYmDSJKOYoU0IIcRIWcrvb51vm6akpNT6MIfCTTb37wN//gns2iV3S7gqKwNu3Kj7cfo0O7aunrf09HTMmjUL6enpfBpuwShrfihrvihvfihrcXQu3lRKS0uRlJSE8vJyKdtjue7cAfr1AwYPlrsl3FRUsC3kgoLqfuzbx46vbY03AMjJycFPP/2EnIf3oCOSo6z5oaz5orz5oazF0fm2aVFREaZOnYrvvvsOAHD58mU0adIEU6dORUBAAGbOnGmQhvIkS7frrVuVo/ErKtiKzmbu7FlWvAGVk21rExYG/P034ORk+HYRQggxTXTbtBazZs1CQkICDh06BIcqv3EjIiKwdetWSRtnUapuLWYhC/UeO8bePvkku2tc1yMujgo3QgghBNCjeNu5cydWrVqFHj16QKFQqJ9v2bIlrl69KmnjLIoFF29du4o/14ULF9C8eXNcUE1NJQZDWfNDWfNFefNDWYujc/F269YteNew2FZhYaFGMUd0ZGdX+T4Vbzpzd3fHwIED4e7uLv5kpE6UNT+UNV+UNz+UtTg6j3nr2bMnhgwZgqlTp8LV1RVnz55FcHAwpk6diuTkZOzZs8dQbeVGtnvmDg6scLtxA2jUiN91ZXDnDlt0FwBu3wYaNJC3PYQQQkyfpYx503mR3k8++QTPPvssEhMTUV5ejhUrViAxMRH//PMPDh8+bIg2Wg57e1a8WcAWWSdOsLfNmklTuN2/fx/Xrl1DkyZN4OjoKP6EpFaUNT+UNV+UNz+UtTg63zbt0aMHzpw5g/LycrRu3Rp//fUXvL29cezYMXTo0MEQbbQcn38OrF0LeHnJ3RKDk/KWKQBcvHgRrVq1wsWLF6U5IakVZc0PZc0X5c0PZS2Ozj1vABASEoJ169ZJ3RYyfrzcLeDm+HH2Vqri7bHHHsM///yDxx57TJoTklpR1vxQ1nxR3vxQ1uLoPOYtIiICI0eOxODBg+Hm5maodsnKUu6Zy6WiAqhXD8jPB+LjgbZt5W4RIYQQc2Apv791vm3asmVLzJo1C76+vhgyZAh+/fVXlJWVGaJtluf0aWD/fjaC34wlJrLCzdkZaNVKmnNmZGTg448/RkZGhjQnJLWirPmhrPmivPmhrMXRuXhbsWIF0tPTsXPnTjg7O2P06NHw8fHBhAkTaMKCWOPHA089BZw8KXdLDEp1y7RzZ8BGrxv31d26dQurV6/GrVu3pDkhqRVlzQ9lzRflzQ9lLY7Ot00fVlxcjF27dmHRokU4d+4cKioqpGqbbGTrdu3WjY3k37EDeOEFftfl7LXXgG++AWbNAj75RO7WEEIIMReWcttUVL9HZmYmtmzZgk2bNuHs2bPo3LmzVO2yTKrtxsx8kV6pZ5oSQgghlkTn26Z5eXn45ptv8NRTTyEwMBBr1qzBwIEDkZycjOOq+2FEP6otssy4eLt3D7h0ib3/+OPSnffixYto3749TTvngLLmh7Lmi/Lmh7IWR+eeNx8fH9SrVw9Dhw5FdHQ0OnbsaIh2WSYLKN5Ui/M2bSrtcnbOzs7o2rUrnJ2dpTspqRFlzQ9lzRflzQ9lLY7Oxdtvv/2Gvn37wspK50478igWULypbplK2esGAI0aNcLq1aulPSmpEWXND2XNF+XND2Utjs4V2FNPPQUrKyvcunULsbGxiI2NpdkiUrGA4k3qxXlVSkpKcP36dZSYcXbGgrLmh7Lmi/Lmh7IWR+firaioCK+99hr8/PzQs2dP9OzZE/7+/hg3bhyKiooM0UbLMXw4sGQJ0Lu33C0xCKWy8rap1MXbhQsXEBwcjAsXLkh7YlINZc0PZc0X5c0PZS2OzrdN3377bRw+fBi7du1C9+7dAQCxsbGYNm0a3nnnHaxZs0byRlqMZ59lj4eVlbGqp7S08rmQEKBxY35tk8DFi0BuLuDkBLRuLfJk584BVXp8mxcW4tTSpWjatGnlMfHxLCcz3QlELk2bNsW+ffs0syYGQVnzRXnzQ1mLo/M6b56envj555/R+6HeoYMHD+Lll182i1uoRrdOzPTpwIoVms9FRwMzZ0p+qdJSYNkyjbpIMpcvA7t2Ab16AYcOiTjRgQNA377Vn/f2BrKyKj/u2ZPtxXX0qIiLEUIIMRVG9/vbQHTueSsqKoKPj0+15729vem2qVj//QfcuMGmYVb9a+T0afa2UaPKXiQpp2pWsWWLQWpCDT16iDzB/v3sracn4OsLACgrL0dWaSlss7LY92dxMSvalEqWX/v2Ii9KVLKysrB582aMGDGixp8FRDqUNV+UNz+UtTg6F29du3bFvHnz8P3338PhwaKy9+/fx4IFC9CVVl0VZ/16YN484I03gLVrK5//7z/2dtMm4IknDNqEv/9mb594wjCL6Lq6Am++KfIkcXHs7UcfARMnAgAunDmDnj174khGBvtB4OAADBkCbN3Kcv2//xN5UaKSkZGB+fPn48knn6QfugZGWfNFefNDWYujc/G2YsUKREZGomHDhggPDwcAJCQkwMHBAXv37pW8gRZFNdu0uLjyOUEAVBv3+vlVPr9zJ1BQALz4IuDoKFkTVLNBo6KA55+X7LTS2rSJjWcLC1M/1bZtW+Tl5Wke9/rrrHjbvBn4/HM22I6IVmPWxCAoa74ob34oa3F0Lt5atWqF5ORkbN68GZceLJU/fPhwjBgxAo4SFhEWqabtsQSBFSv//QcEBFQ+P3w4K/KeeEKyiQu5uYBq4o/U67BJyssLePrpRx/35JNAcDCQkgL89BMwZozh20YIIYQYmF4r7To5OWH8+PFYsmQJlixZgtdff13vwm316tUICgqCg4MDunTpgpMnT9Z6bFlZGRYuXIiQkBA4ODggPDwce/bsqXZceno6Ro4ciQYNGsDR0RGtW7fGv//+q1f7uKppnTcrK7ZJ/eTJmj1sqlWpCwslu/ypU6xWDApSDyUzGUlJSejRoweSkpIqn7SyYr1vALt1SiRRY9bEIChrvihvfsw667w87R960rp4i4uLQ58+fWrs5szNzUWfPn2QkJCg08W3bt2KqKgozJs3D6dPn0Z4eDgiIyORnZ1d4/GzZ8/Gl19+iZUrVyIxMRETJ07ECy+8gPj4ePUx9+7dQ/fu3WFra4s///wTiYmJWLJkCerVq6dT22ShyyK9BijeTGLD+E2bgFmzWKVZhb29PZo2bQp7VYYqr74KWFsDsbFsrRIiWq1ZE8lR1nxR3vyYddYeHkC9enU/VMfoSeulQl555RW0aNECc+bMqfH1Tz75BImJidi0aZPWF+/SpQs6deqEVatWAQCUSiUCAwMxdepUzKxhyqO/vz8+/PBDTJ48Wf3ciy++CEdHR/V1Z86ciaNHj+Jv1ch7LZSUlGis8pyeno6wsDD+U41/+AEYMYLd7ouJYc8lJQEJCUBoKNCmTeWxYWGsGDl4ULJFffv1A/78E/jiC2DqVElOKb3nngN+/x1YuRKYMkW7z3nvPXb7dMQIWvONEELMmFEsFXL4sPbH9uql1yW0HvN24sSJGgsqlQEDBmC9DremSktLERcXh1mzZqmfs7KyQkREBI6puoAeUlJSop7hquLo6IjY2Fj1x7/99hsiIyMxZMgQHD58GAEBAXjzzTcxfvz4WtsSHR2NBQsWaN12g6lpzNvvvwPvvMPGuP3wQ+XzLi7srUQ9b4JQOVnBqMe7qZZNeWjpj7KyMuTk5MDDwwO2traan/PZZ5waZxnqzJpIirLmi/Lmx6yz1rMg04XWt03T09Ph6upa6+suLi7IUM2K1MLt27dRUVFRbYqwj48PMjMza/ycyMhILF26FMnJyVAqldi3bx927Nihcd1r165hzZo1aNasGfbu3YtJkyZh2rRp+O6772pty6xZs5Cbm6t+JCYmav11SKplS2DBAmDChMrnapppClTeNi0okOTSly8D9+6x+vHBJGLjk5HBHlZW1Rp57tw5eHt749y5czI1znJQ1vxQ1nxR3vxYVNZ//w2MHAl06wakp7PnNm5kw3n0pHXx5uXlVefAwkuXLsHT01PvhmhjxYoVaNasGUJDQ2FnZ4cpU6Zg7NixsLKq/DKUSiXat2+PTz75BO3atcOECRMwfvx4rK26btpD7O3t4ebmpn7UVaQaVPPmwNy5wOjRlc89qniTqOdN1dnZsSNgZyfJKaWn6nULDa38+h9o0qQJfv31VzRp0qTmzy0oAL78EnjrLQM30vw9MmsiGcqaL8qbH4vJevt2IDKSTTg8fbryzlpuLvDJJ3qfVuvbphEREVi0aBGeeeaZaq8JgoBFixYhIiJC6wt7enrC2toaWVW3MwJbddm3lqmOXl5e2LlzJ4qLi3Hnzh34+/tj5syZGv/4fn5+CKuy/hcAtGjRAtu3b9e6bUaltuLtrbeAYcMkm11gyrdMAcDDwwMDBw6s/XNv3VIv6IubNwGFQvP1desqB49u2AD88Yfm6717az/Gzsw9MmsiGcqaL8qbH4vJ+uOP2aL7o0ezLYxUundnr+lJ65632bNn49y5c+jSpQu2bduGhIQEJCQkYOvWrejSpQvOnz+PDz/8UOsL29nZoUOHDohRDcwH6zWLiYl55E4NDg4OCAgIQHl5ObZv345BgwapX+vevXu1HsLLly+jsSls4l5czDZcP3u28jnV7gr+/prHPvUU64YNCZHk0iYx01S1s0INxdutW7fw5Zdf1r63bnAwoPrDY8cO9tdQ1UfVhZETEqq/Pm0akJ8v8Rdkmh6ZNZEMZc0X5c2PxWSdlMT22X6YuzuQk6P3abXueQsJCcH+/fvx6quvYtiwYVA86LkQBAFhYWHYt28fmlbdj1MLUVFRGDNmDDp27IjOnTtj+fLlKCwsxNixYwEAo0ePRkBAAKKjowGwSRPp6elo27Yt0tPTMX/+fCiVSrz33nvqc7799tvo1q0bPvnkE7z88ss4efIkvvrqK3z11Vc6tU0Wly+zsVw+PoBq3F9tPW8Sys8Hzp9n7xt18Xb1KnvboUO1l9LS0jB58mR06tQJXrXt+/rtt8CvvwLl5dVfqzoL9aWX2C1slbfeYp9z7x7b38vCaZU1kQRlzRflzY/FZO3rC1y5whZQrSo2FhBzy1jQQ3x8vLBt2zZh69atQnx8vD6nUFu5cqXQqFEjwc7OTujcubNw/Phx9Wu9evUSxowZo/740KFDQosWLQR7e3uhQYMGwqhRo4T09PRq59y1a5fQqlUrwd7eXggNDRW++uorndqUlpYmABDS0tL0/rr0cumSIACC4OHBPi4qYh8DgpCTo3lscrIg/PqrIMTFib5sTAy7ROPGok9lWEqlIFy5Igj37/O9rqcnC+jcOb7XJYQQohPZfn/X5pNPBCEsTBCOHxcEV1dB+PtvQdi0SRC8vAThiy/0Pq3W67xZEtnWibl+nd3ec3QEiorYwMY//2S9bxMnao7T+vhjYM4cYPx4QGSv4qJFwOzZwNChmrfkyQOLFwMVFcDYsQbtASWEECKOUazzVpUgsIkJ0dHs9zrAFuR/913go4/0Pq1e22MRA6m6w4IgsI+ffx6YNKn6AHsJlwoxifFuj5CcnIzIyEgkJydLf/L33wc++IAKtwcMmjXRQFnzRXnzYzFZKxTAhx8Cd++y8UnHj7MJdCIKN0CPjemJAamKN6WSjbGqa+FCiZYKqbo4r1EXb9Ons/Vx3n0X6NKl2svW1tZwc3ODtbU1/7ZZGMqaH8qaL8qbH4vL2s6O7YwkEbptWgPZul0LCyt3TsjPZ4Mck5KA1q2r/6Nv3sxmm/btC+zfr/clk5OBxx5jdWNenhGv8RYSAly7xr7Wvn35Xjszkz18fdmDEEKIUTKK26aDB2t/7I4del2Cbpsak6ob9JaUAFu3srXcalpgWKKeN9Ut0w4djLhwu3ePFW5AjcuEAEBFRQUKCwtRUVEh/fVnzADatWMFMzFs1kQDZc0X5c2PWWft7l75cHNje5X/+2/l63Fx7Dl3d70voVXxdvbsWa0fRAQbGza+as4cVsjVtUyIRMWbSdwyjY9nb4ODKxfSfUhCQgJcXFyQkJAg/fVV/8Fyc6U/twkyaNZEA2XNF+XNj9FkHR0NdOrEloHy9mbjzB/eTap3bzZ2repDteh7Tb75pvLh4wO8/DKQksJ62XbsYJ0Rw4YBInal0mrMW9u2baFQKCAIgnp9t9qYZRXN06efVr5f2wK9QLWN6X//nd1F1bWWKytjb01iZ4Ua1ndTCQoKwg8//ICgh9fSkYJqDbi8POnPbYIMmjXRQFnzRXnzYzRZHz4MTJ7MCrjycjY57emngcREzW0Yx48HFi6s/NjJSbvzb9jA1nSrOrbP2hqIimJ7nf7vf3o1W6viLSUlRf1+fHw83n33XcyYMUO9E8KxY8ewZMkSfPbZZ3o1gtSirp63kBBg9Wp15b5rl/6LNXt6An361PLirVtsVkNNbG01e8Ju32aTLWpiYwPUr1/58Z07bPmNmlhbAw0aVH68bx97W8stUwCoX78+hg8fXuvroqiKN2PueVNlyWHwr0GzJhooa74ob36MJus9ezQ//vZb1gMXF6e5M4KTk35jnsvLgUuXNBd+B9hztf2+1IJWxVvVraWGDBmCL774Av369VM/16ZNGwQGBmLOnDl4/vnn9W4MAZCayrrPgoLqLt68vYE331R/qFoxZM4c4I03dLtkgwaAg0MtL4aE1L4tVI8ewN9/V37cunXlzhAPa9eusgcNADp3rhzH9rDHHtPstv7rL/a2juLt7t27+OOPP9CvXz/Ur1okSkF129RYe96USqBjR1Zknz4NWBl2KKtBsyYaKGu+KG9+DJ11fn4+8qr8zLa3t4d91XHltVH9kf5wmzZvBjZtYgXcgAHsl602vW9jxwLjxrEdgjp3Zs+dOMHusj3YTUofOi8Vcu7cOQQHB1d7Pjg4GImJiXo3hDzQsydw4wZw5AjrnQK0Wl9MdbvU3x8ICBBx/fx84P59VrBo843OQ+vW7K+X7t1rPeT69esYNWoU4uLipP9BYOy3TbOzgTNn2Pu3brExFgZk0KyJBsqaL8qbH0NnHfbQCg3z5s3D/Pnz6/4kpZItS9W9O9CqVeXzr7wCNG7MfsGePcvGpiclaTdT9PPPWcG3ZIlmh8yMGcA77+j0NVWlc/HWokULREdHY/369bB7MD2xtLQU0dHRaNGihd4NIQ+ousBSU9lbW1vNW4gqSiW7V19YCERGorCQrQlX9Ra9XtatY99Qr7zC/tLQpWBRfWNqQ7VPqTa0mAjTtm1bFBcXw7autfH0ZewTFqr+G3l4GPxyBs2aaKCs+aK8+TF01omJiQio0pOhVa/b5MlsId3YWM3nJ0yofL91a1Z89e3Lfo+FhNR9Tisr4L332EP1s7rqXtp60rl4W7t2LQYMGICGDRuiTZs2ANhsVIVCgV27dolukMVTfYM5OwO//MIKhpomiQgC8OST7P3sbBQUsI19VfMY9CbhNxdPVlZW2v3n1Efz5uw/Xg09zkbh3j32tlEjLr2lBs2aaKCs+aK8+TF01q6urnDT5ffYlCnA7t3srtej1odTLRR/5cqjizeVW7cqhwOFhoqaaQrosc5b586dce3aNXz88cdo06YN2rRpg0WLFuHatWvorLqfS/Sn+ma2sWFTlseMqfk4a+vKXrrCQvVtU9E9b6riTcT6M3K4du0aBg8ejGu1jaMTo1kztr9pXVPD5aQq3mpZRkVqBs2aaKCs+aK8+TGarAWBFW6//AIcOKDdH+mqYSrabJlYWAi89ho7tmdP9vDzY+PgVHud6kGv7bGcnZ0xoWo3IpFO1f1NH8XFBSgulrZ4U90aNLGeN6VSiZKSEihFzN4xWappxgkJbHq7hFuw1MSis+aMsuaL8ubHaLKePBn44Qfg11/ZWm+qSXfu7oCjI7s1+sMPQL9+bAjT2bPA22+zIuzB3cc6RUWxIU67dlWO246NBaZNY0OU1qzRq9l6FW/Jyck4ePAgsrOzqwU/d+5cvRpCHlAVb4cOsUXY2rdnsy9r4uzMlucoKFDPNrXU26ZNmzbF77//bpiTK5VsEkleHhvEamx78al63gDW5W/g4s2gWRMNlDVflDc/RpO1qnjq3Vvz+W++AV59lW09tH8/sHw560ULDARefBGYPVu782/fDvz8s+b5+/VjheHLL/Mr3tatW4dJkybB09MTvr6+Gov2KhQKKt7EUhVv//d/wKpVbFHAOXNqPrbKLguWftvUoMrLgSZN2Pt37lSfQi63Z59lExVycjQLOUIIIXV71PbugYGs50xfRUU1rwDg7S3qtqnOY94+/vhjLFq0CJmZmThz5gzi4+PVj9NV1/Ei+hk0iHXJqsYv1XVP/UGlJhQYoHgzsZ6306dPQ6FQGOZ70M6ucnyhMS4XEhTExlQAXIo3g2ZNNFDWfFHe/FhM1l27AvPmsSFOKvfvAwsWiNqXUueet3v37mHIkCF6X5A8gmos4aFDrJdHi+Kt9F6h+o8H0bdN+/cHmjat7GkyEY0aNcK6devQqFEjw1zA3Z395zPW5UJUS4Tou82GDgyeNVGjrPmivPmxmKxXrAAiI9kM1vBw9lxCAusQ2LtX79MqBOFRfYaaxo0bh06dOmGisc68k8DNmzcRGBiItLQ0NHzUlGFD8fNjAyfj4mrfWeDnn4HMTNzt+DQadGXj4srLjW9Illl47DEgOZmNKXviCblbo2nfPuCjj9huFy+9BPz0k9wtIoQQWRjF7++HFRWxdVMvXWIft2gBjBjBxr3pSeeet6ZNm2LOnDk4fvw4WrduXW2BvWnTpundGAJ2W+7u3coZL3X1vL30EvuU6+xDBwfLLdzu3buHgwcPok+fPqhniCUzjHmh3sWLK7cp43Db1OBZEzXKmi/Kmx+LytrJiW1sLyGdi7evvvoKLi4uOHz4MA4/NIhPoVBQ8SbWnDnAF1+w962s2KDGR5BspqkgsNmrbm7GszWWllJSUvDiiy8iLi7OMD8IjHmLrKoFG4fizeBZEzXKmi/Kmx+zz/rIEe2O69lTr9PrXLylpKTodSGipao7xHt7192VlpoKpKSgItsPwGPSrPGmKhaLi02qgGvTpg3u3Lmj24raujCF4m3GDDaF3cAMnjVRo6z5orz5Mfuse/eu3B2pttFpCgVQUaHX6fVa540YkKpgCgxkG9nWZe1aIDoa9Qe/BWC5dDNN7e1NqnADABsbG8NuJD1oEJvIoc2ijLypirexY9lYCgMzeNZEjbLmi/Lmx+yzrlePLfr76qvAqFGit8N6mM5LhQBsQOD//d//YebMmYiKitJ4EJFURVNkJPCoWb0PqjVlPlsnxFIX6AVYj/DIkSMN1zP86qvA//4HdOtmmPPrS6msHIfH6daDwbMmapQ1X5Q3P2afdUYGG4987BjbzH7cOOCff9jvV3f3yoeedO55i4mJwcCBA9GkSRNcunQJrVq1wvXr1yEIAtrXNiuSaE9VvFVdE6Y2qnXeHizyZqlbYwFAWVkZbt68ibKyMrmbwldeXmWX/N69bFbTmDFsgKyBWGzWMqCs+aK8+TH7rO3sgKFD2SM1Ffj2W7aHakkJ+xm9YAHbw1xPOi8V0rlzZzz77LNYsGABXF1dkZCQAG9vb4wYMQLPPPMMJk2apHdjjIWsU41XrQKmTmXvX7tW9ya569YBEybgRvhABCX8iueeY9un6e3PP9m2He3bsyVKSKX794HsbDYG0VimnwNASgpbk0815fz+/Ud/3xBCiJkyyqVCVFJSWA/c4cPArVuiduvR+bbpxYsXMXr0aADsnvX9+/fh4uKChQsXYvHixXo3hDxQdazZoxbwe3CfVFEk0W1TE+55M7jvv2c7GUyZIndLNHl6sr3z1q+vXKiXtsgihBDjUFLCNraPiGB7Y3t6Ar//LnqbRZ2LN2dnZ5SWlgIA/Pz8cPXqVfVrt2/fFtUYAs1Nxeta4w1Q3ye1vl9Q9UP9mfCYtzNnzsDZ2RlnzpwxzAVUYxOMbbapqysweDDwyiuVY94MvMuCwbMmapQ1X5Q3P2af9cmTwKRJgK8vGy89cCCQlgZs2wY884zo0+t8w/Xxxx9HbGwsWrRogX79+uGdd97BuXPnsGPHDjz++OOiG2Txundnt+Vu3tS+eCuRaMxbkybAyJG17+hgxPz9/REdHQ1/f3/DXMCYlwpRURVvBu55M3jWRI2y5ovy5sfss378caBRI2DaNKBDB/ZcbGz14wYO1Ov0Oo95u3btGgoKCtCmTRsUFhbinXfewT///INmzZph6dKlaNy4sV4NMSay3jNXKtmt0/JyNsgxMLD2Y1NTgc2bsemAP0btH4MPPgAWLeLXVIty9CjQowdbLiQ5We7WVLp4ke2T99hjbPPj3bvZWMjXX5e7ZYQQwp3RjHmz0uLGJs913ppU2bDc2dkZa9eu1evCpBZZWaxwA1h3a10aNQJmzcLBK+xD0T1vJiwvLw/Hjh1D165dDbPoo7H2vO3eDbz3HltHiFPPm8GzJmqUNV+UNz9mn7VSadDT67XOGzGgrVsr339o39jaPFgpRHzxVlAAPBjPaGquXLmCZ555BleuXDHMBYx1b1NVoVavXuWEBQOPeTN41kSNsuaL8uaHshaHijdj82CzefTo8ehjKyqA+Hg0So2FAkrxs01Hj2a3bE2wN7VVq1ZIS0tDq1atDHMB1V+GJSXsYSxUhZqHB9thYccOtoaQARk8a6JGWfNFefNDWYtD22MZm4YNgbt3tZvxef8+0L49PgOwGgVwFtv1prol6Ooq7jwysLOzM+z4BldXYPx41gOnT3e4IFRukbJggaiVtTVU7Xlr1449DMzgWRM1ypovypsfyloc6nkzRvXq1b0hvUqVVfRdUCDdDgtSFRYcpaam4o033kBqaqphLmBtDXz1FZvyrVoQVxf5+cDmzcCKFcDSpdK1q2rxxonBsyZqlDVflDc/lLU41PNmyqysWAFXVARnFFr03qZFRUU4ffo0ioqK5G5KzTIyKt//7z/pzlu1eMvOBmJi2FhJ1e13AzD6rM0IZc0X5c0PZS2OTsWbUqnE4cOH8ffff+PGjRsoKiqCl5cX2rVrh4iICATWtawFMQxnZ3XxJtkivSbY8xYaGopTp04Z9iJ5eaxY8vTUfXZI1YKtaiEnVtXi7dIltljvY48ZtHjjkjUBQFnzRnnzY3FZl5ayP7AfHnbTqJFep9Pqtun9+/fx8ccfIzAwEP369cOff/6JnJwcWFtb48qVK5g3bx6Cg4PRr18/HD9+XK+GED09KCIkLd5MsOeNi6eeYltkxcTo/rlVCzYpi7fly9nWWKGh3JYKIYQQoqXkZOCJJ9hwm8aN2b7TwcHsd4mIPai1Kt4ee+wxnD17FuvWrVOvzbJ9+3Zs2rQJf/zxB1JTU3H16lU88cQTGDZsGNatW6d3g4iOqhRvom6blpUBqu5rE+x5O3v2LLy8vHD27FnDXUTMciGGKt769WMbHXt5aRZvuq29rRMuWRMAlDVvlDc/FpP1q6+yIU67dwNxccDp0+wRH8/e6kmr26Z//fUXWrRoUecxjRs3xqxZs/Duu+/SAESOBCdnKCBBz1tZGTBiBOt9M8HZpt7e3oiKioK3t7fhLiJmod6qBVtWFlvmRZtJKbpQFW/l5WzxP9GDIGvGJWsCgLLmjfLmx2KyPnOGFW2hoZKeVuftsSyB0WyvoYX7K9dj0bRM/IyXEF8UqtdESKKl114DvvmG7UH2wQe6fe7IkWy2qcp//z1679pHyc8Hfv+djcGLiGC9bfb2rBB/1NZqhBBihozu93enTsCyZdqt3aoDvZYK+fvvvzFy5Eh07doV6enpAICNGzcitqZNV4lB5bz0OhZhNpKtQuHgIHdr5FNQUIDY2FgUFBQY7iKq26b69Lx9/z3rcQsMBNq0qdwWQ4yUFGD4cNZjCrB98jjsssAlawKAsuaN8ubHYrJevJhtYXjoEHDnDvv9UfWhJ52Lt+3btyMyMhKOjo6Ij49HyYPV5nNzc/HJJ5/o3RCiH9X3vbMz+92tt/Jyk90aCwAuX76MJ554ApcvXzbcRcTcNrWyAry9WY9YQgLb4F6sqrsrqHCYtMAlawKAsuaN8ubHYrKOiACOHwf69mW/A+rVq9zOUMT6nDqv8/bxxx9j7dq1GD16NLZs2aJ+vnv37vj444/1bgjRT0lqFtogE9YODQCI6CLeswcYMADo1g04elSy9vESFhaGS5cuoXHjxoa7iLHtb1rTAr2ff84K8bAwg12WS9YEAGXNG+XNj8VkffCgQU6rc/GWlJSEnj17Vnve3d0dOQbeEJtUV3/tJ0jAF1hTPguAiJ5PVW+Sid57dXBwQPPmzQ17kbZt2RZZjz+u2+fdv8/2G/XzY8WVra007ampeBswQJpz14FL1gQAZc0b5c2PxWTdq5dBTqvzbVNfX19cuXKl2vOxsbFo0qSJJI0i2iu2ZlNM3a1FjqEy4a2xADZINSoqCjdv3jTcRZ58km2R9dprun1eRgbw00/AunXApk1AixbAjBni2yPD1lgAp6wJAMqaN8qbH7PO+uzZysV4z56t+6EnnYu38ePH46233sKJEyegUCjw33//YfPmzXj33XcxadIkvRtC9HPfihVvrlYiizcTX6A3Ly8Pe/fuRZ6IAaAGo9pdwd8fKC5mOyEkJ4s/b01j3hITgR9/BAy4crlRZ21mKGu+KG9+zDrrtm2B27cr32/Xjr19+NGund6X0Pm26cyZM6FUKtG3b18UFRWhZ8+esLe3x7vvvoupU6fq3RCinyIrtpaXi9jiTdXzZqLFW1hYGC5cuGDYiyiVrMgtKmKFmLZUa7z5+VUuDyLFQr019bxt3gx88gkwdSqbom4AXLImAChr3ihvfsw665QUtnC66n0D0Ll4UygU+PDDDzFjxgxcuXIFBQUFCAsLg4uBFgQldSsE63lzgcjp1ia8ryk3ly4BLVsCDRpU/lWlDUMVb6NGAeHhbOkRFdoiixBC5FV1EoaBJmTotc4bANjZ2SEsLAydO3emwk1GBcKD7bEEy75tev78eQQFBeH8+fOGu0jVpUJ0Wdu6avGm6rHLyKi+QbGuOnViW2NV7WHjULxxyZoAoKx5o7z5oazF0bnnrU+fPlDUsaDYgQMHRDWI6CZfyYo3R7HFW9u27Be+xFt48FK/fn2MHDkS9evXN9xFVL2SZWVs7Jq221lULd58fNj75eVswUZV17pUOCzSyyVrAoCy5o3y5oeyFkfnnre2bdsiPDxc/QgLC0NpaSlOnz6N1q1b69WI1atXIygoCA4ODujSpQtOnjxZ67FlZWVYuHAhQkJC4ODggPDwcOzZs6fW4z/99FMoFApMnz5dr7YZuzSn5liM93C6zVhxJ4qKAnbt4rLUhCH4+/vj448/hr8uY9F0VXUlZF0G2WZlsbd+foCdHdvOChB/6/TPP4G//tJsC4eeNy5ZEwCUNW+UNz+UtTg697wtW7asxufnz5+v1zYXW7duRVRUFNauXYsuXbpg+fLliIyMRFJSUo0b1s6ePRubNm3CunXrEBoair179+KFF17AP//8g3YPzdw4deoUvvzyS7SpOibIzFy3b441WIx53YEX5W6MjIqKinDp0iWEhobCycnJMBexsgJcXVmxlJtb2Yv2KL//znrZVD11rVsDd++yHjwxxo1jBeDp05WzljgUb1yyJgAoa94ob34oa3H0HvP2sJEjR2LDhg06f97SpUsxfvx4jB07FmFhYVi7di2cnJxqPdfGjRvxwQcfoF+/fmjSpAkmTZqEfv36YcmSJRrHFRQUYMSIEVi3bh3qcV4HiyfVFpnOziJPVF4uui1yunTpEjp06IBLly4Z9kL67G9qZcVuj6rGhh44AJw5A3ToIK4tNc025VC8ccuaUNacUd78WEzWaWlA1bXsTp4Epk9na4aKIFnxduzYMTjouDp/aWkp4uLiEBERUdkgKytERETg2LFjNX5OSUlJtes4OjoiNjZW47nJkyejf//+GueuTUlJCfLy8tSP/Px8nb4OORXlVyAY19AwV+SU64AA1jN08aI0DeMsNDQUcXFxCDX0mD0x+5tKqbiYPQDN4s3HB9iwAfjhB90mVeiAW9aEsuaM8ubHYrJ+5ZXKLbIyM4GnnmIF3IcfAgsX6n1anW+bDh48WONjQRCQkZGBf//9F3PmzNHpXLdv30ZFRQV8Hrr95OPjU2s1HhkZiaVLl6Jnz54ICQlBTEwMduzYgYqKCvUxW7ZswenTp3FKy4VKo6OjsWDBAp3abiyscu7iGkKARQAWVrBeHn3k5gIlJRJ04cnDyckJ7du3N/yFXngB6NqVbTCsjYwMYNo0Nl3888+la4eqZ011K1fF0REYK3L84yNwy5pQ1pxR3vxYTNbnzwOdO7P3t20DWrVi+4f/9RcwcSIwd65ep9X5N727u7vGo379+ujduzf++OMPzJs3T69G6GLFihVo1qwZQkNDYWdnhylTpmDs2LGwelC0pKWl4a233sLmzZu17gmcNWsWcnNz1Y/ExERDfgmSulNcpdgqKtLvJCUl7AGY7FIh//33H2bPno3/VLsZGMpHH7Ftrlq10u74GzeAn39m22Op7NjBZvWKKbJUs0nd3fUv2PXELWtCWXNGefNjMVmXlQH29uz9/fuBgQPZ+6Ghoiat6dzz9s033+h9sYd5enrC2toaWarZeA9kZWXB19e3xs/x8vLCzp07UVxcjDt37sDf3x8zZ85U76saFxeH7OxsjYq+oqICR44cwapVq1BSUgJra2uNc9rb28NeFS5gUtt13ClyhBIKWEFgA+D0WXOv6tdbtRfHhNy9exebNm3CsGHDjGv2UtVlQlQqKoCkpMpZp/qoa1/Tv/9mYyx699a8rkSMNmszRFnzRXnzYzFZt2wJrF0L9O8P7NvHOgAAtm1igwZ6n1bn4k1KdnZ26NChA2JiYvD8888DAJRKJWJiYjBlypQ6P9fBwQEBAQEoKyvD9u3b8fLLLwMA+vbti3PnzmkcO3bsWISGhuL999+vVriZusIiBYrgBBcUAgUF2s+ArEpVvLm4ACaaT6tWrXD9+nXDX6iiguWsUGjXS1lT8ab6QSXmL866irfp09kM1N272Q8MiXHLmlDWnFHe/FhM1osXs+E2//sfMGYM2xUHAH77rfJ2qh60Kt7q1atX58K8Vd29e1enBkRFRWHMmDHo2LEjOnfujOXLl6OwsBBjH9xSGj16NAICAhAdHQ0AOHHiBNLT09G2bVukp6dj/vz5UCqVeO+99wAArq6uaPXQLS1nZ2c0aNCg2vPmoLCQbZHlgsLKqae6MvF9Tbl6/31gyRLg3XfZf8ZHqal4q7pFliBUrh2ni9atgfXra/43oy2yCCHEOPTuzbZTzMvT/GN7wgRAxBIpWhVvy5cv1/sCjzJ06FDcunULc+fORWZmJtq2bYs9e/aoJzGkpqaqx7MBQHFxMWbPno1r167BxcUF/fr1w8aNG+GhWlnewhQUAAVwgQ+y9S/ezGBf08TERAwZMgQ//fQTwsLCDHchXWeb1lW8FRezwlmf791Gjdg6bzUx8C4L3LImlDVnlDc/FpW1IABxccDVq2z2qasrW7Dd0MXbmDFj9L6ANqZMmVLrbdJDhw5pfNyrVy+dJxQ8fA5zIQiVPW8A9C/eXF3Z7bWAAOkax5mbmxsiIyPhZujeQ32Lt6pjOhwdWaGcm8tel/oPDwP3vHHLmlDWnFHe/FhM1jduAM88A6SmsomBTz3FfucuXsw+XrtWr9OKGvMmCAIOHjyI+/fvo1u3bma9GK4xKi1lQ7A2YSQ+mnoL9g0b6neiDh3Y+CgT1rBhQyxdutTwF1L1TqpuNT9KdjZ7+/DEAT+/yuKtRQvd23HmDNt2q0UL1gtXlYGLN25ZE8qaM8qbH4vJ+q23gI4dgYQEzQkKL7wAjB+v92m1XmMgJycHY8aMQevWrTF+/Hjk5eXhiSeeQEREBAYMGIAWLVrg7NmzejeE6E61G9n/8B6sl/7PZDeVl0JxcTGSkpJQrFq41lB07Xk7cYIVcH36aD7fujXQtq1+490AYMUK9tfc5s3VXzNw8cYta0JZc0Z582M0WUdHA506sd4wb2/g+efZagBVFRcDkyez4svFBXjxxco9qx/l77+B2bPZbdKqgoKA9HS9m6118fbuu+/i2LFjGDZsGM6dO4dnnnkGFRUVOHbsGE6cOIEWLVrgww8/1LshRHequ6T29oCNmD5UA63Ez1NiYiJCQ0MNv0afrj1vqq2xVPuaqmzbBsTHVy/qtFXXbFMOY964ZE0oa84ob36MJuvDh1lhdvw4W8qjrAx4+mnNYUhvvw3s2sXW6zx8mK0U8NCGBbVSKtktsofdvCluaS5BS/7+/sKhQ4cEQRCEmzdvCgqFQjh48KD69RMnTgg+Pj7ans6opaWlCQCEtLQ0uZtSp8REQQAEoXG9XEG4fl0Q7tzR70Tvvy8ITk6CMHu2tA3kKD8/X/j777+F/Px8w17oxAkWeqNGhr3Oo/TsydqxZUv11y5eFISvvxaEI0cMcmluWRPKmjPKmx9DZS3693d2NvvZevgw+zgnRxBsbQXhp58qj7l4kR1z7Nijz/fyy4Iwfjx738VFEK5dE4T8fEF48klBePVV/dooCILW/TVZWVl47LHHAAABAQFwcHBAYGCg+vVGjRrh1q1b+leRRGeq26bRpe8AQevZ4n+zZ+t+otxctjsD55X6peTi4oIePXoY/kJ+fsDLL2u3+G1SEtu/rmVLQOrt11S9ajVNdggNNegtdG5ZE8qaM8qbH0NnnZ+fr7Hg/sOL8ddKdVelfn32Ni6O9cZV3Sc9NJSNNT52DHj88brPt2QJEBkJhIWx26+vvAIkJ7NF2n/8UcevqpLWv62VSqXGArfW1tYaa79puw4ckY6qV7fU3kXzCV2ZwVIhmZmZiI6ORmZmpmEvFBgIbN0KaLN8TnIysH078Pvv1V87cABo3hx47jn92lHXbVMD45Y1oaw5o7z5MXTWYWFhGlt5qtaKrZNSyRY57969cgvEzEw2Xu3hP5R9fNhrj9KwIZus8MEH7PZru3bAp5+yYTPa7pFdA51GSq1fvx4uD7ZfKi8vx7fffgvPB1v85Ofn690Ioh9VrVZu/2CpEFVXnK5UxZsJT9nOzs7G0qVL0b9//1q3VuOupjXeVKytgcuX9R9vWFfxVlwMHDoE5OcDQ4bod/46GGXWZoqy5ovy5sfQWScmJiKgyvJXWvW6TZ7MNpKPjZW2MTY2wMiR0p5S2wMbNWqEdevWqT/29fXFxo0bqx1D+FHVahUOItd5U3UTm3DPW5s2bfjdtlcqWWH0qO3E6irequ6yoKuyssp//JqKt/x84NlnK48VNZulOq5ZWzjKmi/Kmx9DZ+3q6qrbGnJTprAls44cYb1lKr6+bF2unBzN3resLPZaTX77Tfvrqjaq15HWP9UtYg8yE6Oq1QRHkcWbGfS8ceXtDdy5A1y8WPfYMm2Kt4IC9njQo60VQQC+/pr1vtU05q3qc7m5ojY/JoQQsyYIwNSpwC+/sDsWwcGar3foANjaAjExbIkQgI1nTk0Funat+ZwP9mp/JIWi5pmoWjDdEeqksnhzop63S5cuoVOnTrh06ZLhL6aa3v2o5UJUG89X3V2h6jmcH/y76dr7ZmcHvPYa8M47Nfeq2dpWFoMGWOuNa9YWjrLmi/Lmx2iynjwZ2LQJ+OEH9nM5M5M97t9nr7u7s60Io6KAgwfZBIaxY1nhVttkBaVSu4eehRugZfG2ZcsWrU+YlpaGo0eP6t0goj31EDdnkcXb448DPXqw9chMlJOTE9q3bw8nEXvFaU3bhXrr6nmr+rw+t04fxYAL9XLN2sJR1nxR3vwYTdZr1rA/xHv3Zj+TVY+tWyuPWbaMTS578UWgZ092u3THDtmaDGh523TNmjVYsGABxo4dq95Noarc3FwcPXoUmzZtwr59+/D1118bpLFEk6pWy/NrDkyYoN82S4Co6crGolGjRvjyyy/5XEzb4u32bfa2tuLN3x+4ckX34i0zk81eCgionBH1sHr1gLQ0gxRvXLO2cJQ1X5Q3P0aTtTaTxhwcgNWr2UNXCxfW/frcubqfE1oWb4cPH8Zvv/2GlStXYtasWXB2doaPjw8cHBxw7949ZGZmwtPTE6+++irOnz8PHx8fvRpDdKMq3u4FtQOijeA/gYxKS0uRnZ0Nb29v2D28DYnUtN1l4coV4O7d2scStmzJJhfo2t7Dh4Fhw4BevdgYjZoYcJcFrllbOMqaL8qbH4vJ+pdfND8uKwNSUtiQl5AQwxZvADBw4EAMHDgQt2/fRmxsLG7cuIH79+/D09MT7dq1Q7t27WBlwou8miLVbVPVXVNLdv78eXTo0AFxcXFo3769YS+mbc+blRVbiLE2//d/+l1fVZDVtcabAW+bcs3awlHWfFHe/FhM1vHx1Z/LywNefZVtTq8nndcQ8PT0xPPazqQgBqXqeXNxUgJ3c9gAyyrr2mjl8mW2aGBgICD3wFERmjZtij179qBp06aGv5iu+5tKTVWQ1TTTVGXSJGDQILbYpMS4Zm3hKGu+KG9+LDprNze2686AAcCoUXqdQtoFoAhXquLNpyQVaBDM7surZshoKy+PbY1VVCR9Azlyc3NDZGQkn4t17Ai89FLlMiGzZ7Pbn1WdOMHGsr35JvD++7Wfq7SUrbZ9507Nr8+eXTmRZPdutnHyP/+wj+vqeTNgFlyztnCUNV+UNz8Wn3VurqgOACreTJjqtqldvQf3TYuL2dTjuhaOfZjqm8fE13jLzs7Gli1bMGzYMHiL2HJEK+PGsYfKunVAdnbNxx4/Xvt5EhPZekDJybUfM2VKZfF27BjwxReVr1VdSJIjrllbOMqaL8qbH4vJuurPbIBNkMjIADZurFxMXQ9UvJkwVc+bQ4Mqg96KiirXIdOGGexrCgD//fcfZs2ahZ49e/L/QTB9es3LtNja1r0lSosWwIwZwI0btR9TtXetd2+2qCNQufZQbVJT2TYv3t6sp1BCsmZtYShrvihvfiwm62XLND+2smJ/kI8ZA8yapfdpqXgzYerirZ4j+6UuCOxJfYo3E+95a9u2LQr1XedOLH3/AyoUwPjx2h//1FPsoY1du1iv3UsvAT/9pF/7aiFr1haGsuaL8ubHYrJOSan9NV2HOVWh8/TQgwcP6n0xIi31bFMXReWUU103pzeD3RVIDRwd2VsTH8tICCFmp6QEWLq0+lZcOtC5eHvmmWcQEhKCjz/+GGlpaXpfmIinnm3qAv13WTCTnrfLly+jd+/euHz5stxNMQ6q4k3EX3a1oaz5oaz5orz5MfusS0rYXZmOHYFu3YCdO9nzGzawom3ZMuDtt/U+vc7FW3p6OqZMmYKff/4ZTZo0QWRkJLZt24bS0lK9G0H0o6rTnJ2hf/Hm58eWk6hrg3UTYGtri4YNG8LW1lbuphgH1ZYzBijeKGt+KGu+KG9+zD7ruXPZ1ltBQcD168CQIWwnpOXLWa/b9et1r0TwCApB0GZviJqdPn0a33zzDX58sL3SK6+8gnHjxiE8PFzvBhmDmzdvIjAwEGlpaWgo04y+R1EqKyeVZmUB3kveZ6v5v/su0Ly5vI0j8vvrL7ZcSHg4cOaM3K0hhBAujOb3d5MmrFAbOJBNHmvThi3M+/XXlRPPRBC1JUL79u0xa9YsTJkyBQUFBdiwYQM6dOiAJ554AhcuXBDdOFK7qkOZXFwALF7Mlqyw0MKtvLwcd+/eRXl5udxNMQ4GHPNGWfNDWfNFefNj9lnfvAl06MDeb9UKsLdnt0klKNwAPYu3srIy/Pzzz+jXrx8aN26MvXv3YtWqVcjKysKVK1fQuHFjDBkyRJIGkpqp7o4qFJW/py3Z2bNn0aBBA5w9e1buphgHA455o6z5oaz5orz5MfusKyo09622sXnQ0yINnZcKmTp1Kn788UcIgoBRo0bhs88+Q6tWrdSvOzs74/PPP4e/v79kjSTVqSaVOjk9KOTLy9mTdnaV45208fTTrEv322/Z+yYqODgY27dvR7CI2TtmJSgIWLECqF9f8lNT1vxQ1nxR3vyYfdaCwG6T2tuzj4uLgYkTq29GvmOHXqfXecxb37598frrr2Pw4MGwVzXqIeXl5Th69Ch69eqlV6PkZjT3zOtw9iwbzuTjA2Rmgi0Gu3kzsGQJEBWl/YnatAHOnWPbLkVEGKy9hBBCiKEZze/vsWO1O+6bb/Q6vc49bzExMY8+qY2NyRZupkJjpilQ2dv27rvVZ7BkZ1eu1D9pErB+feVrqvEGJr5UyO3bt7Fz5048//zz8PT0lLs5Zo2y5oey5ovy5sfss9azKNOWzmPeoqOjsWHDhmrPb9iwAYsXL5akUeTR1Av0qoq3J56o3GWhvFzzUVVFRfXXvL1NfqmQ1NRUjB8/HqmpqXI3xThUVAB//81mnVZUSHpqypofypovypsfylocnW+bBgUF4YcffkC3bt00nj9x4gSGDRuGlLq2gjARRtPtWoedO4EXXgC6dgX++efBk3fvsvvqD/P1ZfupAUBOTvUZiA0aVN6XJ+ahtLTy3/TePcDDQ9bmEEIID6bw+1sKOt82zczMhJ+fX7Xnvby8kJGRIUmjyKNVu20KaDc43cODfpFbAltbVrArlWzGKf2bE0KI2dD5tmlgYCCOHj1a7fmjR4/SDFOOqt02tXBXrlxB//79ceXKFbmbYhyqriEj8VpvlDU/lDVflDc/lLU4Ove8jR8/HtOnT0dZWRmefPJJAGwSw3vvvYd33nlH8gaSmmnsa0pgZWUFe3t7WFmJWnfavDg6sm8Uidd6o6z5oaz5orz5oazF0bl4mzFjBu7cuYM333xTvZ+pg4MD3n//fcyaNUvyBpKa1Xjb1II1adIEO/RcL8dsGWh/U8qaH8qaL8qbH8paHJ2LN4VCgcWLF2POnDm4ePEiHB0d0axZs1rXfCOGQbdNNSmVSpSVlcHW1pb+klMx0C4LlDU/lDVflDc/lLU4eifm4uKCTp06oVWrVlS4yYBum2o6c+YMHBwccIY2Ya9koOKNsuaHsuaL8uaHshZH5543APj333+xbds2pKamqm+dqlA3KB9021RTUFAQNm7ciKCgILmbYjymT2fLxzz2mKSnpaz5oaz5orz5oazF0Xmdty1btmD06NGIjIzEX3/9haeffhqXL19GVlYWXnjhBXxj4FWFeTCFdWKGDAF+/hlYuRKYMkXu1hBCCCHyM4Xf31LQ+bbpJ598gmXLlmHXrl2ws7PDihUrcOnSJbz88sto1KiRIdpIakC3TTXdvXsXP/74I+7evSt3U8weZc0PZc0X5c0PZS2OzsXb1atX0b9/fwCAnZ0dCgsLoVAo8Pbbb+Orr76SvIGkZnTbVNP169fxyiuv4Pr163I3xXhcvw4cOwbcvCnxaSlrXihrvihvfihrcXQe81avXj3k5+cDAAICAnD+/Hm0bt0aOTk5KJJ4MVBSO5ptqik8PBwFBQVwcHCQuynGY+5cYONG4LPPgBkzJDstZc0PZc0X5c0PZS2OzsVbz549sW/fPrRu3RpDhgzBW2+9hQMHDmDfvn3o27evIdpIakC3TTVZW1vDmSpZTQZa542y5oey5ovy5oeyFkfn26arVq3CsGHDAAAffvghoqKikJWVhRdffBFff/215A0kNaPbppquXbuGIUOG4Nq1a3I3xXgYaKkQypofypovypsfylocnXreysvLsXv3bkRGRgJg21vMnDnTIA0jdaPbppoqKiqQl5eHiooKuZtiPAxUvFHW/FDWfFHe/FDW4ui8VIiTkxMuXryIxo0bG6pNsjOFqcZ2dkBZGZCWBhhpE4ncPvqIjXsbPx6gyUSEEAtgCr+/paDzbdPOnTvTisgyKy1lhRtAPW+kDgbqeSOEECIvnYu3N998E1FRUVi1ahWOHTuGs2fPajyI9AQBSE8Hbtxgj6SkyteoeGNOnz4NGxsbnD59Wu6mGA8DTVigrPmhrPmivPmhrMXRebaparLCtGnT1M8pFAoIggCFQkH3rw1gyhTg//6v+vO2tuz2KQECAwOxevVqBAYGyt0U49GlC7BgAdCypaSnpaz5oaz5orz5oazF0XnM240bN+p83RzGwhnTPfO9e4FnnmHvP7wczvDhwIYN/NtECCGEGCNj+v1tSDr3vJlDcWYqcnOB119n70+bBqxYIW97jFlOTg6OHDmCnj17wsPDQ+7mmDXKmh/Kmi/Kmx/KWhydi7fvv/++ztdHjx6td2OIphkz2M5GISHAJ5/I3Rrjdu3aNQwaNAhxcXFo37693M0xDkVFwNWrgEIBtGol2Wkpa34oa74ob34oa5EEHXl4eGg8nJ2dBYVCIdjb2wv16tXT9XSCIAjCqlWrhMaNGwv29vZC586dhRMnTtR6bGlpqbBgwQKhSZMmgr29vdCmTRvhzz//1Djmk08+ETp27Ci4uLgIXl5ewqBBg4RLly5p3Z60tDQBgJCWlqbX1yOFvXsFgU1VEITDh2VrhskoLS0VsrOzhdLSUrmbYjyOHGHfQI89JulpKWt+KGu+KG9+DJW1Mfz+5kHn2ab37t3TeBQUFCApKQk9evTAjz/+qHPxuHXrVkRFRWHevHk4ffo0wsPDERkZiezs7BqPnz17Nr788kusXLkSiYmJmDhxIl544QXEx8erjzl8+DAmT56M48ePY9++fSgrK8PTTz+NQtW2BEYuL48tzQUAU6cCPXvK2x5TYGtrCy8vL9ja2srdFOOhWipE4j2HKWt+KGu+KG9+KGuRpKoCT506JTRv3lznz+vcubMwefJk9ccVFRWCv7+/EB0dXePxfn5+wqpVqzSeGzx4sDBixIhar5GdnS0AEA5r2YUld+X+xhusw6RJE0EoKJClCSYnJSVFGDNmjJCSkiJ3U4zH+fPsG6lBA0lPS1nzQ1nzRXnzY6is5f79zYvOPW+1sbGxwX///afT55SWliIuLg4RERHq56ysrBAREYFjx47V+DklJSVweGjapaOjI2JjY2u9Tm5uLgCgfv36tZ4zLy9P/cjPz9fp65DS5cvAl1+y97/+mtZx01ZJSQmuXLmCkpISuZtiPAy0SC9lzQ9lzRflzQ9lLY7OExZ+++03jY8FQUBGRgZWrVqF7t2763Su27dvo6KiAj4+PhrP+/j44NKlSzV+TmRkJJYuXYqePXsiJCQEMTEx2LFjR63ryymVSkyfPh3du3dHq1oGbUdHR2PBggU6td1QUlLY2zZtgN69ZW2KSWnevHmdBbxFqlq8CQKbuCABypofypovypsfylocnYu3559/XuNjhUIBLy8vPPnkk1iyZIlU7arVihUrMH78eISGhkKhUCAkJARjx47FhloWPJs8eTLOnz9f5zfJrFmzEBUVpf44PT0dYWFhkrddG6pOPzc3WS5PzImqeBMEtqeavb287SGEECIJnW+bKpVKjUdFRQUyMzPxww8/wM/PT6dzeXp6wtraGllZWRrPZ2VlwdfXt8bP8fLyws6dO1FYWIgbN27g0qVLcHFxQZMmTaodO2XKFOzevRsHDx6sc7E+e3t7uLm5qR+urq46fR1SUhVvMjbBJJ05cwZubm60725VquINkPTWKWXND2XNF+XND2UtjmRj3vRhZ2eHDh06ICYmRv2cUqlETEwMunbtWufnOjg4ICAgAOXl5di+fTsGDRqkfk0QBEyZMgW//PILDhw4gODgYIN9DVKj4k0/fn5+mD9/vs5/QJg1Ozu2WODcuWwvNYlQ1vxQ1nxR3vxQ1iLpOsNh8ODBwqefflrt+cWLFwsvvfSSzjMmtmzZItjb2wvffvutkJiYKEyYMEHw8PAQMjMzBUEQhFGjRgkzZ85UH3/8+HFh+/btwtWrV4UjR44ITz75pBAcHCzcu3dPfcykSZMEd3d34dChQ0JGRob6UVRUpFWb5Jyt8vHHbILguHHcL00IIYSYNJ1/fx8+LAjPPScIfn7sl+8vv2i+PmZM5aKrqkdkpNTN1pnOPW9HjhxBv379qj3/7LPP4siRIzoXj0OHDsXnn3+OuXPnom3btjhz5gz27NmjnsSQmpqKjIwM9fHFxcWYPXs2wsLC8MILLyAgIACxsbEa22usWbMGubm56N27N/z8/NSPrVu36tw+3qjnTT95eXnYv38/8vLy5G6K2aOs+aGs+aK8+TGarAsLgfBwYPXq2o955hkgI6PyoceatlLTecJCQUEB7Ozsqj1va2ur9z/ClClTMGXKlBpfO3TokMbHvXr1QmJiYp3nEwRBr3YYA1WENGFBN1euXMFTTz1FW6087OZN9hdBo0aSrTtDWfNDWfNFefNjNFk/+yx71MXeHqhlHL5cdO55a926dY09WFu2bJFthqY5oZ43/bRs2RIpKSlo2bKl3E0xLhERQFgYEBcn2Skpa34oa74ob34MnXV+fr7G+q2i1pM7dAjw9gaaNwcmTQLu3JGsnfrSuedtzpw5GDx4MK5evYonn3wSABATE4Mff/wRP/30k+QNtDRUvOnH3t4eQUFBcjfD+BhgoV7Kmh/Kmi/Kmx9DZ/1wZ9K8efMwf/583U/0zDPA4MFAcDBw9SrwwQesp+7YMcDaWprG6kHnnrcBAwZg586duHLlCt5880288847uHnzJvbv319tDTiiOyre9JOamorJkycjNTVV7qYYFwMUb5Q1P5Q1X5Q3P4bOOjExEbm5uerHrFmz9DvRsGHAwIFA69bA888Du3cDp06x3jgZ6dzzBgD9+/dH//79pW4LARVv+iosLMSxY8dQWFgod1OMiwGKN8qaH8qaL8qbH0Nn7erqCjdDDB5v0gTw9ASuXAH69pX+/FrSuXg7deoUlEolunTpovH8iRMnYG1tjY4dO0rWOEtExZt+WrRogdOnT8vdDOOjKt6KiiQ7JWXND2XNF+XNj8lmffMmG/Mm8/p0Ot82nTx5MtLS0qo9n56ejsmTJ0vSKEtGxRuRlIE2pyeEELNQUACcOcMeANtg/MwZIDWVvTZjBnD8OHD9OhATAwwaBDRtCkRGytdm6FG8JSYm1jitt127do9cwoM8GhVv+jl79iz8/Pxw9uxZuZtiXAxQvFHW/FDWfFHe/BhN1v/+C7Rrxx4AEBXF3p87l01IOHuWjXl77DFg3DigQwfg779l3yta59um9vb2yMrKqraXaEZGBmxs9BpCRx4QBFboA1S86crLywuTJ0+Gl5eX3E0xLs88w8ZndOgg2Skpa34oa74ob36MJuvevdkv39rs3cutKbpQCDquaDt8+HBkZGTg119/hbu7OwAgJycHzz//PLy9vbFt2zaDNJSnmzdvIjAwEGlpaXVuaC+1wkLAxYW9n59f+T4hhBBCHk2u39+86Xzb9PPPP0daWhoaN26MPn36oE+fPggODkZmZiaWLFliiDZaDNUtU4VCssXwLUZBQQGOHTuGAlXXJTEYypofypovypsfylocnYu3gIAAnD17Fp999hnCwsLQoUMHrFixAufOnUNgYKAh2mgxVMWbiwsr4Ij2Ll++jG7duuHy5ctyN8W4FBYCaWlAdrZkp6Ss+aGs+aK8+aGsxdFrkJqzszMmTJig8dzFixfx9ddf4/PPP5ekYZZIta8pjXfTXYsWLXD+/PlqYzEt3po1bLbUyJHAxo2SnJKy5oey5ovy5oeyFkfnnreqCgsL8fXXX6Nbt25o2bIl9uzZI1W7LJKq5402pdedo6MjWrZsCUfV7ErCGGC2KWXND2XNF+XND2Utjl7F29GjR/Haa6/Bx8cHEyZMQLdu3ZCYmIjz589L3T6LQsuE6O/mzZuYMWMGbt68KXdTjIsBijfKmh/Kmi/Kmx/KWhyti7fs7Gx89tlnCA0NxUsvvQQPDw8cOnQIVlZWeO211xAaGmrIdloEKt70l5ubi99++w25ublyN8W4ODmxtxIWb5Q1P5Q1X5Q3P5S1OFqPeWvcuDFeeuklrFixAk899RSsrETdcSU1oOJNfy1btkRSUpLczTA+Buh5o6z5oaz5orz5oazF0boCa9y4MWJjY3HkyBGaHWIgVLwRydH2WIQQYna0Lt4uXbqETZs2ISMjA506dUKHDh2wbNkyAICC1rWQBBVv+rtw4QKaNm2KCxcuyN0U42KA4o2y5oey5ovy5oeyFkene5/du3fHhg0bkJGRgYkTJ+Knn35CRUUF3nzzTaxbtw63bt0yVDstAhVv+vPw8MCQIUPg4eEhd1OMS0AA8PrrwPDhkp2SsuaHsuaL8uaHshZH5+2xHqZa323jxo24e/cuysrKpGqbbOTaXmP8eGD9euCjj4DZs7ldlhBCCDELtD2Wllq0aIHPP/8c6enp2Lp1qxRtsljU86a/+/fv4+zZs7hPY7sMjrLmh7Lmi/Lmh7IWR7IpozY2Nhg8eLBUp7NIVLzp7+LFiwgPD8fFixflbopxEQQgJwfIyGDvS4Cy5oey5ovy5oeyFkev7bGIYVDxpr/mzZvj5MmTaN68udxNMS55eUC9euz9+/cBBwfRp6Ss+aGs+aK8+aGsxaHizYhQ8aY/Z2dndOrUSe5mGJ+qW89IVLxR1vxQ1nxR3vxQ1uLQSrtGhDam119GRgbmz5+PjIwMuZtiXGxtAdWC2hKNLaGs+aGs+aK8+aGsxaHizYjQxvT6u337NtavX4/bt2/L3RTjolBIvtYbZc0PZc0X5c0PZS2OzkuFvPDCCzUuyqtQKODg4ICmTZvilVdeMen72HJNNXZ0BIqLgZQUICiI22WJufPyAm7fBs6dA1q1krs1hBBiMLRUSC3c3d1x4MABnD59GgqFAgqFAvHx8Thw4ADKy8uxdetWhIeH4+jRo4Zor9kqL2eFG0C3TYnEaIssQggxKzoXb76+vnjllVdw7do1bN++Hdu3b8fVq1cxcuRIhISE4OLFixgzZgzef/99Q7TXbKlumQJUvOkjMTERbdq0QWJiotxNMT4SF2+UNT+UNV+UNz+UtTg6zzb9+uuvcfToUVhZVdZ9VlZWmDp1Krp164ZPPvkEU6ZMwRNPPCFpQ82dqnizs2MPohtXV1f07t0brlT5VvfCC0B2Nrt9KgHKmh/Kmi/Kmx/KWhydx7zVq1cP3333HQYOHKjx/G+//YYxY8bg3r17SE5ORufOnXHv3j1JG8uLHPfML1xgw5EaNGDDkwghhBCiGxrzVotRo0Zh3LhxWLZsGWJjYxEbG4tly5Zh3LhxGD16NADg8OHDaNmypeSNNWe0xps4xcXFuHLlCopVAweJwVDW/FDWfFHe/FDW4uhcvC1btgzTp0/HZ599hp49e6Jnz5747LPP8Pbbb2Pp0qUAgKeffhpbtmyRvLHmjIo3cRITE9GsWTMaP1GT8nK2iKBEPyQpa34oa74ob34oa3F0Lt6sra3x4YcfIiMjAzk5OcjJyUFGRgY++OADWFtbAwAaNWpk1t2VhkDFmzjNmjXDwYMH0axZM7mbYnxeeQVwdwfWr5fkdJQ1P5Q1X5Q3P5S1OKK2x3Kj1WQlQ8WbOKrBr6QGEs82paz5oaz5orz5oazF0bnnLSsrC6NGjYK/vz9sbGxgbW2t8SD6oeJNnKysLPzvf/9DVlaW3E0xPhIXb5Q1P5Q1X5Q3P5S1ODr3vL366qtITU3FnDlz4OfnV+NuC0R3tK+pOJmZmYiOjsbTTz8NHx8fuZtjXFTFW1GRJKejrPmhrPmivPmhrMXRuXiLjY3F33//jbZt2xqgOZaLet7ECQ8Px927d+VuhnGSuOeNsuaHsuaL8uaHshZH59umgYGB0HFpOKIF2pSeGAxtj0UIIWZF5+Jt+fLlmDlzJq5fv26A5lgu6nkTJykpCV27dkVSUpLcTTE+EhdvlDU/lDVflDc/lLU4Ot82HTp0KIqKihASEgInJyfY2tpqvE7doPqh4k0cBwcHtGzZEg4ODnI3xfiEhQFDhgBdukhyOsqaH8qaL8qbH8paHJ23x/ruu+/qfH3MmDGiGmQM5Nhe46mngP37gY0bgZEjuVySEEIIMSuWsj2Wzj1v5lCcGSPqeROnrKwMt2/fhqenZ7XeYCItypofypovypsfylocrca85anWsXjwfl0Poh8q3sQ5d+4c/P39ce7cObmbYpyUSqCkRJJTUdb8UNZ8Ud78UNbiaNXzVq9ePWRkZMDb2xseHh41ru0mCAIUCgUqKiokb6QloOJNnJCQEOzevRshISFyN8X47N/P7su3aQMkJIg+HWXND2XNF+XND2UtjlbF24EDB1C/fn0AwMGDBw3aIEtFxZs47u7u6N+/v9zNME729uytRLNNKWt+KGu+KG9+KGtxtCreevXqVeP7RBqCQMWbWLdu3cK2bdvw8ssvw8vLS+7mGBcnJ/ZWouKNsuaHsuaL8uaHshZHr43pc3JycPLkSWRnZ0OpVGq8Nnr0aEkaZkmKiwHV3WYq3vRz8+ZNREVFoVu3bvSD4GESr/NGWfNDWfNFefNDWYuj81Ihu3btwogRI1BQUAA3NzeN8W8KhcIs1nnjPdU4OxtQbe1WUQFY6bx0MiF1SEkBmjRhPXCFhXK3hhBCDMZSlgrRuUx455138Nprr6GgoAA5OTm4d++e+mEOhZscVJN0nZ2pcCMGULXnjba2I4QQk6dzqZCeno5p06bBSTWOhohG493ES05ORkREBJKTk+VuivFRFW+CAJSWij4dZc0PZc0X5c0PZS2OzsVbZGQk/v33X0kbsXr1agQFBcHBwQFdunTByZMnaz22rKwMCxcuREhICBwcHBAeHo49e/aIOqfcaFN68WxsbODl5QUbG72GcZo3Jyfg2WeBwYMrB1eKQFnzQ1nzRXnzQ1mLo/OYt6+//hoLFy7E2LFj0bp162orIw8cOFCnBmzduhWjR4/G2rVr0aVLFyxfvhw//fQTkpKS4O3tXe34999/H5s2bcK6desQGhqKvXv3IioqCv/88w/atWun1zkfxvue+e+/A889B3ToAEhcFxNCCCEWw1LGvOlcvFnVMShLn0V6u3Tpgk6dOmHVqlUAAKVSicDAQEydOhUzZ86sdry/vz8+/PBDTJ48Wf3ciy++CEdHR2zatEmvc5aUlKCkyurz6enpCAsL4/aPv2ULMHw40Ls3QMvo6aeiogKFhYVwdnaGtbW13M0xa5Q1P5Q1X5Q3P4bK2lKKN51vmyqVylofuhZupaWliIuLQ0RERGWDrKwQERGBY8eO1fg5JSUlcHBw0HjO0dERsbGxep8zOjoa7u7u6kdYWJhOX4dYNOZNvISEBLi7uyNBgh0EzJYgSDJhgbLmh7Lmi/Lmh7IWR9a5jbdv30ZFRQV8VOtkPODj44PMzMwaPycyMhJLly5FcnIylEol9u3bhx07diAjI0Pvc86aNQu5ubnqR2JiogRfnfaoeBMvODgY27ZtQ3BwsNxNMU4tWgA2NsCpU6JPRVnzQ1nzRXnzYzRZHzkCDBgA+PsDCgWwc6fm64IAzJ0L+PmxyV8REYARTLLQaqTgF198gQkTJsDBwQFffPFFncdOmzZNkobVZsWKFRg/fjxCQ0OhUCgQEhKCsWPHYsOGDXqf097eHvaqLYQA5KnW7uCEijfx6tWrhyFDhsjdDOMlCGxzegkW6qWs+aGs+aK8+TGarAsLgfBw4LXX2KSuh332GfDFF8B33wHBwcCcOUBkJJCYCDx0F5AnrYq3ZcuWYcSIEXBwcMCyZctqPU6hUOhUvHl6esLa2hpZWVkaz2dlZcHX17fGz/Hy8sLOnTtRXFyMO3fuwN/fHzNnzkSTJk30PqfcqHgT786dO9i1axcGDBiABg0ayN0c4yPhLguUNT+UNV+UNz9Gk/Wzz7JHTQQBWL4cmD0bGDSIPff992xV/Z07gWHDeLWyGq1um6akpKjDTUlJqfVx7do1nS5uZ2eHDh06ICYmRv2cUqlETEwMunbtWufnOjg4ICAgAOXl5di+fTsGPQhWzDnlQsWbeDdu3MDYsWNx48YNuZtinCQs3ihrfihrvihvfgyddX5+PvLy8tSPqpMStZaSAmRmslulKu7uQJcuQC1j6HmRfYGVqKgojBkzBh07dkTnzp2xfPlyFBYWYuzYsQDYXqkBAQGIjo4GAJw4cQLp6elo27Yt0tPTMX/+fCiVSrz33ntan9PYUPEmXrt27VBWVkYzxGojYfFGWfNDWfNFefNj6Kwfnng4b948zJ8/X7eTqMbJPzSGHj4+la/JRK/i7ebNm/jtt9+QmpqK0odWbF+6dKlO5xo6dChu3bqFuXPnIjMzE23btsWePXvUEw5SU1M1licpLi7G7Nmzce3aNbi4uKBfv37YuHEjPDw8tD6nsaHiTTyFQkGLPdZFwuKNsuaHsuaL8ubH0FknJiYiICBA/XHVce1mQdDR/v37BScnJ6FVq1aCjY2N0LZtW8HDw0Nwd3cX+vTpo+vpjFJaWpoAQEhLS+NyvV692BoOW7ZwuZxZunLlijBgwADhypUrcjfFOA0Zwr7JVq4UfSrKmh/Kmi/Kmx9DZS3q9zcgCL/8Uvnx1avsufh4zeN69hSEadPENFM0nZcKmTVrFt59912cO3cODg4O2L59O9LS0tCrVy/jmDliglSTW6nnjRhM27bAU08BVf4SJYQQUofgYMDXF6gyhh55ecCJE4DMY+h13mHB1dUVZ86cQUhICOrVq4fY2Fi0bNkSCQkJGDRoEK5fv26gpvLDe4XmZs2AK1fYcjNPPGHwyxFCCCFmSeff3wUF7BcwALRrByxdCvTpA9SvDzRqBCxeDHz6qeZSIWfPmsZSIVU5Ozurx7n5+fnh6tWraNmyJQC2QC7RHW1ML54gCKioqIC1tTUUCoXczTFrlDU/lDVflDc/RpP1v/+yYk0lKoq9HTMG+PZb4L332FpwEyYAOTlAjx7Anj2yFm6AHjssPP744+qtqPr164d33nkHixYtwmuvvYbHH39c8gZaApqwIF58fDxsbW0RHx8vd1PMHmXND2XNF+XNj9Fk3bt35daBVR/ffsteVyiAhQvZ7NLiYmD/fuCxx+RsMQA9irelS5eiS5cuAIAFCxagb9++2Lp1K4KCgvD1119L3kBzV1EBFBWx96l401/jxo3xzTffoHHjxnI3xTgtW8bWJ5o8WfSpKGt+KGu+KG9+KGtxdBrzVlFRgaNHj6JNmzYaS3OYG55j3nJzAVWU9+/L3hNLzNWSJcC77wIjRwIbN8rdGkIIMQjeY9blolPPm7W1NZ5++mncu3fPUO2xOKpbpjY2gLktQ8PTvXv38NNPP9H3Zm0kXOeNsuaHsuaL8uaHshZH59umrVq10nkbLFK7quPdaHys/lJSUvDyyy8jJSVF7qYYJ1XxprpHLwJlzQ9lzRflzQ9lLY7Os00//vhjvPvuu/joo4/QoUMHODs7a7zuRlMmdUKTFaQRHh6O3Nzcat+P5AEJe94oa34oa74ob34oa3G0Lt4WLlyId955B/369QMADBw4UGN6ryAIUCgUqKiokL6VZoyKN2lYW1vTHw51kbB4o6z5oaz5orz5oazF0fq26YIFC1BYWIiDBw+qHwcOHFA/VB8T3VDxJo2UlBQMHz6cuuBrI2HxRlnzQ1nzRXnzQ1mLo3XPm2pSaq9evQzWGEtExZs0ysvLcevWLZSXl8vdFOPk5cW2c2nWTPSpKGt+KGu+KG9+KGtxtF4qxMrKCllZWfDy8jJ0m2THc6rx6tXAlCnA4MHA9u0GvRQhhBBi1ixlqRCdJiw89thjj9zG4u7du6IaZGmo540QQgghutCpeFuwYAHc3d0N1RaLRMWbNOLj4/H444/j+PHjaNeundzNMWuUNT+UNV+UNz+UtTg6FW/Dhg2Dt7e3odpikah4k0bDhg2xdOlSs+4mF+X2baBtW7Y3361bohYVpKz5oaz5orz5oazF0XrMm7W1NTIyMiyieON5z3zsWLb/bXQ0MHOmQS9FLFleHtvbFKB92AghZstSxrxpvVSIDlugEh1Qz5s0cnNz8fvvvyM3N1fuphgn1VIhgOjlQihrfihrvihvfihrcbS+bapUKg3ZDotQWMjuXlV16xZ7S8WbOFevXsVzzz2HuLg4tG/fXu7mGB9bW8DaGqioAJKTAR8foHHjytezs+su6ho1Ut9qvf7vv3jzuefw++7dcG/VqvqxDRuyawHAnTtAQUHt5w0IYBv7AsDdu5V/zdTE3599HQCQkwPU9UPf17dys+DcXHZ8bXx8Knsi8/KAuvZa9PauLIQLCtjXVxsvL8DJib1f03/+qho0AFxc2PtFReofDGnnz1fPun79yh8Y9++zf7va1KsHqBZCLSkBMjNrP9bdHfDwYO+XlgIZGbUf6+bGzg0AZWXAf//VfqyrK2szwL7/bt6s/VhnZ8DTk72vVAJpabUf6+TEMgYAQQBSU2s/1tGR/dup3LhR42Fp58/jteeew59Vf46kpbG21MTODvDzq/z45k32NdbE1pZ9D6ukpwO1LZNhY8P+b6hkZLB/k5pYWQGBgZUfZ2ayf+uaKBTs/7JKVhYbSlEbPX9G4PZt9j1fm4YN6We2WAKpJi0tTQAgpKWlSXreH38UBPZTpvpjxw5JL2VxSktLhf/++08oLS2VuynGy8Wl8hvOykrztRdeqP2bExCE+/fVh1a88krdx965U3neN96o+9jU1Mpj33677mMvXqw8dvbsuo+Ni6s8Njq67mOPHKk8dsWKuo/988/KY9evr/vY7dsrj63rPz8gCN9/X3nsr7/WfeyaNZXH7t9f97FLllQee+xY3ccuXFh5bEJC3ce+/37lsVeu1H3s1KmVx2Zk1H3sa69VHpubW/exQ4dWHlteXvexAwYIGuztaz22uEcPzZ8j9erVft4uXTTP27Bh7ce2bq15bPPmtR/bpInmse3a1X6sj4/msT161H6sq6vmsZGRtR8r4meEMHJk3cfeuWOwn9mG+v1tbHTe25Toz9q65qFGgYFA9+7822NObG1t4Vf1L2BS3ciRbIAlwP5ar8rOTutxcFb29tqPmbO1rfvYqhMnbGzkObZqFrX9JzX0saqeStXnSXWsTZUf8QqFPMeqektV5DjWzq76sbVM2rF3cdE8t4ND7edW9e5W/Zj3sQ8/X9f/ZV2OFfEz4pH/70E/s0WTu3o0RpZSuZuT69evC+PGjROuX78ud1PMHmXND2XNF+XNj6GytpTf31pPWCDEmBUXF+PChQsormv8BpEEZc0PZc0X5c0PZS2O1kuFWBJLmWpMCCGEmBNL+f1NPW+EEEIIISaEijdiFhISElC/fn0kJCTI3RSzR1nzQ1nzRXnzQ1mLQ8UbMQu+vr6YNWsWfH195W6K2aOs+aGs+aK8+aGsxaExbzWwlHvmhBBCiDmxlN/f1PNGzEJ+fj4OHTqE/LpW6CeSoKz5oaz5orz5oazFoeKNmIXk5GT06dMHycnJcjfF7FHW/FDWfFHe/FDW4tAOC8QshIWFITk52ay7yY0FZc0PZc0X5c0PZS0OFW/ELDg4OKBp06ZyN8MiUNb8UNZ8Ud78UNbi0G1TYhbS0tIwbdo0pKWlyd0Us0dZ80NZ80V580NZi0PFGzELNPiVH8qaH8qaL8qbH8paHFoqpAaWMtWYEEIIMSeW8vubet4IIYQQQkwITViogVKpBABkZGTI3BKirUuXLmHkyJHYtGkTQkND5W6OWaOs+aGs+aK8+TFU1qrf26rf4+aKbpvW4NSpU+jcubPczSCEEEKIHk6ePIlOnTrJ3QyDoeKtBuXl5YiPj4ePjw+srPS/s5yfn4+wsDAkJibC1dVVwhaSh1HW/FDW/FDWfFHe/Bgqa6VSiaysLLRr1w42NuZ7c5GKNwPKy8uDu7s7cnNz4ebmJndzzBplzQ9lzQ9lzRflzQ9lLQ5NWCCEEEIIMSFUvBFCCCGEmBAq3gzI3t4e8+bNg729vdxNMXuUNT+UNT+UNV+UNz+UtTg05o0QQgghxIRQzxshhBBCiAmh4o0QQgghxIRQ8UYIIYQQYkKoeCOEEEIIMSFUvBnQ6tWrERQUBAcHB3Tp0gUnT56Uu0kmLTo6Gp06dYKrqyu8vb3x/PPPIykpSeOY4uJiTJ48GQ0aNICLiwtefPFFZGVlydRi8/Hpp59CoVBg+vTp6ucoa2mlp6dj5MiRaNCgARwdHdG6dWv8+++/6tcFQcDcuXPh5+cHR0dHREREIDk5WcYWm6aKigrMmTMHwcHBcHR0REhICD766CNUnbtHWevnyJEjGDBgAPz9/aFQKLBz506N17XJ9e7duxgxYgTc3Nzg4eGBcePGoaCggONXYRqoeDOQrVu3IioqCvPmzcPp06cRHh6OyMhIZGdny900k3X48GFMnjwZx48fx759+1BWVoann34ahYWF6mPefvtt7Nq1Cz/99BMOHz6M//77D4MHD5ax1abv1KlT+PLLL9GmTRuN5ylr6dy7dw/du3eHra0t/vzzTyQmJmLJkiWoV6+e+pjPPvsMX3zxBdauXYsTJ07A2dkZkZGRKC4ulrHlpmfx4sVYs2YNVq1ahYsXL2Lx4sX47LPPsHLlSvUxlLV+CgsLER4ejtWrV9f4uja5jhgxAhcuXMC+ffuwe/duHDlyBBMmTOD1JZgOgRhE586dhcmTJ6s/rqioEPz9/YXo6GgZW2VesrOzBQDC4cOHBUEQhJycHMHW1lb46aef1MdcvHhRACAcO3ZMrmaatPz8fKFZs2bCvn37hF69eglvvfWWIAiUtdTef/99oUePHrW+rlQqBV9fX+F///uf+rmcnBzB3t5e+PHHH3k00Wz0799feO211zSeGzx4sDBixAhBEChrqQAQfvnlF/XH2uSamJgoABBOnTqlPubPP/8UFAqFkJ6ezq3tpoB63gygtLQUcXFxiIiIUD9nZWWFiIgIHDt2TMaWmZfc3FwAQP369QEAcXFxKCsr08g9NDQUjRo1otz1NHnyZPTv318jU4Cyltpvv/2Gjh07YsiQIfD29ka7du2wbt069espKSnIzMzUyNvd3R1dunShvHXUrVs3xMTE4PLlywCAhIQExMbG4tlnnwVAWRuKNrkeO3YMHh4e6Nixo/qYiIgIWFlZ4cSJE9zbbMxs5G6AObp9+zYqKirg4+Oj8byPjw8uXbokU6vMi1KpxPTp09G9e3e0atUKAJCZmQk7Ozt4eHhoHOvj44PMzEwZWmnatmzZgtOnT+PUqVPVXqOspXXt2jWsWbMGUVFR+OCDD3Dq1ClMmzYNdnZ2GDNmjDrTmn6mUN66mTlzJvLy8hAaGgpra2tUVFRg0aJFGDFiBABQ1gaiTa6ZmZnw9vbWeN3Gxgb169en7B9CxRsxSZMnT8b58+cRGxsrd1PMUlpaGt566y3s27cPDg4OcjfH7CmVSnTs2BGffPIJAKBdu3Y4f/481q5dizFjxsjcOvOybds2bN68GT/88ANatmyJM2fOYPr06fD396esicmg26YG4OnpCWtr62oz77KysuDr6ytTq8zHlClTsHv3bhw8eBANGzZUP+/r64vS0lLk5ORoHE+56y4uLg7Z2dlo3749bGxsYGNjg8OHD+OLL76AjY0NfHx8KGsJ+fn5ISwsTOO5Fi1aIDU1FQDUmdLPFPFmzJiBmTNnYtiwYWjdujVGjRqFt99+G9HR0QAoa0PRJldfX99qk/rKy8tx9+5dyv4hVLwZgJ2dHTp06ICYmBj1c0qlEjExMejatauMLTNtgiBgypQp+OWXX3DgwAEEBwdrvN6hQwfY2tpq5J6UlITU1FTKXUd9+/bFuXPncObMGfWjY8eOGDFihPp9ylo63bt3r7bszeXLl9G4cWMAQHBwMHx9fTXyzsvLw4kTJyhvHRUVFcHKSvNXn7W1NZRKJQDK2lC0ybVr167IyclBXFyc+pgDBw5AqVSiS5cu3Nts1OSeMWGutmzZItjb2wvffvutkJiYKEyYMEHw8PAQMjMz5W6ayZo0aZLg7u4uHDp0SMjIyFA/ioqK1MdMnDhRaNSokXDgwAHh33//Fbp27Sp07dpVxlabj6qzTQWBspbSyZMnBRsbG2HRokVCcnKysHnzZsHJyUnYtGmT+phPP/1U8PDwEH799Vfh7NmzwqBBg4Tg4GDh/v37Mrbc9IwZM0YICAgQdu/eLaSkpAg7duwQPD09hffee099DGWtn/z8fCE+Pl6Ij48XAAhLly4V4uPjhRs3bgiCoF2uzzzzjNCuXTvhxIkTQmxsrNCsWTNh+PDhcn1JRouKNwNauXKl0KhRI8HOzk7o3LmzcPz4cbmbZNIA1Pj45ptv1Mfcv39fePPNN4V69eoJTk5OwgsvvCBkZGTI12gz8nDxRllLa9euXUKrVq0Ee3t7ITQ0VPjqq680XlcqlcKcOXMEHx8fwd7eXujbt6+QlJQkU2tNV15envDWW28JjRo1EhwcHIQmTZoIH374oVBSUqI+hrLWz8GDB2v8GT1mzBhBELTL9c6dO8Lw4cMFFxcXwc3NTRg7dqyQn58vw1dj3BSCUGVZaUIIIYQQYtRozBshhBBCiAmh4o0QQgghxIRQ8UYIIYQQYkKoeCOEEEIIMSFUvBFCCCGEmBAq3gghhBBCTAgVb4QQQgghJoSKN0IIIYQQE0LFGyGE1CAoKAjLly+XuxmEEFINFW+EENm9+uqreP755wEAvXv3xvTp07ld+9tvv4WHh0e150+dOoUJEyZwawchhGjLRu4GEEKIIZSWlsLOzk7vz/fy8pKwNYQQIh3qeSOEGI1XX30Vhw8fxooVK6BQKKBQKHD9+nUAwPnz5/Hss8/CxcUFPj4+GDVqFG7fvq3+3N69e2PKlCmYPn06PD09ERkZCQBYunQpWrduDWdnZwQGBuLNN99EQUEBAODQoUMYO3YscnNz1debP38+gOq3TVNTUzFo0CC4uLjAzc0NL7/8MrKystSvz58/H23btsXGjRsRFBQEd3d3DBs2DPn5+YYNjRBicah4I4QYjRUrVqBr164YP348MjIykJGRgcDAQOTk5ODJJ59Eu3bt8O+//2LPnj3IysrCyy+/rPH53333Hezs7HD06FGsXbsWAGBlZYUvvvgCFy5cwHfffYcDBw7gvffeAwB069YNy5cvh5ubm/p67777brV2KZVKDBo0CHfv3sXhw4exb98+XLt2DUOHDtU47urVq9i5cyd2796N3bt34/Dhw/j0008NlBYhxFLRbVNCiNFwd3eHnZ0dnJyc4Ovrq35+1apVaNeuHT755BP1cxs2bEBgYCAuX76Mxx57DADQrFkzfPbZZxrnrDp+LigoCB9//DEmTpyI//u//4OdnR3c3d2hUCg0rvewmJgYnDt3DikpKQgMDAQAfP/992jZsiVOnTqFTp06AWBF3rfffgtXV1cAwKhRoxATE4NFixaJC4YQQqqgnjdCiNFLSEjAwYMH4eLion6EhoYCYL1dKh06dKj2ufv370ffvn0REBAAV1dXjBo1Cnfu3EFRUZHW17948SICAwPVhRsAhIWFwcPDAxcvXlQ/FxQUpC7cAMDPzw/Z2dk6fa2EEPIo1PNGCDF6BQUFGDBgABYvXlztNT8/P/X7zs7OGq9dv34dzz33HCZNmoRFixahfv36iI2Nxbhx41BaWgonJydJ22lra6vxsUKhgFKplPQahBBCxRshxKjY2dmhoqJC47n27dtj+/btCAoKgo2N9j+24uLioFQqsWTJElhZsRsN27Zte+T1HtaiRQukpaUhLS1N3fuWmJiInJwchIWFad0eQgiRAt02JYQYlaCgIJw4cQLXr1/H7du3oVQqMXnyZNy9exfDhw/HqVOncPXqVezduxdjx46ts/Bq2rQpysrKsHLlSly7dg0bN25UT2Soer2CggLExMTg9u3bNd5OjYiIQOvWrTFixAicPn0aJ0+exOjRo9GrVy907NhR8gwIIaQuVLwRQozKu+++C2tra4SFhcHLywupqanw9/fH0aNHUVFRgaeffhqtW7fG9OnT4eHhoe5Rq0l4eDiWLl2KxYsXo1WrVti8eTOio6M1junWrRsmTpyIoUOHwsvLq9qEB4Dd/vz1119Rr1499OzZExEREWjSpAm2bt0q+ddPCCGPohAEQZC7EYQQQgghRDvU80YIIYQQYkKoeCOEEEIIMSFUvBFCCCGEmBAq3gghhBBCTAgVb4QQQgghJoSKN0IIIYQQE0LFGyGEEEKICaHijRBCCCHEhFDxRgghhBBiQqh4I4QQQggxIVS8EUIIIYSYkP8HJoCuBZjJVBEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "model_tracking_df = heros.get_model_performance_tracking()\n", "model_tracking_df.to_csv(output_path+'/model_tracking.csv', index=False)\n", @@ -847,7 +1752,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "3f39c1c1", "metadata": {}, "outputs": [], @@ -870,7 +1775,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "75c2551d", "metadata": {}, "outputs": [], @@ -888,10 +1793,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "9a6f9391", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Global Phase 1 Phase 2 Rule Initialization Rule Covering \\\n", + "0 73.129616 183.625493 38.000413 0.33039 0.007684 \n", + "\n", + " Rule Equality Rule Matching Rule Evaluation Feature Tracking \\\n", + "0 1.93028 3.089952 30.904625 0.0 \n", + "\n", + " Rule Subsumption Rule Selection Rule Mating Rule Deletion \\\n", + "0 0.556547 1.157167 4.706649 5.529431 \n", + "\n", + " Rule Compaction Rule Prediction \n", + "0 0.031017 0.809222 \n" + ] + } + ], "source": [ "time_df = heros.get_runtimes()\n", "time_df.to_csv(output_path+'/runtimes.csv', index=False)\n", @@ -919,10 +1842,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "708cf494", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if run_all_cells:\n", " heros.get_rule_pop_heatmap(feature_names, weighting='useful_accuracy', specified_filter=1, display_micro=True, show=True, save=True, output_path=output_path)" @@ -938,10 +1872,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "892ea1a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Save Feature Tracking Scores to .csv\n", "if heros.feat_track != None:\n", @@ -986,10 +1942,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "48a269b0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HEROS Whole Rule Population Testing Data Performance Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.82142857 0.86792453 28\n", + " 1 0.80000000 0.90909091 0.85106383 22\n", + "\n", + " accuracy 0.86000000 50\n", + " macro avg 0.86000000 0.86525974 0.85949418 50\n", + "weighted avg 0.86720000 0.86000000 0.86050582 50\n", + "\n" + ] + } + ], "source": [ "predictions = heros.predict(X_test,whole_rule_pop=True)\n", "print(\"HEROS Whole Rule Population Testing Data Performance Report:\")\n", @@ -1009,10 +1982,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "d4390561", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Condition Indexes Condition Values Action Numerosity Fitness \\\n", + "0 [0, 1, 2] [0, 0, 0] 0 7 1.000000 \n", + "1 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", + "2 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", + "3 [0, 1, 4] [1, 0, 0] 0 3 0.997857 \n", + "4 [0, 1, 3] [0, 1, 1] 1 9 0.998143 \n", + "5 [0, 1, 3] [0, 1, 0] 0 6 0.997714 \n", + "6 [0, 1, 5] [1, 1, 1] 1 6 0.997429 \n", + "7 [0, 1, 2] [0, 0, 1] 1 3 0.998714 \n", + "\n", + " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", + "0 1.0 34.0 1.0 68 68 \n", + "1 1.0 27.5 1.0 55 55 \n", + "2 1.0 24.0 1.0 48 48 \n", + "3 1.0 27.5 1.0 55 55 \n", + "4 1.0 28.5 1.0 57 57 \n", + "5 1.0 27.0 1.0 54 54 \n", + "6 1.0 26.0 1.0 52 52 \n", + "7 1.0 30.5 1.0 61 61 \n", + "\n", + " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", + "0 None None None 0 \n", + "1 None None None 0 \n", + "2 None None None 0 \n", + "3 None None None 0 \n", + "4 None None None 0 \n", + "5 None None None 0 \n", + "6 None None None 0 \n", + "7 None None None 190 \n", + "\n", + " Specified Count Average Match Set Size Deletion Probabiilty \n", + "0 3 105.858535 0.015066 \n", + "1 3 94.666956 0.009920 \n", + "2 3 95.967470 0.006990 \n", + "3 3 85.782919 0.002247 \n", + "4 3 94.068161 0.022172 \n", + "5 3 89.252098 0.009354 \n", + "6 3 86.476752 0.009065 \n", + "7 3 93.214078 0.002440 \n", + "HEROS Top 'Default' Model Testing Data Performance Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "set_df = heros.get_model_rules() #returns top training model by default based on balanced accuracy, then covering, then rule-set size.\n", @@ -1035,10 +2064,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "ddeab87c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Condition Indexes Condition Values Action Numerosity Fitness \\\n", + "0 [0, 1, 2] [0, 0, 0] 0 7 1.000000 \n", + "1 [0, 1, 4] [1, 0, 1] 1 6 0.997857 \n", + "2 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", + "3 [0, 1, 4] [1, 0, 0] 0 3 0.997857 \n", + "4 [0, 1, 3] [0, 1, 1] 1 9 0.998143 \n", + "5 [0, 1, 3] [0, 1, 0] 0 6 0.997714 \n", + "6 [0, 1, 5] [1, 1, 1] 1 6 0.997429 \n", + "7 [0, 1, 2] [0, 0, 1] 1 3 0.998714 \n", + "8 [1, 3, 5] [1, 0, 0] 0 11 0.996714 \n", + "\n", + " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", + "0 1.0 34.0 1.0 68 68 \n", + "1 1.0 27.5 1.0 55 55 \n", + "2 1.0 24.0 1.0 48 48 \n", + "3 1.0 27.5 1.0 55 55 \n", + "4 1.0 28.5 1.0 57 57 \n", + "5 1.0 27.0 1.0 54 54 \n", + "6 1.0 26.0 1.0 52 52 \n", + "7 1.0 30.5 1.0 61 61 \n", + "8 1.0 23.5 1.0 47 47 \n", + "\n", + " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", + "0 None None None 0 \n", + "1 None None None 0 \n", + "2 None None None 0 \n", + "3 None None None 0 \n", + "4 None None None 0 \n", + "5 None None None 0 \n", + "6 None None None 0 \n", + "7 None None None 190 \n", + "8 None None None 0 \n", + "\n", + " Specified Count Average Match Set Size Deletion Probabiilty \n", + "0 3 105.858535 0.015066 \n", + "1 3 94.666956 0.009920 \n", + "2 3 95.967470 0.006990 \n", + "3 3 85.782919 0.002247 \n", + "4 3 94.068161 0.022172 \n", + "5 3 89.252098 0.009354 \n", + "6 3 86.476752 0.009065 \n", + "7 3 93.214078 0.002440 \n", + "8 3 96.162275 0.022698 \n", + "HEROS Top 'Default' Model Testing Data Performance Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "desired_model_index = 1\n", @@ -1062,10 +2151,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "ceac3c53", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rule population evaluation at iteration 500\n", + "Run Time: 0.6587517261505127\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.85185185 0.88461538 27\n", + " 1 0.84000000 0.91304348 0.87500000 23\n", + "\n", + " accuracy 0.88000000 50\n", + " macro avg 0.88000000 0.88244767 0.87980769 50\n", + "weighted avg 0.88320000 0.88000000 0.88019231 50\n", + "\n", + "Rule population evaluation at iteration 1000\n", + "Run Time: 1.012446641921997\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.85185185 0.88461538 27\n", + " 1 0.84000000 0.91304348 0.87500000 23\n", + "\n", + " accuracy 0.88000000 50\n", + " macro avg 0.88000000 0.88244767 0.87980769 50\n", + "weighted avg 0.88320000 0.88000000 0.88019231 50\n", + "\n", + "Rule population evaluation at iteration 5000\n", + "Run Time: 3.8025341033935547\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.85185185 0.88461538 27\n", + " 1 0.84000000 0.91304348 0.87500000 23\n", + "\n", + " accuracy 0.88000000 50\n", + " macro avg 0.88000000 0.88244767 0.87980769 50\n", + "weighted avg 0.88320000 0.88000000 0.88019231 50\n", + "\n", + "Rule population evaluation at iteration 10000\n", + "Run Time: 7.286679744720459\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.82142857 0.86792453 28\n", + " 1 0.80000000 0.90909091 0.85106383 22\n", + "\n", + " accuracy 0.86000000 50\n", + " macro avg 0.86000000 0.86525974 0.85949418 50\n", + "weighted avg 0.86720000 0.86000000 0.86050582 50\n", + "\n", + "Rule population evaluation at iteration 50000\n", + "Run Time: 65.66507577896118\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92000000 0.82142857 0.86792453 28\n", + " 1 0.80000000 0.90909091 0.85106383 22\n", + "\n", + " accuracy 0.86000000 50\n", + " macro avg 0.86000000 0.86525974 0.85949418 50\n", + "weighted avg 0.86720000 0.86000000 0.86050582 50\n", + "\n" + ] + } + ], "source": [ "if stored_rule_iterations != None:\n", " rule_iteration_list = [int(x) for x in stored_rule_iterations.split(',')]\n", @@ -1089,10 +2240,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "4ddb1cb2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top default model evaluation at iteration 10\n", + "Run Time: 4.243201494216919\n", + " precision recall f1-score support\n", + "\n", + " 0 0.96000000 0.92307692 0.94117647 26\n", + " 1 0.92000000 0.95833333 0.93877551 24\n", + "\n", + " accuracy 0.94000000 50\n", + " macro avg 0.94000000 0.94070513 0.93997599 50\n", + "weighted avg 0.94080000 0.94000000 0.94002401 50\n", + "\n", + "Top default model evaluation at iteration 50\n", + "Run Time: 19.17435050010681\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n", + "Top default model evaluation at iteration 100\n", + "Run Time: 37.97588515281677\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -1113,10 +2304,86 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "cf9e32c0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------------------------\n", + "Top model evaluation at iteration 10\n", + "Run Time: 4.243201494216919\n", + "8 non-dominated models on Pareto-front.