{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:45:35.425166Z","iopub.execute_input":"2025-06-28T14:45:35.425508Z","iopub.status.idle":"2025-06-28T14:45:38.364856Z","shell.execute_reply.started":"2025-06-28T14:45:35.425484Z","shell.execute_reply":"2025-06-28T14:45:38.363843Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv\n/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv\n/kaggle/input/child-mind-institute-problematic-internet-use/train.csv\n/kaggle/input/child-mind-institute-problematic-internet-use/test.csv\n/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet/id=00115b9f/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet/id=001f3379/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0745c390/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=eaab7a96/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=8ec2cc63/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=b2987a65/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=7b8842c3/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=b120a337/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=5f9dddb4/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=2c3b09db/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=3ab539f0/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=9f9be55c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=15dbc929/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=e7d08824/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=2a3fcf9a/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=7c194077/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=38cccd6d/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=f4d2f5af/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=948a7025/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=25849c6c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=c00c663c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=79bd36b6/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=f44f1227/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=a922ef3f/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=b95aad8d/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=93904b23/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=f028534c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=a221c60c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=cd39e576/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=cb2752bc/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=d9c82502/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=35d2aa41/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=93c06d4c/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=bd8397cb/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0a418b57/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=8abbbc38/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=91656d27/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=75c13e08/part-0.parquet\n/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=3acb5be3/part-0.p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install pytorch-tabnet\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:49:08.891625Z","iopub.execute_input":"2025-06-28T14:49:08.892942Z","iopub.status.idle":"2025-06-28T14:49:12.821856Z","shell.execute_reply.started":"2025-06-28T14:49:08.892847Z","shell.execute_reply":"2025-06-28T14:49:12.820929Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: pytorch-tabnet in /usr/local/lib/python3.11/dist-packages (4.1.0)\nRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from pytorch-tabnet) (1.26.4)\nRequirement already satisfied: scikit_learn>0.21 in /usr/local/lib/python3.11/dist-packages (from pytorch-tabnet) (1.2.2)\nRequirement already satisfied: scipy>1.4 in /usr/local/lib/python3.11/dist-packages (from pytorch-tabnet) (1.15.2)\nRequirement already satisfied: torch>=1.3 in /usr/local/lib/python3.11/dist-packages (from pytorch-tabnet) (2.6.0+cu124)\nRequirement already satisfied: tqdm>=4.36 in 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(1.13.1)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.3->pytorch-tabnet) (1.3.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.3->pytorch-tabnet) (3.0.2)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17->pytorch-tabnet) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17->pytorch-tabnet) (2022.1.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.17->pytorch-tabnet) (1.3.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.17->pytorch-tabnet) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.17->pytorch-tabnet) (2024.2.0)\n","output_type":"stream"}],"execution_count":61},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport os\nimport re\n\nfrom tqdm import tqdm\nfrom concurrent.futures import ThreadPoolExecutor\n\nfrom scipy.optimize import minimize\nfrom scipy.fft import fft\n\nimport polars as pl\nimport polars.selectors as cs\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom pytorch_tabnet.tab_model import TabNetRegressor\n\nfrom colorama import Fore, Style\n\nfrom IPython.display import clear_output\n\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\n\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.base import clone\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import train_test_split\n\nfrom pytorch_tabnet.callbacks import Callback\n\nimport warnings\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:50:22.602599Z","iopub.execute_input":"2025-06-28T14:50:22.603036Z","iopub.status.idle":"2025-06-28T14:50:22.617628Z","shell.execute_reply.started":"2025-06-28T14:50:22.602999Z","shell.execute_reply":"2025-06-28T14:50:22.616336Z"}},"outputs":[],"execution_count":62},{"cell_type":"code","source":"target_labels = ['None', 'Mild', 'Moderate', 'Severe']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:50:40.250715Z","iopub.execute_input":"2025-06-28T14:50:40.251467Z","iopub.status.idle":"2025-06-28T14:50:40.255716Z","shell.execute_reply.started":"2025-06-28T14:50:40.251436Z","shell.execute_reply":"2025-06-28T14:50:40.254724Z"}},"outputs":[],"execution_count":63},{"cell_type":"code","source":"season_dtype = pl.Enum(['Spring', 'Summer', 'Fall', 'Winter'])\n\n\n\ntrain = (\n\n    pl.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n\n    .with_columns(pl.col('^.*Season$').cast(season_dtype))\n\n)\n\n\n\ntest = (\n\n    pl.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n\n    .with_columns(pl.col('^.*Season$').cast(season_dtype))\n\n)\n\n\n\ntrain\n\ntest\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:51:01.307124Z","iopub.execute_input":"2025-06-28T14:51:01.307499Z","iopub.status.idle":"2025-06-28T14:51:01.357947Z","shell.execute_reply.started":"2025-06-28T14:51:01.307473Z","shell.execute_reply":"2025-06-28T14:51:01.357033Z"}},"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"shape: (20, 59)\n┌──────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n│ id       ┆ Basic_Dem ┆ Basic_Dem ┆ Basic_Dem ┆ … ┆ SDS-SDS_T ┆ SDS-SDS_T ┆ PreInt_Ed ┆ PreInt_Ed │\n│ ---      ┆ os-Enroll ┆ os-Age    ┆ os-Sex    ┆   ┆ otal_Raw  ┆ otal_T    ┆ uHx-Seaso ┆ uHx-compu │\n│ str      ┆ _Season   ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ n         ┆ terintern │\n│          ┆ ---       ┆ i64       ┆ i64       ┆   ┆ i64       ┆ i64       ┆ ---       ┆ et_…      │\n│          ┆ enum      ┆           ┆           ┆   ┆           ┆           ┆ enum      ┆ ---       │\n│          ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ i64       │\n╞══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n│ 00008ff9 ┆ Fall      ┆ 5         ┆ 0         ┆ … ┆ null      ┆ null      ┆ Fall      ┆ 3         │\n│ 000fd460 ┆ Summer    ┆ 9         ┆ 0         ┆ … ┆ 46        ┆ 64        ┆ Summer    ┆ 0         │\n│ 00105258 ┆ Summer    ┆ 10        ┆ 1         ┆ … ┆ 38        ┆ 54        ┆ Summer    ┆ 2         │\n│ 00115b9f ┆ Winter    ┆ 9         ┆ 0         ┆ … ┆ 31        ┆ 45        ┆ Winter    ┆ 0         │\n│ 0016bb22 ┆ Spring    ┆ 18        ┆ 1         ┆ … ┆ null      ┆ null      ┆ null      ┆ null      │\n│ …        ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n│ 00c0cd71 ┆ Winter    ┆ 7         ┆ 0         ┆ … ┆ 35        ┆ 50        ┆ Winter    ┆ 2         │\n│ 00d56d4b ┆ Spring    ┆ 5         ┆ 1         ┆ … ┆ 37        ┆ 53        ┆ Spring    ┆ 0         │\n│ 00d9913d ┆ Fall      ┆ 10        ┆ 1         ┆ … ┆ null      ┆ null      ┆ Fall      ┆ 1         │\n│ 00e6167c ┆ Winter    ┆ 6         ┆ 0         ┆ … ┆ 39        ┆ 55        ┆ Winter    ┆ 3         │\n│ 00ebc35d ┆ Winter    ┆ 10        ┆ 0         ┆ … ┆ null      ┆ null      ┆ Winter    ┆ 2         │\n└──────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘","text/html":"<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (20, 59)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>Basic_Demos-Enroll_Season</th><th>Basic_Demos-Age</th><th>Basic_Demos-Sex</th><th>CGAS-Season</th><th>CGAS-CGAS_Score</th><th>Physical-Season</th><th>Physical-BMI</th><th>Physical-Height</th><th>Physical-Weight</th><th>Physical-Waist_Circumference</th><th>Physical-Diastolic_BP</th><th>Physical-HeartRate</th><th>Physical-Systolic_BP</th><th>Fitness_Endurance-Season</th><th>Fitness_Endurance-Max_Stage</th><th>Fitness_Endurance-Time_Mins</th><th>Fitness_Endurance-Time_Sec</th><th>FGC-Season</th><th>FGC-FGC_CU</th><th>FGC-FGC_CU_Zone</th><th>FGC-FGC_GSND</th><th>FGC-FGC_GSND_Zone</th><th>FGC-FGC_GSD</th><th>FGC-FGC_GSD_Zone</th><th>FGC-FGC_PU</th><th>FGC-FGC_PU_Zone</th><th>FGC-FGC_SRL</th><th>FGC-FGC_SRL_Zone</th><th>FGC-FGC_SRR</th><th>FGC-FGC_SRR_Zone</th><th>FGC-FGC_TL</th><th>FGC-FGC_TL_Zone</th><th>BIA-Season</th><th>BIA-BIA_Activity_Level_num</th><th>BIA-BIA_BMC</th><th>BIA-BIA_BMI</th><th>BIA-BIA_BMR</th><th>BIA-BIA_DEE</th><th>BIA-BIA_ECW</th><th>BIA-BIA_FFM</th><th>BIA-BIA_FFMI</th><th>BIA-BIA_FMI</th><th>BIA-BIA_Fat</th><th>BIA-BIA_Frame_num</th><th>BIA-BIA_ICW</th><th>BIA-BIA_LDM</th><th>BIA-BIA_LST</th><th>BIA-BIA_SMM</th><th>BIA-BIA_TBW</th><th>PAQ_A-Season</th><th>PAQ_A-PAQ_A_Total</th><th>PAQ_C-Season</th><th>PAQ_C-PAQ_C_Total</th><th>SDS-Season</th><th>SDS-SDS_Total_Raw</th><th>SDS-SDS_Total_T</th><th>PreInt_EduHx-Season</th><th>PreInt_EduHx-computerinternet_hoursday</th></tr><tr><td>str</td><td>enum</td><td>i64</td><td>i64</td><td>enum</td><td>i64</td><td>enum</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>enum</td><td>i64</td><td>i64</td><td>i64</td><td>enum</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>enum</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>enum</td><td>f64</td><td>enum</td><td>f64</td><td>enum</td><td>i64</td><td>i64</td><td>enum</td><td>i64</td></tr></thead><tbody><tr><td>&quot;00008ff9&quot;</td><td>&quot;Fall&quot;</td><td>5</td><td>0</td><td>&quot;Winter&quot;</td><td>51</td><td>&quot;Fall&quot;</td><td>16.877316</td><td>46.0</td><td>50.8</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>0</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>7.0</td><td>0</td><td>6.0</td><td>0</td><td>6.0</td><td>1</td><td>&quot;Fall&quot;</td><td>2</td><td>2.66855</td><td>16.8792</td><td>932.498</td><td>1492.0</td><td>8.25598</td><td>41.5862</td><td>13.8177</td><td>3.06143</td><td>9.21377</td><td>1</td><td>24.4349</td><td>8.89536</td><td>38.9177</td><td>19.5413</td><td>32.6909</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>3</td></tr><tr><td>&quot;000fd460&quot;</td><td>&quot;Summer&quot;</td><td>9</td><td>0</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>14.03559</td><td>48.0</td><td>46.0</td><td>22.0</td><td>75</td><td>70</td><td>122</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>5</td><td>0</td><td>11.0</td><td>1</td><td>11.0</td><td>1</td><td>3.0</td><td>0</td><td>&quot;Winter&quot;</td><td>2</td><td>2.57949</td><td>14.0371</td><td>936.656</td><td>1498.65</td><td>6.01993</td><td>42.0291</td><td>12.8254</td><td>1.21172</td><td>3.97085</td><td>1</td><td>21.0352</td><td>14.974</td><td>39.4497</td><td>15.4107</td><td>27.0552</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>2.34</td><td>&quot;Fall&quot;</td><td>46</td><td>64</td><td>&quot;Summer&quot;</td><td>0</td></tr><tr><td>&quot;00105258&quot;</td><td>&quot;Summer&quot;</td><td>10</td><td>1</td><td>&quot;Fall&quot;</td><td>71</td><td>&quot;Fall&quot;</td><td>16.648696</td><td>56.5</td><td>75.6</td><td>null</td><td>65</td><td>94</td><td>117</td><td>&quot;Fall&quot;</td><td>5</td><td>7</td><td>33</td><td>&quot;Fall&quot;</td><td>20</td><td>1</td><td>10.2</td><td>1</td><td>14.7</td><td>2</td><td>7</td><td>1</td><td>10.0</td><td>1</td><td>10.0</td><td>1</td><td>5.0</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Summer&quot;</td><td>2.17</td><td>&quot;Fall&quot;</td><td>38</td><td>54</td><td>&quot;Summer&quot;</td><td>2</td></tr><tr><td>&quot;00115b9f&quot;</td><td>&quot;Winter&quot;</td><td>9</td><td>0</td><td>&quot;Fall&quot;</td><td>71</td><td>&quot;Summer&quot;</td><td>18.292347</td><td>56.0</td><td>81.6</td><td>null</td><td>60</td><td>97</td><td>117</td><td>&quot;Summer&quot;</td><td>6</td><td>9</td><td>37</td><td>&quot;Summer&quot;</td><td>18</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>5</td><td>0</td><td>7.0</td><td>0</td><td>7.0</td><td>0</td><td>7.0</td><td>1</td><td>&quot;Summer&quot;</td><td>3</td><td>3.84191</td><td>18.2943</td><td>1131.43</td><td>1923.44</td><td>15.5925</td><td>62.7757</td><td>14.074</td><td>4.22033</td><td>18.8243</td><td>2</td><td>30.4041</td><td>16.779</td><td>58.9338</td><td>26.4798</td><td>45.9966</td><td>null</td><td>null</td><td>&quot;Winter&quot;</td><td>2.451</td><td>&quot;Summer&quot;</td><td>31</td><td>45</td><td>&quot;Winter&quot;</td><td>0</td></tr><tr><td>&quot;0016bb22&quot;</td><td>&quot;Spring&quot;</td><td>18</td><td>1</td><td>&quot;Summer&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Summer&quot;</td><td>1.04</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;00c0cd71&quot;</td><td>&quot;Winter&quot;</td><td>7</td><td>0</td><td>&quot;Summer&quot;</td><td>51</td><td>&quot;Spring&quot;</td><td>29.315775</td><td>54.0</td><td>121.6</td><td>null</td><td>80</td><td>75</td><td>99</td><td>&quot;Spring&quot;</td><td>4</td><td>5</td><td>32</td><td>&quot;Spring&quot;</td><td>6</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>12.0</td><td>1</td><td>15.0</td><td>1</td><td>12.0</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Spring&quot;</td><td>35</td><td>50</td><td>&quot;Winter&quot;</td><td>2</td></tr><tr><td>&quot;00d56d4b&quot;</td><td>&quot;Spring&quot;</td><td>5</td><td>1</td><td>&quot;Summer&quot;</td><td>80</td><td>&quot;Spring&quot;</td><td>17.284504</td><td>44.0</td><td>47.6</td><td>null</td><td>61</td><td>76</td><td>109</td><td>&quot;Spring&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Spring&quot;</td><td>0</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>10.5</td><td>1</td><td>10.0</td><td>1</td><td>7.0</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Spring&quot;</td><td>37</td><td>53</td><td>&quot;Spring&quot;</td><td>0</td></tr><tr><td>&quot;00d9913d&quot;</td><td>&quot;Fall&quot;</td><td>10</td><td>1</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>19.893157</td><td>55.0</td><td>85.6</td><td>30.0</td><td>null</td><td>81</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>5</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>9.0</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Fall&quot;</td><td>1</td></tr><tr><td>&quot;00e6167c&quot;</td><td>&quot;Winter&quot;</td><td>6</td><td>0</td><td>&quot;Spring&quot;</td><td>60</td><td>&quot;Winter&quot;</td><td>30.094649</td><td>37.5</td><td>60.2</td><td>24.0</td><td>61</td><td>91</td><td>95</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Winter&quot;</td><td>6</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>4.0</td><td>0</td><td>4.0</td><td>0</td><td>7.0</td><td>1</td><td>&quot;Winter&quot;</td><td>2</td><td>2.75035</td><td>17.2738</td><td>1003.07</td><td>1504.61</td><td>15.1456</td><td>49.1034</td><td>14.0898</td><td>3.18407</td><td>11.0966</td><td>1</td><td>23.6182</td><td>10.3396</td><td>46.3531</td><td>19.8886</td><td>38.7638</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Winter&quot;</td><td>39</td><td>55</td><td>&quot;Winter&quot;</td><td>3</td></tr><tr><td>&quot;00ebc35d&quot;</td><td>&quot;Winter&quot;</td><td>10</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Spring&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;Winter&quot;</td><td>2</td></tr></tbody></table></div>"},"metadata":{}}],"execution_count":64},{"cell_type":"code","source":"supervised_usable = (\n\n    train\n\n    .filter(pl.col('sii').is_not_null())\n\n)\n\n\n\nmissing_count = (\n\n    supervised_usable\n\n    .null_count()\n\n    .transpose(include_header=True,\n\n               header_name='feature',\n\n               column_names=['null_count'])\n\n    .sort('null_count', descending=True)\n\n    .with_columns((pl.col('null_count') / len(supervised_usable)).alias('null_ratio'))\n\n)\n\nplt.figure(figsize=(6, 15))\n\nplt.title(f'Missing values over the {len(supervised_usable)} samples which have a target')\n\nplt.barh(np.arange(len(missing_count)), missing_count.get_column('null_ratio'), color='coral', label='missing')\n\nplt.barh(np.arange(len(missing_count)), \n\n         1 - missing_count.get_column('null_ratio'),\n\n         left=missing_count.get_column('null_ratio'),\n\n         color='darkseagreen', label='available')\n\nplt.yticks(np.arange(len(missing_count)), missing_count.get_column('feature'))\n\nplt.gca().xaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\n\nplt.xlim(0, 1)\n\nplt.legend()\n\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:51:35.842518Z","iopub.execute_input":"2025-06-28T14:51:35.843294Z","iopub.status.idle":"2025-06-28T14:51:36.798156Z","shell.execute_reply.started":"2025-06-28T14:51:35.843265Z","shell.execute_reply":"2025-06-28T14:51:36.796959Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x1500 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":65},{"cell_type":"code","source":"print(train.select(pl.col('PCIAT-PCIAT_Total').is_null() == pl.col('sii').is_null()).to_series().mean())\n\n\n\n(train\n\n .select(pl.col('PCIAT-PCIAT_Total'))\n\n .group_by(train.get_column('sii'))\n\n .agg(pl.col('PCIAT-PCIAT_Total').min().alias('PCIAT-PCIAT_Total min'),\n\n      pl.col('PCIAT-PCIAT_Total').max().alias('PCIAT-PCIAT_Total max'),\n\n      pl.col('PCIAT-PCIAT_Total').len().alias('count'))\n\n .sort('sii')\n\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:52:02.75654Z","iopub.execute_input":"2025-06-28T14:52:02.756882Z","iopub.status.idle":"2025-06-28T14:52:02.774831Z","shell.execute_reply.started":"2025-06-28T14:52:02.756856Z","shell.execute_reply":"2025-06-28T14:52:02.773683Z"}},"outputs":[{"name":"stdout","text":"1.0\n","output_type":"stream"},{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"shape: (5, 4)\n┌──────┬───────────────────────┬───────────────────────┬───────┐\n│ sii  ┆ PCIAT-PCIAT_Total min ┆ PCIAT-PCIAT_Total max ┆ count │\n│ ---  ┆ ---                   ┆ ---                   ┆ ---   │\n│ i64  ┆ i64                   ┆ i64                   ┆ u32   │\n╞══════╪═══════════════════════╪═══════════════════════╪═══════╡\n│ null ┆ null                  ┆ null                  ┆ 1224  │\n│ 0    ┆ 0                     ┆ 30                    ┆ 1594  │\n│ 1    ┆ 31                    ┆ 49                    ┆ 730   │\n│ 2    ┆ 50                    ┆ 79                    ┆ 378   │\n│ 3    ┆ 80                    ┆ 93                    ┆ 34    │\n└──────┴───────────────────────┴───────────────────────┴───────┘","text/html":"<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>sii</th><th>PCIAT-PCIAT_Total min</th><th>PCIAT-PCIAT_Total max</th><th>count</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>u32</td></tr></thead><tbody><tr><td>null</td><td>null</td><td>null</td><td>1224</td></tr><tr><td>0</td><td>0</td><td>30</td><td>1594</td></tr><tr><td>1</td><td>31</td><td>49</td><td>730</td></tr><tr><td>2</td><td>50</td><td>79</td><td>378</td></tr><tr><td>3</td><td>80</td><td>93</td><td>34</td></tr></tbody></table></div>"},"metadata":{}}],"execution_count":66},{"cell_type":"code","source":"print('Columns missing in test:')\n\nprint([f for f in train.columns if f not in test.columns])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:52:22.005996Z","iopub.execute_input":"2025-06-28T14:52:22.00634Z","iopub.status.idle":"2025-06-28T14:52:22.013016Z","shell.execute_reply.started":"2025-06-28T14:52:22.006316Z","shell.execute_reply":"2025-06-28T14:52:22.012044Z"}},"outputs":[{"name":"stdout","text":"Columns missing in test:\n['PCIAT-Season', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02', 'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06', 'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10', 'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14', 'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18', 'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total', 'sii']\n","output_type":"stream"}],"execution_count":67},{"cell_type":"code","source":"vc = train.get_column('Basic_Demos-Enroll_Season').value_counts()\n\nplt.pie(vc.get_column('count'), labels=vc.get_column('Basic_Demos-Enroll_Season'))\n\nplt.title('Season of enrollment')\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:55:37.181553Z","iopub.execute_input":"2025-06-28T14:55:37.182089Z","iopub.status.idle":"2025-06-28T14:55:37.273263Z","shell.execute_reply.started":"2025-06-28T14:55:37.182051Z","shell.execute_reply":"2025-06-28T14:55:37.27234Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":68},{"cell_type":"code","source":"vc = train.get_column('Basic_Demos-Sex').value_counts()\n\nplt.pie(vc.get_column('count'), labels=['boys', 'girls'])\n\nplt.title('Sex of participant')\n\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:55:51.590483Z","iopub.execute_input":"2025-06-28T14:55:51.590816Z","iopub.status.idle":"2025-06-28T14:55:51.672064Z","shell.execute_reply.started":"2025-06-28T14:55:51.590789Z","shell.execute_reply":"2025-06-28T14:55:51.671148Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":69},{"cell_type":"code","source":"_, axs = plt.subplots(2, 1, sharex=True)\n\nfor sex in range(2):\n\n    ax = axs.ravel()[sex]\n\n    vc = train.filter(pl.col('Basic_Demos-Sex') == sex).get_column('Basic_Demos-Age').value_counts()\n\n    ax.bar(vc.get_column('Basic_Demos-Age'),\n\n           vc.get_column('count'),\n\n           color=['lightblue', 'coral'][sex],\n\n           label=['boys', 'girls'][sex])\n\n    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n\n    ax.set_ylabel('count')\n\n    ax.legend()\n\nplt.suptitle('Age distribution')\n\naxs.ravel()[1].set_xlabel('years')\n\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:56:38.735787Z","iopub.execute_input":"2025-06-28T14:56:38.736176Z","iopub.status.idle":"2025-06-28T14:56:39.088018Z","shell.execute_reply.started":"2025-06-28T14:56:38.736151Z","shell.execute_reply":"2025-06-28T14:56:39.086968Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":70},{"cell_type":"code","source":"_, axs = plt.subplots(2, 1, sharex=True, sharey=True)\n\nfor sex in range(2):\n\n    ax = axs.ravel()[sex]\n\n    vc = train.filter(pl.col('Basic_Demos-Sex') == sex).get_column('sii').value_counts()\n\n    ax.bar(vc.get_column('sii'),\n\n           vc.get_column('count') / vc.get_column('count').sum(),\n\n           color=['lightblue', 'coral'][sex],\n\n           label=['boys', 'girls'][sex])\n\n    ax.set_xticks(np.arange(4), target_labels)\n\n    ax.yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\n\n    ax.set_ylabel('count')\n\n    ax.legend()\n\nplt.suptitle('Target distribution')\n\naxs.ravel()[1].set_xlabel('Severity Impairment Index (sii)')\n\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:57:00.14615Z","iopub.execute_input":"2025-06-28T14:57:00.147132Z","iopub.status.idle":"2025-06-28T14:57:00.390566Z","shell.execute_reply.started":"2025-06-28T14:57:00.147089Z","shell.execute_reply":"2025-06-28T14:57:00.389622Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 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\n"},"metadata":{}}],"execution_count":71},{"cell_type":"code","source":"plt.figure(figsize=(14, 12))\n\ncorr_matrix = supervised_usable.select([\n\n    'PCIAT-PCIAT_Total', 'Basic_Demos-Age', 'Basic_Demos-Sex', 'Physical-BMI', \n\n    'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n\n    'Physical-Diastolic_BP', 'Physical-Systolic_BP', 'Physical-HeartRate',\n\n    'PreInt_EduHx-computerinternet_hoursday', 'SDS-SDS_Total_T', 'PAQ_A-PAQ_A_Total',\n\n    'PAQ_C-PAQ_C_Total', 'Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Time_Mins','Fitness_Endurance-Time_Sec',\n\n    'FGC-FGC_CU', 'FGC-FGC_GSND','FGC-FGC_GSD','FGC-FGC_PU','FGC-FGC_SRL','FGC-FGC_SRR','FGC-FGC_TL','BIA-BIA_Activity_Level_num', \n\n    'BIA-BIA_BMC', 'BIA-BIA_BMI', 'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n\n    'BIA-BIA_FFMI','BIA-BIA_FMI', 'BIA-BIA_Fat','BIA-BIA_Frame_num','BIA-BIA_ICW','BIA-BIA_LDM','BIA-BIA_LST',\n\n    'BIA-BIA_SMM','BIA-BIA_TBW'\n\n    # Add other relevant columns\n\n]).to_pandas().corr()\n\n\n\nsii_corr = corr_matrix['PCIAT-PCIAT_Total'].drop('PCIAT-PCIAT_Total')\n\nfiltered_corr = sii_corr[(sii_corr > 0.1) | (sii_corr < -0.1)]\n\n\n\nprint(filtered_corr)\n\n\n\nplt.figure(figsize=(8, 6))\n\nfiltered_corr.sort_values().plot(kind='barh', color='coral')\n\nplt.title('Features with Correlation > 0.1 or < -0.1 with PCIAT-PCIAT_Total')\n\nplt.xlabel('Correlation coefficient')\n\nplt.ylabel('Features')\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:57:20.937846Z","iopub.execute_input":"2025-06-28T14:57:20.93853Z","iopub.status.idle":"2025-06-28T14:57:21.239633Z","shell.execute_reply.started":"2025-06-28T14:57:20.938496Z","shell.execute_reply":"2025-06-28T14:57:21.238685Z"}},"outputs":[{"name":"stdout","text":"Basic_Demos-Age                           0.409559\nPhysical-BMI                              0.240858\nPhysical-Height                           0.420765\nPhysical-Weight                           0.353048\nPhysical-Waist_Circumference              0.327013\nPhysical-Systolic_BP                      0.147081\nPreInt_EduHx-computerinternet_hoursday    0.374124\nSDS-SDS_Total_T                           0.237718\nFGC-FGC_CU                                0.287494\nFGC-FGC_GSND                              0.146813\nFGC-FGC_GSD                               0.160472\nFGC-FGC_PU                                0.196006\nFGC-FGC_TL                                0.136696\nBIA-BIA_BMI                               0.248060\nBIA-BIA_FFMI                              0.109694\nBIA-BIA_Frame_num                         0.193631\nName: PCIAT-PCIAT_Total, dtype: float64\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x1200 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAIjCAYAAADMcXIDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADK+ElEQVR4nOzdeVxO6f8/8Nfddrfc7UqltIqUfZelyFRMg0HWoYSJ7HszlmqyDjMGHztliXzMZGwhk1KYGWv4WLImMzIIJSmp8/ujX+fr1l0qNaHX8/E4j4f7nOtc1/uc+3S73/d1netIBEEQQERERERERPSJU6ruAIiIiIiIiIj+DUyAiYiIiIiIqEZgAkxEREREREQ1AhNgIiIiIiIiqhGYABMREREREVGNwASYiIiIiIiIagQmwERERERERFQjMAEmIiIiIiKiGoEJMBEREREREdUITICJiIjek0QiQVBQUJnLjh07tmoD+oCkpKRAIpEgPDy8Uuu1srKCj49PpdZJVNXKc91aWVnh888/r9qAiP6/8PBwSCQSpKSkVHcoVY4JMBHRJ6roPzNFy8yZM6ukzZMnTyIoKAjPnj2rkvo/FlV9HjIzMxEcHIwmTZpAJpNBQ0MDTk5OmDFjBu7fv18lbVaHmnI9bdy4EQ4ODlBXV0e9evWwYsWKMu2XlZWFuXPnwsPDAwYGBlXyQ8OH7mM/d1euXEFQUFCVJB1vfuYrKSnBzMwMn332GeLj44uVzc/PR1hYGFxcXGBgYACpVAorKyv4+vrizJkzYrmi/1feXPem6dOnQyKRoH///iXGUtqiKDYAcHFxkStnYGCAVq1aYdOmTSgoKChWPj4+Hl9++SVMTEygpqYGY2NjeHl5ISoqSixT9OPckiVLFLYZHR0NiUQCMzMzuTbejqWk5V0/iha1X5blXddHdnY2goKCSjx/JE+lugMgIqKqFRISAmtra7l1Tk5OVdLWyZMnERwcDB8fH+jp6VVJGx+ily9fQkXl//5LrcrzcPv2bbi5uSE1NRX9+vXDqFGjoKamhosXL2Ljxo3YvXs3rl+/XqltVpfSzmNycjKUlD7+3/HXrl0Lf39/9OnTB5MnT0ZiYiLGjx+P7OxszJgxo9R9Hz9+jJCQENStWxdNmjSpcV9+P8Zz9/Z1e+XKFQQHB8PFxQVWVlaV3l63bt0wdOhQCIKAO3fuYNWqVejSpQsOHDgAT09PAIWfX19++SUOHTqETp064ZtvvoGBgQFSUlLw3//+F5s3b0ZqairMzc1LbUsQBOzYsQNWVlbYt28fnj9/Dm1tbQDA1q1b5cpu2bIFR44cKbbewcGhxPrNzc2xYMECAMCjR4+wZcsW+Pn54fr161i4cKFYbu7cuQgJCUG9evXw9ddfw9LSEunp6YiOjkafPn0QERGBQYMGvfPcRUREwMrKCikpKTh69Cjc3NwAAN9++y1GjBghljt9+jSWL1+Ob775Ri7+xo0bl1q/kZFRseNfunQp/vrrL/z444/FypYmOzsbwcHBAAoTdHoHgYiIPklhYWECAOH06dP/Wpvff/+9AEC4c+dOpdablZVVqfVVtdLOAwAhICCgQvXm5eUJTZo0ETQ1NYXExMRi2zMyMoRvvvmmQnW/raRznp+fL7x8+bLM9dy5c0cAIISFhZU7hqq6nt7Ho0ePhPv371dKXdnZ2YKhoaHQo0cPufWDBw8WtLS0hCdPnpS6f05OjpCWliYIgiCcPn26wue5Iirjb/Lhw4cVPpcf87l7065duwQAQlxcXLFtlpaWxY6vPBR91ly8eFEAIHz22WfiuoCAAAGA8OOPPxar4/Xr18L3338v3Lt3TxCE0v9fOXr0qABAOHr0qKCqqiqEh4eXGFtRm2XVuXNnwdHRUW7dixcvBHNzc0FLS0t49eqVIAj/dz779u0rrnvToUOHhH379gmC8H+fTd9//32xcllZWYKWlpawfPlyoVmzZoKPj0+JsZX2HpZXjx49BEtLy3Lv9+jRIwGAMHfu3Aq3XfTefkift1Xl4//plIiI3svBgwfRsWNHaGlpQVtbGz169MDly5flyly8eBE+Pj6wsbGBuro6TExMMHz4cKSnp4tlgoKCMG3aNACAtbW13NCt0u4DfXuoWFBQECQSCa5cuYJBgwZBX18fHTp0ELdv27YNLVq0gIaGBgwMDDBgwADcu3dPrs4bN26gT58+MDExgbq6OszNzTFgwABkZGSUeB6WL18OZWVlueG2S5cuhUQiweTJk8V1+fn50NbWluthevMYSjsPb/r111/h5OQEqVQKR0dHHDp0qMTYivzyyy+4cOECvv32W7lzUkRHRwfz5s2TW7dr1y7xfNWqVQtDhgzB33//LVfGx8cHMpkMt27dQvfu3aGtrY3BgweLxzZ27FhERETA0dERUqlUjPXvv//G8OHDUbt2bfE4Nm3a9M7jeN/rCVB8L+Xt27fRr18/GBgYQFNTE23btsWBAwfkysTHx0MikeC///0v5s2bB3Nzc6irq6Nr1664efPmO2P/3//+h7p166Jnz57Yu3cvXr9+/c59ShIXF4f09HSMGTNGbn1AQABevHhRLPa3SaVSmJiYVLh9ADh69Kj496+np4eePXvi6tWrcmXe9TdZHgUFBTh06BD69esHc3Nz/PnnnxWqpzrP3d69eyGRSHDx4kVx3S+//AKJRIIvv/xSrqyDg4PccOA3r9vw8HD069cPAODq6lriMODjx4+jdevWUFdXh42NDbZs2VKhuAGgUaNGqFWrFu7cuQMA+Ouvv7B27Vp069YNEydOLFZeWVkZU6dOfWfvL1DYY9qwYUO4urrCzc0NERERFY6zLIr+xl+8eIFHjx4BAGbPng