{
"cells": [
{
"cell_type": "markdown",
"id": "bfa2092b",
"metadata": {},
"source": [
"# Running experiments to compare different solutions"
]
},
{
"cell_type": "markdown",
"id": "3b3f31d8",
"metadata": {},
"source": [
"### automatically load the modules when any change happens"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "cc8c7ab1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "472595fb",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import dual_loops as dual_loops"
]
},
{
"cell_type": "markdown",
"id": "bc9feeed",
"metadata": {},
"source": [
"## Loading and preparing data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "280d9893",
"metadata": {},
"outputs": [],
"source": [
"dataset_name = 'conference'\n",
"#dataset_name = 'nasa'\n",
"#dataset_name = 'ai4eu'\n",
"#dataset_name = 'anatomy'\n",
"\n",
"with_blocking = True\n",
"\n",
"if with_blocking:\n",
" all_dataset_df = pd.read_csv(dataset_name + '_blocked.csv')\n",
"else:\n",
" all_dataset_df = pd.read_csv(dataset_name + '.csv')\n",
"\n",
"import pickle\n",
"source_ontology = pickle.load( open(dataset_name + \"_source_ontology.pk\", \"rb\" ) )\n",
"target_ontology = pickle.load( open(dataset_name + \"_target_ontology.pk\", \"rb\" ) )\n",
"\n",
"dual_loops.source_ontology = source_ontology\n",
"dual_loops.target_ontology = target_ontology\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "36a9a7eb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9299"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_dataset_df = all_dataset_df[all_dataset_df['selected_after_blocking'] == 1].reset_index(drop=True)\n",
"len(my_dataset_df)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "00dc9d2d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"65"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(my_dataset_df[my_dataset_df['label']==1])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "5ce37709",
"metadata": {},
"outputs": [],
"source": [
"lfs_set = [\n",
"# 'LF_aml', \n",
"# 'LF_logmap', \n",
"# 'LF_yam',\n",
" \n",
" 'LF_class_name_equal', \n",
" 'LF_class_name_stemmed_equal',\n",
" 'LF_acronyms', \n",
" 'LF_class_name_synonyms',\n",
" 'LF_root_nouns_equal', \n",
" \n",
" 'LF_class_name_spacy_distance', \n",
" 'LF_class_name_distance', \n",
" \n",
"# 'LF_name_segment_overlap', \n",
" \n",
" 'LF_label_equal', \n",
" 'LF_label_words_overlap',\n",
" 'LF_subclasses_overlap',\n",
" 'LF_superclasses_overlap',\n",
" 'LF_properties_overlap',\n",
" ]\n",
"\n",
"feature_set = [\n",
" 'shared_word', \n",
" 'levenshtein_distance', \n",
" 'hamming_distance',\n",
" 'class_name_embedding_distance'\n",
" ]\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "ff4f4d42",
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
"balance=[0.9, 0.1]\n",
"\n",
"lfs_stat = dual_loops.show_lfs_stat2(my_dataset_df, lfs_set, balance, epochs)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "920dfcfd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>coverage</th>\n",
" <th>tn</th>\n",
" <th>fp</th>\n",
" <th>fn</th>\n",
" <th>tp</th>\n",
" <th>precision</th>\n",
" <th>recall</th>\n",
" <th>f1</th>\n",
" <th>accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LF_class_name_equal</td>\n",
" <td>0.004839</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>0.955556</td>\n",
" <td>1.000000</td>\n",
" <td>0.977273</td>\n",
" <td>0.955556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LF_class_name_stemmed_equal</td>\n",
" <td>0.005269</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>0.877551</td>\n",
" <td>1.000000</td>\n",
" <td>0.934783</td>\n",
" <td>0.877551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LF_acronyms</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LF_class_name_synonyms</td>\n",
" <td>0.005484</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0.764706</td>\n",
" <td>1.000000</td>\n",
" <td>0.866667</td>\n",
" <td>0.764706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LF_root_nouns_equal</td>\n",
" <td>0.005054</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>0.914894</td>\n",
" <td>1.000000</td>\n",
" <td>0.955556</td>\n",
" <td>0.914894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LF_class_name_spacy_distance</td>\n",
" <td>0.152597</td>\n",
" <td>1367</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>45</td>\n",
" <td>0.882353</td>\n",
" <td>0.978261</td>\n",
" <td>0.927835</td>\n",
" <td>0.995067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>LF_class_name_distance</td>\n",
" <td>0.649962</td>\n",
" <td>5994</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>45</td>\n",
" <td>0.957447</td>\n",
" <td>0.937500</td>\n",
" <td>0.947368</td>\n",
" <td>0.999173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>LF_label_equal</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LF_label_words_overlap</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LF_subclasses_overlap</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>LF_superclasses_overlap</td>\n",
" <td>0.000860</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.125000</td>\n",
" <td>1.000000</td>\n",
" <td>0.222222</td>\n",
" <td>0.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>LF_properties_overlap</td>\n",
" <td>0.000215</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>snorkel</td>\n",
" <td>1.000000</td>\n",
" <td>9221</td>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>46</td>\n",
" <td>0.779661</td>\n",
" <td>0.707692</td>\n",
" <td>0.741935</td>\n",
" <td>0.996559</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name coverage tn fp fn tp precision \\\n",
"0 LF_class_name_equal 0.004839 0 2 0 43 0.955556 \n",
"1 LF_class_name_stemmed_equal 0.005269 0 6 0 43 0.877551 \n",
"2 LF_acronyms 0.000000 0 0 0 0 NaN \n",
"3 LF_class_name_synonyms 0.005484 0 12 0 39 0.764706 \n",
"4 LF_root_nouns_equal 0.005054 0 4 0 43 0.914894 \n",
"5 LF_class_name_spacy_distance 0.152597 1367 6 1 45 0.882353 \n",
"6 LF_class_name_distance 0.649962 5994 2 3 45 0.957447 \n",
"7 LF_label_equal 0.000000 0 0 0 0 NaN \n",
"8 LF_label_words_overlap 0.000000 0 0 0 0 NaN \n",
"9 LF_subclasses_overlap 0.000000 0 0 0 0 NaN \n",
"10 LF_superclasses_overlap 0.000860 0 7 0 1 0.125000 \n",
"11 LF_properties_overlap 0.000215 0 1 0 1 0.500000 \n",
"12 snorkel 1.000000 9221 13 19 46 0.779661 \n",
"\n",
" recall f1 accuracy \n",
"0 1.000000 0.977273 0.955556 \n",
"1 1.000000 0.934783 0.877551 \n",
"2 NaN NaN NaN \n",
"3 1.000000 0.866667 0.764706 \n",
"4 1.000000 0.955556 0.914894 \n",
"5 0.978261 0.927835 0.995067 \n",
"6 0.937500 0.947368 0.999173 \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"10 1.000000 0.222222 0.125000 \n",
"11 1.000000 0.666667 0.500000 \n",
"12 0.707692 0.741935 0.996559 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lfs_stat"
]
},
{
"cell_type": "markdown",
"id": "22f8fb6e",
"metadata": {},
"source": [
"# compare DualLoop with the other approaches"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "b214788a",
"metadata": {},
"outputs": [],
"source": [
"### predefined configuration for different setups\n",
"\n",
"configuration_options = { \n",
" 'WeSAL': {\n",
" 'datapoint_grouping': 'disagreement', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'entropy', # best option for uncertainty sampling\n",
" 'batch_size': 100,\n",
" 'lf_ensemble': 'snorkel_with_corrected_votes',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'AL-RF': {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'batch_size': 100, \n",
" 'lf_ensemble': 'normal_active_learning_rf',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'DualLoop-fastloop': { \n",
" 'datapoint_grouping': 'num_positive_votes', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin, match_confidence\n",
" 'batch_size': 100, \n",
" 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'DualLoop': {\n",
" 'datapoint_grouping': 'num_positive_votes', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin, match_confidence\n",
" 'batch_size': 100, \n",
" 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': True \n",
" }, \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "93d850d1",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"\r\n",
"************* DualLoop *************************\n",
"no selection of lfs due to limited number of annotated samples\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30ba876943ca4bbf94b4a91a9436b6ac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/600 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===1===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 5\n",
"SELECTED DATAPOINT = [ 226 369 714 962 1149 2376 2392 3626 4575 5139 5418 5693 5740]\n",
"\n",
"===14===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 7\n",
"SELECTED DATAPOINT = [6551 1880]\n",
"\n",
"===16===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 3\n",
"SELECTED DATAPOINT = [ 83 2830 3638 4757 4927 5109 5122 5264 5591 6442 6734 6821 6852 6966\n",
" 7375 8360 8533 2908 2768 91 2643 209 418 436 444 584 802 862\n",