\n", + "----------------------------------------\n", + "Model testing accuracies: [0.94, 0.96, 0.88, 0.76, 0.8, 0.7, 0.72, 0.74]\n", + "Model testing coverages: [np.float64(1.0), np.float64(0.9), np.float64(0.94), np.float64(0.82), np.float64(0.96), np.float64(0.82), np.float64(0.76), np.float64(0.52)]\n", + "Model rule counts: [8, 7, 6, 5, 4, 3, 2, 1]\n", + "----------------------------------------\n", + "Best model testing accuracy: 0.94\n", + "Best model testing coverage: 1.0\n", + "Best rule count: 8\n", + "Best model index: 0\n", + "----------------------------------------\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n", + "---------------------------------------------------------------------------------------------\n", + "Top model evaluation at iteration 50\n", + "Run Time: 19.17435050010681\n", + "8 non-dominated models on Pareto-front.\n", + "----------------------------------------\n", + "Model testing accuracies: [1.0, 0.94, 0.8200000000000001, 0.8999999999999999, 0.8, 0.6000000000000001, 0.6, 0.5800000000000001]\n", + "Model testing coverages: [np.float64(1.0), np.float64(0.9), np.float64(0.86), np.float64(0.84), np.float64(0.8), np.float64(0.82), np.float64(0.62), np.float64(0.22)]\n", + "Model rule counts: [8, 7, 6, 5, 4, 3, 2, 1]\n", + "----------------------------------------\n", + "Best model testing accuracy: 1.0\n", + "Best model testing coverage: 1.0\n", + "Best rule count: 8\n", + "Best model index: 0\n", + "----------------------------------------\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n", + "---------------------------------------------------------------------------------------------\n", + "Top model evaluation at iteration 100\n", + "Run Time: 37.97588515281677\n", + "8 non-dominated models on Pareto-front.\n", + "----------------------------------------\n", + "Model testing accuracies: [1.0, 0.96, 0.88, 0.86, 0.8, 0.6200000000000001, 0.62, 0.52]\n", + "Model testing coverages: [np.float64(1.0), np.float64(0.9), np.float64(0.9), np.float64(0.84), np.float64(1.0), np.float64(0.82), np.float64(0.7), np.float64(0.48)]\n", + "Model rule counts: [8, 7, 6, 5, 4, 3, 2, 1]\n", + "----------------------------------------\n", + "Best model testing accuracy: 1.0\n", + "Best model testing coverage: 1.0\n", + "Best rule count: 8\n", + "Best model index: 0\n", + "----------------------------------------\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -1140,7 +2407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "b0a873b6", "metadata": {}, "outputs": [], @@ -1156,10 +2423,209 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "eac4849a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Manage Summary: ------------------------------------------------\n", + "Number of quantitative features: 0\n", + "Number of categorical features: 6\n", + "Total Features: 6\n", + "Total Instances: 450\n", + "Feature Types: [1, 1, 1, 1, 1, 1]\n", + "Missing Values: 0\n", + "Quantiative Feature Range: [[inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf]]\n", + "Categorical Feature Values: [[np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(1), np.int64(0)]]\n", + "Average States: 2.0\n", + "Rule Specificity Limit: 6\n", + "Classes: [np.int64(1), np.int64(0)]\n", + "Class Counts: {np.int64(1): 225, np.int64(0): 225}\n", + "Class Weights: {np.int64(1): 0.5, np.int64(0): 0.5}\n", + "Majority Class: 1\n", + "Expert Knowledge Weights Used: True\n", + "--------------------------------------------------------------------\n", + "Initializing Rule Population via Loaded File!\n", + "Max Rule ID in Loaded Population: 21357\n", + "Loading Rule Population Complete: 87 unique rules and 500 total rules loaded.\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 87\n", + "Post-Cleaning Population Size: 87\n", + "Post-Subsumption Compaction Population Size: 87\n", + "HEROS Evolution Beginning!\n", + "Archiving: 500\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 1000 0.903 201 500 0.707\n", + "Archiving: 1000\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 208\n", + "Post-Cleaning Population Size: 208\n", + "17 rules subsumed with a specificity of 2\n", + "58 rules subsumed with a specificity of 3\n", + "43 rules subsumed with a specificity of 4\n", + "1 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 89\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 2000 0.9 89 500 1.444\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:2000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 1 1.0 1.0 16 0.496\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 2 1.0 1.0 16 0.97\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 3 1.0 1.0 16 1.361\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 4 1.0 1.0 15 1.753\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 5 1.0 1.0 15 2.067\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 6 1.0 1.0 15 2.447\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 7 1.0 1.0 15 2.786\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 8 1.0 1.0 15 3.083\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.5402377299059444\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 3000 0.906 202 500 5.694\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 223\n", + "Post-Cleaning Population Size: 223\n", + "17 rules subsumed with a specificity of 2\n", + "67 rules subsumed with a specificity of 3\n", + "49 rules subsumed with a specificity of 4\n", + "2 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 88\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 4000 0.911 88 500 6.401\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:2000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 9 1.0 1.0 15 3.378\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 10 1.0 1.0 13 3.732\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 11 1.0 1.0 13 4.058\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 12 1.0 1.0 12 4.358\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 13 1.0 1.0 12 4.622\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 14 1.0 1.0 12 4.896\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 15 1.0 1.0 11 5.204\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 16 1.0 1.0 9 5.545\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.8059620494537011\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 5000 0.897 203 500 9.556\n", + "Archiving: 5000\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 217\n", + "Post-Cleaning Population Size: 217\n", + "14 rules subsumed with a specificity of 2\n", + "68 rules subsumed with a specificity of 3\n", + "47 rules subsumed with a specificity of 4\n", + "Post-Subsumption Compaction Population Size: 88\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 6000 0.901 88 500 10.306\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:2000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 17 1.0 1.0 9 5.857\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 18 1.0 1.0 9 6.19\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 19 1.0 1.0 9 6.555\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 20 1.0 1.0 9 6.864\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 21 1.0 1.0 9 7.224\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 22 1.0 1.0 9 7.546\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 23 1.0 1.0 9 7.926\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 24 1.0 1.0 8 8.313\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.10124179437006087\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 7000 0.9 196 500 13.807\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 195\n", + "Post-Cleaning Population Size: 195\n", + "13 rules subsumed with a specificity of 2\n", + "56 rules subsumed with a specificity of 3\n", + "35 rules subsumed with a specificity of 4\n", + "Post-Subsumption Compaction Population Size: 91\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 8000 0.901 91 500 14.526\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:2000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 25 1.0 1.0 8 8.655\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 26 1.0 1.0 8 9.002\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 27 1.0 1.0 8 9.445\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 28 1.0 1.0 8 9.802\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 29 1.0 1.0 8 10.218\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 30 1.0 1.0 8 10.594\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 31 1.0 1.0 8 11.052\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 32 1.0 1.0 8 11.42\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.3385627612391344\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 9000 0.898 198 500 18.311\n", + "--------------------------------------------------------------------\n", + "Original Population Size: 206\n", + "Post-Cleaning Population Size: 206\n", + "14 rules subsumed with a specificity of 2\n", + "60 rules subsumed with a specificity of 3\n", + "43 rules subsumed with a specificity of 4\n", + "1 rules subsumed with a specificity of 5\n", + "Post-Subsumption Compaction Population Size: 88\n", + " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", + " 10000 0.915 88 500 18.983\n", + "Archiving: 10000\n", + "HEROS (Phase 1) run complete!\n", + "Number of Unique Rules Identified: 0\n", + "Number of Iterations Used:2000\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 33 1.0 1.0 8 11.804\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 34 1.0 1.0 8 12.221\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 35 1.0 1.0 8 12.622\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 36 1.0 1.0 8 12.993\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 37 1.0 1.0 8 13.446\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 38 1.0 1.0 8 13.837\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 39 1.0 1.0 8 14.278\n", + " Iteration Training Accuracy Coverage Rules in Model Total Time\n", + " 40 1.0 1.0 8 14.686\n", + "HEROS (Phase 2) run complete!\n", + "Random Seed Check - End: 0.2120077853036706\n" + ] + } + ], "source": [ "rule_pop_init = 'load' # Change the rule population initialization run parameter to load (all other parameters kept the same)\n", "\n", @@ -1175,10 +2641,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "a71e7654", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8 non-dominated models on Pareto-front.