0DAwNs2rQJqqqqxfZxd3cv033Vu3fvxsuXL9GvXz8MGDAAUVFRyMnJqfRjKIuHDx/Cz88PtWvXhrq6Opo0aYLNmzeL21NSUsQe4uDg4GJDsMvyeVvTcAg0EdEnLiMjA48fP5ZbV6tWLQCFw9KGDRsGd3d3LFq0CNnZ2Vi9ejU6dOiA8+fPi0Pyjhw5gtu3b8PX1xcmJia4fPky1q1bh8uXL+OPP/4Qv/hdv34dO3bswI8//ii2YWRkJH45KY9+/fqhXr16mD9/PgRBAADMmzcPs2fPhre3N0aMGIFHjx5hxYoV6NSpE86fPw89PT28evUK7u7uyM3Nxbhx42BiYoK///4b+/fvx7Nnz6Crq6uwvY4dO6KgoADHjx8XvyAlJiZCSUkJiYmJYrnz588jKysLnTp1UlhPaeehyPHjxxEVFYUxY8ZAW1sby5cvR58+fZCamgpDQ8MSz8nevXsBAF999VWZzmF4eDh8fX3RqlUrLFiwAP/88w9++uknnDhxQjxfRV6/fg13d3d06NABS5Ysgaamprjt6NGj+O9//4uxY8eiVq1asLKywj///IO2bduKCbKRkREOHjwIPz8/ZGZmKvwyXeR9rydF/vnnH7Rv3x7Z2dkYP348DA0NsXnzZnzxxRf4+eef0bt3b7nyCxcuhJKSEqZOnYqMjAwsXrwYgwcPfmdC1rRpU8yePRvh4eHo2bMnTE1NMWzYMAwfPhz16tV7xzsi7/z58wCAli1byq1v0aIFlJSUcP78eQwZMqRcdZbHb7/9Bk9PT9jY2CAoKAgvX77EihUr4OzsjHPnzhUbkqvob7KsUlJSsGnTJoSHh+PevXuwsLDA9OnT4ezsXKHYq/PcdejQARKJBAkJCeIw16LPiuPHj4vlHj16hGvXrpU46V2nTp0wfvz4YsNn3xxGe/PmTfTt2xd+fn4YNmwYNm3aBB8fH7Ro0QKOjo7ljv3p06d4+vQp7OzsABT+APr69esyf6aUJDc3F7/88gumTJkCABg4cCB8fX3x4MGD9/6RpjS3b9+GsrIy9PT0cOPGDVy7dg3Dhw8Xh15XVEREBFxdXWFiYoIBAwZg5syZ2Ldvn/iDxb/l5cuXcHFxwc2bNzF27FhYW1tj165d8PHxwbNnzzBhwgQYGRlh9erVGD16NHr37i3+CFN0bZbl87bGqe4uaCIiqhpFw5kULYIgCM+fPxf09PSEkSNHyu334MEDQVdXV259dnZ2sfp37NghABASEhLEdSUNWS1tGCzeGrY1d+5cAYAwcOBAuXIpKSmCsrKyMG/ePLn1ly5dElRUVMT158+fFwAIu3btKvnkKJCfny/o6OgI06dPFwRBEAoKCgRDQ0OhX79+grKysvD8+XNBEAThhx9+EJSUlISnT5+WeAzvGgKtpqYm3Lx5U1x34cIFAYCwYsWKUmNs1qyZoKurW6bjefXqlWBsbCw4OTnJDVnev3+/AECYM2eOuG7YsGECAGHmzJkK41VSUhIuX74st97Pz08wNTUVHj9+LLd+wIABgq6urnjNKHrv3/d6EoTC4aHDhg0TX0+cOFEAIDc0/Pnz54K1tbVgZWUl5OfnC4IgCHFxcQIAwcHBQcjNzRXL/vTTTwIA4dKlS8XaUqSgoEA4evSoMGTIEEFDQ0MAIHTq1EnYvHmzwuNTJCAgQFBWVla4zcjISBgwYECZ6hGEig3jbdq0qWBsbCykp6eL6y5cuCAoKSkJQ4cOFdeV9Df5Ljk5OcKOHTsENzc3QSKRCFKpVOjfv79w+PBh8f2oqOo+d46OjoK3t7f4unnz5kK/fv0EAMLVq1cFQRCEqKgoAYBw4cIFsdzb1+27hkC//Tfx8OFDQSqVClOmTHlnjAAEPz8/4dGjR8LDhw+FP//8U+jatasAQFi6dKkgCIIwadIkAYBw/vz5Mh13SUOgf/75ZwGAcOPGDUEQBCEzM1NQV1dXOKxaECo2BLpBgwbCo0ePhEePHglXr14Vxo8fLwAQvLy8BEEQhD179pQ4lFuRkoZA//PPP4KKioqwfv16cV379u2Fnj17KqynKodAL1u2TAAgbNu2TVz36tUroV27doJMJhMyMzMFQSh9CHRZP285BJqIiD4Z//nPf3DkyBG5BSj8VfjZs2cYOHAgHj9+LC7Kyspo06YN4uLixDo0NDTEf+fk5ODx48do27YtAODcuXNVEre/v7/c66ioKBQUFMDb21suXhMTE9SrV0+Mt6iH9/Dhw8jOzi5ze0pKSmjfvj0SEhIAAFevXkV6ejpmzpwJQRDw+++/Ayjs6XFycnqvya3c3Nxga2srvm7cuDF0dHRw+/btUvfLzMwsc8/GmTNn8PDhQ4wZMwbq6uri+h49eqBBgwYKh4iOHj1aYV2dO3dGw4YNxdeCIOCXX36Bl5cXBEGQez/c3d2RkZFR6nVRFddTdHQ0WrduLTc0VyaTYdSoUUhJScGVK1fkyvv6+kJNTU183bFjRwB453tQRCKRwNXVFVu3bsWDBw+wZs0a5ObmYtiwYTA1NcXo0aPx9OnTUut4+fKlXAxvUldXx8uXL8sUS0WkpaUhKSkJPj4+MDAwENc3btwY3bp1Q3R0dLF93v6bLMmLFy8wYcIEmJmZYeDAgXj69ClWrFiBtLQ0REZG4rPPPnvvCcyq89wBhddL0ciQ58+f48KFCxg1ahRq1aolrk9MTISent57TTrYsGFD8doECkdA1K9fv8zX6caNG2FkZARjY2O0adMGJ06cwOTJk8URGpmZmQBQKT2mLVu2FHuWi26nqcxh0NeuXYORkRGMjIzg4OCAFStWoEePHuJtF5V1LJGRkVBSUkKfPn3EdQMHDsTBgwff+Tdd2aKjo2FiYoKBAweK61RVVTF+/HhkZWXh2LFj76yjOv7//tBxCDQR0SeudevWxYYJAoX3yQJAly5dFO6no6Mj/vvJkycIDg5GZGQkHj58KFeutPtq38fbM1ffuHEDgiCUOMy06H4va2trTJ48GT/88AMiIiLQsWNHfPHFFxgyZEiJw5+LdOzYURwKmpiYCFNTUzRv3hxNmjRBYmIiunXrhuPHj8Pb2/u9jq1u3brF1unr67/zy1VZkuQid+/eBQDUr1+/2LYGDRrIDdUEABUVlRLv83v7vXj06BGePXuGdevWYd26dQr3efs6eVNVXE93795FmzZtiq0vGk569+5duUTk7fdAX18fACr0BVdHRwdff/01hg0bhnnz5mHevHlYs2YNvv76a7FeRTQ0NPDq1SuF23JycuS+uFa20q4PBwcHHD58GC9evICWlpa4/u3roCSPHj3C8uXLAQBTp05FcHCw3JD68nj06BHy8/PF1zKZTHz0V3WdO6Dws2LNmjW4efMmbt26BYlEgnbt2omJ8ciRI5GYmAhnZ+f3SvYr+llRpGfPnhg7diwkEgm0tbXh6Ogo954Wfc4/f/68wjE+e/YM0dHRGDt2rNx99M7Ozvjll19w/fp12Nvbv7OerKwsZGVlia+VlZXlbnmwsrLC+vXrIZFIxMdeGRsbV+qxAIXzTLRu3Rrp6enifbLNmjXDq1evsGvXLowaNeq96i+Pu3fvol69esWuoTc/196lOv7//tAxASYiqqGKnmu4detWhfdovflYH29vb5w8eRLTpk1D06ZNIZPJUFBQAA8PD4XPYHxbSfcYvfnF9m1vf4EtKCiARCLBwYMHoaysXKy8TCYT/7106VL4+Phgz549iImJwfjx47FgwQL88ccfpU7m0qFDB+Tl5eH3339HYmKi2PNS9KX22rVrePTokVyPTEUoih/AO++rbNCgAc6fPy/eQ1mZpFJpiV/UFb0XADBkyBAMGzZM4T6lPQLkfa+nylDR90CR06dPY9OmTYiMjMSzZ8/Qpk0b+Pn5lfpIFwAwNTVFfn4+Hj58KPdF/tWrV0hPT4eZmVm5Y6lKZU0qzc3NER4ejo0bN2LJkiVYu3Yt+vfvD19fX7Rv375cbbZq1UruS/7cuXMRFBRU7eeuaKRBQkICbt++jebNm0NLSwsdO3bE8uXLkZWVhfPnzxeblK683vc6NTc3Fx/fo0iDBg0AAJcuXULTpk3LHR9QONFebm4uli5diqVLlxbbHhERIT6ipzRLliyRK2dpaSk3eaCWllaZj6Wibty4gdOnTwOAwh9bIyIi/tUEuDJ8CJ+3HxomwERENVTREFxjY+NSv1Q8ffoUsbGxCA4Oxpw5c8T1RT3Ibyop0S3qBXtzhmWgbL9evxmvIAiwtrYuU29Co0aN0KhRI8yaNQsnT56Es7Mz1qxZg9DQ0BL3ad26NdTU1JCYmIjExERxFuJOnTph/fr1iI2NFV+XpqomFfHy8sKOHTuwbds2BAYGllrW0tISQOFzR9/u5U9OTha3V4SRkRG0tbWRn59f6rWjSGVcT4pYWloiOTm52Ppr166J2yvTw4cPsXXrVoSFheHy5cswNDSEj48P/Pz8yjzktSjhOHPmDLp37y6uP3PmDAoKCiqckJTFm9fH265du4ZatWrJ9RSWh4qKCoYNG4Zhw4bh+vXr2LBhA7Zs2YINGzbA3t4evr6+GDp0aJmS1IiICLnhzDY2NgCq99wBhT2zdevWRWJiIm7fvi3+KNapUydMnjwZu3btQn5+frV9VpSVp6cnlJWVsW3btgpPhBUREQEnJyfMnTu32La1a9di+/btZUqAhw4dKncLQ3l78e3t7VG/fn3s2bMHP/30k9yPomUVEREBVVVVbN26tdiPD8ePH8fy5cuRmpqqsGe+KlhaWuLixYsoKCiQ+4Hy7c+1kq6j8nze1iS8B5iIqIZyd3eHjo4O5s+fj7y8vGLbi2ZuLvoS8HaPw7Jly4rtU/SF+e1EV0dHB7Vq1RLvry2yatWqMsf75ZdfQllZGcHBwcViEQRBHKqWmZlZ7NE0jRo1gpKSEnJzc0ttQ11dHa1atcKOHTuQmpoq1wP88uVLLF++HLa2tjA1NS21npLOw/vq27cvGjVqhHnz5on3JL/p+fPn+PbbbwEUzo5rbGws3pta5ODBg7h69Sp69OhR4TiUlZXRp08f/PLLL/jf//5XbHtps35XxvWkSPfu3XHq1Cm58/LixQusW7cOVlZWcvcwv4979+6hV69eqFOnDqZNmwZTU1NERkbi/v37+PHHH8t1v2eXLl1gYGCA1atXy61fvXo1NDU15d6jx48f49q1a+W6r700pqamaNq0KTZv3ix3fv/3v/8hJiZGLql8H/b29li8eDH++usvREVFwc7ODrNmzULdunXRvXv3d34Rd3Z2hpubm7gUJcDVee6KdOzYEUePHsWpU6fEz4qmTZtCW1sbCxcuhIaGBlq0aFFqHVX1WVFWFhYWGDlyJGJiYrBixYpi2wsKCrB06VL89ddfCve/d+8eEhIS4O3tjb59+xZbfH19cfPmzTI97srGxkbuva7IDOHBwcFIT0/HiBEjFD6iLCYmBvv37y9x/6LbZvr371/sWIp+EN2xY0e546qo7t2748GDB9i5c6e47vXr11ixYgVkMhk6d+4MAOItBm9fR+X5vK1J2ANMRFRD6ejoYPXq1fjqq6/QvHlzDBgwAEZGRkhNTcWBAwfg7OyMlStXQkdHB506dcLixYuRl5eHOnXqICYmRnyO5JuKvux9++23GDBgAFRVVeHl5QUtLS2MGDECCxcuxIgRI9CyZUskJCTg+vXrZY7X1tYWoaGhCAwMREpKCnr16gVtbW3cuXMHu3fvxqhRozB16lQcPXoUY8eORb9+/WBvb4/Xr1+Lv+a/OalJSTp27IiFCxdCV1cXjRo1AlDYS16/fn0kJycXe/asIqWdh/ehqqqKqKgouLm5oVOnTvD29oazszNUVVVx+fJlbN++Hfr6+pg3bx5UVVWxaNEi+Pr6onPnzhg4cKD4GCQrKytMmjTpvWJZuHAh4uLi0KZNG4wcORINGzbEkydPcO7cOfz222948uSJwv0q63p628yZM7Fjxw54enpi/PjxMDAwwObNm3Hnzh388ssv7z3pUpFbt27h3LlzCAwMxPDhw4s9Kqg8NDQ08N133yEgIAD9+vWDu7s7EhMTsW3bNsybN09ucqqVK1ciODgYcXFxcHFxkVv/7Nkz3L9/HwCwb98+MVkZN25cqfe9f//99/D09ES7du3g5+cnPgZJV1dX7tnclUFFRQW9e/dG79698ffffyMsLAybNm3C5cuXy/34KKD6zx1Q+FkREREBiUQi9lwqKyujffv2OHz4MFxcXEqcqKtI06ZNoaysjEWLFiEjIwNSqRRdunSRG9Zd1ZYuXYpbt25h/PjxiIqKwueffw59fX2kpqZi165duHbtGgYMGKBw3+3bt0MQBHzxxRcKt3fv3h0qKiqIiIhQeI9+Zevfvz8uXbqEefPm4fz58xg4cCAsLS2Rnp6OQ4cOITY2Ftu3b1e4759//ik+bkiROnXqoHnz5oiIiJB7DnxVGjVqFNauXQsfHx+cPXsWVlZW+Pnnn3HixAksW7ZMnPBLQ0MDDRs2xM6dO2Fvbw8DAwM4OTnBycmpzJ+3NUp1TD1NRERVr6THVbwtLi5OcHd3F3R1dQV1dXXB1tZW8PHxEc6cOSOW+euvv4TevXsLenp6gq6urtCvXz/h/v37Ch+78N133wl16tQRlJSU5B6pkJ2dLfj5+Qm6urqCtra24O3tLTx8+LDExyA9evRIYby//PKL0KFDB0FLS0vQ0tISGjRoIAQEBAjJycmCIAjC7du3heHDhwu2traCurq6YGBgILi6ugq//fZbmc7bgQMHBACCp6en3PoRI0YIAISNGzcW26c85wGAEBAQUKyOtx+PUpqnT58Kc+bMERo1aiRoamoK6urqgpOTkxAYGCikpaXJld25c6fQrFkzQSqVCgYGBsLgwYOFv/76S67MsGHDBC0tLYVtlRSvIBQ+LiQgIECwsLAQVFVVBRMTE6Fr167CunXrxDKKHoNUGdeTovN169YtoW/fvoKenp6grq4utG7dWti/f79cmaLHIL39mKzSHtX1puzs7Pd+hM/b1q1bJ9SvX19QU1MTbG1thR9//FEoKCiQK1P0d/H2o1aKHpWjaCnL40x+++03wdnZWdDQ0BB0dHQELy8v4cqVKwrbLulvsqIKCgqEFy9evFcd1XnuLl++LD5S602hoaECAGH27NnF9lF03a5fv16wsbERlJWV5eK0tLQUevToUayOzp07C507d35nfKX97b7t9evXwoYNG4SOHTsKurq6gqqqqmBpaSn4+vrKPSLp7f9XGjVqJNStW7fUul1cXARjY2MhLy9PXFeRxyA5OjqWuXxsbKzQs2dPwdjYWFBRURGMjIwELy8vYc+ePWKZtx+DNG7cOAGAcOvWrRLrDQoKKvZoq6p8DJIgFH7O+vr6CrVq1RLU1NSERo0aKfycOnnypNCiRQtBTU1N7rO0rJ+3NekxSBJBqMBsD0REREREREQfGd4DTERERERERDUC7wEmIiIiIiKqRBkZGXIzmCui6BGEVPU4BJqIiIiIiKgS+fj4YPPmzaWWYRpWPZgAExERERERVaIrV66IM4yXpLzPUafKwQSYiIiIiIiIagROgkVEREREREQ1AifBIiIqRUFBAe7fvw9tbW1IJJLqDoeIiIiI3iIIAp4/fw4zMzMoKZXex8sEmIioFPfv34eFhUV1h0FERERE73Dv3j2Ym5uXWoYJMBFRKbS1tQEUfqDq6OhUczRERERE9LbMzExYWFiI39tKwwSYiKgURcOedXR0mAATERERfcDKcrsaJ8EiIiIiIiKiGoEJMBEREREREdUITICJiIiIiIioRmACTERERERERDUCJ8EiIiqLBYMAqWp1R0FERET04QvaXd0RlIg9wERERERERFQjMAEm+peFh4dDT0+vyuqPj4+HRCLBs2fPKqW+lJQUSCQSJCUlVUp97xIUFISmTZuWax8XFxdMnDixSuIhIiIiok8HE2CiKuDj4wOJRAKJRAI1NTXY2dkhJCQEr1+/rvK227dvj7S0NOjq6lZ5W0VKS7qtrKywbNmyMtc1depUxMbGVl5w/59EIsGvv/5a6fUSERER0ceD9wATVREPDw+EhYUhNzcX0dHRCAgIgKqqKkxNTau0XTU1NZiYmFRpG1VJJpNBJpNVdxhERERE9AliDzBRFZFKpTAxMYGlpSVGjx4NNzc37N27V9x++PBhODg4QCaTwcPDA2lpaQCAhIQEqKqq4sGDB3L1TZw4ER07dgQA3L17F15eXtDX14eWlhYcHR0RHR0NQHFv7IkTJ+Di4gJNTU3o6+vD3d0dT58+BQAcOnQIHTp0gJ6eHgwNDfH555/j1q1bVXZenj17hhEjRsDIyAg6Ojro0qULLly4IG5/ewj069evMX78eDG+GTNmYNiwYejVq5dcvQUFBZg+fToMDAxgYmKCoKAgcZuVlRUAoHfv3pBIJOJrIiIiIqpZmAAT/Us0NDTw6tUrAEB2djaWLFmCrVu3IiEhAampqZg6dSoAoFOnTrCxscHWrVvFffPy8hAREYHhw4cDAAICApCbm4uEhARcunQJixYtKrHXNCkpCV27dkXDhg3x+++/4/jx4/Dy8kJ+fj4A4MWLF5g8eTLOnDmD2NhYKCkpoXfv3igoKKiS89CvXz88fPgQBw8exNmzZ9G8eXN07doVT548UVh+0aJFiIiIQFhYGE6cOIHMzEyFQ5k3b94MLS0t/Pnnn1i8eDFCQkJw5MgRAMDp06cBAGFhYUhLSxNfK5Kbm4vMzEy5hYiIiIg+DRwCTVTFBEFAbGwsDh8+jHHjxgEoTGjXrFkDW1tbAMDYsWMREhIi7uPn54ewsDBMmzYNALBv3z7k5OTA29sbAJCamoo+ffqgUaNGAAAbG5sS21+8eDFatmyJVatWiescHR3Ff/fp00eu/KZNm2BkZIQrV67AycmpXMdqbm5ebF12drb47+PHj+PUqVN4+PAhpFIpAGDJkiX49ddf8fPPP2PUqFHF9l+xYgUCAwPRu3dvAMDKlSvF3u43NW7cGHPnzgUA1KtXDytXrkRsbCy6desGIyMjAICent47h4cvWLAAwcHBZTxiIiIiIvqYsAeYqIrs378fMpkM6urq8PT0RP/+/cVhuZqammLyCwCmpqZ4+PCh+NrHxwc3b97EH3/8AaBw5mhvb29oaWkBAMaPH4/Q0FA4Oztj7ty5uHjxYolxFPUAl+TGjRsYOHAgbGxsoKOjIw4PTk1NVVje0dFRvE/X09NTbltiYiKSkpLkFjMzM3H7hQsXkJWVBUNDQ7EOmUyGO3fuKBx2nZGRgX/++QetW7cW1ykrK6NFixbFyjZu3Fju9dvntKwCAwORkZEhLvfu3St3HURERET0YWIPMFEVcXV1xerVq6GmpgYzMzOoqPzfn5uqqqpcWYlEAkEQxNfGxsbw8vJCWFgYrK2tcfDgQcTHx4vbR4wYAXd3dxw4cAAxMTFYsGABli5dKvYwv0lDQ6PUOL28vGBpaYn169fDzMwMBQUFcHJyEodrvy06Ohp5eXkK67a2ti72iKc3jzsrKwumpqZyx1LkfR8NpeicVmQYt1QqFXuniYiIiOjTwh5goiqipaUFOzs71K1bVy4JLKsRI0Zg586dWLduHWxtbeHs7Cy33cLCAv7+/oiKisKUKVOwfv16hfU0bty4xMcKpaenIzk5GbNmzULXrl3h4OAgTo5VEktLS9jZ2cHOzg516tQp1zE1b94cDx48gIqKilhH0VKrVq1i5XV1dVG7dm25e3bz8/Nx7ty5crULFCbIRfc9ExEREVHNxASY6APl7u4OHR0dhIaGwtfXV27bxIkTcfjwYdy5cwfnzp1DXFwcHBwcFNYTGBiI06dPY8yYMbh48SKuXbuG1atX4/Hjx9DX14ehoSHWrVuHmzdv4ujRo5g8eXKVHZObmxvatWuHXr16ISYmBikpKTh58iS+/fZbnDlzRuE+48aNw4IFC7Bnzx4kJydjwoQJePr0KSQSSbnatrKyQmxsLB48ePDOJJ+IiIiIPk1MgIk+UEpKSvDx8UF+fj6GDh0qty0/Px8BAQFwcHCAh4cH7O3t5Sa5epO9vT1iYmJw4cIFtG7dGu3atcOePXugoqICJSUlREZG4uzZs3BycsKkSZPw/fffV9kxSSQSREdHo1OnTvD19YW9vT0GDBiAu3fvonbt2gr3mTFjBgYOHIihQ4eiXbt2kMlkcHd3h7q6ernaXrp0KY4cOQILCws0a9asMg6HiIiIiD4yEuHNGw+J6IPi5+eHR48eyT0/uKYrKCiAg4MDvL298d1331V5e5mZmdDV1UXGzB7Qkaq+ewciIiKimi5o97/anPh9LSMDOjo6pZblJFhEH6CMjAxcunQJ27dvr/HJ7927dxETE4POnTsjNzcXK1euxJ07dzBo0KB/N5DA7cA7PlCJiIiI6MPGBJjoA9SzZ0+cOnUK/v7+6NatW3WHU62UlJQQHh6OqVOnQhAEODk54bfffivxnmciIiIiopJwCDQRUSnKM6SGiIiIiP595fm+xkmwiIiIiIiIqEZgAkxEREREREQ1AhNgIiIiIiIiqhGYABMREREREVGNwASYiIiIiIiIagQmwERERERERFQjMAEmIiIiIiKiGoEJMBEREREREdUITICJiIiIiIioRmACTERERERERDUCE2AiIiIiIiKqEZgAExERERERUY2gUt0BEBF9FBYMAqSq1R0FERERUdUL2l3dEVQZ9gATERERERFRjcAEmKgahIeHQ09Pr7rDICIiIiKqUZgAEyng4+MDiUQiLoaGhvDw8MDFixcrpf7+/fvj+vXrlVKXi4uLGKdUKkWdOnXg5eWFqKioSqm/Ou3YsQPKysoICAio7lCIiIiI6BPABJioBB4eHkhLS0NaWhpiY2OhoqKCzz//vFLq1tDQgLGxcaXUBQAjR45EWloabt26hV9++QUNGzbEgAEDMGrUqEprozps3LgR06dPx44dO5CTk1Pd4RARERHRR44JMFEJpFIpTExMYGJigqZNm2LmzJm4d+8eHj16BACYMWMG7O3toampCRsbG8yePRt5eXni/hcuXICrqyu0tbWho6ODFi1a4MyZMwAUD4Het28fWrVqBXV1ddSqVQu9e/cuc6yampowMTGBubk52rZti0WLFmHt2rVYv349fvvtN7HcvXv34O3tDT09PRgYGKBnz55ISUkRt/v4+KBXr16YP38+ateuDT09PYSEhOD169eYNm0aDAwMYG5ujrCwMLn2L126hC5dukBDQwOGhoYYNWoUsrKyxO3x8fFo3bo1tLS0oKenB2dnZ9y9e7fUY7pz5w5OnjyJmTNnwt7eXmGP9vr162FhYQFNTU307t0bP/zwQ7HzumfPHjRv3hzq6uqwsbFBcHAwXr9+XeZzS0RERESfDibARGWQlZWFbdu2wc7ODoaGhgAAbW1thIeH48qVK/jpp5+wfv16/Pjjj+I+gwcPhrm5OU6fPo2zZ89i5syZUFVVPIvwgQMH0Lt3b3Tv3h3nz59HbGwsWrdu/V4xDxs2DPr6+mLimJeXB3d3d2hrayMxMREnTpyATCaDh4cHXr16Je539OhR3L9/HwkJCfjhhx8wd+5cfP7559DX18eff/4Jf39/fP311/jrr78AAC9evIC7uzv09fVx+vRp7Nq1C7/99hvGjh0LAHj9+jV69eqFzp074+LFi/j9998xatQoSCSSUuMPCwtDjx49oKuriyFDhmDjxo1y20+cOAF/f39MmDABSUlJ6NatG+bNmydXJjExEUOHDsWECRNw5coVrF27FuHh4cXKvSk3NxeZmZlyCxERERF9GiSCIAjVHQTRh8bHxwfbtm2Duro6gMIkz9TUFPv370fz5s0V7rNkyRJERkaKvbw6OjpYsWIFhg0bVqxseHg4Jk6ciGfPngEA2rdvDxsbG2zbtq3csbq4uKBp06ZYtmxZsW1t27aFgYEBoqOjsW3bNoSGhuLq1ati8vnq1Svo6enh119/xWeffQYfHx/Ex8fj9u3bUFIq/H2sQYMGMDY2RkJCAgAgPz8furq62LBhAwYMGID169djxowZuHfvHrS0tAAA0dHR8PLywv3796GqqgpDQ0PEx8ejc+fOZTqmgoICWFlZYcWKFejZsyceP36MOnXq4Nq1a7C2tgYADBgwAFlZWdi/f7+435AhQ7B//37xvLq5uaFr164IDAwUy2zbtg3Tp0/H/fv3FbYdFBSE4ODgYuszZvaADh+DRERERDXBR/YYpMzMTOjq6iIjIwM6OjqllmUPMFEJXF1dkZSUhKSkJJw6dQru7u7w9PQUh+7u3LkTzs7OMDExgUwmw6xZs5CamiruP3nyZIwYMQJubm5YuHAhbt26VWJbSUlJ6Nq1a6UfgyAIYrJ74cIF3Lx5E9ra2pDJZJDJZDAwMEBOTo5cbI6OjmLyCwC1a9dGo0aNxNfKysowNDTEw4cPAQBXr15FkyZNxOQXAJydnVFQUIDk5GQYGBjAx8cH7u7u8PLywk8//YS0tDQAQGpqqhiLTCbD/PnzAQBHjhzBixcv0L17dwBArVq10K1bN2zatElsIzk5uVgv+duvL1y4gJCQELk2iu6Xzs7OVnjOAgMDkZGRIS737t0r49kmIiIiog+dSnUHQPSh0tLSgp2dnfh6w4YN0NXVxfr169GjRw8MHjwYwcHBcHd3h66uLiIjI7F06VKxfFBQEAYNGoQDBw7g4MGDmDt3LiIjIxXe26uhoVHp8efn5+PGjRto1aoVgMJh3C1atEBERESxskZGRuK/3x6mLZFIFK4rKCgocyxhYWEYP348Dh06hJ07d2LWrFk4cuQIWrZsiaSkJLGcgYEBgMLJr548eSJ3XgoKCnDx4kUEBwfLJeilycrKQnBwML788sti24p6998mlUohlUrLfGxERERE9PFgAkxURhKJBEpKSnj58iVOnjwJS0tLfPvtt+J2RZM62dvbw97eHpMmTcLAgQMRFhamMAFu3LgxYmNj4evrW2nxbt68GU+fPkWfPn0AAM2bN8fOnTthbGz8zqEh5eHg4IDw8HC8ePFC7AU+ceIElJSUUL9+fbFcs2bN0KxZMwQGBqJdu3bYvn072rZtK/cjAwCkp6djz549iIyMhKOjo7g+Pz8fHTp0QExMDDw8PFC/fn2cPn1abt+3Xzdv3hzJycnF2iAiIiKimolDoIlKkJubiwcPHuDBgwe4evUqxo0bh6ysLHh5eaFevXpITU1FZGQkbt26heXLl2P37v+7V+Lly5cYO3Ys4uPjcffuXZw4cQKnT5+Gg4ODwrbmzp2LHTt2YO7cubh69SouXbqERYsWlTnW7OxsPHjwAH/99Rf++OMPzJgxA/7+/hg9ejRcXV0BFE7KVatWLfTs2ROJiYm4c+cO4uPjMX78eHFCq4oYPHgw1NXVMWzYMPzvf/9DXFwcxo0bh6+++gq1a9fGnTt3EBgYiN9//x13795FTEwMbty4UeK52Lp1KwwNDeHt7Q0nJydxadKkCbp37y5OhjVu3DhER0fjhx9+wI0bN7B27VocPHhQbnKtOXPmYMuWLQgODsbly5dx9epVREZGYtasWRU+XiIiIiL6eDEBJirBoUOHYGpqClNTU7Rp00ac4djFxQVffPEFJk2ahLFjx6Jp06Y4efIkZs+eLe6rrKyM9PR0DB06FPb29vD29oanp6fCyZWAwomsdu3ahb1796Jp06bo0qULTp06VeZY169fD1NTU9ja2uLLL7/ElStXsHPnTqxatUoso6mpiYSEBNStWxdffvklHBwc4Ofnh5ycnPfqEdbU1MThw4fx5MkTtGrVCn379kXXrl2xcuVKcfu1a9fQp08f2NvbY9SoUQgICMDXX3+tsL5Nmzahd+/eCmeJ7tOnD/bu3YvHjx/D2dkZa9aswQ8//IAmTZrg0KFDmDRpktzQZnd3d+zfvx8xMTFo1aoV2rZtix9//BGWlpYVPl4iIiIi+nhxFmgi+mSMHDkS165dQ2JiYqXVKc4qyFmgiYiIqKb4hGeB5j3ARPTRWrJkCbp16wYtLS0cPHgQmzdvluv1rlSB24FKvHeaiIiIiP59TICJPmCJiYnw9PQscXtWVta/GM2H59SpU1i8eDGeP38OGxsbLF++HCNGjKjusIiIiIjoA8UEmOgD9vZjgkjef//73+oOgYiIiIg+IkyAiT5gGhoafIQPEREREVEl4SzQREREREREVCMwASYiIiIiIqIagQkwERERERER1QhMgImIiIiIiKhGYAJMRERERERENQITYCIiIiIiIqoRmAATERERERFRjcAEmIiIiIiIiGoEJsBERERERERUIzABJiIiIiIiohqBCTARERERERHVCCrVHQAR0UdhwSBAqlrdURAREdGHLGh3dUdA78AeYCIiIiIiIqoRmAD/i3x8fNCrV6/qDoMUCAoKQtOmTas7jH/dx3ZNuri4YOLEidUdBhERERF9pKo1Afbx8YFEIoFEIoGamhrs7OwQEhKC169fV7jOiiQyVlZWWLZsWZnLx8fHi3G/vTx48KB8AVcwtpqasBUpeg+ePXtWKfVNnToVsbGx5dqnvNdNVUtJSYFEIkFSUlJ1h0JERERE9EGq9nuAPTw8EBYWhtzcXERHRyMgIACqqqoIDAyUK/fq1SuoqalVU5SKJScnQ0dHR26dsbFxNUVDFSEIAvLz8yGTySCTyaolhg/x2q5qNfGYiYiIiKj6VfsQaKlUChMTE1haWmL06NFwc3PD3r17xaGZ8+bNg5mZGerXrw8AuHfvHry9vaGnpwcDAwP07NkTKSkpJdZfVM+SJUtgamoKQ0NDBAQEIC8vD0DhkMq7d+9i0qRJYi9uWRkbG8PExERuUVIqPKX5+fmYPHky9PT0YGhoiOnTp0MQBLn9FfUgNm3aFEFBQWWOAQBycnLg6OiIUaNGietu3boFbW1tbNq0qdR99+3bh1atWkFdXR21atVC7969xW1Pnz7F0KFDoa+vD01NTXh6euLGjRvi9vDwcOjp6WH//v2oX78+NDU10bdvX2RnZ2Pz5s2wsrKCvr4+xo8fj/z8fLnj/u677zBw4EBoaWmhTp06+M9//iNuV9ST+ezZM0gkEsTHxyMlJQWurq4AAH19fUgkEvj4+AAACgoKsGDBAlhbW0NDQwNNmjTBzz//LNZT1HN88OBBtGjRAlKpFMePHy/Wo/4+183x48fRsWNHaGhowMLCAuPHj8eLFy+KHf/QoUOho6ODUaNGiefy8OHDcHBwgEwmg4eHB9LS0uTerw0bNsDBwQHq6upo0KABVq1aJW6ztrYGADRr1gwSiQQuLi6lvvdvKuk4gXdfB4pGIyxbtgxWVlbFzufbf8+rVq1CvXr1oK6ujtq1a6Nv377iPi9evMDQoUMhk8lgamqKpUuXFot769ataNmyJbS1tWFiYoJBgwbh4cOHAAp/3LCzs8OSJUvk9klKSoJEIsHNmzfLfH6IiIiI6NNQ7Qnw2zQ0NPDq1SsAQGxsLJKTk3HkyBHs378feXl5cHd3h7a2NhITE3HixAkxUSjaR5G4uDjcunULcXFx2Lx5M8LDwxEeHg4AiIqKgrm5OUJCQpCWllYs4aiopUuXIjw8HJs2bcLx48fx5MkT7N5dNbPCqaurIyIiAps3b8aePXuQn5+PIUOGoFu3bhg+fHiJ+x04cAC9e/dG9+7dcf78ecTGxqJ169bidh8fH5w5cwZ79+7F77//DkEQ0L17d7nkKDs7G8uXL0dkZCQOHTqE+Ph49O7dG9HR0YiOjsbWrVuxdu1auSQUAL7//ns0adIE58+fx8yZMzFhwgQcOXKkTMdrYWGBX375BUBhL3xaWhp++uknAMCCBQuwZcsWrFmzBpcvX8akSZMwZMgQHDt2TK6OmTNnYuHChbh69SoaN26ssJ2KXDe3bt2Ch4cH+vTpg4sXL2Lnzp04fvw4xo4dK1f3kiVLxOOfPXu2eC6XLFmCrVu3IiEhAampqZg6daq4T0REBObMmYN58+bh6tWrmD9/PmbPno3NmzcDAE6dOgUA+O2335CWloaoqKgync/SjhMo23VQFm//PZ85cwbjx49HSEgIkpOTcejQIXTq1EksP23aNBw7dgx79uxBTEwM4uPjce7cObk68/Ly8N133+HChQv49ddfkZKSIv4YIpFIMHz4cISFhcntExYWhk6dOsHOzk5hnLm5ucjMzJRbiIiIiOjTUO1DoIsIgoDY2FgcPnwY48aNw6NHj6ClpYUNGzaIQyW3bduGgoICbNiwQexxCwsLg56eHuLj4/HZZ58prFtfXx8rV66EsrIyGjRogB49eiA2NhYjR46EgYEBlJWVxR6k8jA3N5d7bWlpicuXLwMo7AELDAzEl19+CQBYs2YNDh8+XK76i8yYMQOzZs2SW/fq1Ss0bNhQfN20aVOEhoZixIgRGDBgAO7evYv9+/eXWu+8efMwYMAABAcHi+uaNGkCALhx4wb27t2LEydOoH379gAKEzALCwv8+uuv6NevH4DCBGT16tWwtbUFAPTt2xdbt27FP//8A5lMhoYNG8LV1RVxcXHo37+/2I6zszNmzpwJALC3t8eJEyfw448/olu3bu88H8rKyjAwMABQ2Auvp6cHoDBxmT9/Pn777Te0a9cOAGBjY4Pjx49j7dq16Ny5s1hHSEjIO9uqyHWzYMECDB48WJyoqV69eli+fDk6d+6M1atXQ11dHQDQpUsXTJkyRdwvMTEReXl5WLNmjXgux44di5CQELHM3LlzsXTpUvGasra2xpUrV7B27VoMGzYMRkZGAABDQ8NyXculHWdZr4OyePvvOSoqClpaWvj888+hra0NS0tLNGvWDACQlZWFjRs3Ytu2bejatSsAYPPmzcX+5t78gcfGxgbLly9Hq1atkJWVBZlMBh8fH8yZMwenTp1C69atkZeXh+3btxfrFX7TggUL5P4miIiIiOjTUe09wPv374dMJoO6ujo8PT3Rv39/cQhwo0aN5O4TvHDhAm7evAltbW3xnk0DAwPk5OTg1q1bJbbh6OgIZWVl8bWpqak4TPJ9JCYmIikpSVyio6MBABkZGUhLS0ObNm3EsioqKmjZsmWF2pk2bZpcO0lJSfD39y9WbsqUKbC3t8fKlSuxadMmGBoaituKzpdMJhP3TUpKEpOLt129ehUqKipyx2BoaIj69evj6tWr4jpNTU0xYQOA2rVrw8rKSu5+2tq1axc730UJ6puv36y3Im7evIns7Gx069ZN7ni3bNlS7Pooy3tRkevmwoULCA8Pl2vf3d0dBQUFuHPnTqntv30u32zvxYsXuHXrFvz8/OTqDg0NLfXaL4vSjrOs10FZvP333K1bN1haWsLGxgZfffUVIiIikJ2dDaCwJ/3Vq1dy7RoYGIhDp4ucPXsWXl5eqFu3LrS1tcUfOVJTUwEAZmZm6NGjh3grwL59+5Cbm1tq4h4YGIiMjAxxuXfvXrmOk4iIiIg+XNXeA+zq6orVq1dDTU0NZmZmUFH5v5C0tLTkymZlZaFFixaIiIgoVk9R75ciqqqqcq8lEgkKCgreM/LCHrii3seKUFJSKnZfsKJhpbVq1So2XLOoB/RNDx8+xPXr16GsrIwbN27Aw8ND3Pbm/bRFE3dpaGhUOPYiis7t+57vovuo3zw3ZRlum5WVBaBwaHedOnXktkmlUrnXb19bilTkOLKysvD1119j/PjxxbbVrVu31PYVtVd0DoqObf369XJJIQC55LUiKuP9Kst1/PYxa2tr49y5c4iPj0dMTAzmzJmDoKAgnD59ukztvnjxAu7u7nB3d0dERASMjIyQmpoKd3d3uVsiRowYga+++go//vgjwsLC0L9/f2hqapZYr1QqLXa9EBEREdGnodoTYC0trRLvxXtb8+bNsXPnThgbGxebffl9qKmpyU3S9L50dXVhamqKP//8U7yn8fXr1zh79iyaN28uljMyMpK75zgzM1Oul7C8hg8fjkaNGsHPzw8jR46Em5sbHBwcAEDhOW7cuDFiY2Ph6+tbbJuDgwNev36NP//8Uxz6mp6ejuTkZLmh1xX1xx9/FHtdFGvRjxlpaWnikNi3H+1T1JP45vvWsGFDSKVSpKamyg13riqKrpvmzZvjypUrZb6my6p27dowMzPD7du3MXjw4BLjAVCp13JZrgMjIyM8ePAAgiCItyaU9VFMKioqcHNzg5ubG+bOnQs9PT0cPXoUn332GVRVVfHnn3+KPxw8ffoU169fF9/ba9euIT09HQsXLoSFhQUA4MyZM8Xa6N69O7S0tLB69WocOnQICQkJ73VOiIiIiOjjVe0JcHkMHjwY33//PXr27ImQkBCYm5vj7t27iIqKwvTp04vdH1hWVlZWSEhIwIABAyCVSlGrVq0y7ffw4UPk5OTIrTM0NISqqiomTJiAhQsXol69emjQoAF++OGHYs+s7dKlC8LDw+Hl5QU9PT3MmTOnwr15//nPf/D777/j4sWLsLCwwIEDBzB48GD88ccfJT5uZu7cuejatStsbW0xYMAAvH79GtHR0ZgxYwbq1auHnj17YuTIkVi7di20tbUxc+ZM1KlTBz179qxQjG86ceIEFi9ejF69euHIkSPYtWsXDhw4AKCwZ7pt27ZYuHAhrK2t8fDhw2L3QFtaWkIikWD//v3o3r07NDQ0oK2tjalTp2LSpEkoKChAhw4dkJGRgRMnTkBHRwfDhg1777jfpOi6mTFjBtq2bYuxY8dixIgR0NLSwpUrV3DkyBGsXLnyvdoLDg7G+PHjoaurCw8PD+Tm5uLMmTN4+vQpJk+eDGNjY2hoaODQoUMwNzeHuro6dHV136vNslwHLi4uePToERYvXoy+ffvi0KFDOHjw4Dt/pNq/fz9u376NTp06QV9fH9HR0SgoKED9+vUhk8ng5+eHadOmwdDQEMbGxvj222/F0QFAYY+6mpoaVqxYAX9/f/zvf//Dd999V6wdZWVl+Pj4IDAwEPXq1Ss2/J6IiIiIao5qvwe4PDQ1NZGQkIC6deviyy+/hIODA/z8/JCTk/NePcIhISFISUmBra1tqUOp31a/fn2YmprKLWfPngVQeD/uV199hWHDhqFdu3bQ1taWe8QQUHivYefOnfH555+jR48e6NWrl9w9oGV17do1TJs2DatWrRJ7wlatWoXHjx+LMwwr4uLigl27dmHv3r1o2rQpunTpIs4kDBROMNaiRQt8/vnnaNeuHQRBQHR0dLEhsxUxZcoUnDlzBs2aNUNoaCh++OEHuLu7i9s3bdqE169fo0WLFpg4cSJCQ0Pl9q9Tpw6Cg4Mxc+ZM1K5dW5xl+bvvvsPs2bOxYMECODg4wMPDAwcOHBAfEVSZFF03jRs3xrFjx3D9+nV07NgRzZo1w5w5c2BmZvbe7Y0YMQIbNmxAWFgYGjVqhM6dOyM8PFw8NhUVFSxfvhxr166FmZlZpfxQAbz7OnBwcMCqVavwn//8B02aNMGpU6fkZq8uiZ6eHqKiotClSxc4ODhgzZo12LFjBxwdHQEUzhTesWNHeHl5wc3NDR06dECLFi3E/Y2MjBAeHo5du3ahYcOGWLhwYYmTW/n5+eHVq1cKRzsQERERUc0hEd6+eY+oillZWWHixIniTMlEVS0xMRFdu3bFvXv3ULt27XLtm5mZCV1dXWTM7AEd6fv/+ENERESfsKCqeewplU78vpaR8c6O0Y9qCDQRUXnk5ubi0aNHCAoKQr9+/cqd/MoJ3A5U4twDRERERPTv+6iGQP9bPD095R418+Yyf/786g6PqMxKuo5lMhkSExOrO7wqt2PHDlhaWuLZs2dYvHhxdYdDRERERNWMQ6AV+Pvvv/Hy5UuF2wwMDBQ+gojoQ3Tz5s0St9WpU6dSHoX1qSvPkBoiIiIi+vdxCPR7evsZskQfq8p+HBMRERER0ceMQ6CJiIiIiIioRmACTERERERERDUCE2AiIiIiIiKqEZgAExERERERUY3ABJiIiIiIiIhqBCbAREREREREVCMwASYiIiIiIqIagQkwERERERER1QhMgImIiIiIiKhGYAJMRERERERENQITYCIiIiIiIqoRVKo7ACKij8KCQYBUtbqjICIiorII2l3dEdAHij3AREREREREVCMwASb6l4SHh0NPT6/K6o+Pj4dEIsGzZ88qpb6UlBRIJBIkJSVVSn1lVZHz5OPjg169elVJPERERET06WACTFSJfHx8IJFIIJFIoKamBjs7O4SEhOD169dV3nb79u2RlpYGXV3dKm8LAGbOnIkGDRrIrbt27RokEgl8fHzk1oeHh0MqleLly5fvrLd///64fv16ZYYKALCyssKyZcsqvV4iIiIi+njwHmCiSubh4YGwsDDk5uYiOjoaAQEBUFVVhampaZW2q6amBhMTkypt402urq5YtGgRHjx4ILYbFxcHCwsLxMfHy5WNi4tD27ZtoaGh8c56NTQ0ylSOiIiIiKi82ANMVMmkUilMTExgaWmJ0aNHw83NDXv37hW3Hz58GA4ODpDJZPDw8EBaWhoAICEhAaqqqnjw4IFcfRMnTkTHjh0BAHfv3oWXlxf09fWhpaUFR0dHREdHA1A8BPrEiRNwcXGBpqYm9PX14e7ujqdPnwIADh06hA4dOkBPTw+Ghob4/PPPcevWrTIfZ4cOHaCqqiqX7MbHxyMgIABPnjxBSkqK3HpXV1cAQG5uLqZOnYo6depAS0sLbdq0katD0RDo0NBQGBsbQ1tbGyNGjMDMmTPRtGnTYjEtWbIEpqamMDQ0REBAAPLy8gAALi4uuHv3LiZNmiT20BMRERFRzcMEmKiKaWho4NWrVwCA7OxsLFmyBFu3bkVCQgJSU1MxdepUAECnTp1gY2ODrVu3ivvm5eUhIiICw4cPBwAEBAQgNzcXCQkJuHTpEhYtWgSZTKaw3aSkJHTt2hUNGzbE77//juPHj8PLywv5+fkAgBcvXmDy5Mk4c+YMYmNjoaSkhN69e6OgoKBMx6WlpYVWrVohLi5OXBcfH4+uXbvC2dlZXH/79m2kpqaKCfDYsWPx+++/IzIyEhcvXkS/fv3g4eGBGzduKGwnIiIC8+bNw6JFi3D27FnUrVsXq1evLlYuLi4Ot27dQlxcHDZv3ozw8HCEh4cDAKKiomBubo6QkBCkpaWJPzookpubi8zMTLmFiIiIiD4NHAJNVEUEQUBsbCwOHz6McePGAShMaNesWQNbW1sAhclgSEiIuI+fnx/CwsIwbdo0AMC+ffuQk5MDb29vAEBqair69OmDRo0aAQBsbGxKbH/x4sVo2bIlVq1aJa5zdHQU/92nTx+58ps2bYKRkRGuXLkCJyenMh2jq6srdu3aBQC4cuUKcnJy0KxZM3Tq1Anx8fHw9fVFfHw81NXV0bZtW6SmpiIsLAypqakwMzMDAEydOhWHDh1CWFgY5s+fX6yNFStWwM/PD76+vgCAOXPmICYmBllZWXLl9PX1sXLlSigrK6NBgwbo0aMHYmNjMXLkSBgYGEBZWRna2trvHCa+YMECBAcHl+n4iYiIiOjjwh5gokq2f/9+yGQyqKurw9PTE/3790dQUBAAQFNTU0x+AcDU1BQPHz4UX/v4+ODmzZv4448/ABQOB/b29oaWlhYAYPz48QgNDYWzszPmzp2LixcvlhhHUQ9wSW7cuIGBAwfCxsYGOjo6sLKyAlCYZCvi6OgImUwGmUwGT09PAIVDi69fv460tDTEx8ejQ4cOUFZWRufOncVhzfHx8Wjfvj2kUikuXbqE/Px82Nvbi3XJZDIcO3asxOHXycnJaN26tdy6t18XxaesrCy+fvvcllVgYCAyMjLE5d69e+Wug4iIiIg+TOwBJqpkrq6uWL16NdTU1GBmZgYVlf/7M1NVVZUrK5FIIAiC+NrY2BheXl4ICwuDtbU1Dh48KHd/7IgRI+Du7o4DBw4gJiYGCxYswNKlS8Ue5je9ayIpLy8vWFpaYv369TAzM0NBQQGcnJzE4dpvi46OFu+pLarb2dkZampqiIuLQ1xcHDp37gwAaNWqFR4/fozbt28jPj4eX3/9NQAgKysLysrKOHv2rFyyCqDEodxlpejclnU495ukUimkUul7xUJEREREHyb2ABNVMi0tLdjZ2aFu3bpyyW9ZjRgxAjt37sS6detga2sLZ2dnue0WFhbw9/dHVFQUpkyZgvXr1yusp3HjxoiNjVW4LT09HcnJyZg1axa6du0KBwcHcXKsklhaWsLOzg52dnaoU6cOgMJEuGgSq2PHjsHFxQVAYTLatm1bbNy4Effu3RPv/23WrBny8/Px8OFDsa6ipaShyfXr18fp06fl1r39uizU1NTE+5+JiIiIqGZiAkz0gXF3d4eOjg5CQ0PF+16LTJw4EYcPH8adO3dw7tw5xMXFwcHBQWE9gYGBOH36NMaMGYOLFy/i2rVrWL16NR4/fgx9fX0YGhpi3bp1uHnzJo4ePYrJkydXKF5XV1dERkYiJycHzZs3F9d37twZK1asECfLAgB7e3sMHjwYQ4cORVRUFO7cuYNTp05hwYIFOHDggML6x40bh40bN2Lz5s24ceMGQkNDcfHixXLP5GxlZYWEhAT8/fffePz4cYWOlYiIiIg+bkyAiT4wSkpK8PHxQX5+PoYOHSq3LT8/HwEBAXBwcICHhwfs7e3lJrl6k729PWJiYnDhwgW0bt0a7dq1w549e6CiogIlJSVERkbi7NmzcHJywqRJk/D9999XKF5XV1c8f/4czs7Ocj3enTt3xvPnz8XHJRUJCwvD0KFDMWXKFNSvXx+9evXC6dOnUbduXYX1Dx48GIGBgZg6dSqaN2+OO3fuwMfHB+rq6uWKMyQkBCkpKbC1tYWRkVGFjpWIiIiIPm4S4c0bEInog+Dn54dHjx7JPT+Y/k+3bt1gYmIi98ioqpKZmQldXV1kzOwBHanqu3cgIiKi6he0u7ojoH+R+H0tIwM6OjqlluUkWEQfkIyMDFy6dAnbt29n8vv/ZWdnY82aNXB3d4eysjJ27NiB3377DUeOHPl3AwncDrzjA5WIiIiIPmxMgIk+ID179sSpU6fg7++Pbt26VXc4HwSJRILo6GjMmzcPOTk5qF+/Pn755Re4ublVd2hERERE9JHhEGgiolKUZ0gNEREREf37yvN9jZNgERERERERUY3ABJiIiIiIiIhqBCbAREREREREVCMwASYiIiIiIqIagQkwERERERER1QhMgImIiIiIiKhGYAJMRERERERENQITYCIiIiIiIqoRmAATERERERFRjcAEmIiIiIiIiGoEJsBERERERERUI6hUdwBERB+FBYMAqWp1R0FERPRhCtpd3REQlQl7gImIiIiIiKhGYAJMRERERERENQIT4BogPDwcenp6VVZ/fHw8JBIJnj17Vin1paSkQCKRICkpqVLqK6uqPk9lUV3HXlbZ2dno06cPdHR0KvU9JyIiIiL6NzAB/kT4+PhAIpFAIpFATU0NdnZ2CAkJwevXr6u87fbt2yMtLQ26urpV3hYAzJw5Ew0aNJBbd+3aNUgkEvj4+MitDw8Ph1QqxcuXL99Zb//+/XH9+vUyxVDRZPnmzZvw9fWFubk5pFIprK2tMXDgQJw5cwYAYGFhgbS0NDg5OZW77n/D5s2bkZiYiJMnT/6r7zkRERERUWVgAvwJ8fDwQFpaGm7cuIEpU6YgKCgI33//fZW3q6amBhMTE0gkkipvCwBcXV2RnJyMBw8eiOvi4uJgYWGB+Ph4ubJxcXFo27YtNDQ03lmvhoYGjI2NKztc0ZkzZ9CiRQtcv34da9euxZUrV7B79240aNAAU6ZMAQAoKyvDxMQEKiqK56cTBOFf+VGjJLdu3YKDgwOcnJwq/J7n5+ejoKCgCqIjIiIiIiodE+BPiFQqhYmJCSwtLTF69Gi4ublh79694vbDhw/DwcEBMplMTJYBICEhAaqqqnIJJQBMnDgRHTt2BADcvXsXXl5e0NfXh5aWFhwdHREdHQ1A8RDoEydOwMXFBZqamtDX14e7uzuePn0KADh06BA6dOgAPT09GBoa4vPPP8etW7fKfJwdOnSAqqqqXLIbHx+PgIAAPHnyBCkpKXLrXV1dAQA//PADGjVqBC0tLVhYWGDMmDHIysoSy77dq3vhwgW4urpCW1sbOjo6aNGiBc6cOYP4+Hj4+voiIyND7HUPCgoqNWZBEODj44N69eohMTERPXr0gK2tLZo2bYq5c+diz549AIoPgS46twcPHkSLFi0glUpx/PhxFBQUYPHixbCzs4NUKkXdunUxb968Et+PpKQkSCQS8dwUHev+/ftRv359aGpqom/fvsjOzsbmzZthZWUFfX19jB8/Hvn5+QAAFxcXLF26FAkJCZBIJHBxcQEA5ObmYurUqahTpw60tLTQpk0bufemqK29e/eiYcOGkEqlSE1NLfN+JV23RTZt2gRHR0dIpVKYmppi7Nix4rZnz55hxIgRMDIygo6ODrp06YILFy6U+l4RERER0aeLCfAnTENDA69evQJQeO/mkiVLsHXrViQkJCA1NRVTp04FAHTq1Ak2NjbYunWruG9eXh4iIiIwfPhwAEBAQAByc3ORkJCAS5cuYdGiRZDJZArbTUpKQteuXdGwYUP8/vvvOH78OLy8vMRE6sWLF5g8eTLOnDmD2NhYKCkpoXfv3mXuFdTS0kKrVq0QFxcnrouPj0fXrl3h7Owsrr99+zZSU1PFBFhJSQnLly/H5cuXsXnzZhw9ehTTp08vsZ3BgwfD3Nwcp0+fxtmzZzFz5kyoqqqiffv2WLZsGXR0dJCWloa0tDTxXJYkKSkJly9fxpQpU6CkVPzP7l3DqWfOnImFCxfi6tWraNy4MQIDA7Fw4ULMnj0bV65cwfbt21G7du1S63hbdnY2li9fjsjISBw6dAjx8fHo3bs3oqOjER0dja1bt2Lt2rX4+eefAQBRUVEYOXIk2rVrh7S0NERFRQEAxo4di99//x2RkZG4ePEi+vXrBw8PD9y4cUOurUWLFmHDhg24fPkyjI2Ny7xfSdctAKxevRoBAQEYNWoULl26hL1798LOzk7c3q9fPzx8+BAHDx7E2bNn0bx5c3Tt2hVPnjwp8bzk5uYiMzNTbiEiIiKiTwOfA/wJEgQBsbGxOHz4MMaNGwegMKFds2YNbG1tARQmLSEhIeI+fn5+CAsLw7Rp0wAA+/btQ05ODry9vQEAqamp6NOnDxo1agQAsLGxKbH9xYsXo2XLlli1apW4ztHRUfx3nz595Mpv2rQJRkZGuHLlSpnvfXV1dcWuXbsAAFeuXEFOTg6aNWuGTp06iT208fHxUFdXR9u2bQEU9mgXsbKyQmhoKPz9/eXifFNqaiqmTZsm3m9cr149cZuuri4kEglMTEzKFG9RUvf2vctlFRISgm7dugEAnj9/jp9++gkrV67EsGHDAAC2trbo0KFDuerMy8vD6tWrxWuib9++2Lp1K/755x/IZDI0bNgQrq6uiIuLQ//+/WFgYABNTU1xyDtQeI7CwsKQmpoKMzMzAMDUqVNx6NAhhIWFYf78+WJbq1atQpMmTcq9X2nXbWhoKKZMmYIJEyaI61q1agUAOH78OE6dOoWHDx9CKpUCAJYsWYJff/0VP//8M0aNGqXwvCxYsADBwcHlOpdERERE9HFgD/AnZP/+/ZDJZFBXV4enpyf69+8vDs3V1NQUkwgAMDU1xcOHD8XXPj4+uHnzJv744w8AhcNPvb29oaWlBQAYP348QkND4ezsjLlz5+LixYslxlHUA1ySGzduYODAgbCxsYGOjg6srKwAFCZFijg6OkImk0Emk8HT0xNA4XDc69evIy0tDfHx8ejQoQOUlZXRuXNncRhtfHw82rdvLyY/v/32G7p27Yo6depAW1sbX331FdLT05Gdna2w3cmTJ2PEiBFwc3PDwoULyzVM+22CIFR4XwBo2bKl+O+rV68iNze31HNcFm9fE7Vr14aVlZVcz37t2rXlrpO3Xbp0Cfn5+bC3txffI5lMhmPHjsmdLzU1NTRu3Ljc+5V23T58+BD3798v8TxcuHABWVlZMDQ0lGvjzp07pb6XgYGByMjIEJd79+6VWJaIiIiIPi7sAf6EuLq6YvXq1VBTU4OZmZncREqqqqpyZSUSiVxSZmxsDC8vL4SFhcHa2hoHDx6Uux9zxIgRcHd3x4EDBxATE4MFCxZg6dKlYg/zm9414ZSXlxcsLS2xfv16mJmZoaCgAE5OTuJw7bdFR0cjLy9Prm5nZ2eoqakhLi4OcXFx6Ny5M4DC3r/Hjx/j9u3biI+Px9dffw2g8N7azz//HKNHj8a8efNgYGCA48ePw8/PD69evYKmpmaxdoOCgjBo0CAcOHAABw8exNy5cxEZGYnevXuXenyK2NvbAyicrbpZs2bl3r/oh4g3z0FJioZYv/n+Fp2/Nym6JhStK21oelZWFpSVlXH27FkoKyvLbXszkdbQ0JCbMKus+5V23b7rPGRlZcHU1LTYxGhA6UPOpVKp+KMJEREREX1a2AP8CdHS0oKdnR3q1q1b4izCpRkxYgR27tyJdevWwdbWFs7OznLbLSws4O/vj6ioKEyZMgXr169XWE/jxo0RGxurcFt6ejqSk5Mxa9YsdO3aFQ4ODuLkWCWxtLSEnZ0d7OzsUKdOHQCFyU/RpEnHjh0TJ2RSVVVF27ZtsXHjRty7d0+8//fs2bMoKCjA0qVL0bZtW9jb2+P+/fvvPCf29vaYNGkSYmJi8OWXXyIsLAxAYY9m0T3NZdG0aVM0bNgQS5cuVZhQlud5uvXq1YOGhkaJ59jIyAgA5CaLqqrnCjdr1gz5+fl4+PCh+B4VLaUND6/ofm/S1taGlZVVieehefPmePDgAVRUVIq1UatWrQodLxERERF93JgAk8jd3R06OjoIDQ2Fr6+v3LaJEyfi8OHDuHPnDs6dO4e4uDg4ODgorCcwMBCnT5/GmDFjcPHiRVy7dg2rV6/G48ePoa+vD0NDQ6xbtw43b97E0aNHMXny5ArF6+rqisjISOTk5KB58+bi+s6dO2PFihXiZFkAYGdnh7y8PKxYsQK3b9/G1q1bsWbNmhLrfvnyJcaOHYv4+HjcvXsXJ06cwOnTp8VjtrKyQlZWFmJjY/H48eMSh1EXkUgkCAsLw/Xr19GxY0dER0fj9u3buHjxIubNm4eePXuW+bjV1dUxY8YMTJ8+HVu2bMGtW7fwxx9/YOPGjeKxWlhYICgoCDdu3MCBAwewdOnSMtdfHvb29hg8eDCGDh2KqKgo3LlzB6dOncKCBQtw4MCBSt/vbUFBQVi6dCmWL1+OGzdu4Ny5c1ixYgUAwM3NDe3atUOvXr0QExODlJQUnDx5Et9++6343GUiIiIiqlmYAJNISUkJPj4+yM/Px9ChQ+W25efnIyAgAA4ODvDw8IC9vX2Jk0fZ29sjJiYGFy5cQOvWrdGuXTvs2bMHKioqUFJSQmRkJM6ePQsnJydMmjSpws8qdnV1xfPnz+Hs7CzX4925c2c8f/5cfFwSADRp0gQ//PADFi1aBCcnJ0RERGDBggUl1q2srIz09HQMHToU9vb28Pb2hqenpzg5Uvv27eHv74/+/fvDyMgIixcvfme8rVu3xpkzZ2BnZ4eRI0fCwcEBX3zxBS5fvoxly5aV69hnz56NKVOmYM6cOXBwcED//v3Fe2NVVVWxY8cOXLt2DY0bN8aiRYsQGhparvrLIywsDEOHDsWUKVNQv3599OrVC6dPn0bdunWrZL83DRs2DMuWLcOqVavg6OiIzz//XJxwTCKRIDo6Gp06dYKvry/s7e0xYMAA3L17t9wzZhMRERHRp0EivO/sPPRJ8fPzw6NHj+SeH0xUk2VmZkJXVxcZM3tAR6r67h2IiIhqoqDd1R0B1WDi97WMDOjo6JRalpNgEQAgIyMDly5dwvbt25n8EikSuB14xwcqEREREX3YmAATAKBnz544deoU/P39xefNUtklJiaKj2hSJCsr61+MhoiIiIiIFGECTACg8FExVHYtW7asspmWiYiIiIiocjABJqoEGhoasLOzq+4wiIiIiIioFJwFmoiIiIiIiGoEJsBERERERERUIzABJiIiIiIiohqBCTARERERERHVCEyAiYiIiIiIqEZgAkxEREREREQ1AhNgIiIiIiIiqhGYABMREREREVGNwASYiIiIiIiIagQmwERERERERFQjqFR3AEREH4UFgwCpanVHQUREH6ug3dUdARGBPcBERERERERUQzABJiIiIiIiohqBCTARERERERHVCEyAiT5wPj4+kEgkxZabN28CAB48eIAJEybAzs4O6urqqF27NpydnbF69WpkZ2fL1XX+/Hn069cPtWvXhrq6OurVq4eRI0fi+vXrJbYfHx+vsP1Zs2aJZQRBwPr169GuXTvo6OhAJpPB0dEREyZMEOMskpmZiW+//RYNGjSAuro6TExM4ObmhqioKAiCUKZzcvPmTfj6+sLc3BxSqRTW1tYYOHAgzpw5AwBISUmBRCJBUlJSsX1dXFwwceLEMrVDRERERJ8WToJF9BHw8PBAWFiY3DojIyPcvn0bzs7O0NPTw/z589GoUSNIpVJcunQJ69atQ506dfDFF18AAPbv348+ffrA3d0dERERsLW1xcOHD7Fr1y7Mnj0bO3fuLDWG5ORk6OjoiK9lMhmAwuR30KBB+PXXX/HNN9/gxx9/hJmZGe7fv4/du3cjNDQU4eHhAIBnz56hQ4cOyMjIQGhoKFq1agUVFRUcO3YM06dPR5cuXaCnp1dqHGfOnEHXrl3h5OSEtWvXokGDBnj+/Dn27NmDKVOm4NixY+U8u0RERERUUzABJvoISKVSmJiYFFs/ZswYqKio4MyZM9DS0hLX29jYoGfPnmKPanZ2Nnx9fdG9e3fs3v1/s1BaW1ujTZs2ePbs2TtjMDY2Vpic7ty5E5GRkdizZ4+YbANA3bp10bZtW7le3W+++QYpKSm4fv06zMzMxPX29vYYOHAg1NXVS41BEAT4+PigXr16SExMhJLS/w1iadq0KSZMmPDO4yAiIiKimosJMNFHKj09HTExMZg/f75c8vsmiUQCADh8+DAeP36M6dOnKyz3rl7X0uzYsQP169eXS34VxVBQUIDIyEgMHjxYLvktUtSjXJqkpCRcvnwZ27dvl0t+i7zPcRTJzc1Fbm6u+DozM/O96yQiIiKiDwPvASb6COzfvx8ymUxc+vXrh5s3b0IQBNSvX1+ubK1atcRyM2bMAADcuHEDANCgQYMKx2Bubi4XQ3p6OgDg+vXrxWKYOHGiWM7c3BwA8PjxYzx9+vS9YqiM43iXBQsWQFdXV1wsLCyqrC0iIiIi+ncxASb6CLi6uiIpKUlcli9fXmLZU6dOISkpCY6OjmJPZlknl3J0dBQTV09PT7ltiYmJcjHo6+uXWM+3336LpKQkzJkzB1lZWeWKoTSVUce7BAYGIiMjQ1zu3btX5W0SERER0b+DQ6CJPgJaWlqws7OTW6empgaJRILk5GS59TY2NgAADQ0NcZ29vT0A4Nq1a2jXrl2J7URHRyMvL6/Y/kDh/cKKhhjXq1evWAxGRkYwMjKCsbGx3Do9PT1cu3atxPbf5c3jaNasWYnliibrysjIKLbt2bNn0NXVLXFfqVQKqVRa4RiJiIiI6MPFHmCij5ShoSG6deuGlStX4sWLF6WW/eyzz1CrVi0sXrxY4faiSbAsLS1hZ2cHOzs71KlTp0xxDBw4EMnJydizZ0+p5ZSUlDBgwABERETg/v37xbZnZWXh9evXpdbRtGlTNGzYEEuXLkVBQUGJx2FgYIBatWrh7NmzctszMzNx8+ZNMZEmIiIiopqFCTDRR2zVqlV4/fo1WrZsiZ07d+Lq1atITk7Gtm3bcO3aNSgrKwMo7EHesGEDDhw4gC+++AK//fYbUlJScObMGUyfPh3+/v4VjmHAgAHo27cvBgwYgJCQEPz5559ISUnBsWPHsHPnTjEGAJg3bx4sLCzQpk0bbNmyBVeuXMGNGzewadMmNGvWTBwuXRKJRIKwsDBcv34dHTt2RHR0NG7fvo2LFy9i3rx56Nmzp1h28uTJmD9/PiIiInDr1i2cOnUKgwcPhpGREb788ssKHy8RERERfbw4BJroI2Zra4vz589j/vz5CAwMxF9//QWpVIqGDRti6tSpGDNmjFi2Z8+eOHnyJBYsWIBBgwYhMzMTFhYW6NKlC0JDQyscg0Qiwc6dO7F+/XqEhYVh8eLFyMvLg7m5Obp27YoffvhBLGtgYIA//vgDCxcuRGhoKO7evQt9fX00atQI33//falDk4u0bt0aZ86cwbx58zBy5Eg8fvwYpqamaN++PZYtWyaWmz59OmQyGRYtWoRbt27BwMAAzs7OiIuLKza8m4iIiIhqBonwb8wqQ0T0kcrMzISuri4yZvaAjlS1usMhIqKPVdDu6o6A6JMlfl/LyBDngikJe4CJiMoicDvwjg9UIiIiIvqw8R5gIvpgJCYmyj1r+O2FiIiIiOh9sAeYiD4YLVu2RFJSUnWHQURERESfKCbARPTB0NDQKPa8YyIiIiKiysIh0ERERERERFQjMAEmIiIiIiKiGoEJMBEREREREdUITICJiIiIiIioRmACTERERERERDUCE2AiIiIiIiKqEZgAExERERERUY3ABJiIiIiIiIhqBCbAREREREREVCMwASYiIiIiIqIaQaW6AyAi+igsGARIVas7CiKiT1vQ7uqOgIg+cewBJiIiIiIiohqBCTARERERERHVCEyAiYiIiIiIqEZgAkz0kfLx8YFEIhEXQ0NDeHh44OLFi2IZiUSCX3/9tdi+X3/9NZSVlbFr1653thMeHi7XjkwmQ4sWLRAVFSVXzsXFBRMnTiy2/44dO6CsrIyAgIByHV98fLxcuxoaGnB0dMS6devkyhWdB39//2J1BAQEQCKRwMfHR658r169yhULEREREX0amAATfcQ8PDyQlpaGtLQ0xMbGQkVFBZ9//nmp+2RnZyMyMhLTp0/Hpk2bytSOjo6O2M758+fh7u4Ob29vJCcnv3PfjRs3Yvr06dixYwdycnLK1N6bkpOTkZaWhitXruDrr7/G6NGjERsbK1fGwsICkZGRePnypbguJycH27dvR926dcvdJhERERF9mpgAE33EpFIpTExMYGJigqZNm2LmzJm4d+8eHj16VOI+u3btQsOGDTFz5kwkJCTg3r1772xHIpGI7dSrVw+hoaFQUlKS621W5M6dOzh58iRmzpwJe3v7Yr3GZWFsbAwTExNYW1tj/PjxsLa2xrlz5+TKNG/eHBYWFnL1R0VFoW7dumjWrFm52svNzUVmZqbcQkRERESfBibARJ+IrKwsbNu2DXZ2djA0NCyx3MaNGzFkyBDo6urC09MT4eHh5WonPz8fmzdvBlCYeJYmLCwMPXr0gK6uLoYMGYKNGzeWq603CYKAQ4cOITU1FW3atCm2ffjw4QgLCxNfb9q0Cb6+vuVuZ8GCBdDV1RUXCwuLCsdMRERERB8WJsBEH7H9+/dDJpNBJpNBW1sbe/fuxc6dO6GkpPhP+8aNG/jjjz/Qv39/AMCQIUMQFhYGQRBKbScjI0NsR01NDaNHj8a6detga2tb4j4FBQUIDw/HkCFDAAADBgzA8ePHcefOnXIdo7m5udhujx49MHfuXHTq1KlYuSFDhuD48eO4e/cu7t69ixMnTohtl0dgYCAyMjLEpSw95ERERET0cWACTPQRc3V1RVJSEpKSknDq1Cm4u7vD09MTd+/eVVh+06ZNcHd3R61atQAA3bt3R0ZGBo4ePQoAmD9/vpjoymQypKamAgC0tbXFds6fP4/58+fD398f+/btKzG2I0eO4MWLF+jevTsAoFatWujWrVuZ7zsukpiYKLa9YcMGzJ8/H6tXry5WzsjICD169EB4eLjY81x0nOUhlUqho6MjtxARERHRp0GlugMgoorT0tKCnZ2d+HrDhg3Q1dXF+vXrERoaKle2aOjygwcPoKKiIrd+06ZN6Nq1K/z9/eHt7S1uMzMzAwAoKSnJtdO4cWPExMRg0aJF8PLyUhjbxo0b8eTJE2hoaIjrCgoKcPHiRQQHB5fYS/02a2tr6OnpAQAcHR3x559/Yt68eRg9enSxssOHD8fYsWMBAP/5z3/KVD8RERER1RxMgIk+IRKJBEpKSnKzIReJjo7G8+fPcf78eSgrK4vr//e//8HX1xfPnj2DgYEBDAwMytSWsrKywnYAID09HXv27EFkZCQcHR3F9fn5+ejQoQNiYmLg4eFRzqN7d7seHh549eoVJBIJ3N3dK1Q/EREREX26mAATfcRyc3Px4MEDAMDTp0+xcuVKZGVlKeyV3bhxI3r06IEmTZrIrW/YsCEmTZqEiIiIEp/VKwiC2M7Lly9x5MgRHD58GHPmzFFYfuvWrTA0NIS3tzckEonctu7du2Pjxo1lToAfPnyInJwc5Obm4tSpU9i6dSv69u2rsKyysjKuXr0q/puIiIiI6E1MgIk+YocOHYKpqSmAwvt0GzRogF27dsHFxUWu3D///IMDBw5g+/btxepQUlJC7969sXHjxhIT4MzMTLEdqVQKS0tLhISEYMaMGQrLb9q0Cb179y6W/AJAnz598NVXX+Hx48dluke3fv36AAAVFRVYWFjg66+/RlBQUInlec8uEREREZVEIrxr+lciohosMzMTurq6yJjZAzpS1eoOh4jo0xa0u7ojIKKPkPh9LSPjnZ0h7AEmIiqLwO0Ae5eJiIiIPmp8DBIRVQtPT0+5Ry69ucyfP7+6wyMiIiKiTxB7gImoWmzYsKHE2ZzLOhM1EREREVF5MAEmompRp06d6g6BiIiIiGoYDoEmIiIiIiKiGoEJMBEREREREdUITICJiIiIiIioRmACTERERERERDUCE2AiIiIiIiKqESotAX727FllVUVERERERERU6SqUAC9atAg7d+4UX3t7e8PQ0BB16tTBhQsXKi04IiIiIiIiospSoQR4zZo1sLCwAAAcOXIER44cwcGDB+Hp6Ylp06ZVaoBERERERERElUGlIjs9ePBATID3798Pb29vfPbZZ7CyskKbNm0qNUAiIiIiIiKiylChHmB9fX3cu3cPAHDo0CG4ubkBAARBQH5+fuVFR0RERERERFRJKtQD/OWXX2LQoEGoV68e0tPT4enpCQA4f/487OzsKjVAIqIPwoJBgFS1uqMgIvo4Be2u7giIiABUMAH+8ccfYWVlhXv37mHx4sWQyWQAgLS0NIwZM6ZSAyQiIiIiIiKqDBUaAq2qqoqpU6fip59+QrNmzcT1kyZNwogRIyotOCIqm/DwcOjp6VVZ/fHx8ZBIJJX2uLOUlBRIJBIkJSVVSn1ERERERGVR4ecAb926FR06dICZmRnu3r0LAFi2bBn27NlTacER0f/x8fGBRCKBRCKBmpoa7OzsEBISgtevX1d52+3bt0daWhp0dXWrvK0iLi4u4vFKJBLUrl0b/fr1Ez9vgP9LpJWVlfH333/L7Z+WlgYVFRVIJBKkpKTIlWfiTURERFQzVSgBXr16NSZPngxPT088e/ZMnPhKT08Py5Ytq8z4iOgNHh4eSEtLw40bNzBlyhQEBQXh+++/r/J21dTUYGJiAolEUuVtvWnkyJFIS0vD/fv3sWfPHty7dw9DhgwpVq5OnTrYsmWL3LrNmzejTp06/1aoRERERPQRqFACvGLFCqxfvx7ffvstlJWVxfUtW7bEpUuXKi04IpInlUphYmICS0tLjB49Gm5ubti7d6+4/fDhw3BwcIBMJhOTZQBISEiAqqoqHjx4IFffxIkT0bFjRwDA3bt34eXlBX19fWhpacHR0RHR0dEAFA+BPnHiBFxcXKCpqQl9fX24u7vj6dOnAApnh+/QoQP09PRgaGiIzz//HLdu3Sr38WpqasLExASmpqZo27Ytxo4di3PnzhUrN2zYMISFhcmtCwsLw7Bhw8rdJhERERF9uiqUAN+5c0fu3t8iUqkUL168eO+giKhsNDQ08OrVKwBAdnY2lixZgq1btyIhIQGpqamYOnUqAKBTp06wsbHB1q1bxX3z8vIQERGB4cOHAwACAgKQm5uLhIQEXLp0CYsWLRInuHtbUlISunbtioYNG+L333/H8ePH4eXlJY4GefHiBSZPnowzZ84gNjYWSkpK6N27NwoKCip8rE+ePMF///tfhc8a/+KLL/D06VMcP34cAHD8+HE8ffoUXl5e5W4nNzcXmZmZcgsRERERfRoqNAu0tbU1kpKSYGlpKbf+0KFDcHBwqJTAiKhkgiAgNjYWhw8fxrhx4wAUJrRr1qyBra0tAGDs2LEICQkR9/Hz80NYWBimTZsGANi3bx9ycnLg7e0NAEhNTUWfPn3QqFEjAICNjU2J7S9evBgtW7bEqlWrxHWOjo7iv/v06SNXftOmTTAyMsKVK1fg5ORU5uNctWoVNmzYAEEQkJ2dDXt7exw+fLhYOVVVVQwZMgSbNm1Chw4dsGnTJgwZMgSqquV/bNGCBQsQHBxc7v2IiIiI6MNXoR7gyZMnIyAgADt37oQgCDh16hTmzZuHwMBATJ8+vbJjJKL/b//+/ZDJZFBXV4enpyf69++PoKAgAIXDhYuSXwAwNTXFw4cPxdc+Pj64efMm/vjjDwCFM0d7e3tDS0sLADB+/HiEhobC2dkZc+fOxcWLF0uMo6gHuCQ3btzAwIEDYWNjAx0dHVhZWQEoTLIVcXR0hEwmg0wmE58rDgCDBw9GUlISLly4gOPHj8POzg6fffYZnj9/XqyO4cOHY9euXXjw4AF27dol9myXV2BgIDIyMsTl3r17FaqHiIiIiD48FeoBHjFiBDQ0NDBr1ixkZ2dj0KBBMDMzw08//YQBAwZUdoxE9P+5urpi9erVUFNTg5mZGVRU/u9P+O3eTolEAkEQxNfGxsbw8vJCWFgYrK2tcfDgQcTHx4vbR4wYAXd3dxw4cAAxMTFYsGABli5dKvYwv0lDQ6PUOL28vGBpaYn169fDzMwMBQUFcHJyEodrvy06Ohp5eXnF6tbV1YWdnR0AwM7ODhs3boSpqSl27txZ7JFrjRo1QoMGDTBw4EA4ODjAycmpQrM9S6VSSKXScu9HRERERB++cvcAv379Glu2bIGbmxtu3LiBrKwsPHjwAH/99Rf8/PyqIkYi+v+0tLRgZ2eHunXryiW/ZTVixAjs3LkT69atg62tLZydneW2W1hYwN/fH1FRUZgyZQrWr1+vsJ7GjRsjNjZW4bb09HQkJydj1qxZ6Nq1KxwcHMTJsUpiaWkJOzs72NnZlTpzc9Gkey9fvlS4ffjw4YiPj69w7y8RERERfdrK/Q1aRUUF/v7+uHr1KoDCYZeampqVHhgRVT53d3fo6OggNDRU7v5goHBGaE9PT9jb2+Pp06eIi4sr8Z7+wMBANGrUCGPGjIG/vz/U1NQQFxeHfv36wcDAAIaGhli3bh1MTU2RmpqKmTNnVije7Oxscebqf/75B9999x3U1dXx2WefKSw/cuRI9OvXD3p6ehVqj4iIiIg+bRW6B7h169Y4f/58ZcdCRFVMSUkJPj4+yM/Px9ChQ+W25efnIyAgAA4ODvDw8IC9vb3cJFdvsre3R0xMDC5cuIDWrVujXbt22LNnD1RUVKCkpITIyEicPXsWTk5OmDRpUoWfVbx+/XqYmprC1NQUrq6uePz4MaKjo1G/fn2F5VVUVFCrVq0K9Y4TERER0adPIrx5k2AZ/fe//0VgYCAmTZqEFi1aiJPoFGncuHGlBUhElcvPzw+PHj2Se34wlSwzMxO6urrImNkDOtLyzypNREQAgnZXdwRE9AkTv69lZEBHR6fUshXqJima6Gr8+PHiuqIJdyQSifgsUCL6cGRkZODSpUvYvn07k18iIiIiqpEqlADfuXOnsuMgoirWs2dPnDp1Cv7+/ujWrVt1h/PxCdwOvOMXRSIiIiL6sFUoAba0tKzsOIioir35yCMiIiIiopqoQgnwli1bSt3+9uQ6RERERERERNWtQpNg6evry73Oy8tDdnY21NTUoKmpiSdPnlRagERE1ak8kyoQERER0b+vPN/XKvQYpKdPn8otWVlZSE5ORocOHbBjx44KBU1ERERERERUlSqUACtSr149LFy4EBMmTKisKomIiIiIiIgqTaUlwACgoqKC+/fvV2aVRERERERERJWiQpNgvf0MUUEQkJaWhpUrV8LZ2blSAiMiIiIiIiKqTBVKgHv16iX3WiKRwMjICF26dMHSpUsrIy4iIiIiIiKiSlWhBLigoKCy4yAiIiIiIiKqUhW6BzgkJATZ2dnF1r98+RIhISHvHRQRERERERFRZavQc4CVlZWRlpYGY2NjufXp6ekwNjZGfn5+pQVIRFSd+BxgIiIiog9blT8HWBAESCSSYusvXLgAAwODilRJREREREREVKXKdQ+wvr4+JBIJJBIJ7O3t5ZLg/Px8ZGVlwd/fv9KDJCIiIiIiInpf5UqAly1bBkEQMHz4cAQHB0NXV1fcpqamBisrK7Rr167SgyQiqnYLBgFS1eqOgojo4xC0u7ojICJSqFwJ8LBhwwAA1tbWaN++PVRV+WWQiIiIiIiIPg4VegxS586dxX/n5OTg1atXcts5UQwRVSUfHx88e/YMv/76a3WHQkREREQfkQpNgpWdnY2xY8fC2NgYWlpa0NfXl1uIPnWPHj3C6NGjUbduXUilUpiYmMDd3R0nTpwAAFhZWYn3y2toaMDKygre3t44evRosbp2796Ntm3bQldXF9ra2nB0dMTEiRNLbT87OxuBgYGwtbWFuro6jIyM0LlzZ+zZs0cs4+LiIsYglUpRp04deHl5ISoqqlh9x44dQ5cuXWBgYABNTU3Uq1cPw4YNK/bj1tt8fHzENhQtVlZWpe7v4uLyzmN9H6XFJpFIEBQUVGVtExEREdGHp0IJ8LRp03D06FGsXr0aUqkUGzZsQHBwMMzMzLBly5bKjpHog9OnTx+cP38emzdvxvXr17F37164uLggPT1dLBMSEoK0tDQkJydjy5Yt0NPTg5ubG+bNmyeWiY2NRf/+/dGnTx+cOnUKZ8+exbx585CXl1dq+/7+/oiKisKKFStw7do1HDp0CH379pVrHwBGjhyJtLQ03Lp1C7/88gsaNmyIAQMGYNSoUWKZK1euwMPDAy1btkRCQgIuXbqEFStWQE1N7Z2PNPvpp5+QlpYmLgAQFhYmvj59+nSZz2lVeDO2ZcuWQUdHR27d1KlTqzU+IiIiIvp3VWgI9L59+7Blyxa4uLjA19cXHTt2hJ2dHSwtLREREYHBgwdXdpxEH4xnz54hMTER8fHx4u0AlpaWaN26tVw5bW1tmJiYAADq1q2LTp06wdTUFHPmzEHfvn1Rv3597Nu3D87Ozpg2bZq4n729PXr16lVqDHv37sVPP/2E7t27AyjscW7RokWxcpqammIM5ubmaNu2LRo0aIDhw4fD29sbbm5uiImJgYmJCRYvXizuZ2trCw8Pj3eeC11dXbnJ8ABAT09PbPPYsWOYNm2a+Ii0YcOGITQ0FCoqKvDx8cGxY8dw7Ngx/PTTTwCAO3fuwMLCAqNGjcLRo0fx4MED1K1bF2PGjMGECRPeGc/biuIoilUikcitIyIiIqKapUI9wE+ePIGNjQ2Awvt9nzx5AgDo0KEDEhISKi86og+QTCaDTCbDr7/+itzc3HLtO2HCBAiCIA5VNjExweXLl/G///2vXPWYmJggOjoaz58/L9d+QOFkdvr6+uJQaBMTE6SlpVX63+7ff/+N7t27o1WrVrhw4QJWr16NjRs3IjQ0FEBh73G7du3EXuq0tDRYWFigoKAA5ubm2LVrF65cuYI5c+bgm2++wX//+99Kja8kubm5yMzMlFuIiIiI6NNQoQTYxsYGd+7cAQA0aNBA/GK6b98+6OnpVVpwRB8iFRUVhIeHY/PmzdDT04OzszO++eYbXLx48Z37GhgYwNjYGCkpKQCAcePGoVWrVmjUqBGsrKwwYMAAbNq06Z2J9bp163Dy5EkYGhqiVatWmDRpknj/8bsoKSnB3t5ejKFfv34YOHAgOnfuDFNTU/Tu3RsrV65878Rv1apVsLCwwMqVK9GgQQP06tULwcHBWLp0KQoKCqCrqws1NTWxl9rExATKyspQVVVFcHAwWrZsCWtrawwePBi+vr7/WgK8YMECsWdbV1cXFhYW/0q7RERERFT1KpQA+/r64sKFCwCAmTNn4j//+Q/U1dUxadIkuaGcRJ+qPn364P79+9i7dy88PDwQHx+P5s2bIzw8/J37CoIAiUQCANDS0sKBAwdw8+ZNzJo1CzKZDFOmTEHr1q2RnZ2N1NRUscdZJpNh/vz5AIBOnTrh9u3biI2NRd++fXH58mV07NgR3333XZnifzMGZWVlhIWF4a+//sLixYtRp04dzJ8/H46OjuJ9vRVx9epVtGvXTmwHAJydnZGVlYW//vqr1H3/85//oEWLFjAyMoJMJsO6deuQmppa4VjKIzAwEBkZGeJy7969f6VdIiIiIqp6FUqAJ02ahPHjxwMA3NzccO3aNWzfvh3nz5+v0H16RB8jdXV1dOvWDbNnz8bJkyfh4+ODuXPnlrpPeno6Hj16BGtra7n1tra2GDFiBDZs2IBz587hypUr2LlzJ8zMzJCUlCQu/v7+4j6qqqro2LEjZsyYgZiYGISEhOC7775758zN+fn5uHHjRrEY6tSpg6+++gorV67E5cuXkZOTgzVr1pTzrLy/yMhITJ06FX5+foiJiUFSUhJ8fX3feVyVRSqVQkdHR24hIiIiok9DhSbBelNOTg4sLS1haWlZGfEQfbQaNmz4zufS/vTTT1BSUip1kisrKytoamrixYsXUFFRgZ2dXZnbf/36NXJycqCmplZiuc2bN+Pp06fo06dPiWX09fVhamqKFy9elKltRRwcHPDLL7/I9TafOHEC2traMDc3BwCFM02fOHEC7du3x5gxY8R1t27dqnAcRERERERFKpQA5+fnY/78+VizZg3++ecfXL9+HTY2Npg9ezasrKzg5+dX2XESfTDS09PRr18/DB8+HI0bN4a2tjbOnDmDxYsXo2fPnmK558+f48GDB8jLy8OdO3ewbds2bNiwAQsWLBCT2qCgIGRnZ6N79+6wtLTEs2fPsHz5cuTl5aFbt24lxuDi4oKBAweiZcuWMDQ0xJUrV/DNN9/A1dVVrscyOzsbDx48wOvXr/HXX39h9+7d+PHHHzF69Gi4uroCANauXYukpCT07t0btra2yMnJwZYtW3D58mWsWLGiwudpzJgxWLZsGcaNG4exY8ciOTkZc+fOxeTJk6GkVDj4xMrKCn/++SdSUlIgk8lgYGCAevXqYcuWLTh8+DCsra2xdetWnD59uliPNRERERFReVVoCPS8efMQHh6OxYsXy/U0OTk5YcOGDZUWHNGHSCaToU2bNvjxxx/RqVMnODk5Yfbs2Rg5ciRWrlwplpszZw5MTU1hZ2eHr776ChkZGYiNjcWMGTPEMp07d8bt27cxdOhQNGjQAJ6ennjw4AFiYmJQv379EmNwd3fH5s2b8dlnn8HBwQHjxo2Du7t7sYmi1q9fD1NTU9ja2uLLL78Uh1avWrVKLNO6dWtkZWXB398fjo6O6Ny5M/744w/8+uuv4mOeKqJOnTqIjo7GqVOn0KRJE/j7+8PPzw+zZs0Sy0ydOhXKyspo2LAhjIyMkJqaiq+//hpffvkl+vfvjzZt2iA9PV2uN5iIiIiIqKIkgiAI5d3Jzs4Oa9euRdeuXaGtrY0LFy7AxsYG165dQ7t27fD06dOqiJWI6F+XmZkJXV1dZMzsAR2panWHQ0T0cQjaXd0REFENIn5fy8h45/wtFRoC/ffffyu8L7GgoAB5eXkVqZKI6MMWuB3ghFhEREREH7UKDYFu2LAhEhMTi63/+eef0axZs/cOiog+HJ6ennKPYlL0WKbq8vZjot5e/q1HJxERERHRx6FCPcBz5szBsGHD8Pfff6OgoABRUVFITk7Gli1bsH///sqOkYiq0YYNG/Dy5UuF2wwMDP7laOQVPSaqtO1EREREREXKdQ/w7du3YW1tDYlEgsTERISEhODChQvIyspC8+bNMWfOHHz22WdVGS8R0b+qPPeUEBEREdG/r8ruAa5Xrx7S0tJgbGyMjh07wsDAAJcuXULt2rXfK2AiIiIiIiKiqlaue4Df7iw+ePAgXrx4UakBEREREREREVWFCk2CVaQCT1AiIiIiIiIiqhblSoAlEgkkEkmxdUREREREREQfunLdAywIAnx8fCCVSgEAOTk58Pf3h5aWlly5qKioyouQiIiIiIiIqBKUKwEeNmyY3OshQ4ZUajBEREREREREVaVcCXBYWFhVxUFERERERERUpd5rEiwiIiIiIiKijwUTYCIiIiIiIqoRmAATERERERFRjcAEmIiIiIiIiGqEck2CRURUYy0YBEhVqzsKIvqYBe2u7giIiGo89gATERERERFRjcAEmIiIiIiIiGoEJsBEHwEfHx9IJJJiy82bNwEADx48wIQJE2BnZwd1dXXUrl0bzs7OWL16NbKzs+XqOn/+PPr164fatWtDXV0d9erVw8iRI3H9+vUS24+Pj1fY/qxZs8QygiBg/fr1aNeuHXR0dCCTyeDo6IgJEyaIcRbJzMzEt99+iwYNGkBdXR0mJiZwc3NDVFQUBEF45/lwcXERY1BXV0fDhg2xatUqcXtQUBCaNm1abL+UlBRIJBIkJSW9sw0iIiIi+vQwASb6SHh4eCAtLU1usba2xu3bt9GsWTPExMRg/vz5OH/+PH7//XdMnz4d+/fvx2+//SbWsX//frRt2xa5ubmIiIjA1atXsW3bNujq6mL27NnvjCE5OVmu/ZkzZwIoTH4HDRqE8ePHo3v37oiJicGVK1ewceNGqKurIzQ0VKzj2bNnaN++PbZs2YLAwECcO3cOCQkJ6N+/P6ZPn46MjIwynY+RI0ciLS0NV65cgbe3NwICArBjx45ynlUiIiIiqkk4CRbRR0IqlcLExKTY+jFjxkBFRQVnzpyBlpaWuN7GxgY9e/YUe1Szs7Ph6+uL7t27Y/fu/5uIxdraGm3atMGzZ8/eGYOxsTH09PSKrd+5cyciIyOxZ88efPHFF+L6unXrom3btnK9ut988w1SUlJw/fp1mJmZievt7e0xcOBAqKurvzMOANDU1BTPR1BQELZv3469e/di4MCBZdqfiIiIiGoeJsBEH7H09HSx5/fN5PdNEokEAHD48GE8fvwY06dPV1hOUWJbVjt27ED9+vXlkl9FMRQUFCAyMhKDBw+WS36LyGSyCsegoaGBV69eVXj/Irm5ucjNzRVfZ2ZmvnedRERERPRh4BBooo/E/v37IZPJxKVfv364efMmBEFA/fr15crWqlVLLDdjxgwAwI0bNwAADRo0qHAM5ubmcjGkp6cDAK5fv14shokTJ4rlzM3/X3v3HRbFtf4B/LuALMjK0qSIiCJFIdZgQZMoNkqiXmPDFonGxBY1iUFQo6IIBr0xxWgiUmI0QryxRY2iXFGsaGTVCCIWLFewoICIIZT5/eHD/FxZYEGQst/P88xzs2feOeedw9yVlzM72xIA8ODBAzx69OilcnhRcXExNm3ahPPnz6Nfv34v3V9ISAjkcrm42djY1ECWRERERFQfsAAmaiDc3d2hUCjE7dtvvy03NjExEQqFAi4uLuJqpjoPlwIAFxcXsXD18vJS2peQkKCUg7Gxcbn9LFiwAAqFAosWLUJeXl6VclDH2rVrIZPJoK+vjylTpuCTTz7BtGnTXrrfgIAA5OTkiNutW7dqIFsiIiIiqg94CzRRA2FgYAB7e3ulNl1dXUgkEqSmpiq129nZAXh2W3ApR0dHAMClS5fg5uZW7jh79+5FYWFhmeOBZ58XVnWrtIODQ5kcmjdvjubNm8Pc3FypzcjICJcuXSp3fHWNGzcOCxYsgL6+PqysrKCl9f9/zzM0NFT5MK3SzznL5fJy+5VKpZBKpS+dHxERERHVP1wBJmrATE1NMXDgQKxZswZPnjypMHbQoEEwMzNDaGioyv2lxaGtrS3s7e1hb28Pa2trtfIYM2YMUlNTsXPnzgrjtLS04OPjg82bN+POnTtl9ufl5aGoqEitMeVyuZjj88UvADg5OeH27du4e/euUvvZs2ehp6eHVq1aqTUGERERETUuLICJGri1a9eiqKgIrq6uiImJQUpKClJTU7Fp0yZcunQJ2traAJ6tIG/YsAF79uzBkCFDcPDgQaSnp+PMmTPw8/PD1KlTq52Dj48PRowYAR8fHyxduhSnTp1Ceno6Dh8+jJiYGDEHAFi+fDlsbGzQo0cPbNy4EcnJyUhLS0NERAS6dOki3i79Mjw8PODk5IQxY8bg+PHjuHbtGv7zn/9g4cKFmD17tlI+RERERKQ5eAs0UQPXtm1bJCUlITg4GAEBAbh9+zakUimcnZ0xd+5cTJ8+XYwdOnQojh8/jpCQEIwdOxa5ubmwsbFBv379lL6rt6okEgliYmIQFhaGyMhIhIaGorCwEC1btkT//v3x1VdfibEmJiY4efIkVqxYgaCgINy4cQPGxsbo0KEDVq5cWeHtyerS0dFBbGws5s+fjzFjxuD+/fto06YNZs+ejU8//fSl+yciIiKihkki1ORTaYiIGpnc3FzI5XLk+L8NQ2mTuk6HiBqyJdsrjyEioioTf1/LyYGhoWGFsVwBJiJSR8AvQCVvqERERERUv/EzwERUryQkJCh91/CLGxERERFRdXEFmIjqFVdXVygUirpOg4iIiIgaIRbARFSv6Ovrl/m+YyIiIiKimsBboImIiIiIiEgjsAAmIiIiIiIijcACmIiIiIiIiDQCC2AiIiIiIiLSCCyAiYiIiIiISCOwACYiIiIiIiKNwAKYiIiIiIiINAILYCIiIiIiItIILICJiIiIiIhII7AAJiIiIiIiIo3AApiIiIiIiIg0gk5dJ0BE1CCEjAWkTeo6CyJqaJZsr+sMiIjoOVwBJiIiIiIiIo3AApiIiIiIiIg0AgtgarR8fX0hkUjEzdTUFJ6enjh//rwYI5FIsGPHjjLHfvTRR9DW1sbWrVsrHScqKkppHJlMhtdffx3btm1Tiuvbty/mzJlT5vgtW7ZAW1sbM2bMqNL5xcfHK41bui1cuLBK/RARERERaQoWwNSoeXp6IiMjAxkZGYiLi4OOjg7eeeedCo/Jz89HdHQ0/Pz8EBERodY4hoaG4jhJSUnw8PDAqFGjkJqaWumx4eHh8PPzw5YtW/D333+rNd7zUlNTxbEzMjLg7+9fJqa4uBglJSVV7puIiIiIqDFhAUyNmlQqhaWlJSwtLdG5c2f4+/vj1q1buH//frnHbN26Fc7OzvD398eRI0dw69atSseRSCTiOA4ODggKCoKWlpbSarMq169fx/Hjx+Hv7w9HR8cyq8bqMDc3F8e2tLSETCZDVFQUjIyMsGvXLjg7O0MqleLmzZs4ffo0Bg4cCDMzM8jlcvTp0wdnz54tcy4//vgj3nnnHTRt2hTt27fHiRMncOXKFfTt2xcGBgbo1asXrl69qnTczp070bVrV+jp6cHOzg6BgYEoKipS6xwkEgk2bNiAYcOGoWnTpnBwcMCuXbvE/aXn87wdO3ZAIpGIr5csWYLOnTsjIiICrVq1gkwmw/Tp01FcXIzQ0FBYWlrC3Nwcy5cvr+IMExEREVFjwQKYNEZeXh42bdoEe3t7mJqalhsXHh6O8ePHQy6Xw8vLC1FRUVUap7i4GD/99BMAoGvXrhXGRkZG4u2334ZcLsf48eMRHh5epbEqkp+fjy+//BIbNmzAxYsXYW5ujsePH2PixIk4evQoTp48CQcHB3h7e+Px48dKxy5btgzvvfceFAoF2rVrh7Fjx+Kjjz5CQEAAzpw5A0EQMHPmTDE+ISEB7733HmbPno3k5GT8+OOPiIqKqlKxGRgYiFGjRuH8+fPw9vbGuHHj8PDhwyqd89WrV/HHH39g37592LJlC8LDw/H222/j9u3bOHz4ML788kssXLgQp06dKrePgoIC5ObmKm1ERERE1DiwAKZGbffu3ZDJZJDJZGjWrBl27dqFmJgYaGmpvvTT0tJw8uRJjB49GgAwfvx4REZGQhCECsfJyckRx9HV1cW0adOwfv16tG3bttxjSkpKEBUVhfHjxwMAfHx8cPToUVy/fr1K59iyZUtxbJlMhqysLABAYWEh1q5di169esHJyQlNmzZFv379MH78eLRr1w7t27fH+vXrkZ+fj8OHDyv1+f7772PUqFFwdHTEvHnzkJ6ejnHjxsHDwwPt27fH7NmzER8fL8YHBgbC398fEydOhJ2dHQYOHIhly5bhxx9/VPs8fH19MWbMGNjb2yM4OBh5eXlITEys0lyUlJQgIiICzs7OGDx4MNzd3ZGamoqvv/4aTk5OeP/99+Hk5IRDhw6V20dISAjkcrm42djYVCkHIiIiIqq/WABTo+bu7g6FQgGFQoHExER4eHjAy8sLN27cUBkfEREBDw8