" 1164 1222 1415 2061 2227 2528 2560 2590 9075]\n",
"\n",
"===53===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 6\n",
"SELECTED DATAPOINT = [ 929 1078 1096 6124 7194 7980]\n",
"\n",
"===59===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 4\n",
"SELECTED DATAPOINT = [153 272]\n",
"\n",
"===61===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 2\n",
"SELECTED DATAPOINT = [2299 540 46 8508 4250 685 1642 4131 1831 6825 859 8206 8292 8780\n",
" 9019 9026 7385]\n",
"\n",
"===78===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 1\n",
"SELECTED DATAPOINT = [6933 6936 6937 6938 6939 6941 6942 6943 6945 6946 6947 6948 6951 6953\n",
" 6955 6958 6965 6968 6973 6974 6976 6977 6978]\n",
"SELECTED set of LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap']\n",
"combined LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap']\n",
"[0.6758620689655173, 0.632258064516129, 0.7, 0.6375, 0.6533333333333333, 0.6343749999999999, 0.6766666666666666, 0.7, 0.7, 0.7, 0.5764705882352941, 0.7]\n",
"========= SLOW LOOP =============\n",
"========= random_forest ===========\n",
"0.9672131147540983 0.9076923076923077 0.9365079365079365\n",
"========= logistic_regression ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"[18:18:15] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"========= xgboost ===========\n",
"0.9629629629629629 0.8 0.8739495798319328\n",
"========= mlp ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"===== LF_class_name_distance_ma =========\n",
"x0 [1.0]\n",
"bounds [(0.0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 45, 'fp': 2, 'tn': 39, 'fn': 14, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.9206366539001465 s\n",
" direc: array([[0.14290182]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 64\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.23135423])\n",
"new x0 = [0.23135423]\n",
"-------NEW---------\n",
"{'x0': [array([0.23135423])], 'annotated': {'tp': 59, 'fp': 41, 'tn': 0, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 9067, 'tn': 132, 'fn': 0, 'coverage': 1.0}}\n",
"LF_class_name_distance_ma_tuned\n",
"===== LF_class_name_distance_mb =========\n",
"x0 [1.0]\n",
"bounds [(0.0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 45, 'fp': 2, 'tn': 39, 'fn': 14, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 9.369488000869751 s\n",
" direc: array([[-7.72183695e-06]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 89\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.23134662])\n",
"new x0 = [0.23134662]\n",
"-------NEW---------\n",
"{'x0': [array([0.23134662])], 'annotated': {'tp': 59, 'fp': 28, 'tn': 13, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 5102, 'tn': 4097, 'fn': 0, 'coverage': 1.0}}\n",
"LF_class_name_distance_mb_tuned\n",
"===== LF_comment_distance_ma =========\n",
"x0 [1.0]\n",
"bounds [(0.0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 1.9601781368255615 s\n",
" direc: array([[1.]])\n",
" fun: 59\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.97993522])\n",
"new x0 = [0.97993522]\n",
"-------NEW---------\n",
"{'x0': [array([0.97993522])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"LF_comment_distance_ma_tuned\n",
"===== LF_comment_distance_mb =========\n",
"x0 [1.0]\n",
"bounds [(0.0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 2.132521390914917 s\n",
" direc: array([[1.]])\n",
" fun: 59\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.97993522])\n",
"new x0 = [0.97993522]\n",
"-------NEW---------\n",
"{'x0': [array([0.97993522])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"LF_comment_distance_mb_tuned\n",
"===== LF_num_common_words =========\n",
"x0 [5]\n",
"bounds [(1, 5)]\n",
"-------OLD---------\n",
"{'x0': [5], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 4.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.872936248779297 s\n",
" direc: array([[0.72637048]])\n",
" fun: 49.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 67\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([1.17565718])\n",
"new x0 = [1.17565718]\n",
"-------NEW---------\n",
"{'x0': [array([1.17565718])], 'annotated': {'tp': 10, 'fp': 3, 'tn': 38, 'fn': 49, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 19, 'tn': 9180, 'fn': 0, 'coverage': 1.0}}\n",
"LF_num_common_words_tuned\n",
"===== LF_random_forest_prob =========\n",
"x0 [1.0]\n",
"bounds [(0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 27, 'fp': 0, 'tn': 41, 'fn': 32, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.352594614028931 s\n",
" direc: array([[0.14290182]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 64\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.23135423])\n",
"new x0 = [0.23135423]\n",
"-------NEW---------\n",
"{'x0': [array([0.23135423])], 'annotated': {'tp': 59, 'fp': 3, 'tn': 38, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 1, 'tn': 9198, 'fn': 0, 'coverage': 1.0}}\n",
"LF_random_forest_prob_tuned\n",
"===== LF_logistic_regression_prob =========\n",
"x0 [1.0]\n",
"bounds [(0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.628280401229858 s\n",
" direc: array([[-0.51739079]])\n",
" fun: 10.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 63\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.08833415])\n",
"new x0 = [0.08833415]\n",
"-------NEW---------\n",
"{'x0': [array([0.08833415])], 'annotated': {'tp': 49, 'fp': 8, 'tn': 33, 'fn': 10, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 38, 'tn': 9161, 'fn': 0, 'coverage': 1.0}}\n",
"LF_logistic_regression_prob_tuned\n",
"===== LF_xgboost_prob =========\n",
"x0 [1.0]\n",
"bounds [(0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 5.69774055480957 s\n",
" direc: array([[0.01796911]])\n",
" fun: 4\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 58\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.21329306])\n",
"new x0 = [0.21329306]\n",
"-------NEW---------\n",
"{'x0': [array([0.21329306])], 'annotated': {'tp': 55, 'fp': 3, 'tn': 38, 'fn': 4, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"LF_xgboost_prob_tuned\n",
"===== LF_mlp_prob =========\n",
"x0 [1.0]\n",
"bounds [(0, 1.0)]\n",
"-------OLD---------\n",
"{'x0': [1.0], 'annotated': {'tp': 0, 'fp': 0, 'tn': 41, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9199, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, 0.98)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.930649280548096 s\n",
" direc: array([[-0.59286483]])\n",
" fun: 4.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 63\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.01286011])\n",
"new x0 = [0.01286011]\n",
"-------NEW---------\n",
"{'x0': [array([0.01286011])], 'annotated': {'tp': 55, 'fp': 11, 'tn': 30, 'fn': 4, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 292, 'tn': 8907, 'fn': 0, 'coverage': 1.0}}\n",
"LF_mlp_prob_tuned\n",
"LF augumentation time: 63.06979036331177 s\n",
"\n",
"===101===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 1\n",
"SELECTED DATAPOINT = [6988 6989 6990 6995 6997 6998 7001 7002 7004 7005 7007 7008 7009 7013\n",
" 7019 7020 7021 7023 7025 7027 7028 7030 7033 7035 7039 7045 7046 7048\n",
" 7049 7050 7051 7053 7054 7055 7063 7066 7067 7068 7070 7074 7075 7076\n",
" 7077 7079 7080 7081 7082 7083 7085 7088 7089 7090 7091 7092 7097 7110\n",
" 7112 7113 7115 7116 7117 7118 7119 7127 7128 7129 7134 7135 7137 7139\n",
" 7141 7143 7146 7158 7161 7162 7163 7172 7174 7176 7179 7184 7186 7193\n",
" 7195 7197 7198 7199 7200 7204 7205 7209 7211 7213 7217 7221 7223 7225\n",
" 7227 7237]\n",
"SELECTED set of LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap']\n",
"combined LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap', 'mlp', 'LF_num_common_words_tuned', 'LF_xgboost_prob_tuned', 'LF_class_name_distance_mb_tuned', 'LF_comment_distance_ma_tuned', 'LF_logistic_regression_prob_tuned', 'LF_class_name_distance_ma_tuned', 'random_forest', 'LF_random_forest_prob_tuned', 'LF_comment_distance_mb_tuned', 'LF_mlp_prob_tuned', 'xgboost', 'logistic_regression']\n",
"[0.6758620689655173, 0.632258064516129, 0.7, 0.6375, 0.6533333333333333, 0.6343749999999999, 0.6766666666666666, 0.7, 0.7, 0.7, 0.5764705882352941, 0.7, 0.6702127659574468, 0.5384615384615384, 0.6637931034482758, 0.258125, 0.1, 0.5913793103448276, 0.20858585858585857, 0.6770491803278688, 0.6661290322580645, 0.1, 0.47530864197530864, 0.674074074074074, 0.6702127659574468]\n",
"========= SLOW LOOP =============\n",
"========= random_forest ===========\n",
"0.9672131147540983 0.9076923076923077 0.9365079365079365\n",
"========= logistic_regression ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"[18:19:19] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"========= xgboost ===========\n",
"0.9629629629629629 0.8 0.8739495798319328\n",
"========= mlp ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"===== LF_class_name_distance_ma =========\n",
"x0 [array([0.23135423])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.23135423])], 'annotated': {'tp': 59, 'fp': 139, 'tn': 2, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 8969, 'tn': 130, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 8969\n",