\n", + "----------------------------------------\n", + "Model testing accuracies: [1.0, 0.94, 0.98, 0.88, 0.74, 0.8200000000000001, 0.54, 0.5]\n", + "Model testing coverages: [np.float64(1.0), np.float64(0.98), np.float64(0.94), np.float64(0.8), np.float64(0.94), np.float64(0.94), np.float64(1.0), np.float64(0.52)]\n", + "Model rule counts: [8, 7, 7, 6, 5, 4, 2, 1]\n", + "----------------------------------------\n", + "Best model testing accuracy: 1.0\n", + "Best model testing coverage: 1.0\n", + "Best rule count: 8\n", + "Best model index: 0\n", + "----------------------------------------\n", + " Condition Indexes Condition Values Action Numerosity Fitness \\\n", + "0 [0, 1, 2] [0, 0, 0] 0 6 1.000000 \n", + "1 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", + "2 [0, 1, 3] [0, 1, 1] 1 5 0.998143 \n", + "3 [0, 1, 2] [0, 0, 1] 1 5 0.998714 \n", + "4 [0, 1, 4] [1, 0, 1] 1 5 0.997857 \n", + "5 [0, 1, 4] [1, 0, 0] 0 5 0.997857 \n", + "6 [0, 1, 5] [1, 1, 1] 1 6 0.997429 \n", + "7 [0, 1, 3] [0, 1, 0] 0 3 0.997714 \n", + "\n", + " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", + "0 1.0 34.0 1.0 68 68 \n", + "1 1.0 24.0 1.0 48 48 \n", + "2 1.0 28.5 1.0 57 57 \n", + "3 1.0 30.5 1.0 61 61 \n", + "4 1.0 27.5 1.0 55 55 \n", + "5 1.0 27.5 1.0 55 55 \n", + "6 1.0 26.0 1.0 52 52 \n", + "7 1.0 27.0 1.0 54 54 \n", + "\n", + " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", + "0 None None None 0 \n", + "1 None None None 0 \n", + "2 None None None 0 \n", + "3 None None None 0 \n", + "4 None None None 0 \n", + "5 None None None 0 \n", + "6 None None None 0 \n", + "7 None None None 0 \n", + "\n", + " Specified Count Average Match Set Size Deletion Probabiilty \n", + "0 3 102.729662 0.009896 \n", + "1 3 95.218564 0.006390 \n", + "2 3 94.068630 0.006305 \n", + "3 3 96.647311 0.006474 \n", + "4 3 104.730319 0.007021 \n", + "5 3 107.803887 0.007227 \n", + "6 3 96.066937 0.009278 \n", + "7 3 94.339126 0.002277 \n" + ] + } + ], "source": [ "# Get the best model (as before)\n", "best_model_index = heros_reboot.auto_select_top_model(X_test,y_test,verbose=True)\n", @@ -1188,10 +2711,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "b08f7f25", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HEROS Top Model Testing Data Performance Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00000000 1.00000000 1.00000000 25\n", + " 1 1.00000000 1.00000000 1.00000000 25\n", + "\n", + " accuracy 1.00000000 50\n", + " macro avg 1.00000000 1.00000000 1.00000000 50\n", + "weighted avg 1.00000000 1.00000000 1.00000000 50\n", + "\n" + ] + } + ], "source": [ "# Report performance results for the top model\n", "predictions = heros_reboot.predict(X_test,whole_rule_pop=False, target_model=best_model_index)\n", @@ -1201,7 +2741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "81dbd9e3", "metadata": {}, "outputs": [], @@ -1217,7 +2757,7 @@ ], "metadata": { "kernelspec": { - "display_name": "heros_dev_12", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1231,7 +2771,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.13" + "version": "3.12.8" } }, "nbformat": 4, From 54e6e629e98af9101356d490ab54dfe6b2464eaf Mon Sep 17 00:00:00 2001 From: Ryan Urbanowicz Date: Wed, 1 Jul 2026 23:43:23 -0700 Subject: [PATCH 3/4] Update HEROS_Demo_Notebook.ipynb --- HEROS_Demo_Notebook.ipynb | 436 +++++++++++++++++++------------------- 1 file changed, 218 insertions(+), 218 deletions(-) diff --git a/HEROS_Demo_Notebook.ipynb b/HEROS_Demo_Notebook.ipynb index 6c61fc9..e9b7209 100644 --- a/HEROS_Demo_Notebook.ipynb +++ b/HEROS_Demo_Notebook.ipynb @@ -192,7 +192,7 @@ " if await self.run_code(code, result, async_=asy):\n", " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_92620\\1537052189.py\", line 12, in \n", + " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_56168\\777087974.py\", line 12, in \n", " from src.skheros.heros import HEROS\n", " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\__init__.py\", line 7, in \n", " from .heros import HEROS\n", @@ -290,7 +290,7 @@ " if await self.run_code(code, result, async_=asy):\n", " File \"c:\\Users\\ryanu\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_92620\\1537052189.py\", line 12, in \n", + " File \"C:\\Users\\ryanu\\AppData\\Local\\Temp\\ipykernel_56168\\777087974.py\", line 12, in \n", " from src.skheros.heros import HEROS\n", " File \"c:\\Users\\ryanu\\Documents\\GitHub\\heros\\src\\skheros\\__init__.py\", line 7, in \n", " from .heros import HEROS\n", @@ -361,7 +361,7 @@ "import matplotlib.pyplot as plt\n", "#from skrebate import MultiSURF, TURF \n", "from skrebate import MultiSWRFDB,TURF\n", - "from skrebate import TURF \n", + "#from skrebate import TURF \n", "from sklearn.metrics import classification_report\n", "from sklearn.inspection import permutation_importance\n", "from sklearn.metrics import roc_curve, roc_auc_score\n", @@ -690,25 +690,25 @@ "HEROS Evolution Beginning!\n", "Archiving: 500\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 1000 0.885 199 500 1.008\n", + " 1000 0.885 199 500 0.988\n", "Archiving: 1000\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 2000 0.902 216 500 1.718\n", + " 2000 0.902 216 500 1.691\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 3000 0.903 216 500 2.415\n", + " 3000 0.903 216 500 2.392\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 4000 0.905 210 500 3.108\n", + " 4000 0.905 210 500 3.086\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 5000 0.907 226 500 3.798\n", + " 5000 0.907 226 500 3.771\n", "Archiving: 5000\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 6000 0.909 219 500 4.52\n", + " 6000 0.909 219 500 4.482\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 7000 0.898 211 500 5.21\n", + " 7000 0.898 211 500 5.165\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 8000 0.902 209 500 5.893\n", + " 8000 0.902 209 500 5.852\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 9000 0.907 219 500 6.586\n", + " 9000 0.907 219 500 6.541\n", "--------------------------------------------------------------------\n", "Original Population Size: 221\n", "Post-Cleaning Population Size: 221\n", @@ -718,71 +718,71 @@ "2 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 89\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 10000 0.914 89 500 7.284\n", + " 10000 0.914 89 500 7.239\n", "Archiving: 10000\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:10000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 1 0.891 1.0 37 0.48\n", + " 1 0.891 1.0 37 0.479\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 2 0.891 1.0 37 1.01\n", + " 2 0.891 1.0 37 1.005\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 3 0.92 1.0 23 1.476\n", + " 3 0.92 1.0 23 1.463\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 4 0.922 1.0 27 1.92\n", + " 4 0.922 1.0 27 1.906\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 5 0.922 1.0 27 2.395\n", + " 5 0.922 1.0 27 2.378\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 6 0.956 1.0 18 2.785\n", + " 6 0.956 1.0 18 2.776\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 7 0.956 1.0 18 3.124\n", + " 7 0.956 1.0 18 3.121\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 8 0.956 1.0 18 3.49\n", + " 8 0.956 1.0 18 3.494\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 9 0.956 1.0 18 3.872\n", + " 9 0.956 1.0 18 3.875\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 10 0.962 0.984 25 4.243\n", + " 10 0.962 0.984 25 4.249\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 11 0.962 0.984 25 4.62\n", + " 11 0.962 0.984 25 4.632\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 12 0.962 0.984 25 4.99\n", + " 12 0.962 0.984 25 4.992\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 13 0.962 0.984 25 5.398\n", + " 13 0.962 0.984 25 5.383\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 14 0.962 0.984 25 5.758\n", + " 14 0.962 0.984 25 5.733\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 15 0.962 0.984 25 6.192\n", + " 15 0.962 0.984 25 6.151\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 16 0.969 1.0 25 6.567\n", + " 16 0.969 1.0 25 6.523\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 17 0.969 1.0 25 6.927\n", + " 17 0.969 1.0 25 6.879\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 18 0.969 1.0 25 7.261\n", + " 18 0.969 1.0 25 7.206\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 19 0.978 1.0 27 7.569\n", + " 19 0.978 1.0 27 7.514\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 20 0.978 1.0 27 7.952\n", + " 20 0.978 1.0 27 7.896\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.11457692579230672\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 11000 0.895 198 500 16.397\n", + " 11000 0.895 198 500 16.285\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 12000 0.906 207 500 17.077\n", + " 12000 0.906 207 500 16.952\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 13000 0.904 203 500 17.757\n", + " 13000 0.904 203 500 17.613\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 14000 0.912 224 500 18.439\n", + " 14000 0.912 224 500 18.292\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 15000 0.906 212 500 19.12\n", + " 15000 0.906 212 500 18.964\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 16000 0.908 225 500 19.792\n", + " 16000 0.908 225 500 19.637\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 17000 0.91 216 500 20.474\n", + " 17000 0.91 216 500 20.316\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 18000 0.909 221 500 21.149\n", + " 18000 0.909 221 500 20.983\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 19000 0.914 231 500 21.827\n", + " 19000 0.914 231 500 21.644\n", "--------------------------------------------------------------------\n", "Original Population Size: 212\n", "Post-Cleaning Population Size: 212\n", @@ -792,70 +792,70 @@ "3 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 90\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 20000 0.9 90 500 22.526\n", + " 20000 0.9 90 500 22.324\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:10000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 21 0.978 1.0 27 8.285\n", + " 21 0.978 1.0 27 8.236\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 22 0.978 1.0 26 8.659\n", + " 22 0.978 1.0 26 8.612\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 23 0.978 1.0 26 9.034\n", + " 23 0.978 1.0 26 8.983\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 24 0.978 1.0 26 9.359\n", + " 24 0.978 1.0 26 9.303\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 25 0.978 1.0 26 9.78\n", + " 25 0.978 1.0 26 9.692\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 26 0.978 1.0 19 10.17\n", + " 26 0.978 1.0 19 10.08\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 27 0.978 1.0 19 10.533\n", + " 27 0.978 1.0 19 10.438\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 28 0.978 1.0 19 10.851\n", + " 28 0.978 1.0 19 10.753\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 29 0.991 1.0 23 11.22\n", + " 29 0.991 1.0 23 11.116\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 30 0.991 1.0 19 11.545\n", + " 30 0.991 1.0 19 11.438\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 31 0.991 1.0 19 11.96\n", + " 31 0.991 1.0 19 11.843\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 32 0.991 1.0 19 12.387\n", + " 32 0.991 1.0 19 12.264\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 33 0.991 1.0 19 12.794\n", + " 33 0.991 1.0 19 12.653\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 34 1.0 1.0 25 13.166\n", + " 34 1.0 1.0 25 13.013\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 35 1.0 1.0 19 13.515\n", + " 35 1.0 1.0 19 13.359\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 36 1.0 1.0 8 13.807\n", + " 36 1.0 1.0 8 13.649\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 