PmJmZAQC8vb2Rk5OD//73vwCA4OBgpWLz5s2bAIBmzZqJ4yQlJSE4OBhTp07F77//Xm5uBw4cwJMnT+Dt7Q0AMDMzw8CBA9X+3HGphIQEcWyFQgFjY2MAgK6uLjp27KgUe/fuXUyZMgUODg6Qy+UwNDREXl6eeB6lnj/OwsICANChQweltr///ltcHT137hyWLl2qNDdTpkxBRkYG8vPz1TqP58c0MDCAoaEh7t27V4WZAFq3bo1mzZop5ens7Kz0Bw8LC4sK+w0ICEBOTo64qXMLPBERERE1DDp1nQBRbTIwMIC9vb34esOGDZDL5QgLC0NQUJBSbOmty5mZmdDR0VFqj4iIQP/+/TF16lSMGjVK3NeiRQsAgJaWltI4HTt2RGxsLL788ksMHjxYZW7h4eF4+PAh9PX1xbaSkhKcP38egYGB5a5Sv6hNmzZlPh8LAPr6+kqfkQWAiRMnIisrC9988w1sbW0hlUrh5uaGf/75RymuSZMm4n+X9qGqrfTBWnl5eQgMDMS7775bJg89PT21zuP5/kvHKO1fS0urzCp8YWGhWn1U1K8qUqkUUqlUrZyJiIiIqGFhAUwaRSKRQEtLC0+fPi2zb+/evXj8+DGSkpKgra0ttv/11194//33kZ2dDRMTE5iYmKg1lra2tspxACArKws7d+5EdHQ0XFxcxPbi4mK88cYbiI2NhaenZxXPrnLHjh3D2rVrxVXnW7du4cGDBy/db9euXZGamqr0R4Ca1Lx5czx+/BhPnjyBgYEBAEChUNTKWERERETUeLEApkatoKAAmZmZAIBHjx5hzZo1yMvLU7kqW/rApE6dOim1Ozs745NPPsHmzZvL/a5eQRDEcZ4+fYoDBw5g//79WLRokcr4n3/+Gaamphg1alSZVVpvb2+Eh4fXSgHs4OCAn3/+Ga6ursjNzcXnn3+utAJdXYsWLcI777yDVq1aYcSIEdDS0sK5c+fw119/lVlpr44ePXqgadOmmD9/PmbNmoVTp05V+eFkRERERET8DDA1avv27YOVlRWsrKzQo0cPnD59Glu3bkXfvn2V4u7evYs9e/Zg+PDhZfrQ0tLCsGHDKnxCc25urjhO+/bt8e9//xtLly7FggULVMZHRERg2LBhZYpfABg+fDh27dpVIyuzLwoPD8ejR4/QtWtXTJgwAbNmzYK5uflL9+vh4YHdu3cjNjYW3bp1Q8+ePbF69WrY2trWQNaAiYkJNm3ahL1796JDhw7YsmULlixZUiN9ExEREZHmkAiVPd6WiEiD5ebmQi6XI8f/bRhKm1R+ABHR85Zsr+sMiIgaPfH3tZwcGBoaVhjLW6CJiNQR8AtQyRsqEREREdVvvAWaqJ7y8vJS+lqh57fg4OC6Tk9tmzdvLvc8nn8AGBERERFRbeMt0ET11P/+979ynyJdladR17XHjx/j7t27Kvc1adKkxj4nXFuqcksNEREREb16vAWaqBGwtrau6xRqRLNmzdCsWbO6ToOIiIiIiLdAExERERERkWZgAUxEREREREQagQUwERERERERaQQWwERERERERKQRWAATERERERGRRmABTERERERERBqBBTARERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGoEFMBEREREREWkEnbpOgIioQQgZC0ib1HUWRJppyfa6zoCIiBoJrgATERERERGRRmABTERERERERBqBBTBRHfH19YVEIimzXblyBQCQmZmJ2bNnw97eHnp6erCwsEDv3r2xbt065OfnK/WVlJSEkSNHwsLCAnp6enBwcMCUKVNw+fLlcsePj49XOf7ChQvFGEEQEBYWBjc3NxgaGkImk8HFxQWzZ88W8yyVm5uLBQsWoF27dtDT04OlpSUGDBiAbdu2QRAEtebkypUrmDRpElq1agWpVApra2v0798fmzdvRlFRkRh3+PBh9OvXDyYmJmjatCkcHBwwceJE/PPPP2XOTUtLC3K5HF26dIGfnx8yMjLUyoWIiIiIGh8WwER1yNPTExkZGUpbmzZtcO3aNXTp0gWxsbEIDg5GUlISTpw4AT8/P+zevRsHDx4U+9i9ezd69uyJgoICbN68GSkpKdi0aRPkcjm++OKLSnNITU1VGt/f3x/As+J37NixmDVrFry9vREbG4vk5GSEh4dDT08PQUFBYh/Z2dno1asXNm7ciICAAJw9exZHjhzB6NGj4efnh5ycnErzSExMRNeuXZGSkoLvv/8ef/31F+Lj4/HBBx9g3bp1uHjxIgAgOTkZnp6ecHV1xZEjR3DhwgV899130NXVRXFxcZlzu3PnDk6fPo158+bh4MGDeO2113DhwgW1fj5ERERE1LhIBHWXZoioRvn6+iI7Oxs7duwos8/T0xMXL17EpUuXYGBgUGa/IAiQSCTIz8+Hra0t3njjDWzfXvYhMdnZ2TAyMlI5fnx8PNzd3fHo0SOVMdHR0RgzZgx27tyJIUOGlJsDAEyfPh0bN27E5cuX0aJFC6W4vLw86OnpQUen/GfuCYIAFxcXNG3aFImJidDSKvu3udLxvv76a3zzzTe4fv16uf2Vd25Pnz5Fly5dYGZmhqNHj5Z7/PNyc3Mhl8uR4/82DPkQLKK6wYdgERFRBcTf13JyYGhoWGEsV4CJ6pmsrCzExsZixowZKotfAGLhuX//fjx48AB+fn4q48orftWxZcsWODk5qSx+n8+hpKQE0dHRGDduXJniFwBkMlmFxS8AKBQKpKSkYO7cuSqL3+fHs7S0REZGBo4cOVKV0wEA6OvrY+rUqTh27Bju3bunMqagoAC5ublKGxERERE1DiyAierQ7t27IZPJxG3kyJG4cuUKBEGAk5OTUqyZmZkYN2/ePABAWloaAKBdu3bVzqFly5ZKOWRlZQEALl++XCaHOXPmiHEtW7YEADx48ACPHj16qRxKP6v8/Hj37t1Tymvt2rUAgJEjR2LMmDHo06cPrKysMGzYMKxZs0btQrU0z/T0dJX7Q0JCIJfLxc3Gxqba50VERERE9QsLYKI65O7uDoVCIW7ffvttubGJiYlQKBRwcXFBQUEBAKj9cCkXFxexkPTy8lLal5CQoJSDsbFxuf0sWLAACoUCixYtQl5eXpVyqCpTU1MxJyMjI/EBV9ra2oiMjMTt27cRGhoKa2trBAcHw8XFRa0HXJXmW7qi/KKAgADk5OSI261bt2rupIiIiIioTlV8XyIR1SoDAwPY29srtenq6kIikSA1NVWp3c7ODsCz23hLOTo6AgAuXboENze3csfZu3cvCgsLyxwPAG3atFF5q7SDg0OZHJo3b47mzZvD3Nxcqc3IyAiXLl0qd/zKODg4AHj20KouXboAeFbols6Nqluora2tMWHCBEyYMAHLli2Do6MjfvjhBwQGBlY4VkpKCgCgdevWKvdLpVJIpdLqngoRERER1WNcASaqZ0xNTTFw4ECsWbMGT548qTB20KBBMDMzQ2hoqMr92dnZAABbW1vY29vD3t4e1tbWauUxZswYpKamYufOnRXGaWlpwcfHB5s3b8adO3fK7M/Ly1P6CiNVunTpgnbt2mHVqlUoKSlRK7/nGRsbw8rKqtL5evr0KdavX4+33noLzZs3r/I4RERERNSwsQAmqofWrl2LoqIiuLq6IiYmBikpKUhNTcWmTZtw6dIlaGtrA3i2grxhwwbs2bMHQ4YMwcGDB5Geno4zZ87Az88PU6dOrXYOPj4+GDFiBHx8fLB06VKcOnUK6enpOHz4MGJiYsQcAGD58uWwsbFBjx49sHHjRiQnJyMtLQ0RERHo0qWLeLt0eSQSCSIjI5GamorevXtj165dSEtLQ3JyMn744Qfcv39fHO/HH3/EtGnTEBsbi6tXr+LixYuYN28eLl68iMGDByv1e+/ePWRmZiItLQ3R0dHo3bs3Hjx4gHXr1lV7XoiIiIio4eIt0ET1UNu2bZGUlITg4GAEBATg9u3bkEqlcHZ2xty5czF9+nQxdujQoTh+/DhCQkIwduxY5ObmwsbGBv369VP6rt6qkkgkiImJQVhYGCIjIxEaGorCwkK0bNkS/fv3x1dffSXGmpiY4OTJk1ixYgWCgoJw48YNGBsbo0OHDli5ciXkcnml4/Xs2RN//vkngoODMWPGDGRmZsLAwACdOnXC6tWrMWnSJABA9+7dcfToUUydOhV37tyBTCaDi4sLduzYgT59+ij16eTkBIlEAplMBjs7OwwaNAiffvopLC0tqz0vRERERNRw8XuAiYgqwO8BJqoH+D3ARERUgap8DzBXgImI1BHwC1DJGyoRERER1W/8DDAR1bqEhASl7/R9cSMiIiIiehW4AkxEtc7V1RUKhaKu0yAiIiIiDccCmIhqnb6+fpnvOyYiIiIietV4CzQRERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGoEFMBEREREREWkEFsBERERERESkEVgAExERERERkUZgAUxEREREREQagQUwERERERERaQQWwERERERERKQRdOo6ASKiBiFkLCBtUtdZEDUcS7bXdQZERERlcAWYiIiIiIiINAILYCIiIiIiItIILIBJ40RFRcHIyKjW+o+Pj4dEIkF2dnaN9Jeeng6JRAKFQlEj/b1KL85Fbc89EREREVFFWABTo+Tr6wuJRAKJRAJdXV3Y29tj6dKlKCoqqvWxe/XqhYyMDMjl8lof63lhYWHo1KkTZDIZjIyM0KVLF4SEhNRI3zVV1I8ePRqXL1+ukZz69u0r/owlEgksLCwwcuRI3LhxQ4wp/eNB6WZqaopBgwYhKSmpRnIgIiIiooaFBTA1Wp6ensjIyEBaWho+++wzLFmyBCtXrqz1cXV1dWFpaQmJRFLrY5WKiIjAnDlzMGvWLCgUChw7dgx+fn7Iy8t7ZTmoQ19fH+bm5jXW35QpU5CRkYE7d+5g586duHXrFsaPH18m7uDBg8jIyMD+/fuRl5cHLy+vGluhJyIiIqKGgwUwNVpSqRSWlpawtbXFtGnTMGDAAOzatUvcv3//frRv3x4ymUwslgHgyJEjaNKkCTIzM5X6mzNnDt58800AwI0bNzB48GAYGxvDwMAALi4u2Lt3LwDVq6XHjh1D37590bRpUxgbG8PDwwOPHj0CAOzbtw9vvPEGjIyMYGpqinfeeQdXr16t0rnu2rULo0aNwuTJk2Fvbw8XFxeMGTMGy5cvf+lzSk9Ph7u7OwDA2NgYEokEvr6+AICCggLMmjUL5ubm0NPTwxtvvIHTp0+Xm6eqW6B///13dOvWDXp6ejAzM8OwYcPUPu+mTZvC0tISVlZW6NmzJ2bOnImzZ8+WiTM1NYWlpSVcXV2xatUq3L17F6dOnVJ7HCIiIiJqHFgAk8bQ19fHP//8AwDIz8/HqlWr8PPPP+PIkSO4efMm5s6dCwB46623YGdnh59//lk8trCwEJs3b8akSZMAADNmzEBBQQGOHDmCCxcu4Msvv4RMJlM5rkKhQP/+/eHs7IwTJ07g6NGjGDx4MIqLiwEAT548waeffoozZ84gLi4OWlpaGDZsGEpKStQ+N0tLS5w8eVLp9t/nvcw52djY4LfffgMApKamIiMjA9988w0AwM/PD7/99ht++uknnD17Fvb29vDw8MDDhw/VynvPnj0YNmwYvL29kZSUhLi4OHTv3l3t837ew4cP8euvv6JHjx4Vxunr6wOAeC28qKCgALm5uUobERERETUO/B5gavQEQUBcXBz279+Pjz/+GMCz4u+HH35A27ZtAQAzZ87E0qVLxWMmT56MyMhIfP755wCerVL+/fffGDVqFADg5s2bGD58ODp06AAAsLOzK3f80NBQuLq6Yu3atWKbi4uL+N/Dhw9Xio+IiEDz5s2RnJyM1157Ta1zXLx4Md599120bt0ajo6OcHNzg7e3N0aMGAEtLa2XPicTExMAgLm5ubiC++TJE6xbtw5RUVHw8vIC8OxzyAcOHEB4eLg4TkWWL18OHx8fBAYGim2dOnVS65wBYO3atdiwYQMEQUB+fj4cHR2xf//+cuOzs7OxbNkyyGSycgvtkJAQpXyIiIiIqPHgCjA1Wrt374ZMJoOenh68vLwwevRoLFmyBMCzW2dLi18AsLKywr1798TXvr6+uHLlCk6ePAng2a27o0aNgoGBAQBg1qxZCAoKQu/evbF48WKcP3++3DxKV4DLk5aWhjFjxsDOzg6GhoZo3bo1gGcFqSouLi6QyWSQyWRi4WllZYUTJ07gwoULmD17NoqKijBx4kR4enqKK8k1eU4AcPXqVRQWFqJ3795iW5MmTdC9e3ekpKRUeKy6c1OZcePGQaFQ4Ny5czh69Cjs7e0xaNAgPH78WCmuV69ekMlkMDY2xrlz5xATEwMLCwuVfQYEBCAnJ0fcbt26Ve38iIiIiKh+YQFMjZa7uzsUCgXS0tLw9OlT/PTTT2Kx16RJE6VYiUQCQRDE1+bm5hg8eDAiIyNx9+5d/PHHH+KtwgDwwQcf4Nq1a5gwYQIuXLgAV1dXfPfddyrzKL3ltjyDBw/Gw4cPERYWhlOnTomfTS3vFt29e/dCoVBAoVBgw4YNSvtee+01TJ8+HZs2bcKBAwdw4MABHD58uMbPqaZUNjeVkcvlsLe3h729PXr37o3w8HCkpaUhJiZGKS4mJgbnzp3Do0ePcPXqVXh7e5fbp1QqhaGhodJGRERERI0DC2BqtAwMDGBvb49WrVpBR6fqd/t/8MEHiImJwfr169G2bVullU4AsLGxwdSpU7Ft2zZ89tlnCAsLU9lPx44dERcXp3JfVlYWUlNTsXDhQvTv3x/t27cXH45VHltbW7Hos7a2LjfO2dkZwLNblV/2nHR1dQFA/NwyALRt2xa6uro4duyY2FZYWIjTp0+LY1emormpDm1tbQDA06dPldptbGzQtm1bfgcxERERkYbjZ4CJyuHh4QFDQ0MEBQUpfT4YePb0ZC8vLzg6OuLRo0c4dOgQ2rdvr7KfgIAAdOjQAdOnT8fUqVOhq6uLQ4cOYeTIkTAxMYGpqSnWr18PKysr3Lx5E/7+/lXOddq0aWjRogX69euHli1bIiMjA0FBQWjevDnc3Nxe+pxsbW0hkUiwe/dueHt7Q19fHzKZDNOmTcPnn38OExMTtGrVCqGhocjPz8fkyZPVynvx4sXo378/2rZtCx8fHxQVFWHv3r2YN2+eWsfn5+eLT7a+e/culi1bBj09PQwaNEit44mIiIhIs3AFmKgcWlpa8PX1RXFxMd577z2lfcXFxZgxYwbat28PT09PODo6Kj3k6nmOjo6IjY3FuXPn0L17d7i5uWHnzp3Q0dGBlpYWoqOj8eeff+K1117DJ598Uq3vKh4wYABOnjyJkSNHwtHREcOHD4eenh7i4uJgamr60udkbW2NwMBA+Pv7w8LCAjNnzgQArFixAsOHD8eECRPQtWtXXLlyBfv374exsbFaefft2xdbt27Frl270LlzZ/Tr1w+JiYlqn3dYWBisrKxgZWUFd3d3PHjwAHv37oWTk5PafRARERGR5pAIz3/wkYiUTJ48Gffv31f6/uCGrjGeU23Kzc2FXC5Hjv/bMJQ2qfwAInpmyfa6zoCIiDSE+PtaTk6lz2/hLdBEKuTk5ODChQv45ZdfGk2h2BjP6ZUK+AXgA7GIiIiIGjQWwEQqDB06FImJiZg6dSoGDhxY1+nUiIZ0TgkJCeJXPKmSl5f3CrMhIiIiosaCt0ATUb3z9OlT/O9//yt3v729/SvLpSq31BARERHRq8dboImoQdPX13+lRS4RERERaQY+BZqIiIiIiIg0AgtgIiIiIiIi0ggsgImIiIiIiEgjsAAmIiIiIiIijcACmIiIiIiIiDQCC2AiIiIiIiLSCCyAiYiIiIiISCOwACYiIiIiIiKNwAKYiIiIiIiINAILYCIiIiIiItIIOnWdABFRgxAyFpA2qessiBqOJdvrOgMiIqIyuAJMREREREREGoEFMBEREREREWkEFsBERERERESkEVgAE9UgX19fSCSSMtuVK1cAAJmZmZg9ezbs7e2hp6cHCwsL9O7dG+vWrUN+fr5SX0lJSRg5ciQsLCygp6cHBwcHTJkyBZcvXy53/Pj4eJXjL1y4UIwRBAFhYWFwc3ODoaEhZDIZXFxcMHv2bDHPUrm5uViwYAHatWsHPT09WFpaYsCAAdi2bRsEQVBrTq5cuYJJkyahVatWkEqlsLa2Rv/+/bF582YUFRWJcYcPH0a/fv1gYmKCpk2bwsHBARMnTsQ///yjdG4uLi4oLi5WGsPIyAhRUVHi69atW4vnrq+vj9atW2PUqFH473//q1bORERERNQ4sQAmqmGenp7IyMhQ2tq0aYNr166hS5cuiI2NRXBwMJKSknDixAn4+flh9+7dOHjwoNjH7t270bNnTxQUFGDz5s1ISUnBpk2bIJfL8cUXX1SaQ2pqqtL4/v7+AJ4Vv2PHjsWsWbPg7e2N2NhYJCcnIzw8HHp6eggKChL7yM7ORq9evbBx40YEBATg7NmzOHLkCEaPHg0/Pz/k5ORUmkdiYiK6du2KlJQUfP/99/jrr78QHx+PDz74AOvWrcPFixcBAMnJyfD09ISrqyuOHDmCCxcu4LvvvoOurm6ZYvfatWvYuHFjpWMvXboUGRkZSE1NxcaNG2FkZIQBAwZg+fLllR5LRERERI0TnwJNVMOkUiksLS3LtE+fPh06Ojo4c+YMDAwMxHY7OzsMHTpUXFHNz8/H+++/D29vb2zf/v9PUW3Tpg169OiB7OzsSnMwNzeHkZFRmfaYmBhER0dj586dGDJkiNjeqlUr9OzZU2lVd/78+UhPT8fly5fRokULsd3R0RFjxoyBnp5ehTkIggBfX184Ojri2LFj0NL6/7+3OTg4YMyYMeJ4sbGxsLS0RGhoqBjTtm1beHp6lun3448/xuLFizF27FhIpdJyx2/WrJn4c2jVqhXeeustWFlZYdGiRRgxYgScnJwqzJ+IiIiIGh+uABO9AllZWYiNjcWMGTOUit/nSSQSAMD+/fvx4MED+Pn5qYxTVdiqa8uWLXByclIqflXlUFJSgujoaIwbN06p+C0lk8mgo1Px388UCgVSUlIwd+5cpeJX1XiWlpbIyMjAkSNHKj2HOXPmoKioCN99912lsS+aPXs2BEHAzp07y40pKChAbm6u0kZEREREjQMLYKIatnv3bshkMnEbOXIkrly5AkEQyqw6mpmZiXHz5s0DAKSlpQEA2rVrV+0cWrZsqZRDVlYWAODy5ctlcpgzZ44Y17JlSwDAgwcP8OjRo5fKofSzys+Pd+/ePaW81q5dCwAYOXIkxowZgz59+sDKygrDhg3DmjVrVBafTZs2xeLFixESEqLWbdjPMzExgbm5OdLT08uNCQkJgVwuFzcbG5sqjUFERERE9RcLYKIa5u7uDoVCIW7ffvttubGJiYlQKBRwcXFBQUEBAKj9cCkXFxexkPTy8lLal5CQoJSDsbFxuf0sWLAACoUCixYtQl5eXpVyqCpTU1MxJyMjI/EBV9ra2oiMjMTt27cRGhoKa2trBAcHw8XFBRkZGWX6mTx5MkxNTfHll19WOQdBEMSVZ1UCAgKQk5Mjbrdu3aryGERERERUP/EzwEQ1zMDAAPb29kpturq6kEgkSE1NVWq3s7MDAOjr64ttjo6OAIBLly7Bzc2t3HH27t2LwsLCMscDzz4vrOpWaQcHhzI5NG/eHM2bN4e5ublSm5GRES5dulTu+JVxcHAA8OyBXF26dAHwrNAtnRtVt1BbW1tjwoQJmDBhApYtWwZHR0f88MMPCAwMVIrT0dHB8uXL4evri5kzZ6qdU1ZWFu7fv482bdqUGyOVSiv8bDERERERNVxcASZ6BUxNTTFw4ECsWbMGT548qTB20KBBMDMzU3og1PNKH4Jla2sLe3t72Nvbw9raWq08xowZg9TU1Ao/AwsAWlpa8PHxwebNm3Hnzp0y+/Py8pS+wkiVLl26oF27dli1ahVKSkrUyu95xsbGsLKyKne+Ro4cCRcXlzLFcUW++eYbaGlp4V//+leV8yEiIiKiho8rwESvyNq1a9G7d2+4urpiyZIl6NixI7S0tHD69GlcunQJr7/+OoBnK8gbNmzAyJEjMWTIEMyaNQv29vZ48OABfv31V9y8eRPR0dHVysHHxwfbtm2Dj48PAgIC4OHhAQsLC9y4cQMxMTHQ1tYWY5cvX474+Hj06NEDy5cvh6urK5o0aYKEhASEhITg9OnTFT6QSyKRIDIyEgMHDkTv3r0REBCA9u3bo7CwEEeOHMH9+/fF8X788UcoFAoMGzYMbdu2xd9//42NGzfi4sWLFT7sasWKFfDw8FC57/Hjx8jMzERhYSGuX7+OTZs2YcOGDQgJCSmzQk9EREREmoEFMNEr0rZtWyQlJSE4OBgBAQG4ffs2pFIpnJ2dMXfuXEyfPl2MHTp0KI4fP46QkBCMHTsWubm5sLGxQb9+/ZS+q7eqJBIJYmJiEBYWhsjISISGhqKwsBAtW7ZE//798dVXX4mxJiYmOHnyJFasWIGgoCDcuHEDxsbG6NChA1auXAm5XF7peD179sSff/6J4OBgzJgxA5mZmTAwMECnTp2wevVqTJo0CQDQvXt3HD16FFOnTsWdO3cgk8ng4uKCHTt2oE+fPuX2369fP/Tr1w+xsbFl9i1atAiLFi2Crq4uLC0t0bNnT8TFxcHd3b0aM0dEREREjYFEqK2n3RARNQK5ubmQy+XI8X8bhtImdZ0OUcOxZHvlMURERDVA/H0tJweGhoYVxnIFmIhIHQG/AJW8oRIRERFR/caHYBFRtSQkJCh9p++LGxERERFRfcMVYCKqFldXVygUirpOg4iIiIhIbSyAiaha9PX1+TRlIiIiImpQeAs0ERERERERaQQWwERERERERKQRWAATERERERGRRmABTERERERERBqBBTARERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGoEFMBEREREREWkEFsBERERERESkEXTqOgEiogYhZCwgbVLXWRC9eku213UGRERENYYrwERERERERKQRWAATERERERGRRmABTERERERERBqBBTBRA+Pr6wuJRFJmu3LlCgAgMzMTs2fPhr29PfT09GBhYYHevXtj3bp1yM/PV+orKSkJI0eOhIWFBfT09ODg4IApU6bg8uXL5Y4fHx+vcvyFCxeKMYIgICwsDG5ubjA0NIRMJoOLiwtmz54t5lkqNzcXCxYsQLt27aCnpwdLS0sMGDAA27ZtgyAI5eaRnp6uMo/nt6ioKDHf7Ozsasw2ERERETUmfAgWUQPk6emJyMhIpbbmzZvj2rVr6N27N4yMjBAcHIwOHTpAKpXiwoULWL9+PaytrTFkyBAAwO7duzF8+HB4eHhg8+bNaNu2Le7du4etW7fiiy++QExMTIU5pKamwtDQUHwtk8kAPCt+x44dix07dmD+/PlYvXo1WrRogTt37mD79u0ICgpCVFQUACA7OxtvvPEGcnJyEBQUhG7dukFHRweHDx+Gn58f+vXrByMjI5Xj29jYICMjQ3y9atUq7Nu3DwcPHhTb5HI5Tp06pfa8EhEREVHjxgKYqAGSSqWwtLQs0z59+nTo6OjgzJkzMDAwENvt7OwwdOhQcUU1Pz8f77//Pry9vbF9+/8/4bVNmzbo0aOHWqul5ubmKovTmJgYREdHY+fOnWKxDQCtWrVCz549lVZ158+fj/T0dFy+fBktWrQQ2x0dHTFmzBjo6emVO762trbSHMhkMujo6Kicl6ooKChAQUGB+Do3N/el+iMiIiKi+oO3QBM1EllZWYiNjcWMGTOUit/nSSQSAMD+/fvx4MED+Pn5qYwrb9VVHVu2bIGTk5NS8asqh5KSEkRHR2PcuHFKxW+p0oL2VQsJCYFcLhc3GxubV54DEREREdUOFsBEDdDu3bshk8nEbeTIkbhy5QoEQYCTk5NSrJmZmRg3b948AEBaWhoAoF27dtXOoWXLlko5ZGVlAQAuX75cJoc5c+aIcS1btgQAPHjwAI8ePXqpHGpDQEAAcnJyxO3WrVt1nRIRERER1RDeAk3UALm7u2PdunXiawMDA9y8eVNlbGJiIkpKSjBu3Djx1t6KHi71PBcXF9y4cQMA8Oabb+KPP/4Q9yUkJKBZs2bia2Nj43L7WbBgAWbOnIlt27YhODi4Sjm8alKpFFKptK7TICIiIqJawAKYqAEyMDCAvb29Upuuri4kEglSU1OV2u3s7AAA+vr6YpujoyMA4NKlS3Bzcyt3nL1796KwsLDM8cCzzwurulXawcGhTA7NmzdH8+bNYW5urtRmZGSES5culTs+EREREVFN4i3QRI2EqakpBg4ciDVr1uDJkycVxg4aNAhmZmYIDQ1Vub/0IVi2trawt7eHvb09rK2t1cpjzJgxSE1Nxc6dOyuM09LSgo+PDzZv3ow7d+6U2Z+Xl4eioiK1xiQiIiIiUgcLYKJGZO3atSgqKoKrqytiYmKQkpKC1NRUbNq0CZcuXYK2tjaAZyvIGzZswJ49ezBkyBAcPHgQ6enpOHPmDPz8/DB16tRq5+Dj44MRI0bAx8cHS5cuxalTp5Ceno7Dhw8jJiZGzAEAli9fDhsbG/To0QMbN25EcnIy0tLSEBERgS5duiAvL++l56TUhQsXoFAoxO3cuXM11jcRERERNQy8BZqoEWnbti2SkpIQHByMgIAA3L59G1KpFM7Ozpg7dy6mT58uxg4dOhTHjx9HSEgIxo4di9zcXNjY2KBfv34ICgqqdg4SiQQxMTEICwtDZGQkQkNDUVhYiJYtW6J///746quvxFgTExOcPHkSK1asQFBQEG7cuAFjY2N06NABK1euhFwuf6n5eN5bb72l9FpbW5srzEREREQaRiLU1yfREBHVA7m5uZDL5cjxfxuG0iZ1nQ7Rq7dke+UxREREdUj8fS0nB4aGhhXGcgWYiEgdAb8AlbyhEhEREVH9xs8AE1G9lZCQoPRdwy9uRERERERVwRVgIqq3XF1doVAo6joNIiIiImokWAATUb2lr69f5vuOiYiIiIiqi7dAExERERERkUZgAUxEREREREQagQUwERERERERaQQWwERERERERKQRWAATERERERGRRmABTERERERERBqBBTARERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGkGnrhMgImoQQsYC0iZ1nQU1Zku213UGREREjR5XgImIiIiIiEgjsAAmIiIiIiIijcACmKge8/X1hUQiETdTU1N4enri/PnzYoxEIsGOHTvKHPvRRx9BW1sbW7durXScqKgopXFkMhlef/11bNu2TSmub9++mDNnTpnjt2zZAm1tbcyYMaNK5xcfH680bum2cOHCKu03NjbG33//rdT36dOnxfgXx8vOzq5SnkRERETUOLAAJqrnPD09kZGRgYyMDMTFxUFHRwfvvPNOhcfk5+cjOjoafn5+iIiIUGscQ0NDcZykpCR4eHhg1KhRSE1NrfTY8PBw+Pn5YcuWLWUKUXWkpqaKY2dkZMDf379K+5s1a4bt25U/PxkeHo5WrVpVORciIiIiarxYABPVc1KpFJaWlrC0tETnzp3h7++PW7du4f79++Ues3XrVjg7O8Pf3x9HjhzBrVu3Kh1HIpGI4zg4OCAoKAhaWlpKq82qXL9+HcePH4e/vz8cHR3LrBqrw9zcXBzb0tISMpmsSvsnTpyoVOg/ffoU0dHRmDhxYpVzISIiIqLGiwUwUQOSl5eHTZs2wd7eHqampuXGhYeHY/z48ZDL5fDy8kJUVFSVxikuLsZPP/0EAOjatWuFsZGRkXj77bchl8sxfvx4hIeHV2msmjBhwgQkJCTg5s2bAIDffvsNrVu3rjR3VQoKCpCbm6u0EREREVHjwAKYqJ7bvXs3ZDIZZDIZmjVrhl27diEmJgZaWqr/75uWloaTJ09i9OjRAIDx48cjMjISgiBUOE5OTo44jq6uLqZNm4b169ejbdu25R5TUlKCqKgojB8/HgDg4+ODo0eP4vr161U6x5YtW4pjy2QyZGVlVWm/ubm5UqEfERGBSZMmVSmHUiEhIZDL5eJmY2NTrX6IiIiIqP5hAUxUz7m7u0OhUEChUCAxMREeHh7w8vLCjRs3VMZHRETAw8MDZmZmAABvb2/k5OTgv//9LwAgODhYqZgsXTVt1qyZOE5SUhKCg4MxdepU/P777+XmduDAATx58gTe3t4AADMzMwwcOFDtzx2XSkhIEMdWKBQwNjau0n4AmDRpEqKionDt2jWcOHEC48aNq1IOpQICApCTkyNu6tw+TkREREQNg05dJ0BEFTMwMIC9vb34esOGDZDL5QgLC0NQUJBSbOmty5mZmdDR0VFqj4iIQP/+/TF16lSMGjVK3NeiRQsAgJaWltI4HTt2RGxsLL788ksMHjxYZW7h4eF4+PAh9PX1xbaSkhKcP38egYGB5a5Sv6hNmzYwMjKq9n4A8PLywocffojJkydj8ODBFd4iXhGpVAqpVFqtY4mIiIiofmMBTNTASCQSaGlp4enTp2X27d27F48fP0ZSUhK0tbXF9r/++gvvv/8+srOzYWJiAhMTE7XG0tbWVjkOAGRlZWHnzp2Ijo6Gi4uL2F5cXIw33ngDsbGx8PT0rOLZVZ+Ojg7ee+89hIaG4o8//nhl4xIRERFRw8ECmKieKygoQGZmJgDg0aNHWLNmDfLy8lSuyoaHh+Ptt99Gp06dlNqdnZ3xySefYPPmzeV+V68gCOI4T58