"Optimizing Time: 2.2377641201019287 s\n",
" direc: array([[1.]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 20\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.23139809])\n",
"new x0 = [0.23139809]\n",
"-------NEW---------\n",
"{'x0': [array([0.23139809])], 'annotated': {'tp': 59, 'fp': 139, 'tn': 2, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 8969, 'tn': 130, 'fn': 0, 'coverage': 1.0}}\n",
"LF_class_name_distance_ma_tuned\n",
"===== LF_class_name_distance_mb =========\n",
"x0 [array([0.23134662])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.23134662])], 'annotated': {'tp': 59, 'fp': 101, 'tn': 40, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 5029, 'tn': 4070, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 5029\n",
"Optimizing Time: 2.3973960876464844 s\n",
" direc: array([[1.]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 22\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.23131305])\n",
"new x0 = [0.23131305]\n",
"-------NEW---------\n",
"{'x0': [array([0.23131305])], 'annotated': {'tp': 59, 'fp': 101, 'tn': 40, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 5030, 'tn': 4069, 'fn': 0, 'coverage': 1.0}}\n",
"LF_class_name_distance_mb_tuned\n",
"===== LF_comment_distance_ma =========\n",
"x0 [array([0.97993522])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.97993522])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 141, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, array([0.95993522]))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 2.0602402687072754 s\n",
" direc: array([[1.]])\n",
" fun: 59\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.95987176])\n",
"new x0 = [0.95987176]\n",
"-------NEW---------\n",
"{'x0': [array([0.95987176])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 141, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"LF_comment_distance_ma_tuned\n",
"===== LF_comment_distance_mb =========\n",
"x0 [array([0.97993522])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.97993522])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 141, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, array([0.95993522]))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 2.193847179412842 s\n",
" direc: array([[1.]])\n",
" fun: 59\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.95987176])\n",
"new x0 = [0.95987176]\n",
"-------NEW---------\n",
"{'x0': [array([0.95987176])], 'annotated': {'tp': 0, 'fp': 0, 'tn': 141, 'fn': 59, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"LF_comment_distance_mb_tuned\n",
"===== LF_num_common_words =========\n",
"x0 [array([1.17565718])]\n",
"bounds [(0, 4.98)]\n",
"-------OLD---------\n",
"{'x0': [array([1.17565718])], 'annotated': {'tp': 10, 'fp': 3, 'tn': 138, 'fn': 49, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 19, 'tn': 9080, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 19\n",
"Optimizing Time: 2.7192747592926025 s\n",
" direc: array([[1.]])\n",
" fun: 49.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 23\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([1.1756814])\n",
"new x0 = [1.1756814]\n",
"-------NEW---------\n",
"{'x0': [array([1.1756814])], 'annotated': {'tp': 10, 'fp': 3, 'tn': 138, 'fn': 49, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 19, 'tn': 9080, 'fn': 0, 'coverage': 1.0}}\n",
"LF_num_common_words_tuned\n",
"===== LF_random_forest_prob =========\n",
"x0 [array([0.23135423])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.23135423])], 'annotated': {'tp': 59, 'fp': 3, 'tn': 138, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 1, 'tn': 9098, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 1\n",
"Optimizing Time: 1.9541094303131104 s\n",
" direc: array([[1.]])\n",
" fun: 0.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 20\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.23139809])\n",
"new x0 = [0.23139809]\n",
"-------NEW---------\n",
"{'x0': [array([0.23139809])], 'annotated': {'tp': 59, 'fp': 3, 'tn': 138, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 1, 'tn': 9098, 'fn': 0, 'coverage': 1.0}}\n",
"LF_random_forest_prob_tuned\n",
"===== LF_logistic_regression_prob =========\n",
"x0 [array([0.08833415])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.08833415])], 'annotated': {'tp': 49, 'fp': 9, 'tn': 132, 'fn': 10, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 37, 'tn': 9062, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 37\n",
"Optimizing Time: 2.2215030193328857 s\n",
" direc: array([[1.]])\n",
" fun: 14\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.60573809])\n",
"new x0 = [0.60573809]\n",
"-------NEW---------\n",
"{'x0': [array([0.60573809])], 'annotated': {'tp': 45, 'fp': 2, 'tn': 139, 'fn': 14, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"LF_logistic_regression_prob_tuned\n",
"===== LF_xgboost_prob =========\n",
"x0 [array([0.21329306])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.21329306])], 'annotated': {'tp': 55, 'fp': 3, 'tn': 138, 'fn': 4, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 0\n",
"updated boundary [(0, array([0.19329306]))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bcheng/aaai22/dual_loops.py:1244: OptimizeWarning: Initial guess is not within the specified bounds\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizing Time: 6.249705076217651 s\n",
" direc: array([[0.01738137]])\n",
" fun: 1.0\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 61\n",
" nit: 2\n",
" status: 0\n",
" success: True\n",
" x: array([0.02820201])\n",
"new x0 = [0.02820201]\n",
"-------NEW---------\n",
"{'x0': [array([0.02820201])], 'annotated': {'tp': 58, 'fp': 8, 'tn': 133, 'fn': 1, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 51, 'tn': 9048, 'fn': 0, 'coverage': 1.0}}\n",
"LF_xgboost_prob_tuned\n",
"===== LF_mlp_prob =========\n",
"x0 [array([0.01286011])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.01286011])], 'annotated': {'tp': 55, 'fp': 26, 'tn': 115, 'fn': 4, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 277, 'tn': 8822, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 277\n",
"Optimizing Time: 2.106564521789551 s\n",
" direc: array([[1.]])\n",
" fun: 14\n",
" message: 'Optimization terminated successfully.'\n",
" nfev: 21\n",
" nit: 1\n",
" status: 0\n",
" success: True\n",
" x: array([0.60573809])\n",
"new x0 = [0.60573809]\n",
"-------NEW---------\n",
"{'x0': [array([0.60573809])], 'annotated': {'tp': 45, 'fp': 2, 'tn': 139, 'fn': 14, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 0, 'tn': 9099, 'fn': 0, 'coverage': 1.0}}\n",
"LF_mlp_prob_tuned\n",
"LF augumentation time: 35.50675296783447 s\n",
"\n",
"===201===\n",
"# of GROUPs = 7\n",
"SELECTED GROUP = 1\n",
"SELECTED DATAPOINT = [7470 8102 8294 9012 8849 8855 8994 8583 9003 8339 7257 7263 7266 7275\n",
" 7276 7278 7292 7297 7305 7310 7312 7313 7314 7318 7321 7323 7324 7326\n",
" 7329 7336 7340 7341 7344 7366 7368 7371 7373 7374 7376 7378 7444 7463\n",
" 7464 7466 7469 7472 7474 7475 7476 7478 7479 7483 7485 7490 7550 7557\n",
" 7568 7569 7570 7574 7576 7578 7579 7583 7584 7586 7595 7599 7601 7602\n",
" 7604 7612 7615 7641 7668 7669 7671 7674 7681 7684 7689 7692 7761 7762\n",
" 7765 7767 7788 7789 7791 7798 7800 7801 7811 7812 7815 7825 7827 7831\n",
" 7839 7841]\n",
"SELECTED set of LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap']\n",
"combined LFs = ['LF_class_name_equal', 'LF_class_name_stemmed_equal', 'LF_acronyms', 'LF_class_name_synonyms', 'LF_root_nouns_equal', 'LF_class_name_spacy_distance', 'LF_class_name_distance', 'LF_label_equal', 'LF_label_words_overlap', 'LF_subclasses_overlap', 'LF_superclasses_overlap', 'LF_properties_overlap', 'mlp', 'LF_num_common_words_tuned', 'LF_xgboost_prob_tuned', 'LF_class_name_distance_mb_tuned', 'LF_comment_distance_ma_tuned', 'LF_logistic_regression_prob_tuned', 'LF_class_name_distance_ma_tuned', 'random_forest', 'LF_random_forest_prob_tuned', 'LF_comment_distance_mb_tuned', 'LF_mlp_prob_tuned', 'xgboost', 'logistic_regression']\n",
"[0.6758620689655173, 0.632258064516129, 0.7, 0.6375, 0.6533333333333333, 0.6343749999999999, 0.6766666666666666, 0.7, 0.7, 0.7, 0.5764705882352941, 0.7, 0.6702127659574468, 0.41363636363636364, 0.5970588235294118, 0.16692307692307692, 0.1, 0.6702127659574468, 0.14563758389261744, 0.6770491803278688, 0.6661290322580645, 0.1, 0.6702127659574468, 0.674074074074074, 0.6702127659574468]\n",
"========= SLOW LOOP =============\n",
"========= random_forest ===========\n",
"0.96875 0.9538461538461539 0.9612403100775194\n",
"========= logistic_regression ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"[18:19:55] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"========= xgboost ===========\n",
"0.9642857142857143 0.8307692307692308 0.8925619834710744\n",
"========= mlp ===========\n",
"0.9574468085106383 0.6923076923076923 0.8035714285714286\n",