37 1.0 1.0 8 14.17\n", + " 37 1.0 1.0 8 13.998\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 38 1.0 1.0 8 14.546\n", + " 38 1.0 1.0 8 14.367\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 39 1.0 1.0 8 14.936\n", + " 39 1.0 1.0 8 14.753\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 40 1.0 1.0 8 15.296\n", + " 40 1.0 1.0 8 15.099\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.007092506344841265\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 21000 0.906 195 500 30.553\n", + " 21000 0.906 195 500 30.197\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 22000 0.899 207 500 31.247\n", + " 22000 0.899 207 500 30.851\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 23000 0.906 219 500 31.962\n", + " 23000 0.906 219 500 31.532\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 24000 0.907 212 500 32.632\n", + " 24000 0.907 212 500 32.213\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 25000 0.899 225 500 33.32\n", + " 25000 0.899 225 500 32.925\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 26000 0.908 221 500 34.017\n", + " 26000 0.908 221 500 33.604\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 27000 0.897 224 500 34.691\n", + " 27000 0.897 224 500 34.293\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 28000 0.905 218 500 35.364\n", + " 28000 0.905 218 500 34.966\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 29000 0.909 222 500 36.056\n", + " 29000 0.909 222 500 35.627\n", "--------------------------------------------------------------------\n", "Original Population Size: 215\n", "Post-Cleaning Population Size: 215\n", @@ -865,70 +865,70 @@ "2 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 89\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 30000 0.913 89 500 36.73\n", + " 30000 0.913 89 500 36.291\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:10000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 41 1.0 1.0 8 15.664\n", + " 41 1.0 1.0 8 15.468\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 42 1.0 1.0 8 16.074\n", + " 42 1.0 1.0 8 15.864\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 43 1.0 1.0 8 16.398\n", + " 43 1.0 1.0 8 16.174\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 44 1.0 1.0 8 16.826\n", + " 44 1.0 1.0 8 16.578\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 45 1.0 1.0 8 17.234\n", + " 45 1.0 1.0 8 16.987\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 46 1.0 1.0 8 17.606\n", + " 46 1.0 1.0 8 17.359\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 47 1.0 1.0 8 17.941\n", + " 47 1.0 1.0 8 17.703\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 48 1.0 1.0 8 18.297\n", + " 48 1.0 1.0 8 18.053\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 49 1.0 1.0 8 18.768\n", + " 49 1.0 1.0 8 18.508\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 50 1.0 1.0 8 19.174\n", + " 50 1.0 1.0 8 18.896\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 51 1.0 1.0 8 19.533\n", + " 51 1.0 1.0 8 19.251\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 52 1.0 1.0 8 19.833\n", + " 52 1.0 1.0 8 19.551\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 53 1.0 1.0 8 20.19\n", + " 53 1.0 1.0 8 19.909\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 54 1.0 1.0 8 20.593\n", + " 54 1.0 1.0 8 20.316\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 55 1.0 1.0 8 21.088\n", + " 55 1.0 1.0 8 20.817\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 56 1.0 1.0 8 21.458\n", + " 56 1.0 1.0 8 21.18\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 57 1.0 1.0 8 21.889\n", + " 57 1.0 1.0 8 21.598\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 58 1.0 1.0 8 22.299\n", + " 58 1.0 1.0 8 22.011\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 59 1.0 1.0 8 22.653\n", + " 59 1.0 1.0 8 22.351\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 60 1.0 1.0 8 23.009\n", + " 60 1.0 1.0 8 22.705\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.5444387138775688\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 31000 0.905 190 500 45.151\n", + " 31000 0.905 190 500 44.592\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 32000 0.904 220 500 45.868\n", + " 32000 0.904 220 500 45.254\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 33000 0.904 225 500 46.582\n", + " 33000 0.904 225 500 45.942\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 34000 0.905 217 500 47.256\n", + " 34000 0.905 217 500 46.628\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 35000 0.902 223 500 47.926\n", + " 35000 0.902 223 500 47.326\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 36000 0.903 218 500 48.603\n", + " 36000 0.903 218 500 48.04\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 37000 0.914 220 500 49.28\n", + " 37000 0.914 220 500 48.793\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 38000 0.902 220 500 49.962\n", + " 38000 0.902 220 500 49.579\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 39000 0.91 211 500 50.633\n", + " 39000 0.91 211 500 50.327\n", "--------------------------------------------------------------------\n", "Original Population Size: 218\n", "Post-Cleaning Population Size: 218\n", @@ -938,70 +938,70 @@ "2 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 91\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 40000 0.906 91 500 51.331\n", + " 40000 0.906 91 500 51.001\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:10000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 61 1.0 1.0 8 23.403\n", + " 61 1.0 1.0 8 23.095\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 62 1.0 1.0 8 23.799\n", + " 62 1.0 1.0 8 23.494\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 63 1.0 1.0 8 24.149\n", + " 63 1.0 1.0 8 23.848\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 64 1.0 1.0 8 24.47\n", + " 64 1.0 1.0 8 24.188\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 65 1.0 1.0 8 24.865\n", + " 65 1.0 1.0 8 24.575\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 66 1.0 1.0 8 25.174\n", + " 66 1.0 1.0 8 24.878\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 67 1.0 1.0 8 25.613\n", + " 67 1.0 1.0 8 25.317\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 68 1.0 1.0 8 26.022\n", + " 68 1.0 1.0 8 25.725\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 69 1.0 1.0 8 26.352\n", + " 69 1.0 1.0 8 26.057\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 70 1.0 1.0 8 26.722\n", + " 70 1.0 1.0 8 26.437\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 71 1.0 1.0 8 27.133\n", + " 71 1.0 1.0 8 26.844\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 72 1.0 1.0 8 27.502\n", + " 72 1.0 1.0 8 27.217\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 73 1.0 1.0 8 27.805\n", + " 73 1.0 1.0 8 27.511\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 74 1.0 1.0 8 28.162\n", + " 74 1.0 1.0 8 27.859\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 75 1.0 1.0 8 28.651\n", + " 75 1.0 1.0 8 28.329\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 76 1.0 1.0 8 29.084\n", + " 76 1.0 1.0 8 28.751\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 77 1.0 1.0 8 29.416\n", + " 77 1.0 1.0 8 29.079\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 78 1.0 1.0 8 29.838\n", + " 78 1.0 1.0 8 29.495\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 79 1.0 1.0 8 30.237\n", + " 79 1.0 1.0 8 29.893\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 80 1.0 1.0 8 30.569\n", + " 80 1.0 1.0 8 30.223\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.5459455361616103\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 41000 0.9 212 500 59.572\n", + " 41000 0.9 212 500 59.197\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 42000 0.904 217 500 60.25\n", + " 42000 0.904 217 500 59.866\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 43000 0.911 210 500 60.93\n", + " 43000 0.911 210 500 60.538\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 44000 0.902 213 500 61.606\n", + " 44000 0.902 213 500 61.219\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 45000 0.902 209 500 62.276\n", + " 45000 0.902 209 500 61.885\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 46000 0.909 212 500 62.935\n", + " 46000 0.909 212 500 62.555\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 47000 0.891 217 500 63.597\n", + " 47000 0.891 217 500 63.222\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 48000 0.9 218 500 64.269\n", + " 48000 0.9 218 500 63.902\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 49000 0.907 217 500 64.963\n", + " 49000 0.907 217 500 64.568\n", "--------------------------------------------------------------------\n", "Original Population Size: 215\n", "Post-Cleaning Population Size: 215\n", @@ -1011,51 +1011,51 @@ "2 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 87\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 50000 0.905 87 500 65.662\n", + " 50000 0.905 87 500 65.246\n", "Archiving: 50000\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:10000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 81 1.0 1.0 8 31.055\n", + " 81 1.0 1.0 8 30.687\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 82 1.0 1.0 8 31.514\n", + " 82 1.0 1.0 8 31.136\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 83 1.0 1.0 8 31.818\n", + " 83 1.0 1.0 8 31.439\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 84 1.0 1.0 8 32.236\n", + " 84 1.0 1.0 8 31.86\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 85 1.0 1.0 8 32.488\n", + " 85 1.0 1.0 8 32.115\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 86 1.0 1.0 8 32.91\n", + " 86 1.0 1.0 8 32.535\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 87 1.0 1.0 8 33.225\n", + " 87 1.0 1.0 8 32.845\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 88 1.0 1.0 8 33.614\n", + " 88 1.0 1.0 8 33.23\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 89 1.0 1.0 8 34.032\n", + " 89 1.0 1.0 8 33.644\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 90 1.0 1.0 8 34.403\n", + " 90 1.0 1.0 8 33.998\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 91 1.0 1.0 8 34.77\n", + " 91 1.0 1.0 8 34.351\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 92 1.0 1.0 8 35.128\n", + " 92 1.0 1.0 8 34.705\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 93 1.0 1.0 8 35.51\n", + " 93 1.0 1.0 8 35.074\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 94 1.0 1.0 8 35.883\n", + " 94 1.0 1.0 8 35.433\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 95 1.0 1.0 8 36.179\n", + " 95 1.0 1.0 8 35.718\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 96 1.0 1.0 8 36.562\n", + " 96 1.0 1.0 8 36.088\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 97 1.0 1.0 8 36.909\n", + " 97 1.0 1.0 8 36.421\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 98 1.0 1.0 8 37.239\n", + " 98 1.0 1.0 8 36.741\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 99 1.0 1.0 8 37.603\n", + " 99 1.0 1.0 8 37.089\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 100 1.0 1.0 8 37.976\n", + " 100 1.0 1.0 8 37.444\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.5410935159323432\n" ] @@ -1802,16 +1802,16 @@ "output_type": "stream", "text": [ " Global Phase 1 Phase 2 Rule Initialization Rule Covering \\\n", - "0 73.129616 183.625493 38.000413 0.33039 0.007684 \n", + "0 72.518429 182.195208 37.467329 0.319728 0.007509 \n", "\n", " Rule Equality Rule Matching Rule Evaluation Feature Tracking \\\n", - "0 1.93028 3.089952 30.904625 0.0 \n", + "0 1.872399 3.018794 31.186308 0.0 \n", "\n", " Rule