+xYEDB7B//34sWrRIZfzPP/8MU1NTjBo1Sum7doFnt12Hh4e/0gIYAJYtW4bPP/+82qu/RERERNS4sQAmquf27dsHKysrAM8+p9uuXTts3boVffv2VYq7e/cu9uzZg19++aVMH1paWhg2bBjCw8PLLYBzc3PFcaRSKWxtbbF06VLMmzdPZXxERASGDRtWpvgFgOHDh2PChAl48OCB+FnkV0FXV/eVjkdEREREDYtEqOzRsEREGiw3NxdyuRw5/m/DUNqkrtOhxmzJ9rrOgIiIqEESf1/LyYGhoWGFsXwKNBEREREREWkE3gJNRLXGy8sLCQkJKvfNnz8f8+fPf8UZvYSAX4BK/qJIRERERPUbC2AiqjUbNmwo9ynS6j6JmoiIiIioprAAJqJaY21tXdcpEBERERGJ+BlgIiIiIiIi0ggsgImIiIiIiEgjsAAmIiIiIiIijcDPABMRVaD0q9Jzc3PrOBMiIiIiUqX097TS39sqwgKYiKgCWVlZAAAbG5s6zoSIiIiIKvL48WPI5fIKY1gAExFVoPTrmm7evFnpG6omyc3NhY2NDW7dugVDfj+yiPNSPs6NapwX1TgvqnFeVOO8qKZJ8yIIAh4/fowWLVpUGssCmIioAlpazx6VIJfLG/0/HtVhaGjIeVGB81I+zo1qnBfVOC+qcV5U47yopinzou5CBR+CRURERERERBqBBTARERERERFpBBbAREQVkEqlWLx4MaRSaV2nUq9wXlTjvJSPc6Ma50U1zotqnBfVOC+qcV5UkwjqPCuaiIiIiIiIqIHjCjARERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGoEFMBFpnO+//x6tW7eGnp4eevTogcTExArjt27dinbt2kFPTw8dOnTA3r17lfYLgoBFixbBysoK+vr6GDBgANLS0mrzFGpFTc+Lr68vJBKJ0ubp6Vmbp1ArqjIvFy9exPDhw9G6dWtIJBJ8/fXXL91nfVXT87JkyZIy10u7du1q8QxqR1XmJSwsDG+++SaMjY1hbGyMAQMGlInXxPcXdealsby/AFWbm23btsHV1RVGRkYwMDBA586d8fPPPyvFaOI1o868NJZrprr/fkRHR0MikeBf//qXUntjuV6qRCAi0iDR0dGCrq6uEBERIVy8eFGYMmWKYGRkJNy9e1dl/LFjxwRtbW0hNDRUSE5OFhYuXCg0adJEuHDhghizYsUKQS6XCzt27BDOnTsnDBkyRGjTpo3w9OnTV3VaL6025mXixImCp6enkJGRIW4PHz58VadUI6o6L4mJicLcuXOFLVu2CJaWlsLq1atfus/6qDbmZfHixYKLi4vS9XL//v1aPpOaVdV5GTt2rPD9998LSUlJQkpKiuDr6yvI5XLh9u3bYowmvr+oMy+N4f1FEKo+N4cOHRK2bdsmJCcnC1euXBG+/vprQVtbW9i3b58Yo4nXjDrz0hiumer++3H9+nXB2tpaePPNN4WhQ4cq7WsM10tVsQAmIo3SvXt3YcaMGeLr4uJioUWLFkJISIjK+FGjRglvv/22UluPHj2Ejz76SBAEQSgpKREsLS2FlStXivuzs7MFqVQqbNmypRbOoHbU9LwIwrNfNl78h7ahqeq8PM/W1lZlofcyfdYXtTEvixcvFjp16lSDWb56L/uzLSoqEpo1ayb89NNPgiBo7vvLi16cF0FoHO8vglAz7wddunQRFi5cKAgCr5nnPT8vgtA4rpnqzEtRUZHQq1cvYcOGDWXmoLFcL1XFW6CJSGP8888/+PPPPzFgwACxTUtLCwMGDMCJEydUHnPixAmleADw8PAQ469fv47MzEylGLlcjh49epTbZ31TG/NSKj4+Hubm5nBycsK0adOQlZVV8ydQS6ozL3XR56tWm+eQlpaGFi1awM7ODuPGjcPNmzdfNt1XpibmJT8/H4WFhTAxMQGgue8vL3pxXko15PcX4OXnRhAExMXFITU1FW+99RYAXjOA6nkp1ZCvmerOy9KlS2Fubo7JkyeX2dcYrpfq0KnrBIiIXpUHDx6guLgYFhYWSu0WFha4dOmSymMyMzNVxmdmZor7S9vKi6nvamNeAMDT0xPvvvsu2rRpg6tXr2L+/Pnw8vLCiRMnoK2tXfMnUsOqMy910eerVlvn0KNHD0RFRcHJyQkZGRkIDAzEm2++ib/++gvNmjV72bRrXU3My7x589CiRQvxl1FNfX950YvzAjT89xeg+nOTk5MDa2trFBQUQFtbG2vXrsXAgQMBaPY1U9G8AA3/mqnOvBw9ehTh4eFQKBQq9zeG66U6WAATEVGt8PHxEf+7Q4cO6NixI9q2bYv4+Hj079+/DjOj+sjLy0v8744dO6JHjx6wtbXFr7/+qnLlorFZsWIFoqOjER8fDz09vbpOp94ob140+f2lWbNmUCgUyMvLQ1xcHD799FPY2dmhb9++dZ1anapsXjTtmnn8+DEmTJiAsLAwmJmZ1XU69QpvgSYijWFmZgZtbW3cvXtXqf3u3buwtLRUeYylpWWF8aX/W5U+65vamBdV7OzsYGZmhitXrrx80q9AdealLvp81V7VORgZGcHR0VEjrpdVq1ZhxYoViI2NRceOHcV2TX1/KVXevKjS0N5fgOrPjZaWFuzt7dG5c2d89tlnGDFiBEJCQgBo9jVT0byo0tCumarOy9WrV5Geno7BgwdDR0cHOjo62LhxI3bt2gUdHR1cvXq1UVwv1cECmIg0hq6uLl5//XXExcWJbSUlJYiLi4Obm5vKY9zc3JTiAeDAgQNifJs2bWBpaakUk5ubi1OnTpXbZ31TG/Oiyu3bt5GVlQUrK6uaSbyWVWde6qLPV+1VnUNeXh6uXr3a6K+X0NBQLFu2DPv27YOrq6vSPk19fwEqnhdVGtr7C1Bz/18qKSlBQUEBAM2+Zl70/Lyo0tCumarOS7t27XDhwgUoFApxGzJkCNzd3aFQKGBjY9MorpdqqeuncBERvUrR0dGCVCoVoqKihOTkZOHDDz8UjIyMhMzMTEEQBGHChAmCv7+/GH/s2DFBR0dHWLVqlZCSkiIsXrxY5dcgGRkZCTt37hTOnz8vDB06tMF9hUBNz8vjx4+FuXPnCidOnBCuX78uHDx4UOjatavg4OAg/P3333VyjtVR1XkpKCgQkpKShKSkJMHKykqYO3eukJSUJKSlpandZ0NQG/Py2WefCfHx8cL169eFY8eOCQMGDBDMzMyEe/fuvfLzq66qzsuKFSsEXV1d4T//+Y/SV7M8fvxYKUbT3l8qm5fG8v4iCFWfm+DgYCE2Nla4evWqkJycLKxatUrQ0dERwsLCxBhNvGYqm5fGcs1UdV5epOpJ2I3heqkqFsBEpHG+++47oVWrVoKurq7QvXt34eTJk+K+Pn36CBMnTlSK//XXXwVHR0dBV1dXcHFxEfbs2aO0v6SkRPjiiy8ECwsLQSqVCv379xdSU1NfxanUqJqcl/z8fGHQoEFC8+bNhSZNmgi2trbClClTGlSRV6oq83L9+nUBQJmtT58+avfZUNT0vIwePVqwsrISdHV1BWtra2H06NHClStXXuEZ1YyqzIutra3KeVm8eLEYo4nvL5XNS2N6fxGEqs3NggULBHt7e0FPT08wNjYW3NzchOjoaKX+NPGaqWxeGtM1U9V/q5+nqgBuLNdLVUgEQRBe7ZozERERERER0avHzwATERERERGRRmABTERERERERBqBBTARERERERFpBBbAREREREREpBFYABMREREREZFGYAFMREREREREGoEFMBEREREREWkEFsBERERERESkEVgAExEREdUSiUSCHTt21Jt+6lp+fj6GDx8OQ0NDSCQSZGdnq2xr3bo1vv76a7X6jIqKgpGRUa3mTUSNBwtgIiIiahQyMzPx8ccfw87ODlKpFDY2Nhg8eDDi4uLqOjW1LVmyBJ07dy7TnpGRAS8vr1efUA376aefkJCQgOPHjyMjIwNyuVxl2+nTp/Hhhx+q1efo0aNx+fLlGs0zPj5eLMaJqHHRqesEiIiIiF5Weno6evfuDSMjI6xcuRIdOnRAYWEh9u/fjxkzZuDSpUvV6veff/6Brq5umfbCwkI0adLkZdNWm6Wl5SsbqzZdvXoV7du3x2uvvVZhW/PmzdXuU19fH/r6+jWaJxE1XlwBJiIiogZv+vTpkEgkSExMxPDhw+Ho6AgXFxd8+umnOHnypBh38+ZNDB06FDKZDIaGhhg1ahTu3r0r7i9dgd2wYQPatGkDPT09AM9uQV63bh2GDBkCAwMDLF++HACwc+dOdO3aFXp6erCzs0NgYCCKiorKzXPevHlwdHRE06ZNYWdnhy+++AKFhYUAnt3KGxgYiHPnzkEikUAikSAqKkoc//lboC9cuIB+/fpBX18fpqam+PDDD5GXlyfu9/X1xb/+9S+sWrUKVlZWMDU1xYwZM8SxyvP777+jW7du0NPTg5mZGYYNGybue/ToEd577z0YGxujadOm8PLyQlpamtLxR48exZtvvgl9fX3Y2Nhg1qxZePLkCQCgb9+++Pe//40jR45AIpGgb9++KtsAlLkFOjs7Gx999BEsLCygp6eH1157Dbt37xbn7cVboCv7uUgkEmzYsAHDhg1D06ZN4eDggF27dgF49scUd3d3AICxsTEkEgl8fX0rnDciajhYABMREVGD9vDhQ+zbtw8zZsyAgYFBmf2lxVFJSQmGDh2Khw8f4vDhwzhw4ACuXbuG0aNHK8VfuXIFv/32G7Zt2waFQiG2L1myBMOGDcOFCxcwadIkJCQk4L333sPs2bORnJyMH3/8EVFRUWJxrEqzZs0QFRWF5ORkfPPNNwgLC8Pq1asBPLuV97PPPoOLiwsyMjKQkZFRJjcAePLkCTw8PGBsbIzTp09j69atOHjwIGbOnKkUd+jQIVy9ehWHDh3CTz/9hKioKLGgVmXPnj0YNmwYvL29kZSUhLi4OHTv3l3c7+vrizNnzmDXrl04ceIEBEGAt7e3WFRfvXoVnp6eGD58OM6fP4+YmBgcPXpUzGvbtm2YMmUK3NzckJGRgW3btqlse1FJSQm8vLxw7NgxbNq0CcnJyVixYgW0tbVVnoe6P5fAwECMGjUK58+fh7e3N8aNG4eHDx/CxsYGv/32GwAgNTUVGRkZ+Oabb8qdNyJqYAQiIiKiBuzUqVMCAGHbtm0VxsXGxgra2trCzZs3xbaLFy8KAITExERBEARh8eLFQpMmTYR79+4pHQtAmDNnjlJb//79heDgYKW2n3/+WbCyslI6bvv27eXmtHLlSuH1118XXy9evFjo1KlTmbjn+1m/fr1gbGws5OXlifv37NkjaGlpCZmZmYIgCMLEiRMFW1tboaioSIwZOXKkMHr06HJzcXNzE8aNG6dy3+XLlwUAwrFjx8S2Bw8eCPr6+sKvv/4qCIIgTJ48Wfjwww+VjktISBC0tLSEp0+fCoIgCLNnzxb69OmjFKOqzdbWVli9erUgCIKwf/9+QUtLS0hNTVWZW2RkpCCXy8XX6v5cFi5cKL7Oy8sTAAh//PGHIAiCcOjQIQGA8OjRI5VjElHDxc8AExERUYMmCIJacSkpKbCxsYGNjY3Y5uzsDCMjI6SkpKBbt24AAFtbW5WfQXV1dVV6fe7cORw7dkxpZbG4uBh///038vPz0bRp0zJ9xMTE4Ntvv8XVq1eRl5eHoqIiGBoaqpX/8+fRqVMnpdXu3r17o6SkBKmpqbCwsAAAuLi4KK2SWllZ4cKFC+X2q1AoMGXKlHLH1NHRQY8ePcQ2U1NTODk5ISUlBcCz+Th//jw2b94sxgiCgJKSEly/fh3t27ev0nk+n1fLli3h6OioVry6P5eOHTuK+w0MDGBoaIh79+5VK0ciajhYABMREVGD5uDgAIlEUu0HXb1I1W3Uqtrz8vIQGBiId999t0xs6WeHn3fixAmMGzcOgYGB8PDwgFwuR3R0NP7973/XSN4vevEhXRKJBCUlJeXGv+yDpPLy8vDRRx9h1qxZZfa1atWq2v1WNS91fy5VnR8iahxYABMREVGDZmJiAg8PD3z//feYNWtWmUI1OzsbRkZGaN++PW7duoVbt26Jq8DJycnIzs6Gs7Nzlcft2rUrUlNTYW9vr1b88ePHYWtriwULFohtN27cUIrR1dVFcXFxhf20b98eUVFRePLkiXiux44dg5aWFpycnKp4Fv+vY8eOiIuLw/vvv69yzKKiIpw6dQq9evUCAGRlZSE1NVWcu65duyI5OVnt+ahKXrdv38bly5fVWgWu6s9FldInf1f2syCihocPwSIiIqIG7/vvv0dxcTG6d++O3377DWlpaUhJScG3334LNzc3AMCAAQPQoUMHjBs3DmfPnkViYiLee+899OnTp8ztzepYtGgRNm7ciMDAQFy8eBEpKSmIjo7GwoULVcY7ODjg5s2biI6OxtWrV/Htt99i+/btSjGtW7fG9evXoVAo8ODBAxQUFJTpZ9y4cdDT08PEiRPx119/4dChQ/j4448xYcIE8fbn6li8eDG2bNmCxYsXIyUlBRcuXMCXX34p5j506FBMmTIFR48exblz5zB+/HhYW1tj6NChAJ494fr48eOYOXMmFAoF0tLSsHPnzjIP56qqPn364K233sLw4cNx4MABXL9+HX/88Qf27dunMr6qPxdVbG1tIZFIsHv3bty/f1/pCdtE1LCxACYiIqIGz87ODmfPnoW7uzs+++wzvPbaaxg4cCDi4uKwbt06AM9ucd25cyeMjY3x1ltvYcCAAbCzs0NMTEy1xvTw8MDu3bsRGxuLbt26oWfPnli9ejVsbW1Vxg8ZMgSffPIJZs6cic6dO+P48eP44osvlGKGDx8OT09PuLu7o3nz5tiyZUuZfpo2bYr9+/fj4cOH6NatG0aMGIH+/ftjzZo11TqPUn379sXWrVuxa9cudO7cGf369UNiYqK4PzIyEq+//jreeecduLm5QRAE7N27V7yVuGPHjjh8+DAuX76MN998E126dMGiRYvQokWLl8oLAH777Td069YNY8aMgbOzM/z8/Mpdna3qz0UVa2trBAYGwt/fHxYWFi9dxBNR/SER1H1yBBEREREREVEDxhVgIiIiIiIi0ggsgImIiIiIiEgjsAAmIiIiIiIijcACmIiIiIiIiDQCC2AiIiIiIiLSCCyAiYiIiIiISCOwACYiIiIiIiKNwAKYiIiIiIiINAILYCIiIiIiItIILICJiIiIiIhII7AAJiIiIiIiIo3wf4kvME1vz8PeAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":72},{"cell_type":"code","source":"actigraphy = pl.read_parquet('/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0417c91e/part-0.parquet')\n\nactigraphy","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:57:36.829532Z","iopub.execute_input":"2025-06-28T14:57:36.829868Z","iopub.status.idle":"2025-06-28T14:57:36.858628Z","shell.execute_reply.started":"2025-06-28T14:57:36.829844Z","shell.execute_reply":"2025-06-28T14:57:36.857727Z"}},"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"shape: (287_179, 13)\n┌────────┬───────────┬───────────┬───────────┬───┬──────────────┬─────────┬─────────┬──────────────┐\n│ step   ┆ X         ┆ Y         ┆ Z         ┆ … ┆ time_of_day  ┆ weekday ┆ quarter ┆ relative_dat │\n│ ---    ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---          ┆ ---     ┆ ---     ┆ e_PCIAT      │\n│ u32    ┆ f32       ┆ f32       ┆ f32       ┆   ┆ i64          ┆ i8      ┆ i8      ┆ ---          │\n│        ┆           ┆           ┆           ┆   ┆              ┆         ┆         ┆ f32          │\n╞════════╪═══════════╪═══════════╪═══════════╪═══╪══════════════╪═════════╪═════════╪══════════════╡\n│ 0      ┆ 0.014375  ┆ -0.020112 ┆ -0.995358 ┆ … ┆ 441000000000 ┆ 2       ┆ 2       ┆ 5.0          │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 1      ┆ 0.014167  ┆ -0.023278 ┆ -0.996164 ┆ … ┆ 441050000000 ┆ 2       ┆ 2       ┆ 5.0          │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 2      ┆ 0.014036  ┆ -0.022964 ┆ -0.99632  ┆ … ┆ 441100000000 ┆ 2       ┆ 2       ┆ 5.0          │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 3      ┆ 0.013593  ┆ -0.022048 ┆ -0.996762 ┆ … ┆ 441150000000 ┆ 2       ┆ 2       ┆ 5.0          │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 4      ┆ -0.061772 ┆ -0.065317 ┆ -0.973063 ┆ … ┆ 447800000000 ┆ 2       ┆ 2       ┆ 5.0          │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ …      ┆ …         ┆ …         ┆ …         ┆ … ┆ …            ┆ …       ┆ …       ┆ …            │\n│ 287174 ┆ -0.407433 ┆ 0.091612  ┆ -0.377763 ┆ … ┆ 328750000000 ┆ 1       ┆ 3       ┆ 53.0         │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 287175 ┆ -0.703572 ┆ 0.016187  ┆ 0.15956   ┆ … ┆ 328800000000 ┆ 1       ┆ 3       ┆ 53.0         │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 287176 ┆ -0.209607 ┆ -0.4697   ┆ 0.636573  ┆ … ┆ 328850000000 ┆ 1       ┆ 3       ┆ 53.0         │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 287177 ┆ -0.390378 ┆ 0.284386  ┆ 0.147654  ┆ … ┆ 328900000000 ┆ 1       ┆ 3       ┆ 53.0         │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n│ 287178 ┆ -0.48903  ┆ 0.179624  ┆ -0.509611 ┆ … ┆ 328950000000 ┆ 1       ┆ 3       ┆ 53.0         │\n│        ┆           ┆           ┆           ┆   ┆ 00           ┆         ┆         ┆              │\n└────────┴───────────┴───────────┴───────────┴───┴──────────────┴─────────┴─────────┴──────────────┘","text/html":"<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (287_179, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>step</th><th>X</th><th>Y</th><th>Z</th><th>enmo</th><th>anglez</th><th>non-wear_flag</th><th>light</th><th>battery_voltage</th><th>time_of_day</th><th>weekday</th><th>quarter</th><th>relative_date_PCIAT</th></tr><tr><td>u32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>i64</td><td>i8</td><td>i8</td><td>f32</td></tr></thead><tbody><tr><td>0</td><td>0.014375</td><td>-0.020112</td><td>-0.995358</td><td>0.00106</td><td>-88.445251</td><td>0.0</td><td>41.0</td><td>4195.0</td><td>44100000000000</td><td>2</td><td>2</td><td>5.0</td></tr><tr><td>1</td><td>0.014167</td><td>-0.023278</td><td>-0.996164</td><td>0.000289</td><td>-88.3722</td><td>0.0</td><td>41.0</td><td>4194.833496</td><td>44105000000000</td><td>2</td><td>2</td><td>5.0</td></tr><tr><td>2</td><td>0.014036</td><td>-0.022964</td><td>-0.99632</td><td>0.000301</td><td>-88.356422</td><td>0.0</td><td>41.5</td><td>4194.666504</td><td>44110000000000</td><td>2</td><td>2</td><td>5.0</td></tr><tr><td>3</td><td>0.013593</td><td>-0.022048</td><td>-0.996762</td><td>0.002278</td><td>-88.575943</td><td>0.0</td><td>37.5</td><td>4194.5</td><td>44115000000000</td><td>2</td><td>2</td><td>5.0</td></tr><tr><td>4</td><td>-0.061772</td><td>-0.065317</td><td>-0.973063</td><td>0.092321</td><td>-88.391273</td><td>0.0</td><td>55.666668</td><td>4199.0</td><td>44780000000000</td><td>2</td><td>2</td><td>5.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>287174</td><td>-0.407433</td><td>0.091612</td><td>-0.377763</td><td>0.039733</td><td>-43.319416</td><td>0.0</td><td>7.0</td><td>3695.0</td><td>32875000000000</td><td>1</td><td>3</td><td>53.0</td></tr><tr><td>287175</td><td>-0.703572</td><td>0.016187</td><td>0.15956</td><td>0.03598</td><td>14.12139</td><td>0.0</td><td>7.0</td><td>3695.0</td><td>32880000000000</td><td>1</td><td>3</td><td>53.0</td></tr><tr><td>287176</td><td>-0.209607</td><td>-0.4697</td><td>0.636573</td><td>0.097799</td><td>44.998573</td><td>0.0</td><td>7.0</td><td>3695.0</td><td>32885000000000</td><td>1</td><td>3</td><td>53.0</td></tr><tr><td>287177</td><td>-0.390378</td><td>0.284386</td><td>0.147654</td><td>0.057826</td><td>7.726313</td><td>0.0</td><td>7.0</td><td>3695.0</td><td>32890000000000</td><td>1</td><td>3</td><td>53.0</td></tr><tr><td>287178</td><td>-0.48903</td><td>0.179624</td><td>-0.509611</td><td>0.077749</td><td>-36.995014</td><td>0.0</td><td>7.0</td><td>3695.0</td><td>32895000000000</td><td>1</td><td>3</td><td>53.0</td></tr></tbody></table></div>"},"metadata":{}}],"execution_count":73},{"cell_type":"code","source":"def analyze_actigraphy(id, only_one_week=False, small=False):\n\n    actigraphy = pl.read_parquet(f'/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id={id}/part-0.parquet')\n\n    day = actigraphy.get_column('relative_date_PCIAT') + actigraphy.get_column('time_of_day') / 86400e9\n\n    sample = train.filter(pl.col('id') == id)\n\n    age = sample.get_column('Basic_Demos-Age').item()\n\n    sex = ['boy', 'girl'][sample.get_column('Basic_Demos-Sex').item()]\n\n    actigraphy = (\n\n        actigraphy\n\n        .with_columns(\n\n            (day.diff() * 86400).alias('diff_seconds'),\n\n            (np.sqrt(np.square(pl.col('X')) + np.square(pl.col('Y')) + np.square(pl.col('Z'))).alias('norm'))\n\n        )\n\n    )\n\n\n\n    if only_one_week:\n\n        start = np.ceil(day.min())\n\n        mask = (start <= day.to_numpy()) & (day.to_numpy() <= start + 7*3)\n\n        mask &= ~ actigraphy.get_column('non-wear_flag').cast(bool).to_numpy()\n\n    else:\n\n        mask = np.full(len(day), True)\n\n        \n\n    if small:\n\n        timelines = [\n\n            ('enmo', 'forestgreen'),\n\n            ('light', 'orange'),\n\n        ]\n\n    else:\n\n        timelines = [\n\n            ('X', 'm'),\n\n            ('Y', 'm'),\n\n            ('Z', 'm'),\n\n#             ('norm', 'c'),\n\n            ('enmo', 'forestgreen'),\n\n            ('anglez', 'lightblue'),\n\n            ('light', 'orange'),\n\n            ('non-wear_flag', 'chocolate')\n\n    #         ('diff_seconds', 'k'),\n\n        ]\n\n        \n\n    _, axs = plt.subplots(len(timelines), 1, sharex=True, figsize=(12, len(timelines) * 1.1 + 0.5))\n\n    for ax, (feature, color) in zip(axs, timelines):\n\n        ax.set_facecolor('#eeeeee')\n\n        ax.scatter(day.to_numpy()[mask],\n\n                   actigraphy.get_column(feature).to_numpy()[mask],\n\n                   color=color, label=feature, s=1)\n\n        ax.legend(loc='upper left', facecolor='#eeeeee')\n\n        if feature == 'diff_seconds':\n\n            ax.set_ylim(-0.5, 20.5)\n\n    axs[-1].set_xlabel('day')\n\n    axs[-1].xaxis.set_major_locator(MaxNLocator(integer=True))\n\n    plt.tight_layout()\n\n    axs[0].set_title(f'id={id}, {sex}, age={age}')\n\n    plt.show()\n\n\n\nanalyze_actigraphy('0417c91e', only_one_week=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:58:05.367084Z","iopub.execute_input":"2025-06-28T14:58:05.367405Z","iopub.status.idle":"2025-06-28T14:58:07.654725Z","shell.execute_reply.started":"2025-06-28T14:58:05.367382Z","shell.execute_reply":"2025-06-28T14:58:07.653754Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x820 with 7 Axes>","image/png":"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= 42\n\nn_splits = 5","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:58:19.476987Z","iopub.execute_input":"2025-06-28T14:58:19.477324Z","iopub.status.idle":"2025-06-28T14:58:19.481832Z","shell.execute_reply.started":"2025-06-28T14:58:19.477301Z","shell.execute_reply":"2025-06-28T14:58:19.480765Z"}},"outputs":[],"execution_count":75},{"cell_type":"code","source":"def process_file(filename, directory):\n    df = pd.read_parquet(os.path.join(directory, filename, 'part-0.parquet'))\n    df.drop('step', axis=1, inplace=True)\n\n    stats = df.describe().values.reshape(-1)\n    \n    # Frequency domain features using FFT\n    fft_features = np.abs(fft(df, axis=0)).mean(axis=0)  # Mean magnitude of FFT for each feature\n    \n    combined_features = np.concatenate([stats, fft_features])\n    \n    return combined_features, filename.split('=')[1]\n\n\n\ndef load_time_series(dirname) -> pd.DataFrame:\n\n    ids = os.listdir(dirname)\n\n    \n\n    with ThreadPoolExecutor() as executor:\n\n        results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n\n    \n\n    stats, indexes = zip(*results)\n\n    \n\n    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n\n    df['id'] = indexes\n\n    return df\n\n\n\n\n\nclass AutoEncoder(nn.Module):\n\n    def __init__(self, input_dim, encoding_dim):\n\n        super(AutoEncoder, self).