"===== LF_class_name_distance_ma =========\n",
"x0 [array([0.23139809])]\n",
"bounds [(0, 0.98)]\n",
"-------OLD---------\n",
"{'x0': [array([0.23139809])], 'annotated': {'tp': 62, 'fp': 236, 'tn': 2, 'fn': 0, 'coverage': 1.0}, 'predicted': {'tp': 0, 'fp': 8869, 'tn': 130, 'fn': 0, 'coverage': 1.0}}\n",
"num_predicted_matches = 8869\n",
"Unexpected exception formatting exception. Falling back to standard exception\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3398, in run_code\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
" File \"/tmp/ipykernel_1993635/2926300259.py\", line 21, in <cell line: 18>\n",
" result, result_df = dual_loops.run_experiment(experiment_config, my_dataset_df, lfs_set, feature_set,\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 1576, in run_experiment\n",
" new_lfs = trigger_slow_loop(my_df, feature_set, num_slow_loop)\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 1341, in trigger_slow_loop\n",
" tuned_lfs = augmented_with_tuned_lf(df, iteration)\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 1296, in augmented_with_tuned_lf\n",
" optimal_parameter, my_lf = compute_optimal_parameter(df, lf_name, iteration)\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 1244, in compute_optimal_parameter\n",
" res = minimize(optimize_wrapper, x0, method='Powell', bounds=bounds,\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_minimize.py\", line 690, in minimize\n",
" res = _minimize_powell(fun, x0, args, callback, bounds, **options)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_optimize.py\", line 3182, in _minimize_powell\n",
" fval, x, direc1 = _linesearch_powell(func, x, direc1,\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_optimize.py\", line 2920, in _linesearch_powell\n",
" res = _minimize_scalar_bounded(myfunc, bound, xatol=tol / 100)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_optimize.py\", line 2164, in _minimize_scalar_bounded\n",
" fu = func(x, *args)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_optimize.py\", line 2903, in myfunc\n",
" return func(p + alpha*xi)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/scipy/optimize/_optimize.py\", line 496, in function_wrapper\n",
" fx = function(np.copy(x), *(wrapper_args + args))\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 1138, in optimize_wrapper\n",
" y_df = predicted_df.apply(lf_func, x0=x0, axis = 1).to_numpy()\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/frame.py\", line 7768, in apply\n",
" return op.get_result()\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/apply.py\", line 185, in get_result\n",
" return self.apply_standard()\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/apply.py\", line 276, in apply_standard\n",
" results, res_index = self.apply_series_generator()\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/apply.py\", line 290, in apply_series_generator\n",
" results[i] = self.f(v)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/apply.py\", line 110, in f\n",
" return func(x, *args, **kwds)\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 971, in __call__\n",
" return self.func(*args, **kwargs)\n",
" File \"/home/bcheng/aaai22/dual_loops.py\", line 985, in LF_class_name_distance_ma\n",
" r = row['class_long_name_distance_a']\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/pandas/core/series.py\", line 853, in __getitem__\n",
" return self._get_value(key)\n",
"KeyboardInterrupt\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 1993, in showtraceback\n",
" stb = self.InteractiveTB.structured_traceback(\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 1118, in structured_traceback\n",
" return FormattedTB.structured_traceback(\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 1012, in structured_traceback\n",
" return VerboseTB.structured_traceback(\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 865, in structured_traceback\n",
" formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 818, in format_exception_as_a_whole\n",
" frames.append(self.format_record(r))\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 736, in format_record\n",
" result += ''.join(_format_traceback_lines(frame_info.lines, Colors, self.has_colors, lvals))\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
" value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/core.py\", line 698, in lines\n",
" pieces = self.included_pieces\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
" value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/core.py\", line 649, in included_pieces\n",
" pos = scope_pieces.index(self.executing_piece)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
" value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/stack_data/core.py\", line 628, in executing_piece\n",
" return only(\n",
" File \"/home/bcheng/anaconda3/envs/dualloop/lib/python3.8/site-packages/executing/executing.py\", line 164, in only\n",
" raise NotOneValueFound('Expected one value, found 0')\n",
"executing.executing.NotOneValueFound: Expected one value, found 0\n"
]
}
],
"source": [
"epochs = 100\n",
"balance=[0.9, 0.1]\n",
"\n",
"total_size = len(my_dataset_df)\n",
"num_iteration = len(my_dataset_df)\n",
"num_iteration = 600\n",
"interval_slow_loop = 10\n",
"\n",
"results = {}\n",
"\n",
"experiments = [ \n",
"# 'WeSAL', \n",
"# 'AL-RF', \n",
"# 'DualLoop-fastloop',\n",
" 'DualLoop', \n",
"]\n",
"\n",
"for exp in experiments:\n",
" print(\"\\r\\n\\r\\n************* \" + exp + \" *************************\")\n",
" experiment_config = configuration_options[exp]\n",
" result, result_df = dual_loops.run_experiment(experiment_config, my_dataset_df, lfs_set, feature_set, \n",
" total_size, num_iteration, interval_slow_loop, \n",
" balance, epochs, True) \n",
"\n",
" results[exp] = result "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "0472479c",
"metadata": {},
"outputs": [],
"source": [
"pickle.dump(results, open(\"dualloop_result_\" + dataset_name + \".pk\", \"wb\" ))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "9848a458",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IwSaEEEIIQZ7TRFiXy7BF2Ehf9RQArW59jgJ3END4HlVqvNMthBBCCBGwVFVB9RRyauPb3mP5P2/Conj8GkegPOgvPWxCCCGECDjFQ6I7UMxmWt36HPk/b8JxdD/h/a7065CoisrCobMINYcQYg4ufobNb5/+hzq/4/z8fKZMmcK///1vjhw5wnPPPedtS0tLo0ePHrzyyiu8+OKLfPzxx0REFE/Xveaaa7juuus4cOAA8+fPJz8/n759+/Loo4961yoTQgghRMNU6DbjOfdidFsPHnj7EJd26834SVdi16yA/3rZXIVwJP0EbcNb06RJM8MGY+u08tmzZw/z588nKSkJgCFDhjBkyBAAMjIymDp1Kg899BAA+/bt47nnnqNXr14lrjF79mwef/xxevbsybx581i5ciXTpk2ry7ANk5DwG9OnT+Hxx5/issuuAGDy5HH861+vEBvbqsL3XnJJXzp27AwUb5Cbn59H//4DeeCBuaiqWqL9jNmz53HBBd3q5maEEEKIWnAD8/+9lbnK63Qs7M17/+vGV/GpLL5rEP6bI1qsc7P2NLUau/5bnRZsK1euZOHChTz44IOl2pYuXcqUKVNo164dUFywvfbaaxw7doyLLrqIOXPmkJmZicPhoGfPngBMnDiRF154wZCCzZ1yAOf37xIyZjba6VTv16bQZj77jP/+9wsuu+wKVq/+2FuwVcdbb73v/bqgIJ8bbriW7dt/YODAQaXahRBCiEDm0XQcOdnQDAq14p2G0rPteDTdr89zJeUe5Z+7X+H+XrfTObKjHz+5pDqddPDEE0/Qt2/fUseTkpLYvn0706cX73FZUFDA+eefz5w5c/j000/Jzc1l+fLlpKenY7PZvO+z2WykpaXVZchlcqccwL5uGdrpVBxfv+L9umjX5777DLebDRu+5Pbb7+Lw4UOcOHG8Vtc7ffo0TqeDiIimPopQCCGE8B/VpNC2afEj/3l6CADRkSGoJv/2r0WHRnF9l6tpGRbj18/9M0MeBvvwww+ZNm0aQUFBAISFhfHaa69522+++WbmzZvnHT49m6JU7xtV1qrB6ekmzOY/atW81U+W+d7wCcXbNDm+ehHcRQB4Ug5yZs6I68gPNLlsBs6D31F08Lty318VW7duJTY2lvbtz2Xw4KF8/vkn3HvvTABUtWS85bnppmm43W6ys0/Rrt25/O1vD9Kjx4Ul2s/o06cvM2f+vcrxVYXJZMJmCy+3vaI2UXWSR9+QPPqG5LH2JIdliz9wkmsvjYUtkKsFEx0Zwvyb+xPVLBRTGUVbXeUxpMhEZGRPmoc2w6QYt7iGIQXbxo0bef31172vU1JS2Lp1K5MnF++lqes6ZrOZmJgYMjMzvedlZGQQHR1drc8qa2sqTdNKbDdV3u5cbreG2WxCbdcHd+IOKHLwxwRfhaCeY3G7NTSPVuY1qrOl1RdffMYVVwzH7da4/PK/8OijC7j11jsB8Hi0Kl3rzTeLhzw//PA9/vvfz+nff1CJ951pr0l8VaFpWrlbgzSEbUMCgeTRNySPviF5rD3JYdlyC4t45r1dTG6dQi/g4bv+giUiCjPFf9f/rC7z+PXRb/n4tzU8femjhFpC6uQzIAC3pjp16hQOh4M2bdp4jwUHB/P000/Tv39/WrduzXvvvcewYcOIi4vDarUSHx9Pnz59WL16NYMHD/Z5TKHjHqqw3dJpIO4jP1BiNRaTCT03o7j9vEuxnHdp2W+uguzsU2zb9j0HDx7go49WoOs6eXm5bNq0sczzDx78hSVLHgegS5fzmTt3QYn2a6+9jh9/3Mby5f/kwQcfrnFcQgghhBE+3HgYu9PN+bHBcErBEhyO4tH8ODf0D12ad+a6LpMJNlsN+PQ/+L1gO378OC1btixxrHnz5ixatIg777wTl8tF7969uemmmwB45plnmD9/PgUFBXTt2tX73Js/Ob9/Fzzu4heqBXQNNA+uxJ0EX3pjra+/fv1a+vTpx7PPvuA99vrrr/DZZ5+UeX6XLl0rnUBwzz2zuPnm65g48Ro6duxU6xiFEEIIf/g5IYtt+9O4clA7bBe3Rx8wDsVk3FBkmCWUri3OM3Q4FPxUsH399dfery+88EJWrlxZ6pwRI0YwYsSIUse7dOnCqlWr6jS+yoSMmU3Rrs9xJewg+JLpeE78gitxJyF/ucsn11+79gtuv/3uEscmTrya999/m7CwJtxwwzUlnt376qvSz8v9Wfv2HRg1aiwvvriM559f7pM4hRBCiLrkKHLz9rqDxLYIZczAdgCGFmsAq4+s5cjpRBZdXPFoXF1T9PIe4GogynqG7eTJZFq2bFul95vNJp8/69VQVZRXeU7DNySPviF59A3JY+1JDkt6/3+/snHncR66vg8dWzfF/s2rKMHhBA+cWuH76jKPR04nkVeUR8/o7nVy/TMC7hk2IYQQQog/O3Iih407jzO0dxwdWxcvSeVJO4Jqa2doXE2tEUSHRhkaA8jm70IIIYQwmNuj8daXB2kWbmXSkA7e47o9ByXE2B0GXtn7FisOlf1MuT9JD5sQQgghDKGqJtyAvcjDjWMvwKRrhFiLSxPd7QSXAyXU2AXgr+wwEqtq7AxRkIJNCCGEEAZQVRPZhS6efGs76dl2oiNDmDejH6pqwuPR0AtzATCFGFuw2UJaGL6PKMiQqBBCCCEM4AZvsQbF+4Q++dZ2fl9EC92eA2DokKhbc/PYj8+y6dhWw2I4Q3rYhBBCCOF3Hk33FmtnnL25u6lFG0InPoopwlb2BfxAQeHmC6YRG9ay8pPrmPSwCSGEEMLvVJNCdGTJrZ7O3txdMVtRo9qiBIUaER4AHl0jrkkrokKaGxbDGVKwBYDU1BQuu2wAM2ZMY8aMaUydOpH58+dw6lRWja53ySV9geLdEl5//RVfhiqEEEL4hBmYNbW3t2g78wzbmaE/V2I8jh9WlLvftz+kFKTy2I/P8Gv2EcNiOEOGRKto7pZF5BX9seFseFATllzyD59dPyrK5t1uStd1XnnlJebPn8Py5f/ns88QQgghAkVOvpP//PcX/n5dX5qFB6GaFMyAx1O8WL3nxH7cCTsIHjDFsBijgltwU9eptAmPMyyGM6SHDXh+17/ZlrqzzK+3ntgBUKJYO/t1Re+tKUVRuOWWO0hIOMJHH63gnntu97Y98cQjrF37BQCvvPISt98+gylTJvLXv95MVlZmla7vcDh49NH53HDDNdx44xS+/HINAJqm8fzzT3P99ddwww3X8O67bwGwa9dO7rnndu6//y6mTp3IY48toKioqMb3J4QQQuz5LZODydnk5dkx63rx5u6eP3YW0guNX4MNBdqEx9HEEmZsHEjBFrAsFgtt2rShefMWZbYfP36Mo0eT+Pe/32DFik+Ii2vNhg3rqnTtN954haZNm/LOOyv55z//zRtvvMZvvx1m9eqPSUtL4z//+YDXXnubzZu/ZuvWLQD88st+HnjgQd5//2OcziI++aT0frBCCCFEVcUfyqB5hJV2LcPLbNfsOYavwbY7fS+LfnyGPFd+5SfXMRkSBWb2/mu5X1e2l2hF7609Bau17MX6Wrduwz33zOKLL1Zz9Ggy+/f/TFxc6ypdNT5+J3PnLgCgWbNmXHrpYHbvjmfPnl2MHj0WVVVRVZVhw0YRH7+dQYMG07NnL845px0AI0eO5vPPP2XKlOt9cI9CCCEaG0eRm32Jp7isVysURSnzHL0wB1NMhzLb/KVLZGdu7DpFetjqk/CgJhW+9jWXy8WxY8mEhZX8IXG7i1eoOXjwALNm3YOuawwdegWDB19W5QczdV3702vweNxo2p/fr+PxeABQVdV7VNP0Eq+FEEKI6vg54RRuj0afzuUv2aHbc1EMXjTXbFJpF3EOZpPx/VtSsFXRkkv+wUuXL/X+58sJB3+maRqvv/4KXbt2p2nTZqSknMDpdJKbm8OePbsB+OmneHr16sOECZNp164927f/iKaV3xN4tt69L+K///0MgNOnT/Pdd5vo1asvffr05csv/4vH48HhcLBhwzp69Sqecbp3709kZKSjaRrr1v2X/v0vrotbF0II0QjEH0onPNRCp9bNymzXdY3gi6/D0v4i/wb2J18mbeS5+OWGxnCG8SWjACAzM4MZM6YBoGkeOnU6j0ceeZyIiKYMHDiIG264htjYVvTo0QuAK64Yzrx5s7nxximoqpkOHTqSmppS6rrvvPMmK1a86309e/Y8brrpVp599immT78WTdOYPv1mzjuvCx06dOTYsaPMmDEVt9vNiBGjGTJkKLt27SQqysbjjy8kIyOdiy7qz7hxE/ySFyGEEA2Ly+1hz5Es+p8fg8lU9nCoopiwdBns58hKu6RVfy5o0cXoMABQdCMXOPGDrKz8UkN9J08m07Jl2yq9v7Jn2BqDXbt28sYbr/Lii69WeF5FebXZwsnIyKuL8BoVyaNvSB59Q/JYe40xhz/9lskLq/byt2t60K192RPrtIJsPCkHUNt0xxRc9qSEs9VVHtMKMwgyWYgMbubza/+ZyaTQokX5j1vJkKgQQggh/GbXoQxCrGa6tI0s9xxP+hEc37yKnn/Kj5GV9p/9K3j/4MeGxnCGDImKSvXu3ZfevfsaHYYQQoh6zqNp7D6cQc+OLTCr5fcZ6YW/b/xu8LIe4zuMwqQERt9Woy3YdF0vdyqxqL4GPrIuhBDCB349epoCh5venaMrPE+35wIKShWGQ+tSmCWUplaDF+/9XWCUjX5mNgdRUJArRYaP6LpOQUEuZnOQ0aEIIYQIYDt/zSDIYqJb+4o3Uy/e5SAcxWTcElK6rvPUzhf45tgWw2I4W6PsYYuMtJGdnUF+/ulKzzWZTFVeLqMxM5uDiIwsfz0dIYQQjZum6+z6NYPu7VtgtVRciOl247el0tG5tdsN2ELKnhjhb42yYFNVM1FRsVU6tzHO4BFCCCF8LSEll5z8ogoXyz1DjT0Pk61d3QdVAY/moXlwpF9miFZFnQ+J5ufnM3bsWI4fPw7AQw89xPDhwxk/fjzjx4/nq6++AmDr1q2MGzeO4cOHs2zZMu/7Dxw4wKRJkxgxYgQPP/ywd6V/IYQQQtQfuw5loJoULuwQVem5QReOxNp7vB+iKl+6PZMlO57nwKlfDY3jjDot2Pbs2cPUqVNJSkryHtu3bx/vvvsun332GZ999hnDhg3D4XAwb948li9fztq1a9m3bx+bN28GYPbs2SxYsID169ej6zorV8qm40IIIUR9ous68b+m07Vdc0KDKx7c03UdT/YJ9CK7n6IrW6S1Gbd1n077plVbt7Wu1WnBtnLlShYuXEh0dPFskMLCQlJSUliwYAHjxo3jhRdeQNM09u7dS9u2bWnTpg1ms5lx48axbt06Tpw4gcPhoGfPngBMnDiRdevW1WXIQgghhPCxY+n5ZJx20Oe8Kjzr7HJQ+NHDuA5sqvO4KqKhERXcnLAA2Pgd6vgZtieeeKLE66ysLAYMGMCiRYsIDQ3ljjvuYNWqVYSGhmKz/fFNjI6OJi0tjfT09BLHbTYbaWlpdRmyEEIIIXws/lAGigI9O1U+HOpdg83gSQf7Mw/y9oEPeWTAHGyhxk888OukgzZt2vDSSy95X99www2sXr2akSNHljpXUZQyl92o7tppFW3zUFU2m7HrwDQUkkffkDz6huTRNySPtdcYcrgnIYtu7aPo0LbywsduP0YB0Cw2ltBq5MbXeRwQdiG25k3p2LIVQQGwbJVfC7ZDhw6RlJTEiBEjgOJxarPZTExMDJmZmd7z0tPTiY6OLnU8IyPDO7xaVWXtJVodMkvUNySPviF59A3Jo29IHmuvoedQVU3YXRp3TrwQq0Xl1KkCPJ6Kl8pypaYCkOuyUFDF3NRFHk858rG6w8jJdgJOn167LAG1l6iu6zz55JPk5OTgcrn48MMPGTZsGD169CAxMZHk5GQ8Hg9r1qxh8ODBxMXFYbVaiY+PB2D16tUMHjzYnyELIYQQogZU1UR2oYt/vLaNh5Z/z5K3d5Bd6EKtYEsqAL0wFwAlxNhtqb4+9h3PxS83NIaz+bWHrUuXLtx+++1MnToVt9vN8OHDGTt2LABLlizh3nvvxel0MmTIEO8w6TPPPMP8+fMpKCiga9euTJ8+3Z8hCyGEEKIG3MCTb20nPbt4tmd6tp0n39rO4rsGUeHDTaoZpWkMirX2jzTVxsWx/egS2cnQGM6m6A18fyYZEg0MkkffkDz6huTRNySPtdeQc+hWFG5fvLHU8VcfugKzj0uPusjjyYI0TIpKdGjlEyV8IaCGRIUQQgjROKgmhejIkBLHoiNDUE3VmzxolBWHPuX9g6uMDsNLCjYhhBBC+JwZmDW1t7doi44MYd6MfpU+i1X4+ZPYv36lzuOrzPgOoxjXvvQqFkZplHuJCiGEEKJuncp18NaaX5h9fV+aNglCNSmYodJZolpeJmp4FRbYrWMmxUQza+AsuSI9bEIIIYTwud2HMzh0NJvCAidmXUfxaJUWa7quo9tzMYUaO0MU4IXdr7Lx2LdGh+ElPWxCCCGE8LldhzKIbhZCa1s1tnYqKgTNbfguBwC3druBplbj4zhDetiEEEII4VOFDhcHkrPpc56tWjsUafbft6UyuIfNpbmxmq1EyJCoEEIIIRqqPb9l4dF0eldls/ezBMqiudmO0zwb/xL7Mw8aGsfZZEhUCCGEED4V/2sGkeFWzo2t3pCiGnseTWa8DKqljiKrmoigcO7qcQtxTVoaGsfZpIdNCCGEED7jLPKwLyGL3p1smKoxHAqgKApKUAiKamx/klt3E2oOJtQcUvnJfiIFmxBCCCF85ueELIrcWrWHQwGKfvka+1cv1kFU1fNr9hGeiX+JDHuW0aF4ScEmhBBCCJ/Z9WsGTUIsdG5T/efQPOmJeNJ+q4Ooqqdjs3O5q8fNRIW0MDoULynYhBBCCOETLrfGniOZ9OoUhWqqfomh23MMnyEK4PK4CLOEEmQy9lm6s0nBJoQQQgifOJB8CrvTQ58aDIfC7wWbwTNEAbak/Miz8cuNDqMEmSUqhBBCCJ+IP5RBcJDK+W2b1+j9uj0XU/NzfBxV9Q2MvYhOzdpXaw25uiY9bEIIIYSoNY+msftwJj06RmEx12A4VNcCZlsql+aimdX4OM4mBZsQQgghau3wsRzy7S76dK75xu0ho/+OpfMlPoyqZlb/tpZ3D3xkdBglyJCoEEIIIWot/tcMLGYT3dvXbGalopgwtzrfx1HVzPgOo3BpLqPDKEF62IQQQghRK5qus+vXDLqd2xxrkFqja3hOp+CMX41WkO3j6KqvSHMRHtTE6DBKkIJNCCGEELWSmJpLdp6zxrNDAbTMZIriV6MX2X0YWc28uvc/fJW8yegwSpAhUSGEEELUyq5DGagmhR4do2p8jTMbv5tCqrf/aF24tfsNhFlCjQ6jBOlhE0IIIUSN6bpO/K8ZdGkbSVhwzRea1e05YFLBGubD6KrPo3nwaB5CzVKwCSGEEKKBOJFRQHq2vVbDoQDa74vmGr32WZ4rnxd+epW9mfsNjePPZEhUCCGEEDWiqiZCwqwsvmsQLSKCUVUFj0er0bV0e25AbEsVZg7l/l53YAugfUTBDz1s+fn5jB07luPHjwPw4YcfMnbsWMaNG8dDDz1EUVERAC+++CJDhw5l/PjxjB8/nvfeew+AAwcOMGnSJEaMGMHDDz+M2+2u65CFEEIIUQlVNZFd6OLpd3fy0PLvmf/KVrILXahqzUoLS6eLCep6uY+jrD6nVlyXBJuDDY6kpDot2Pbs2cPUqVNJSkoCIDExkddff50VK1bw+eefo2ka77//PgD79u3jueee47PPPuOzzz7juuuuA2D27NksWLCA9evXo+s6K1eurMuQhRBCCFEFbuDJt7aTnl08qzM9286Tb22npt0qlo4DsZx3qc/iq6mknKP8c/crpBWmGx1KCXVasK1cuZKFCxcSHR0NQFBQEI888ghNmjRBURQ6d+5MSkoKUFywvfbaa4wbN45FixbhdDo5ceIEDoeDnj17AjBx4kTWrVtXlyELIYQQogo8mu4t1s5Iz7bj0fRqX0vXNdzJu9Hys3wVXo21a3oO9/e6nZah0UaHUkKdPsP2xBNPlHgdFxdHXFwcAKdOneK9995j8eLFFBQUcP755zNnzhzi4uKYO3cuy5cv57LLLsNm++MhRpvNRlpaWrViaNGi9gvf2Wzhtb6GkDz6iuTRNySPviF5rL36msPsPAfRkSElirboyBCCrWYiw6s3nOgpyCF5/T9pMfwWmp47ukbx+CqPer6TSHMYcVEtMJkCZ26mIZMO0tLSuPXWW5k0aRL9+/cH4LXXXvO233zzzcybN48hQ4aUem91Z49kZeWj1aDaP8NmCycjI6/G7xfFJI++IXn0Dcmjb0gea68+51BVTcyc2pvnP9hFerad6MgQ5s3oh+7yVPuePKeKR9sKPFaKapAPX+ZxXeIW1iSu54XLFqOaarZrQ02YTEqFnUx+L9iOHDnCbbfdxvXXX8/NN98MQEpKClu3bmXy5MlA8ZouZrOZmJgYMjMzve/NyMjwDq8KIYQQwjgOp4u3//sLf7+uD83CragmBTPUaJaoXpgDEBCzRAfE9qF907Z+Ldaqwq99ffn5+dxyyy3cf//93mINIDg4mKeffppjx46h6zrvvfcew4YNIy4uDqvVSnx8PACrV69m8ODB/gxZCCGEEGU4mpbPweRskk/kYNZ1FI9WiyU9igu2QNjlIN9ViNUcZHQYpfi1h23VqlVkZmbyxhtv8MYbbwBw+eWXc//997No0SLuvPNOXC4XvXv35qabbgLgmWeeYf78+RQUFNC1a1emT5/uz5CFEEIIUYaE1OKtpNq3qn2RdaZgC4Qeti+T/kdGYSYP9/+b0aGUoOi6XvMHvOoBeYYtMEgefUPy6BuSR9+QPNZefc7hq5/v59Cx0zx796BaX8v12w+4Dm8lZOSsGu104Ms8phWk4/A4aRvRxifXq6qAe4ZNCCGEEPVfQkou7WN9M4Rp6TgAS8cBPrlWbeW5CgJu43eQvUSFEEIIUU15hUWkn7b7ZDgUQHfko3sCYyejt39ZwYbkb4wOoxTpYRNCCCFEtST68Pk1gMI1T6E0aUHoyJk+uV5t3Nr9BqymwJt0ID1sQgghhKiWhJRcFAXatvTRYrX2HEwBMOFA0zVynXlYVIvRoZQiBZsQQgghqiUhNZe4qDCCg2o/UKdrGrojLyBmiDrcDl7e+yY/pf9sdCilyJCoEEIIIapM13USU3Lpc56t8pOrcj1HHug6SgCswWZVrfy9zz1EBhtfPP6Z9LAJIYQQosrSs+0UONy0b+Wbosa7BluI8UWSw+Ok0F2IxSRDokIIIYSoxxJSfp9w4KMlPXSXEyU4HCW0mU+uVxvH81JYvucNTuSnGh1KKTIkKoQQQogqS0jNxWpRaRUV5pPrmVt2osn0f/nkWrV1TkRr/t7nblqGxRgdSinSwyaEEEKIKktIyaVdy3BMpurvSBDoClwF2N0OLKbA68+Sgk0IIYQQVeJyaxxLz/PZ+msAzvjVFHy6yGfXq42fMvbx0p7XcWmBsYjv2QKvhBRCCCFEQDqanofbo/usYFNVhcguvck48iOqqhBq9pDnNK4vqV/L3rRv2harKgvnCiGEEKKe8k448MEMUVVVCDPZcaUlEHnJZMJMdhyHvifYrNf62jV12pmDW/NgUgKvPAq8iIQQQggRkBJTc2nWJIjIcGutrxVq9mD/dTvZ361EDQ4h7f1Hydu9gSDV44NIa2bj0W9578BHhn1+RapUsM2bN6/UsXvvvdfnwQghhBAicCWk5Pps/bU8pwlTs5bYxtxJ+qqluLNTsY2fRYHbuOHIK9uP5Nbu0w37/IpU+AzbwoULSUtLIz4+nlOnTnmPu91uEhIS6jw4IYQQQgSGfLuL9Gw7g3u08sn1TIpOUERz0j959o/P+HkT1j5X4cGYGahZjlMEm4MN+ezKVFiwTZ48mcOHD3Po0CFGjBjhPa6qKr169arz4IQQQggRGHy9YG5YkI4j8RcUs5lWtz5H/s+bcBzdT3i/K3G4jZkT+eGh1cSGxXBr9xsM+fyKVJiR7t270717dy6++GJatmwJQH5+Prm5ubRq5ZsKWwghhBCBLyElB0WBdrHhtb5W0b7/kd08jiYdLyX6vIspcAdh7XMV4f2upMBtAYyZeHBb9xsCcsIBVPEZtp9//pnHHnuM/Px8rrzySsaPH89//vOfuo5NCCGEEAEiMTWPVlFhBAfVrvfLc+oYzm0f4Dr4LQ63Qq7TjMejnfW1cbNETxZmGPbZlalSwfbKK69wzTXXsGHDBnr27Mk333zD559/XtexCSGEECIA6LpOYmpurYdDdU3D8e2bKNZQrBdP81F0vuHS3Lz289vsSv/Z6FDKVKWCTdd1zjvvPLZu3crgwYNp0qQJum5cBSyEEEII/0k/bSff7qr1grmu/f9DS0/AevE0TMG1H1r1JVUxMa/fLAbE9jE6lDJVqWAzmUysXbuW7777jkGDBrF58+a6jksIIYQQAcIXC+ZqeRk4d3yM2uZCzB0G+Co0n3G4nWTYs+r3M2xz5sxh5cqVPPDAA9hsNl5++WXmz59f17EJIYQQIgAkpORitajERYXV+BpawWlMYZEEX3ojihJ4G8enFabz2s9vcyzvhNGhlKlKBVvfvn156623GDRoEMnJyaxYsYLevXtX+r78/HzGjh3L8ePHAdi6dSvjxo1j+PDhLFu2zHvegQMHmDRpEiNGjODhhx/G7S7edDUlJYXrrruOkSNHcuedd1JQUFCTexRCCCFENamqQrhVA4rXYHvspp6YTEq555z9dVnt1rjOxEx/ElOTFn6IvvrimsQy96KZtG/a1uhQylSlgi0pKYkxY8YwYcIEJk6cyF/+8heOHDlS4Xv27NnD1KlTSUpKAsDhcDBv3jyWL1/O2rVr2bdvn3dodfbs2SxYsID169ej6zorV64E4NFHH2XatGmsW7eObt26sXz58lrcqhBCCCGq4sw+n45D3xMcpHDb8NbEnN5TYp/Ps88JCzaV2gv07HZr1iHCKMBp8F6hFcktyuOU4xSqohodSpmqVLA99thj3HrrrezYsYP4+HjuvPNOHn300Qrfs3LlShYuXEh0dDQAe/fupW3btrRp0waz2cy4ceNYt24dJ06cwOFw0LNnTwAmTpzIunXrcLlc7Nixw7tg75njQgghhKhboWYP9sPbyd78AZw8gPOLJRTs+Yog1YMn/QjOn/5LsD0N+6Efyd78AXrqL3/sBaq4SrWb9CLSVjxm+F6hFfkl61de/fltHB6n0aGUqUqLqWRlZXHVVVd5X0+aNIm33nqrwvc88cQTJV6np6djs9m8r6Ojo0lLSyt13GazkZaWRnZ2Nk2aNMFsNpc4Xl0tWjSp9nv+zGYLrJks9ZXk0Tckj74hefQNyWPtBWoOg3oNxxJhI23VEgBa3bwUa0QkwbmbObX9I9LiVxN52VRsY+4k7aPic1rftgy9qICiP7Wf2X6q9W3LCIqIxFbup9ZcbfM4oukgerXtQttmMaimwOtlq1LB5vF4OH36NM2aNQMosa9oVZW1DIiiKNU+Xl1ZWfloWs27X222cDIy8mr8flFM8ugbkkffkDz6huSx9gI1h6qqEKblkrXxbe+x/P1bcAXbKGo/hCbtLkVVTYRYXKR9sMh7Tu7ebwjqM4EmN79abru1z1U43L6ddOCLPB7NPU5uUR7hnkgfRVU9JpNSYSdTlYZEr7/+eq699lqef/55nn/+eaZOncrUqVOrFUhMTAyZmZne1+np6URHR5c6npGRQXR0NM2bNyc/Px+Px1PiuBBCCCHqVojJhf23nShmMy1vebZ4f8+j+wlSPSgmM4o5iLBgE/bfdnj3Ao3oX3yOVdUqbA/UIdHvTvzA+wdXGR1GuapUsA0ZMgQAl8tFQkICaWlpDBs2rFof1KNHDxITE0lOTsbj8bBmzRoGDx5MXFwcVquV+Ph4AFavXs3gwYOxWCz07duXtWvXljguhBBCiLqVn5GGqzAXdfgsZr+TwEZ3b1pMegi7ZvWek+c0oXa8lOgpCym0RGHtcxW2yQ/9vhdo5e2BZmz74dzT8zajwyhXlYZE586dy3XXXcf06dNxOp188MEHzJs3j9dee63KH2S1WlmyZAn33nsvTqeTIUOGMHLkSACeeeYZ5s+fT0FBAV27dmX69OkALFy4kLlz5/Lyyy8TGxvLc889V4NbFEIIIUR16C3a8PCHiZz8708AvHcyj6/iU1l81yDOHsx0uBUcbjOg4eHM13qV2wNJSsFJgkxBRodRrioVbNnZ2d4iymq1MmPGDFavXl2lD/j666+9Xw8cOLDMPUi7dOnCqlWluyHj4uJ45513qvQ5QgghhKgd3ePC+eNHqD3HcDK7qERberYdj6ZXrXCohz4/8iXhQeHc1exmo0MpU5UnHaSlpRETEwNAZmam7CUqhBBCNDBFu9fg2rcB9ZzuREeGkJ5t97ZFR4agmhTwNMy//7d2uwE9QHv/oIoF24wZM5gwYQKXXnopiqKwdetWHnzwwbqOTQghhBB+4jl1nKKf1mDuOIDgtj2YN8PFk29tJz3bTnRkCPNm9MMMBOaUgdpLyj1GbFiM0WGUq0oF2+TJk+nWrRs//PADqqpyyy230Llz57qOTQghhBB+oGsajm/fQLGEYB04DY9HQ/d4uHV8d1q2CCUs2FxcrHm0Sq9VH2m6xpv732dE26G0ajLS6HDKVOWh6C5dutClS5e6jEUIIYQQBnD9shEtPYHgobdjCokA4EDiKV794hceuekimsSEN9ieNQAFhQUD/k6wGmx0KOWq0rIeQgghhKhfqrMxu2pWiZn2COaOA73tCSm5BFlMxNnC/Be0QRweJ8dyj6PpgVuWSsEmhBBCNDDV2Zg9LNhEZPeLcWUeI+SsJdISUnNpFxOOamr4pUKW/RRv/vIBSbnHjA6lXA11dq4QQgjRaIWaPdgPbSd78wqim0aT9r+3UMxmolp1ovB/7xJ16STs6Ulkb/moRHv0eQNxuM24PRpH0/L4S582Rt+KX8SERfOP/n8nwhqY+7qC9LAJIYQQDU6e04S5be/fN2ZfjDs7Fdu4+zi17TP0okIyN32AEhKObezdf7SPn0WBu3jh2GPp+bg9Ou1bRRh8J/6R48zlWH4K4Ns9Tn1JCjYhhBCigTEpOmZPIae+ed97LH//d0RccTthE/5B+ISHCW7bjVNfv/tH+8+bsCjFz3AlpOQCNJqC7fDpBN7c/z4FrkKjQymXFGxCCCFEAxPizMCRvK/cjddDzR7sh7eX256QkkPTJkFEhlsr+pgGo5etOwv6P0CktanRoZRLnmETQgghGhAtJ420Tx8lYvBUoqcspMAdhLXPVYT3u/L3jdd18pwmgjteSvR5F5fZnpCSS/vYCBQlcIcIfSmtMJ0sRzYtA3jhXOlhE0IIIRoQrSAbJbQp7phu5DrNeDwaDrfy+9clN2Yvqz3f7iIt295ohkMBfjy5i/cPlt7TPJBID5sQQgjRgJhbdUG9+gkUpWZ9Mkmpvz+/Ftt4CrYx5w5jSOuLjQ6jQtLDJoQQQjQAWuFpHN++gWbPrXGxBsUTDhSgXSMq2JJzj5HtOG10GBWSgk0IIYRoAJzfv4vr8FYoqt1Mx4TUXGKjwgixNp5BuHVJG1mf9LXRYVSo8Xw3hBBCiAbKlRiPO3EnQf0mY2rassbX0fXiCQc9O0b5MLrAd0u3G/DobqPDqJD0sAkhhBD1mO4swPn9O5hatCHowpG1ulZGjoN8u6tRTTgA+DX7N/KK8o0Oo0JSsAkhhBD10JnN2507PkENDiFm8lwUU+0GzhJScoDGs2DuGe8f+pidaT8ZHUaFZEhUCCGEqGfObN5uP7SDpt0GYh00FnviLoI7XoLDXfO10xJScgkym4izhfkw2sBmCvbw1LB5KIBHcWBRLGgO1eiwSpGCTQghhKhnSmzuftUDpK1aWmLz9ppKTMmlbctwVFPjGYBzakXMWveI9/ULoxahEngFW+P5jgghhBANRJ7ThKlFW6KvvJ+0FY+V2ry9JtwejeS0/EY3HOrWXEaHUCVSsAkhhBD1TU4qQdYgsv73lvfQ2Zu318Sx9HzcHo32rQJ3P826EKTWvMj1JxkSFUIIIeoRXfMQRj6OY8e8m7fn/7wJx9H9hPe7ssZDogkpjW+HA4fbSb6ew3MjFmI2FQ+DWhQLmsFxlcXvBdtHH33Eu+++6319/Phxxo8fj91uJz4+npCQEADuuecehg0bxtatW1m8eDFOp5NRo0Yxa9Ysf4cshBBCBAzXzxtI3/kJkVcvIHrK4DI3b6+JhJRcIsKCaB5h9W3AASy14CT/+uk17rzwJjpFdgAIyGINDCjYrr76aq6++moADh8+zN13380999zDjTfeyLvvvkt0dLT3XIfDwbx583jnnXeIjY3ljjvuYPPmzQwZMsTfYQshhBCG03LTce78FHPrbhSFt8HlVAAND8rvPWs1K9ageIeD9rERKErNZ5nWN+c2bctTlyxENQXeJIM/M/QZtkceeYRZs2YRHBxMSkoKCxYsYNy4cbzwwgtomsbevXtp27Ytbdq0wWw2M27cONatW2dkyEIIIYRxNA9qy05YB93g08KqwOEi7VRho5pw4NLc/Gv3a/yWk4ipFnuv+othz7Bt3boVh8PBqFGjOHbsGAMGDGDRokWEhoZyxx13sGrVKkJDQ7HZbN73REdHk5aWVq3PadGiSa1jtdnCa30NIXn0Fcmjb0gefUPyWHvVyqEtHDot8nkMxw6lA9D7/Jb19nta3bhP5mfg0O1ERATXi3s2rGBbsWIFN910EwBt2rThpZde8rbdcMMNrF69mpEjS2+xUd3/o8jKykfTat5FbLOFk5GRV+P3i2KSR9+QPPqG5NE3JI+1V9UcaoU5OL55BevA61Cbx/k8jt0HTqIAzULM9fJ7WpOfRZNuZXbv+9B1PSDu2WRSKuxkMqQPsKioiB07dnD55ZcDcOjQIdavX+9t13Uds9lMTEwMmZmZ3uPp6eklnnETQgghGgPn1vfwpP6KUkcL2iak5NKyRSihwY1j8QiP5mHRj0/z/Ykf680ze4YUbIcOHaJdu3aEhoYCxQXak08+SU5ODi6Xiw8//JBhw4bRo0cPEhMTSU5OxuPxsGbNGgYPHmxEyEIIIYQh3Em7cSdsJ6j3lZiaxfr8+rquk5ia26ieX7N7HLQNP4em1vpzz4aU0seOHaNly5be1126dOH2229n6tSpuN1uhg8fztixYwFYsmQJ9957L06nkyFDhpQ5TCqEEEI0NKqqEGIqIm3bewSd24sWA8eQ7/T952TmOMgrdDWqBXPNisqMC6YYHUa1KLqu1/wBr3pAnmELDJJH35A8+obk0Tckj7VXXg7/2Nz9R9A9hHTqhz1hN2otN3cvy4+/pPHK5/tZOOMi2rYM/Ifvy1Kdn0VN11iwdTF9Y3pyVccxdRxZ1QXkM2xCCCGEKF+o2YP98Hayv/sQS/NWpH34BHm7NxCk1nzrqfIkpORiMZuIs4X5/NqByKN5GNL6Ys6L7Gh0KNXSOJ4uFEIIIeqRXIdCky6XEd08jrQVjwHQ6tbnft/c3bdr8Sek5tC2ZThmtXH04ZxynubyNpdiNtWvEqhxfHeEEEKIekQ78D/Uwkyy1v+f91h1N3dXVRO6asKtKOiqCfVPBZmqmtBMJm4d353bJnQv1d4Q6brO8p9e54397xsdSrXVr/JSCCGEaOA8WcdoEh2LPWlvjTd3V1UT2YUunnxrO+nZdqIjQ5g3ox+RoRY8Hq3S9oZKR+fqzuOxqkFGh1JtDb+cFkIIIeoJXdNwfPsGmevfwNR+ANFTFlJoicLa5ypskx/6fXP3yrnBW4wBpGfbi1/nOvl48xHSc51ltrvr6sYCRFLuUVqGRXs3eq9PpGATQgghAoRr/1doGYkE9b8GJ8HkOs14PBoOt/L711Vb9cCj6d5i7Iz0bDtuj8a6H4/i9mhltntqsapCffDBwU9458BKo8OoERkSFUIIIQKAlpuBc8fHqOf0wNyhf62upZoUoiNDShRl0ZEhREeG8NqDQ9FVU5ntqkmBKhaF9dEdF86g0F1odBg1Ij1sQgghRABQgkKwdLyY4Eum13q7JDMwZ/pFREeGAHifUTOf1T5vRr9y2xuifZkHSC04yTnhrY0OpUYa8vdGCCGEqBd0XUMJbkLw4Bk+uZ7Ho/Fb8iluHd/99yU7FMy/Hz/THhlqYfFdg/BoOqqpZHtDtPHotzi1IrpHdTU6lBqRHjYhhBDCQJo9l8KPHsZ9fJ9Pr7vjYDqvfroXCzqKRytVjHk8GopHw6yX3d7Q3N3zFm654Hqjw6gxKdiEEEIIP1NVhXDr771de9cSdfl1KE2a+/QzElNyGtWG7hXZlb6XjUe/JTK4/u6XKgWbEEII4Udn9gl1HPqeoswTNO81BHdeFmFRsT77jJx8J1m5TtrHNu6CzRTswWN10LFla/q264Y5pP5OqJBn2IQQQgg/CjV7sB/aTvbmFZibRHLqm/dQzGaiuwyq0qK4VZGQkgvAuY28h82lu7jvy394X78wahEqqoER1ZwUbEIIIYQf5TlNhHW5DFuEjfRVTwG+3yc0ITUX1aTQNibcJ9cTxpMhUSGEEMKPVFVB9RRyauPb3mPV3Se0MgkpubS2NSHIUj97k3yl/g6AliY9bEIIIYQfhZjd2A9tRzGbaX3bMnL3flOtfUIro+k6SSdzGdC1pQ+irb9cmhu328U/Ry3izKp2FsXioz5M/5MeNiGEEMKPsratwVPkIvrafxAUfU619wmtTGpWIXanh3Mb+YSDbSnb+ftXj3EiIwPVGYzqDEZz1N8eR+lhE0IIIfzEc+o4Rbs+R2vfD63zFdgAh1v5vWfNNwN4CSk5AI16SQ9d1xnUqj/NgyNpG9HG6HB8QnrYhBBCCD/QNQ3Ht2+gBIVivXhanX1OYmoeIVYzLVuE1tlnBDJN13hpz+vsSNtNt6jzjQ7HZ6RgE0IIIfzA9ctGtPQErAOnYgquu9mbCSk5nBsbjqmW+5HWVw63A13XMSkNq8SRIVEhhBCijmn5WTi3r0JtcyHmjgPr7HOcLg/H0wsYPfCcOvuMQJbtOI3T4+SenrcaHYrPNazyUwghhAhASmgzrH0nEHzJdJQ67PlKPpmHpuuNdsLBp7/9l+fiX6ZIc9Vpno0gPWxCCCFEHdLdThSzlaALR9X5ZyWmFu9w0L5V/d0zszau7jyeo3knsKpBRofic4b0sE2fPp0xY8Ywfvx4xo8fz549e/jiiy8YPXo0w4YN47333vOeu3XrVsaNG8fw4cNZtmyZEeEKIYRoZM7enP3sr6vbrjnycK57ljB3ll/iTkjJpUVEME3DGl7BUpFcRx7P7/o3p505XNDiPKPDqRN+72HTdZ2EhAQ2bdqE2Vz88WlpacyaNYtPPvmEoKAgpkyZQv/+/WndujXz5s3jnXfeITY2ljvuuIPNmzczZMgQf4cthBCikTizObv90A7CugxB9RRiP7SD4I6X4HAr1WoPbtKCpsNvwnH8F297XUpIyW1Uy3mYgj24dBf5RRrX976KZpYIcBkdVd3we8GWkJCAoijcdtttZGVlcc011xAWFsaAAQNo1qwZACNGjGDdunX069ePtm3b0qZN8Roq48aNY926dVKwCSGEqDNnb85uC29B5tfvFG/O3rk/DoIItp/EfuwXsrd8VKLd1r43DsIIcZ/GnrCruH3MnaR/9kLx+88b6LPN3cuSU1BEVq6Dv/RtXWefEWjK2ty9ofJ7wZabm8vAgQN55JFHcDgcTJ8+nVGjRmGz2bznREdHs3fvXtLT00sdT0tLq9bntWjRpNYx22yyea4vSB59Q/LoG5JH32iIedQcBbhDmxYXWx8vBaDV9CcwB1mxNW1C9pb1mCNjiB57D2lntev2HGytW1Jw+BBqeGSJ9ta3LSMoIhJbGZ/nqxwmpKUC0Ov8lg3y+1KW9IKiEq9NqqnB3rvfC7ZevXrRq1cvAEJDQ5k8eTKLFy/mr3/9a4nzFEVB10uv+lzdWR9ZWfloWs1Xj7bZwsnIyKvx+0UxyaNvSB59Q/LoGw01j54jW2nW5lzSP3vBeyz/0I9Ym7TCkZ+H2nUkQSY7ae8/WrK9z1VkZOShtuhC2J/ac/d+g7XPVaWGRH2Zw58OpWFSFJoGqw3y+1IW7U/PDmoerd7eu8mkVNjJ5PeCbefOnbhcLgYOLF6HRtd14uLiyMzM9J6Tnp5OdHQ0MTExZR4XQgghfE0vsqMEhdC06wAcB7egmM20uvU58n/eVGJz9jNDpjVtrysJKbm0jg7Daqm/+2VWl0WxsGzkI1hUFU3T6/Xm7pXx+yzRvLw8li5ditPpJD8/n08//ZSnn36abdu2cerUKex2Oxs2bGDw4MH06NGDxMREkpOT8Xg8rFmzhsGDB/s7ZCGEEA2c+9he8j/4O+4Tv5DvNKF2upToKQsptESV2pw9z2lC7Vjz9rqg6TqJqbmNbjkPVyE8sH4RGxO+r/ebu1fG7wXb0KFDGTJkCBMmTGDSpElMmjSJPn36MGvWLKZPn86ECRMYO3YsF154IVarlSVLlnDvvfcyevRo2rdvz8iRI/0dco35alp4XbUHQgwNIcaGcA+BEENjuIdAiKEx3EN1r+FJ2kl4iAm1aQymZrFA8YbsuU4zHo921td/PF5T23ZfO5lViN3p4dzYhvn8Vnk0XWNal8n0b93T6FDqnCHrsM2cOZMvv/yS9evXc+ONNwLFM0DXrFnD+vXrue2227znDhw4kM8//5z169czb968erNy8Zlp3Y5D3xMWbPJ+HWzWA6Ld3zFoblfAx1gf8lyTPEqe/ZPHxphnI/Lo6zxbcxJo1ioOd24mkVfNxRQWSX2UkNI4F8x1akV0iezIuZENfysu2emgjpSYFh4RRebGt73TugtPnSLMXIT95G9kf7eyVLvDbSY4/xj21AraHenYj+0v1W7r2BcHIcXTyhN3l24/tycOwtFy0/0agyUiiqw/xRCCHfuhH0q//5xuOEzF/2jWdR4qiyHU7MF+oIz21l2wmyIJtZqwH/iR7O8+LNVekOtEbd7apzGUyGMVYwiPbYX94I9kf1u6Pe9oEpb2F9V5HiqLIfz8PtgPbif72xWl2nN+/oGoAWPK/H2yte7C6e+/wHrp9GrF8Oc8+iKGqOE3Vthu6T68zvNQWQxRQ6diP7yj3PYWl07EfnRvue1KhK1EDH/Ooz9iaDFwDPaUQ+W2A9WLYcydpG94rfjflfMvwek08i9HzSWm5hJiVYltEWp0KH71Q+pOPv3tv7wx4RmjQ6lzil7WVMwGxMhZoqFmF/rJg6R/8iwAsTOWYA9pRdHJBBwbX6LZwAmYw5qS/knxD1rs9Cewh7XB49EoWPkQzfqPLdl+w2MUhsah6Sbsm14j4tyupdrz7R5o3o6iX74mLMxaqj3neBJqp0vxZCQZH0NmMsEFx0u1Z+/5FsuA6wACIgbL0W2YQ8NLtGdtXkHQsJkoqoWgzF9Q0Uq1qxeOxdzq/ICIIbjgBIo9u1Q7Medj7TU2IGII0fMhK6lUuwcLoSNnEhZsQju+t1S7KyuFsClLAyKGJmFWtJT9pK96qlR78NDbjY/h8jtoem5X9NQDpH20uFS7pdswmvcfi552iLQPnyjVbmrR1vgYbO2xXXk/evph0lY8VqodqFEMrW59jkJLFB6Pfx9Z99Us0Uff3EFosJnZU3v5IKr6I7UgjYTTSUzo+Zd6Ozv0jICbJdpYqKqCWXOQ9s373mMFB7Zi7XMVHls7Iq57hpA/TfsuOPQj1j6t8KAQMXVJ6fZfd2Dt0xqHG5pccXuZ7cF9rsLhhpDuV5TZHvZ7uxoAMQTFtCPEFFOqPfySaTjcxa+NjkFVFUI69CrV3nTs370rmgfHti+3HYyPQVUVgpq1IG3ti+XGaHQMqqpgMZlJ++a9cttVTyGZ5bQDhsfgbd/4doUxGhnDmfa0/71VcfuGNyq8ByNj8Lav/7/KY6xGDPk/byr+95n68djN2YpcHo5n5DOyf8MfFvwzXdfpFd3d6DD8wpBn2BqDULMH+2+7iqd13/IsEf2vxHF0P0Gq54/2w39M+/Z3u79jaH3bsoCPsT7kuSZ5lDz7J4+NMc9G5NEfea5vktPy8Gg67WMbz5ZUZ/zrp9f4+Lc1RofhFzIkWonadFdbPfkEmXUKTU2xKB6CVA8Fbot3plCwWf/9WJAh7f6MwRoRSV52bkDHWB/yXNM8Sp79k8fGlmej8uiPPPuLL4ZEN2w/yoqvf2PZPYNo2sTqo8gCn67r7M86SHhQE/p26Nrgh0SlYKtEQ13J298kj74hefQNyaNvSB5rzxc5/Pdn+zhyIoen7xrko6jqh3xXAdmO08SGxRAbE1nvfxYrK9hkSLSO6O4inD+uxHPqmNGhCCGEaMASUnI5t5Et5wFwIOtXluz4J+mFmZWf3ABIwVZHtFPHKdqzFi0n3ehQhBBCNFC5BUVk5jga5fNrnSM7cEu367GFRhkdil/ILNE64sk6CoDaovHN2hFCCFH3VNWEajGz+K5BRIQGoaomvy9LYqTTzhzaNInDYmocpUzjuEsDaFlHwRKCEt44Kn8hhBD+o6omsgtdPPnWdtKz7URHhjBvRj8iQy2Npmh7/+DHRFjDubvHLUaH4hcyJFpHPFlHUaPOqTdbaQkhhKg/3OAt1gDSs+08+dZ23MaG5VfXdZnM2HOHGx2G30jBVgd0XUPLOoZJhkOFEELUAY+me4u1M9Kz7XhqsSpCfWJ3OyhwFWILaTyjWFKw1QVNw3rxNCwd+hsdiRBCiAZINSlER4aUOBYdGYJqahyjOkdzj/Pinv/jaN5xo0PxGynY6oCimgnqMgQ1pqPRoQghhGiADiRkct+1vbxF25ln2BrLg+ltI1ozs9cdtI1oY3QoftNYvrd+5