Subsumption Rule Selection Rule Mating Rule Deletion \\\n", - "0 0.556547 1.157167 4.706649 5.529431 \n", + "0 0.535832 1.108148 4.856153 5.279404 \n", "\n", " Rule Compaction Rule Prediction \n", - "0 0.031017 0.809222 \n" + "0 0.02652 0.883465 \n" ] } ], @@ -2160,7 +2160,7 @@ "output_type": "stream", "text": [ "Rule population evaluation at iteration 500\n", - "Run Time: 0.6587517261505127\n", + "Run Time: 0.6417346000671387\n", " precision recall f1-score support\n", "\n", " 0 0.92000000 0.85185185 0.88461538 27\n", @@ -2171,7 +2171,7 @@ "weighted avg 0.88320000 0.88000000 0.88019231 50\n", "\n", "Rule population evaluation at iteration 1000\n", - "Run Time: 1.012446641921997\n", + "Run Time: 0.991980791091919\n", " precision recall f1-score support\n", "\n", " 0 0.92000000 0.85185185 0.88461538 27\n", @@ -2182,7 +2182,7 @@ "weighted avg 0.88320000 0.88000000 0.88019231 50\n", "\n", "Rule population evaluation at iteration 5000\n", - "Run Time: 3.8025341033935547\n", + "Run Time: 3.7754905223846436\n", " precision recall f1-score support\n", "\n", " 0 0.92000000 0.85185185 0.88461538 27\n", @@ -2193,7 +2193,7 @@ "weighted avg 0.88320000 0.88000000 0.88019231 50\n", "\n", "Rule population evaluation at iteration 10000\n", - "Run Time: 7.286679744720459\n", + "Run Time: 7.24245285987854\n", " precision recall f1-score support\n", "\n", " 0 0.92000000 0.82142857 0.86792453 28\n", @@ -2204,7 +2204,7 @@ "weighted avg 0.86720000 0.86000000 0.86050582 50\n", "\n", "Rule population evaluation at iteration 50000\n", - "Run Time: 65.66507577896118\n", + "Run Time: 65.24907279014587\n", " precision recall f1-score support\n", "\n", " 0 0.92000000 0.82142857 0.86792453 28\n", @@ -2249,7 +2249,7 @@ "output_type": "stream", "text": [ "Top default model evaluation at iteration 10\n", - "Run Time: 4.243201494216919\n", + "Run Time: 4.249011516571045\n", " precision recall f1-score support\n", "\n", " 0 0.96000000 0.92307692 0.94117647 26\n", @@ -2260,7 +2260,7 @@ "weighted avg 0.94080000 0.94000000 0.94002401 50\n", "\n", "Top default model evaluation at iteration 50\n", - "Run Time: 19.17435050010681\n", + "Run Time: 18.896023988723755\n", " precision recall f1-score support\n", "\n", " 0 1.00000000 1.00000000 1.00000000 25\n", @@ -2271,7 +2271,7 @@ "weighted avg 1.00000000 1.00000000 1.00000000 50\n", "\n", "Top default model evaluation at iteration 100\n", - "Run Time: 37.97588515281677\n", + "Run Time: 37.444315910339355\n", " precision recall f1-score support\n", "\n", " 0 1.00000000 1.00000000 1.00000000 25\n", @@ -2314,7 +2314,7 @@ "text": [ "---------------------------------------------------------------------------------------------\n", "Top model evaluation at iteration 10\n", - "Run Time: 4.243201494216919\n", + "Run Time: 4.249011516571045\n", "8 non-dominated models on Pareto-front.\n", "----------------------------------------\n", "Model testing accuracies: [0.94, 0.96, 0.88, 0.76, 0.8, 0.7, 0.72, 0.74]\n", @@ -2337,7 +2337,7 @@ "\n", "---------------------------------------------------------------------------------------------\n", "Top model evaluation at iteration 50\n", - "Run Time: 19.17435050010681\n", + "Run Time: 18.896023988723755\n", "8 non-dominated models on Pareto-front.\n", "----------------------------------------\n", "Model testing accuracies: [1.0, 0.94, 0.8200000000000001, 0.8999999999999999, 0.8, 0.6000000000000001, 0.6, 0.5800000000000001]\n", @@ -2360,7 +2360,7 @@ "\n", "---------------------------------------------------------------------------------------------\n", "Top model evaluation at iteration 100\n", - "Run Time: 37.97588515281677\n", + "Run Time: 37.444315910339355\n", "8 non-dominated models on Pareto-front.\n", "----------------------------------------\n", "Model testing accuracies: [1.0, 0.96, 0.88, 0.86, 0.8, 0.6200000000000001, 0.62, 0.52]\n", @@ -2458,7 +2458,7 @@ "HEROS Evolution Beginning!\n", "Archiving: 500\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 1000 0.903 201 500 0.707\n", + " 1000 0.903 201 500 0.689\n", "Archiving: 1000\n", "--------------------------------------------------------------------\n", "Original Population Size: 208\n", @@ -2469,30 +2469,30 @@ "1 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 89\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 2000 0.9 89 500 1.444\n", + " 2000 0.9 89 500 1.399\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:2000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 1 1.0 1.0 16 0.496\n", + " 1 1.0 1.0 16 0.49\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 2 1.0 1.0 16 0.97\n", + " 2 1.0 1.0 16 0.96\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 3 1.0 1.0 16 1.361\n", + " 3 1.0 1.0 16 1.458\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 4 1.0 1.0 15 1.753\n", + " 4 1.0 1.0 15 1.841\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 5 1.0 1.0 15 2.067\n", + " 5 1.0 1.0 15 2.148\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 6 1.0 1.0 15 2.447\n", + " 6 1.0 1.0 15 2.528\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 7 1.0 1.0 15 2.786\n", + " 7 1.0 1.0 15 2.858\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 8 1.0 1.0 15 3.083\n", + " 8 1.0 1.0 15 3.135\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.5402377299059444\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 3000 0.906 202 500 5.694\n", + " 3000 0.906 202 500 5.729\n", "--------------------------------------------------------------------\n", "Original Population Size: 223\n", "Post-Cleaning Population Size: 223\n", @@ -2502,30 +2502,30 @@ "2 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 88\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 4000 0.911 88 500 6.401\n", + " 4000 0.911 88 500 6.43\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:2000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 9 1.0 1.0 15 3.378\n", + " 9 1.0 1.0 15 3.502\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 10 1.0 1.0 13 3.732\n", + " 10 1.0 1.0 13 3.914\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 11 1.0 1.0 13 4.058\n", + " 11 1.0 1.0 13 4.24\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 12 1.0 1.0 12 4.358\n", + " 12 1.0 1.0 12 4.543\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 13 1.0 1.0 12 4.622\n", + " 13 1.0 1.0 12 4.799\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 14 1.0 1.0 12 4.896\n", + " 14 1.0 1.0 12 5.069\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 15 1.0 1.0 11 5.204\n", + " 15 1.0 1.0 11 5.372\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 16 1.0 1.0 9 5.545\n", + " 16 1.0 1.0 9 5.695\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.8059620494537011\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 5000 0.897 203 500 9.556\n", + " 5000 0.897 203 500 9.678\n", "Archiving: 5000\n", "--------------------------------------------------------------------\n", "Original Population Size: 217\n", @@ -2535,30 +2535,30 @@ "47 rules subsumed with a specificity of 4\n", "Post-Subsumption Compaction Population Size: 88\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 6000 0.901 88 500 10.306\n", + " 6000 0.901 88 500 10.388\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:2000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 17 1.0 1.0 9 5.857\n", + " 17 1.0 1.0 9 6.002\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 18 1.0 1.0 9 6.19\n", + " 18 1.0 1.0 9 6.329\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 19 1.0 1.0 9 6.555\n", + " 19 1.0 1.0 9 6.708\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 20 1.0 1.0 9 6.864\n", + " 20 1.0 1.0 9 7.013\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 21 1.0 1.0 9 7.224\n", + " 21 1.0 1.0 9 7.372\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 22 1.0 1.0 9 7.546\n", + " 22 1.0 1.0 9 7.69\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 23 1.0 1.0 9 7.926\n", + " 23 1.0 1.0 9 8.059\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 24 1.0 1.0 8 8.313\n", + " 24 1.0 1.0 8 8.448\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.10124179437006087\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 7000 0.9 196 500 13.807\n", + " 7000 0.9 196 500 13.805\n", "--------------------------------------------------------------------\n", "Original Population Size: 195\n", "Post-Cleaning Population Size: 195\n", @@ -2567,30 +2567,30 @@ "35 rules subsumed with a specificity of 4\n", "Post-Subsumption Compaction Population Size: 91\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 8000 0.901 91 500 14.526\n", + " 8000 0.901 91 500 14.471\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:2000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 25 1.0 1.0 8 8.655\n", + " 25 1.0 1.0 8 8.79\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 26 1.0 1.0 8 9.002\n", + " 26 1.0 1.0 8 9.147\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 27 1.0 1.0 8 9.445\n", + " 27 1.0 1.0 8 9.581\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 28 1.0 1.0 8 9.802\n", + " 28 1.0 1.0 8 9.939\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 29 1.0 1.0 8 10.218\n", + " 29 1.0 1.0 8 10.332\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 30 1.0 1.0 8 10.594\n", + " 30 1.0 1.0 8 10.686\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 31 1.0 1.0 8 11.052\n", + " 31 1.0 1.0 8 11.117\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 32 1.0 1.0 8 11.42\n", + " 32 1.0 1.0 8 11.482\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.3385627612391344\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 9000 0.898 198 500 18.311\n", + " 9000 0.898 198 500 18.178\n", "--------------------------------------------------------------------\n", "Original Population Size: 206\n", "Post-Cleaning Population Size: 206\n", @@ -2600,27 +2600,27 @@ "1 rules subsumed with a specificity of 5\n", "Post-Subsumption Compaction Population Size: 88\n", " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - " 10000 0.915 88 500 18.983\n", + " 10000 0.915 88 500 18.863\n", "Archiving: 10000\n", "HEROS (Phase 1) run complete!\n", "Number of Unique Rules Identified: 0\n", "Number of Iterations Used:2000\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 33 1.0 1.0 8 11.804\n", + " 33 1.0 1.0 8 11.863\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 34 1.0 1.0 8 12.221\n", + " 34 1.0 1.0 8 12.293\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 35 1.0 1.0 8 12.622\n", + " 35 1.0 1.0 8 12.692\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 36 1.0 1.0 8 12.993\n", + " 36 1.0 1.0 8 13.076\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 37 1.0 1.0 8 13.446\n", + " 37 1.0 1.0 8 13.539\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 38 1.0 1.0 8 13.837\n", + " 38 1.0 1.0 8 13.931\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 39 1.0 1.0 8 14.278\n", + " 39 1.0 1.0 8 14.368\n", " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - " 40 1.0 1.0 8 14.686\n", + " 40 1.0 1.0 8 14.768\n", "HEROS (Phase 2) run complete!\n", "Random Seed Check - End: 0.2120077853036706\n" ] From 74e6dff1364d430d19454ef06408a1dc525aed60 Mon Sep 17 00:00:00 2001 From: Ryan Urbanowicz Date: Wed, 1 Jul 2026 23:57:01 -0700 Subject: [PATCH 4/4] Update pyproject.toml updated to use most recent version of skrebate --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a2dd391..173d4ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ dependencies = [ "matplotlib", "seaborn", "scipy", -"skrebate==0.7", +"skrebate", "networkx"] [project.optional-dependencies]