T6+D93txNKuj9GhCCGEaGByC4p4+dN99L+gJYvvGoRH01FNCmZoNBMOUgvSUE1mQszBRofiN9LDVgeKft5A0c7VRochhBCiAfpg42GcLg9De8WheDTMuo7i0RpNsQawNul/fHjoU6PD8CvpYasDWtZR1LgLjA5DCCFEA7Pnt0x+/CWNCZecS1xUmNHhGGbqeRMpdNkrP7EBkR42H9PsueiFp1FbNJ5xdSGEEHXP7nTz9vpDxEWFMXpgW6PDMYxbc/Pb6UTCLKFGh+JXUrD5mPb7DgeypIcQQghf+mRzAqfznMwY1QWz2nj/fGfYs/jPLys4fDrB6FD8SoZEfUyTLamEEEL42G/Hc/h613Gu6NOaDnGNb6P3s0WHRLGg/wOEB4UbHYpfNd4SvY6YotoR1GM0SnATo0MRQgjRALjcGm9+eYDmEVYmDmlvdDiGO5p3nNSCdELNIZWf3IBID5uPmeO6Yo7ranQYQgghApiqmnAD6dmFoJrKXJLjzDl2p4ebxnVDRSc4SP5sb0n5kV+yDtErurvRofiVfOd9SHcX4T6+D3PLztLDJoQQokxV2bi9vHNU1dSolu8oy9WdriSnbZ7RYfidIUOiL774ImPGjGHMmDEsXboUgIceeojhw4czfvx4xo8fz1dffQXA1q1bGTduHMOHD2fZsmVGhFtl2qnjODa8gDv1oNGhCCGECFDlbdyeluPgw68P8+HXh0nLcTT6zd3L8+PJXbi1xpcJv/ewbd26lS1btvDpp5+iKAq33norX331Ffv27ePdd98lOjrae67D4WDevHm88847xMbGcscdd7B582aGDBni77CrxCMTDoQQQlSioo3bN+1OAeCyvueUe05jHhordBWy8tfVXNVxDHFNYo0Ox6/8/n232WzMnTuXoKAgADp06EBKSgopKSksWLCAlJQUhg0bxj333MPevXtp27YtbdoUr2k2btw41q1bF7AFm5Z1FCwhKOFRRocihBAiQJ3ZuP3sgiw6MoToyBBefqD475uumso8RzUp4NH9HnOgCDGH8NQlCzEpjWOT+7P5vWDr1KmT9+ukpCTWrl3L+++/z/bt21m0aBGhoaHccccdrFq1itDQUGw2m/f86Oho0tLSqvV5LVrU/lkym61qU4dP5KYQ3LId0dGNe8p1eaqaR1ExyaNvSB59Q/JYfUUuNzOn9ub5D3Z5n0+bf3N/opqFYjIVFyKapjP/5v48/saP5Z7TGP2WlURC7lEuazeAIHNQibaG/rNoWM/q4cOHueOOO5gzZw7t27fnpZde8rbdcMMNrF69mpEjR5Z6n1LNqjorKx9Nq/n/jdhs4WRkVP5wo65rONOSsHS+pErnNzZVzaOomOTRNySPviF5rJnPtyRy4OhpFtzcn9AQC+jFw5xZWfklzmsabC61ufufz2lsvk3YyYbkb7gw/EJUk9N7vCH8LJpMSoWdTIZMOoiPj2fGjBk88MADXHXVVRw6dIj169d723Vdx2w2ExMTQ2Zmpvd4enp6iWfcAoq7CEv7fphlD1EhhBDlSM0qYM22JJqGWQizmIiODC1343aPR2u0m7uXZ8y5w3j84nmoJtXoUPzO7wVbamoqd999N8888wxjxowBigu0J598kpycHFwuFx9++CHDhg2jR48eJCYmkpycjMfjYc2aNQwePNjfIVeJYgkmeMjNmNv1MjoUIYQQAUjXdd5Zf4ggs8rUKzpV/gZRyubjWzlZkG50GIbw+5Do66+/jtPpZMmSJd5jU6ZM4fbbb2fq1Km43W6GDx/O2LFjAViyZAn33nsvTqeTIUOGlDlMGgi0nJNgtmIKizQ6FCGEEAFoy8+pHDx6mhtHnkfTJlajw6l3dF1nTcJ6BrXqz3nNOxodjt8puq436Okm/nqGrXDdMvS8LMKufrzGn9WQNYTnCwKB5NE3JI++IXmsutyCIh5+7QdaRYUx57re3lmOksPq8WgeXJqLYHNwieMNIY8B+QxbQ6RlHcXUoo3RYQghhAhAK74+jKPIw40juzTKJSl84UR+KhuSv8GteYwOxRBSsPmAZs9FL8hGjZIFc4UQQpS0P/EUP+xPY/SAtrSKCjM6nHorOfc4axI34NEb5+SLxrxgss9oWccAMLVoa3AkQgghAsGZjdvdHp2wJlYGdGvJ2Ivlb0RtXNzqIvrG9MBishgdiiGkYPMB7fctqWRIVAghRFkbt8+ZfhHBVosszVELXx/9liZBTejXsrfRoRhChkR9wRKMGncBpuCGvcqyEEKIypW1uftTb++QjdtraXvabvZnHTQ6DMNID5sPBHUdSlDXoUaHIYQQIgBUtLm7/NGtubkX3Y+nkU44AOlhqzVdc6PZc40OQwghRIA4s7n72bwbt4saybRnsfLX1WQ5ThkdimGkYKslLes4Be/chytpl9GhCCGECAD5+U7uu7aXt2iLjgxh3ox+0rtWC5n2U/yQuhOnp8joUAwjPz+15MlKBkCNjDM4EiGEEEbTdZ1XP98PCjx2+8UoJrwbt8uEg5rr0rwTzw5+zOgwDCU9bLWkZR0FSzBKhM3oUIQQQhhs2/6T7E88Rc8OUVgUXTZu95Fvjm3hq+RNKI140WEp2GpBVRWad78Y1XYuZrNKuFV+IYUQorHKLSjig/8dpkNcBEN7yaiLLyXlHuVwToLRYRhKhkRrSFUVwkx27BlHiRx0FVaTHfuhHQR3vASHu/H+H4AQQjRWH2ws3n5qxqjzMckEA5+66YJpNPCtzyslPWw1FGr2YP91O9lbPkJFJ+39R8nbvYEgtfFOORZCiMZqz2+Z/PhLGmMvbkecbD/lU/muAt7Y9x6JuUeNDsVQ0sNWQ3lOE2HnX0Z0izjSVhQ/CNnq1ucocAcBMjQqhBCNhd3p5p0Nh2gVFcboAbL9lK/lFeWTnHsMu9thdCiGUvQG3seYlZWPptX8Fm22cDIy8kodV1UFk7WIwtxM+H0hP6tqIcga7R0SNQV7cOku73ssigXNoXpfG93uzxhMqgnNowV0jHXV7svPqGkeJc8l2+sqj40tz0blMdDynG93kXGqCJxmOsY1rdb7y8uhv++hJu2BEgOU/7e6PjGZFFq0aFJuu/Sw1VCo2cNpZwEzv13mPfbsZX9jU/I3DGx5KSkFJ2mhhjPnqye97f8ctYgTuSn8mn2Ey1oPQvMU8bf1j3rbnxo2D6snjJSCk/yafYShnQdw/5f/8LY/N2Ih3ybv4LLWg3xy/UCIobLrB0IMkmfJs+RZ8lxZDIsvn8+Pybto6ynv+o9yIjfzrOs7+dv6RZXkueJzhnbuz/1fLjzrHv/Bt8k7K7jH6sVQ2fWNiOGFUYtQKV2wNQbyDFsNFbrNYA4uccwTFMyHh9bh0twk5iZT6C65NYmmayTmJrP6yFo0RaNIc5VoL3Tb0RSNIzlJxefoJYdWizQXq4+s9d31AyGGCq4fCDFIniXPkmfJc1ViUFStRAx/vr6u432vS3Pj0kruLFrotpeIvyrn/Hl8zKW5fRpDZdc3IobGTHrYasgNZOc5SxwrLFR4oPs8nnp3LzeN64HiKjkBISffRbvgHkQdNZF2yk1IRMlZRIorhLRcN5v/Z+GBsfPIyS/5D4bD7rvrRykTDI+hsuuD8TFIniXPkmfJc1VjeO7yJwnSVYbEXYweXHJFfkVRGBJ3MZe06o/FZMFjLnn95sHNUB3B1TpHUUoWjcHmYJYNeRyLyeKTGCq7vhExNGbyDFslyhsXdysKSRmZhDf5o2s2L99D89CmvLv2F64f3ZVThTkB2w4YHkNDiLEh3EN9iLEh3EN9iLEh3IORMbazRWH+/U+qPMPmvxigcTzDJgVbJcr7IdBVEw8t/5707D+6e6MjQ1h81yAUjxbw7fXhHupDjA3hHupDjA3hHupDjA3hHgIhxupoCIVGIGgIeaysYJNn2GrIDMyb0a/czX0DvT0QYmgIMTaEewiEGBrDPQRCDI3hHgIlBiF8TXrYKlFR1a6qJtyAR9PL3Nw30Nv9GQOKAroe0DHWhzzXNI+SZ//ksbHl2ag81oc8V1VD6BkKBA0hjw1iSPSLL77g5ZdfxuVyMWPGDK677roqv7cuCzZRdZJH35A8+obk0Tckj7UnOfSNhpDHer8OW1paGsuWLeOTTz4hKCiIKVOm0L9/fzp27Gh0aEIIIYQQfhHwz7Bt3bqVAQMG0KxZM0JDQxkxYgTr1q0zOiwhhBBCCL8J+IItPT0dm83mfR0dHU1aWpqBEQkhhBBC+FfAD4mW9YidoihlnFm2isaDq8pmC6/1NYTk0Vckj74hefQNyWPtSQ59o6HnMeALtpiYGHbu3Ol9nZ6eTnR0dJXfL5MOAoPk0Tckj74hefQNyWPtSQ59oyHksd6vw3bxxRezbds2Tp06hd1uZ8OGDQwePNjosIQQQggh/KZe9LDNmjWL6dOn43K5mDx5MhdeeGGV328yVX34tC6vISSPviJ59A3Jo29IHmtPcugb9T2PlcVfL9ZhE0IIIYRozAJ+SFQIIYQQorGTgk0IIYQQIsBJwSaEEEIIEeCkYBNCCCGECHBSsAkhhBBCBDgp2IQQQgghApwUbEIIIYQQAU4KNiGEEEKIACcFmxBCCCFEgJOCTQghhBAiwEnBVoEvvviC0aNHM2zYMN577z2jw6lX8vPzGTt2LMePHwdg69atjBs3juHDh7Ns2TKDo6sfXnzxRcaMGcOYMWNYunQpIHmsiX/+85+MHj2aMWPG8OabbwKSx9p46qmnmDt3LgAHDhxg0qRJjBgxgocffhi3221wdIFv+vTpjBkzhvHjxzN+/Hj27Nkjf2tq4Ouvv2bixImMHDmSxx9/HGgEv9e6KNPJkyf1oUOH6tnZ2XpBQYE+btw4/fDhw0aHVS/89NNP+tixY/ULLrhAP3bsmG632/UhQ4boR48e1V0ul37zzTfrmzZtMjrMgPb999/r1157re50OvWioiJ9+vTp+hdffCF5rKYff/xRnzJliu5yuXS73a4PHTpUP3DggOSxhrZu3ar3799fnzNnjq7ruj5mzBh99+7duq7r+kMPPaS/9957BkYX+DRN0wcNGqS7XC7vMflbU31Hjx7VL7nkEj01NVUvKirSp06dqm/atKnB/15LD1s5tm7dyoABA2jWrBmhoaGMGDGCdevWGR1WvbBy5UoWLlxIdHQ0AHv37qVt27a0adMGs9nMuHHjJJeVsNlszJ07l6CgICwWCx06dCApKUnyWE39+vXj7bffxmw2k5WVhcfjITc3V/JYA6dPn2bZsmX89a9/BeDEiRM4HA569uwJwMSJEyWPlUhISEBRFG677TauvPJK3n33XflbUwNfffUVo0ePpmXLllgsFpYtW0ZISEiD/72Wgq0c6enp2Gw27+vo6GjS0tIMjKj+eOKJJ+jbt6/3teSy+jp16uT9Q5iUlMTatWtRFEXyWAMWi4UXXniBMWPGMHDgQPl5rKF//OMfzJo1i4iICKD077XNZpM8ViI3N5eBAwfy0ksv8dZbb7FixQpSUlLk57GakpOT8Xg83HLLLVx55ZW8//77jeL3Wgq2cui6XuqYoigGRFL/SS5r7vDhw9x8883MmTOHc845p1S75LFq7rvvPrZt20ZqaipJSUml2iWPFfvoo4+IjY1l4MCB3mPye119vXr1YunSpYSGhtK8eXMmT57MCy+8UOo8yWPFPB4P27Zt4+mnn2blypX8/PPP3uelz9bQ8mg2OoBAFRMTw86dO72v09PTvUN8onpiYmLIzMz0vpZcVk18fDz33Xcf8+bNY8yYMWzfvl3yWE1HjhyhqKiI888/n5CQEIYPH866detQVdV7juSxcmvXriUjI4Px48eTk5NDYWEhiqKU+HnMyMiQPFZi586duFwub+Gr6zpxcXHye11NUVFRDBw4kObNmwNwxRVXNIrfa+lhK8fFF1/Mtm3bOHXqFHa7nQ0bNjB48GCjw6qXevToQWJiorcbe82aNZLLSqSmpnL33XfzzDPPMGbMGEDyWBPHjx9n/vz5FBUVUVRUxMaNG5kyZYrksZrefPNN1qxZw2effcZ9993H5ZdfzuLFi7FarcTHxwOwevVqyWMl8vLyWLp0KU6nk/z8fD799FOefvpp+VtTTUOHDmXLli3k5ubi8Xj47rvvGDlyZIP/vZYetnLExMQwa9Yspk+fjsvlYvLkyVx44YVGh1UvWa1WlixZwr333ovT6WTIkCGMHDnS6LAC2uuvv47T6WTJkiXeY1OmTJE8VtOQIUPYs2cPEyZMQFVVhg8fzpgxY2jevLnk0QeeeeYZ5s+fT0FBAV27dmX69OlGhxTQhg4d6v151DSNadOm0adPH/lbU009evTg1ltvZdq0abhcLgYNGsTUqVNp3759g/69VvSyHkQQQgghhBABQ4ZEhRBCCCECnBRsQgghhBABTgo2IYQQQogAJwWbEEIIIUSAk4JNCCGEECLAScEmhBA1NH78eHJzc40OQwjRCMiyHkIIIYQQAU4WzhVCiN+9+uqrrFq1irCwMPr27cvGjRt5/fXXWbRoEYWFhaSnp9OlSxeef/55rFYr5513Htu2bWPTpk189dVXmEwmkpOTsVgsPPXUU3Tu3NnoWxJCNBAyJCqEEMB3333HJ598wqpVq/jkk08oKCgAYOXKlUyYMIEPP/yQDRs2cPz4cTZt2lTq/Tt27GDBggWsWbOG3r178/rrr/v5DoQQDZkUbEIIAWzevJmRI0cSERGBoihcd911AMyePZvmzZvz2muv8cgjj5Cenk5hYWGp919wwQW0bNkSgK5du5KTk+PX+IUQDZsMiQohBGA2mzn7kV5VVQH429/+hsfjYdSoUVx22WWkpqZS1qO/wcHB3q8VRSnzHCGEqCnpYRNCCIo3it+wYQN5eXkArFq1CoAtW7Zw9913M3r0aBRFYc+ePXg8HiNDFUI0QtLDJoQQwMCBA7nmmmu49tprCQ4OplOnToSEhHDLLbdw991307RpU0JCQrjooos4evSo0eEKIRoZWdZDCCGAn3/+md27dzN9+nQA3nzzTfbs2cPzzz9vbGBCCIEUbEIIAUB+fj7z5s0jISEBRVGIjY3lscceIyYmxujQhBBCCjYhhBBCiEAnkw6EEEIIIQKcFGxCCCGEEAFOCjYhhBBCiAAnBZsQQgghRICTgk0IIYQQIsD9P6Z3OFzWZ5iQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a4c68f6",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c91f29d4",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87c6135a",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12b52ba2",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5219db92",
"metadata": {},
"outputs": [],
"source": [
"results['AL-RF']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15843d49",
"metadata": {},
"outputs": [],
"source": [
"results['DualLoop']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "2e06c01d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)\n",
"\n",
"dual_loops.plot_result(results, 'precision', 100.0) \n",
"dual_loops.plot_result(results, 'recall', 100.0) \n",
"\n",
"dual_loops.plot_result(results, 'human_effort', 1.0) \n",
"dual_loops.plot_result(results, 'num_true_matches', 1.0) \n",
"\n",
"dual_loops.plot_result(results, 'a-tp', 1.0) \n",
"dual_loops.plot_result(results, 'p-tp', 1.0) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3bf59af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3542c021",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "35b83840",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ee7bcbf",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a0d2748",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb6240e9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecc86bfe",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e1d8ecee",
"metadata": {},
"source": [
"# compare different query strategies for traditional active learning"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b1cb6e1",
"metadata": {},
"outputs": [],
"source": [
"configuration_options = { \n",
" 'ActiveLearning_rf': {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'lf_ensemble': 'normal_active_learning_rf',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'ActiveLearning_lg': {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'lf_ensemble': 'normal_active_learning_lg',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'ActiveLearning_mlp': {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'lf_ensemble': 'normal_active_learning_mlp',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec4b2b41",
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
"balance=[0.9, 0.1]\n",
"\n",
"total_size = len(my_dataset_df)\n",
"num_iteration = len(my_dataset_df)\n",
"batch_size = 100\n",
"interval_slow_loop = 4\n",
"\n",
"results = {}\n",
"\n",
"experiments = [ \n",
" 'ActiveLearning_rf', \n",
" 'ActiveLearning_lg', \n",
" 'ActiveLearning_mlp', \n",
"]\n",
"\n",
"for exp in experiments:\n",
" print(\"\\r\\n\\r\\n************* \" + exp + \" *************************\")\n",
" experiment_config = configuration_options[exp]\n",
" result, result_df = dual_loops.run_experiment(experiment_config, my_dataset_df, lfs_set, feature_set, \n",
" total_size, num_iteration, batch_size, interval_slow_loop, \n",
" balance, epochs, False) \n",
"\n",
" results[exp] = result "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "662379cc",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_cost_over_gain(results)\n",
"\n",
"#dual_loops.plot_result(results, 'precision', 100.0) \n",
"#dual_loops.plot_result(results, 'recall', 100.0) \n",
"\n",
"# dual_loops.plot_result(results, 'human_effort', 1.0) \n",
"# dual_loops.plot_result(results, 'num_true_matches', 1.0) \n",
"\n",
"#dual_loops.plot_result(results, 'a-tp', 1.0) \n",
"#dual_loops.plot_result(results, 'p-tp', 1.0) \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97b824ff",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"# dual_loops.plot_result(results, 'precision', 100.0) \n",
"# dual_loops.plot_result(results, 'recall', 100.0) \n",
"\n",
"dual_loops.plot_result(results, 'human_effort', 1.0) \n",
"dual_loops.plot_result(results, 'num_true_matches', 1.0) \n",
"\n",
"# dual_loops.plot_result(results, 'a-tp', 1.0) \n",
"# dual_loops.plot_result(results, 'p-tp', 1.0) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1caf3c8",
"metadata": {},
"outputs": [],
"source": [
"### predefined configuration for different setups\n",
"\n",
"# configuration_options = {\n",
"# 'KDD19': {\n",
"# 'datapoint_grouping': 'disagreement', \n",
"# 'group_selection': 'max',\n",
"# 'datapoint_selection': 'random',\n",
" \n",
"# 'lf_ensemble': 'snorkel',\n",
" \n",
"# 'lf_selection': 'all', \n",
"# 'with_slow_loop': False \n",
"# }, \n",
"# 'WeSAL': {\n",
"# 'datapoint_grouping': 'disagreement', \n",
"# 'group_selection': 'max',\n",
"# 'datapoint_selection': 'entropy', # best option for uncertainty sampling\n",
" \n",
"# 'lf_ensemble': 'snorkel_with_corrected_votes',\n",
" \n",
"# 'lf_selection': 'all', \n",
"# 'with_slow_loop': False \n",
"# }, \n",
"# 'ActiveWeaSul': {\n",
"# 'datapoint_grouping': 'uniqueness_votes', \n",
"# 'group_selection': 'max_kl',\n",
"# 'datapoint_selection': 'random',\n",
" \n",
"# 'lf_ensemble': 'snorkel_with_corrected_votes', #'snorkel_with_annotated_labels',\n",
" \n",
"# 'lf_selection': 'all', \n",
"# 'with_slow_loop': False \n",
"# }, \n",
"# 'DualLoops': {\n",
"# 'datapoint_grouping': 'num_positive_votes', \n",
"# 'group_selection': 'max',\n",
"# 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin\n",
" \n",
"# 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
"# 'lf_selection': 'all', \n",
"# 'with_slow_loop': False \n",
"# }, \n",
"# 'ActiveLearning': {\n",
"# 'datapoint_grouping': 'none', \n",
"# 'group_selection': 'none',\n",
"# 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
"# 'lf_ensemble': 'normal_active_learning_rf',\n",
" \n",
"# 'lf_selection': 'all', \n",
"# 'with_slow_loop': False \n",
"# } \n",
"# }\n",
"\n",
"configuration_options = { \n",
" 'WeSAL': {\n",
" 'datapoint_grouping': 'disagreement', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'entropy', # best option for uncertainty sampling\n",
" \n",
" 'lf_ensemble': 'snorkel_with_corrected_votes',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'ActiveLearning': {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'lf_ensemble': 'normal_active_learning_rf',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'DualLoops-1': {\n",
" 'datapoint_grouping': 'num_positive_votes', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin\n",
" \n",
" 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'DualLoops-2': {\n",
" 'datapoint_grouping': 'num_positive_votes', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin\n",
" \n",
" 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
" 'lf_selection': 'f1_first', \n",
" 'with_slow_loop': False \n",
" }, \n",
" 'DualLoops-3': {\n",
" 'datapoint_grouping': 'num_positive_votes', \n",
" 'group_selection': 'max',\n",
" 'datapoint_selection': 'match_confidence', #entropy, least_confidence, margin\n",
" \n",
" 'lf_ensemble': 'snorkel_with_init_precision', #snorkel_with_init_precision\n",
" \n",
" 'lf_selection': 'f1_first', \n",
" 'with_slow_loop': True\n",
" }, \n",
"}\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50d40033",
"metadata": {},
"outputs": [],
"source": [
"results = {}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5677d3f5",
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
"balance=[0.9, 0.1]\n",
"\n",
"num_iteration = 1000\n",
"batch_size = 20\n",
"interval_slow_loop = 4\n",
"budget = 20000\n",
"\n",
"exp = 'ActiveLearning' \n",
"\n",
"experiment_config = {\n",
" 'datapoint_grouping': 'none', \n",
" 'group_selection': 'none',\n",
" 'datapoint_selection': 'entropy', #entropy, least_confidence, margin\n",
" 'lf_ensemble': 'normal_active_learning_rf',\n",
" \n",
" 'lf_selection': 'all', \n",
" 'with_slow_loop': False \n",
" }\n",
"\n",
"print(\"\\r\\n\\r\\n************* \" + exp + \" *************************\")\n",
"\n",
"result, result_df = dual_loops.run_experiment(experiment_config, my_dataset_df, lfs_set, feature_set, \n",
" num_iteration, batch_size, interval_slow_loop, \n",
" balance, epochs, budget, True) \n",
"results[exp] = result "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1d591ff",
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
"balance=[0.9, 0.1]\n",
"\n",
"num_iteration = 1000\n",
"batch_size = 100\n",
"interval_slow_loop = 4\n",
"budget = 20000\n",
"\n",
"exp = 'DualLoops'\n",
"print(\"\\r\\n\\r\\n************* \" + exp + \" *************************\")\n",
"experiment_config = configuration_options[exp]\n",
"result, result_df = dual_loops.run_experiment(experiment_config, my_dataset_df, lfs_set, feature_set, \n",
" num_iteration, batch_size, interval_slow_loop, \n",
" balance, epochs, budget, True) \n",
"results[exp] = result "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8961d0f3",
"metadata": {},
"outputs": [],
"source": [
"# plot the results generated from all experiments\n",
"dual_loops.plot_result(results, 'f1', 100.0) \n",
"dual_loops.plot_result(results, 'precision', 100.0) \n",
"dual_loops.plot_result(results, 'recall', 100.0) \n",
"\n",
"dual_loops.plot_result(results, 'human_effort', 1.0) \n",
"dual_loops.plot_result(results, 'num_true_matches', 1.0) \n",
"\n",
"dual_loops.plot_result(results, 'a-tp', 1.0) \n",
"dual_loops.plot_result(results, 'p-tp', 1.0) "
]
},
{
"cell_type": "markdown",
"id": "de88255f",
"metadata": {},
"source": [
"# Produce the figures for the comparison"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a48fbde",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
" \n",
"def plot_comparison_result(results, methods, names, interval): \n",
" \n",
" metrics = ['human_effort', 'num_true_matches', 'a-tp', 'p-tp']\n",
" labels = ['cost', 'gain', '#annoate_matches', '#verified_matches']\n",
" markers = ['o', '*', '+', 'x']\n",
" fsize=20 \n",
"\n",
" num_fig = len(metrics)\n",
" fig, axs = plt.subplots(1, num_fig, figsize=(28, 5)) \n",
" lines = []\n",
" \n",
" for i in range(len(metrics)):\n",
" metric = metrics[i] \n",
" for j in range(len(methods)):\n",
" method = methods[j]\n",
" rdf = pd.DataFrame(results[method]) \n",
" x = rdf.columns\n",
" xticks = np.arange(min(x), max(x)+1, interval)\n",
" y = rdf.loc[metric] \n",
" \n",
" axs[i].set_xticks(xticks)\n",
" line = axs[i].plot(x, y, linestyle = '-', marker=markers[j])\n",
" lines.append(lines)\n",
" \n",
" axs[i].set_ylabel(labels[i], fontsize=fsize)\n",
" axs[i].grid(True)\n",
"\n",
"\n",
" fig.legend(lines, # The line objects\n",
" labels=names, # The labels for each line\n",
" loc=\"upper center\", # Position of legend,\n",
" bbox_to_anchor=(0.45, 1.0), ncol=len(methods)\n",
" ) \n",
" \n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d4bf4c3",
"metadata": {},
"outputs": [],
"source": [
"plot_comparison_result(results, ['WeSAL', 'ActiveLearning', 'DualLoops-3'], ['WeSAL', 'Active Learning', 'DualLoops (fastloop and slowloop)' ], 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a044809",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}