__init__()\n\n        self.encoder = nn.Sequential(\n\n            nn.Linear(input_dim, encoding_dim*3),\n\n            nn.ReLU(),\n\n            nn.Linear(encoding_dim*3, encoding_dim*2),\n\n            nn.ReLU(),\n\n            nn.Linear(encoding_dim*2, encoding_dim),\n\n            nn.ReLU()\n\n        )\n\n        self.decoder = nn.Sequential(\n\n            nn.Linear(encoding_dim, input_dim*2),\n\n            nn.ReLU(),\n\n            nn.Linear(input_dim*2, input_dim*3),\n\n            nn.ReLU(),\n\n            nn.Linear(input_dim*3, input_dim),\n\n            nn.Sigmoid()\n\n        )\n\n        \n\n    def forward(self, x):\n\n        encoded = self.encoder(x)\n\n        decoded = self.decoder(encoded)\n\n        return decoded\n\n\n\n\n\ndef perform_autoencoder(df, encoding_dim=50, epochs=50, batch_size=32):\n\n    scaler = StandardScaler()\n\n    df_scaled = scaler.fit_transform(df)\n\n    \n\n    data_tensor = torch.FloatTensor(df_scaled)\n\n    \n\n    input_dim = data_tensor.shape[1]\n\n    autoencoder = AutoEncoder(input_dim, encoding_dim)\n\n    \n\n    criterion = nn.MSELoss()\n\n    optimizer = optim.Adam(autoencoder.parameters())\n\n    \n\n    for epoch in range(epochs):\n\n        for i in range(0, len(data_tensor), batch_size):\n\n            batch = data_tensor[i : i + batch_size]\n\n            optimizer.zero_grad()\n\n            reconstructed = autoencoder(batch)\n\n            loss = criterion(reconstructed, batch)\n\n            loss.backward()\n\n            optimizer.step()\n\n            \n\n        if (epoch + 1) % 10 == 0:\n\n            print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}]')\n\n                 \n\n    with torch.no_grad():\n\n        encoded_data = autoencoder.encoder(data_tensor).numpy()\n\n        \n\n    df_encoded = pd.DataFrame(encoded_data, columns=[f'Enc_{i + 1}' for i in range(encoded_data.shape[1])])\n\n    \n\n    return df_encoded\n\n\n\ndef feature_engineering(df):\n\n    season_cols = [col for col in df.columns if 'Season' in col]\n\n    df = df.drop(season_cols, axis=1) \n\n    df['BMI_Age'] = df['Physical-BMI'] * df['Basic_Demos-Age']\n\n    df['Internet_Hours_Age'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['Basic_Demos-Age']\n\n    df['BMI_Internet_Hours'] = df['Physical-BMI'] * df['PreInt_EduHx-computerinternet_hoursday']\n\n    df['BFP_BMI'] = df['BIA-BIA_Fat'] / df['BIA-BIA_BMI']\n\n    df['FFMI_BFP'] = df['BIA-BIA_FFMI'] / df['BIA-BIA_Fat']\n\n    df['FMI_BFP'] = df['BIA-BIA_FMI'] / df['BIA-BIA_Fat']\n\n    df['LST_TBW'] = df['BIA-BIA_LST'] / df['BIA-BIA_TBW']\n\n    df['BFP_BMR'] = df['BIA-BIA_Fat'] * df['BIA-BIA_BMR']\n\n    df['BFP_DEE'] = df['BIA-BIA_Fat'] * df['BIA-BIA_DEE']\n\n    df['BMR_Weight'] = df['BIA-BIA_BMR'] / df['Physical-Weight']\n\n    df['DEE_Weight'] = df['BIA-BIA_DEE'] / df['Physical-Weight']\n\n    df['SMM_Height'] = df['BIA-BIA_SMM'] / df['Physical-Height']\n\n    df['Muscle_to_Fat'] = df['BIA-BIA_SMM'] / df['BIA-BIA_FMI']\n\n    df['Hydration_Status'] = df['BIA-BIA_TBW'] / df['Physical-Weight']\n\n    df['ICW_TBW'] = df['BIA-BIA_ICW'] / df['BIA-BIA_TBW']\n\n    \n\n    return df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:58:55.051015Z","iopub.execute_input":"2025-06-28T14:58:55.051331Z","iopub.status.idle":"2025-06-28T14:58:55.070756Z","shell.execute_reply.started":"2025-06-28T14:58:55.051309Z","shell.execute_reply":"2025-06-28T14:58:55.069769Z"}},"outputs":[],"execution_count":76},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\n\n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\n\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\n\n\ndf_train = train_ts.drop('id', axis=1)\n\ndf_test = test_ts.drop('id', axis=1)\n\n\n\ntrain_ts_encoded = perform_autoencoder(df_train, encoding_dim=60, epochs=100, batch_size=32)\n\ntest_ts_encoded = perform_autoencoder(df_test, encoding_dim=60, epochs=100, batch_size=32)\n\n\n\ntime_series_cols = train_ts_encoded.columns.tolist()\n\ntrain_ts_encoded[\"id\"]=train_ts[\"id\"]\n\ntest_ts_encoded['id']=test_ts[\"id\"]\n\n\n\ntrain = pd.merge(train, train_ts_encoded, how=\"left\", on='id')\n\ntest = pd.merge(test, test_ts_encoded, how=\"left\", on='id')\n\n\n\nimputer = KNNImputer(n_neighbors=5)\n\nnumeric_cols = train.select_dtypes(include=['float64', 'int64']).columns\n\nimputed_data = imputer.fit_transform(train[numeric_cols])\n\ntrain_imputed = pd.DataFrame(imputed_data, columns=numeric_cols)\n\ntrain_imputed['sii'] = train_imputed['sii'].round().astype(int)\n\nfor col in train.columns:\n\n    if col not in numeric_cols:\n\n        train_imputed[col] = train[col]\n\n        \n\ntrain = train_imputed\n\n\n\ntrain = feature_engineering(train)\n\ntrain = train.dropna(thresh=10, axis=0)\n\ntest = feature_engineering(test)\n\n\n\ntrain = train.drop('id', axis=1)\n\ntest  = test .drop('id', axis=1)   \n\n\n\n\n\nfeaturesCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n\n                'CGAS-CGAS_Score', 'Physical-BMI',\n\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n\n                'Fitness_Endurance-Max_Stage',\n\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n\n                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n\n                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n\n                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n\n                'SDS-SDS_Total_T',\n\n                'PreInt_EduHx-computerinternet_hoursday', 'sii', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n\n                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n\n                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n\n\n\nfeaturesCols += time_series_cols\n\n\n\ntrain = train[featuresCols]\n\ntrain = train.dropna(subset='sii')\n\n\n\nfeaturesCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n\n                'CGAS-CGAS_Score', 'Physical-BMI',\n\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n\n                'Fitness_Endurance-Max_Stage',\n\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n\n                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n\n                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n\n                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n\n                'SDS-SDS_Total_T',\n\n                'PreInt_EduHx-computerinternet_hoursday', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n\n                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n\n                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n\n\n\nfeaturesCols += time_series_cols\n\ntest = test[featuresCols]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T14:59:28.167757Z","iopub.execute_input":"2025-06-28T14:59:28.168767Z","iopub.status.idle":"2025-06-28T15:05:31.552667Z","shell.execute_reply.started":"2025-06-28T14:59:28.16873Z","shell.execute_reply":"2025-06-28T15:05:31.55182Z"}},"outputs":[{"name":"stderr","text":"100%|██████████| 996/996 [05:39<00:00,  2.93it/s]\n100%|██████████| 2/2 [00:00<00:00,  4.08it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch [10/100], Loss: 1.5936]\nEpoch [20/100], Loss: 1.5364]\nEpoch [30/100], Loss: 1.5010]\nEpoch [40/100], Loss: 1.4638]\nEpoch [50/100], Loss: 1.4474]\nEpoch [60/100], Loss: 1.4334]\nEpoch [70/100], Loss: 1.4284]\nEpoch [80/100], Loss: 1.4222]\nEpoch [90/100], Loss: 1.4227]\nEpoch [100/100], Loss: 1.4066]\nEpoch [10/100], Loss: 1.0011]\nEpoch [20/100], Loss: 0.5420]\nEpoch [30/100], Loss: 0.4306]\nEpoch [40/100], Loss: 0.4306]\nEpoch [50/100], Loss: 0.4306]\nEpoch [60/100], Loss: 0.4306]\nEpoch [70/100], Loss: 0.4306]\nEpoch [80/100], Loss: 0.4306]\nEpoch [90/100], Loss: 0.4306]\nEpoch [100/100], Loss: 0.4306]\n","output_type":"stream"}],"execution_count":77},{"cell_type":"code","source":"if np.any(np.isinf(train)):\n\n    train = train.replace([np.inf, -np.inf], np.nan)\n\n\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\n\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n\n    return np.where(oof_non_rounded < thresholds[0], 0,\n\n                    np.where(oof_non_rounded < thresholds[1], 1,\n\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\n\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:07:38.311487Z","iopub.execute_input":"2025-06-28T15:07:38.31199Z","iopub.status.idle":"2025-06-28T15:07:38.328137Z","shell.execute_reply.started":"2025-06-28T15:07:38.311955Z","shell.execute_reply":"2025-06-28T15:07:38.327105Z"}},"outputs":[],"execution_count":78},{"cell_type":"code","source":"def TrainML(model_class, test_data):\n\n    X = train.drop(['sii'], axis=1)\n\n    y = train['sii']\n\n\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n\n    \n\n    train_S = []\n\n    test_S = []\n\n    \n\n    oof_non_rounded = np.zeros(len(y), dtype=float) \n\n    oof_rounded = np.zeros(len(y), dtype=int) \n\n    test_preds = np.zeros((len(test_data), n_splits))\n\n\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n\n\n        model = clone(model_class)\n\n        model.fit(X_train, y_train)\n\n\n\n        y_train_pred = model.predict(X_train)\n\n        y_val_pred = model.predict(X_val)\n\n\n\n        oof_non_rounded[test_idx] = y_val_pred\n\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n\n\n        train_S.append(train_kappa)\n\n        test_S.append(val_kappa)\n\n        \n\n        test_preds[:, fold] = model.predict(test_data)\n\n        \n\n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n\n        clear_output(wait=True)\n\n\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n\n                              method='Nelder-Mead')\n\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n\n    \n\n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n\n\n    tpm = test_preds.mean(axis=1)\n\n    tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n\n    \n\n    submission = pd.DataFrame({\n\n        'id': sample['id'],\n\n        'sii': tpTuned\n\n    })\n\n\n\n    return submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:08:04.572348Z","iopub.execute_input":"2025-06-28T15:08:04.57268Z","iopub.status.idle":"2025-06-28T15:08:04.58481Z","shell.execute_reply.started":"2025-06-28T15:08:04.572654Z","shell.execute_reply":"2025-06-28T15:08:04.583751Z"}},"outputs":[],"execution_count":79},{"cell_type":"code","source":"Params = {\n    'learning_rate': 0.046,\n    'max_depth': 12,\n    'num_leaves': 478,\n    'min_data_in_leaf': 13,\n    'feature_fraction': 0.893,\n    'bagging_fraction': 0.784,\n    'bagging_freq': 4,\n    'lambda_l1': 10,  # Increased from 6.59\n    'lambda_l2': 0.01,  # Increased from 2.68e-06\n    'device': 'cpu'\n\n}\n\n\n# XGBoost parameters\nXGB_Params = {\n    'learning_rate': 0.05,\n    'max_depth': 6,\n    'n_estimators': 200,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'reg_alpha': 1,  # Increased from 0.1\n    'reg_lambda': 5,  # Increased from 1\n    'random_state': SEED,\n    'tree_method': 'gpu_hist',\n\n}\n\n\nCatBoost_Params = {\n    'learning_rate': 0.05,\n    'depth': 6,\n    'iterations': 200,\n    'random_seed': SEED,\n    'verbose': 0,\n    'l2_leaf_reg': 10,  # Increase this value\n    'task_type': 'GPU'\n\n}\n\n\nTabNet_Params = {\n    'n_d': 64,              # Width of the decision prediction layer\n    'n_a': 64,              # Width of the attention embedding for each step\n    'n_steps': 5,           # Number of steps in the architecture\n    'gamma': 1.5,           # Coefficient for feature selection regularization\n    'n_independent': 2,     # Number of independent GLU layer in each GLU block\n    'n_shared': 2,          # Number of shared GLU layer in each GLU block\n    'lambda_sparse': 1e-4,  # Sparsity regularization\n    'optimizer_fn': torch.optim.Adam,\n    'optimizer_params': dict(lr=2e-2, weight_decay=1e-5),\n    'mask_type': 'entmax',\n    'scheduler_params': dict(mode=\"min\", patience=10, min_lr=1e-5, factor=0.5),\n    'scheduler_fn': torch.optim.lr_scheduler.ReduceLROnPlateau,\n    'verbose': 1,\n    'device_name': 'cuda' if torch.cuda.is_available() else 'cpu'\n}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:08:33.78631Z","iopub.execute_input":"2025-06-28T15:08:33.787115Z","iopub.status.idle":"2025-06-28T15:08:33.795643Z","shell.execute_reply.started":"2025-06-28T15:08:33.787081Z","shell.execute_reply":"2025-06-28T15:08:33.794576Z"}},"outputs":[],"execution_count":80},{"cell_type":"code","source":"class TabNetWrapper(BaseEstimator, RegressorMixin):\n    def __init__(self, **kwargs):\n        self.model = TabNetRegressor(**kwargs)\n        self.kwargs = kwargs\n        self.imputer = SimpleImputer(strategy='median')\n        self.best_model_path = 'best_tabnet_model.pt'\n        \n    def fit(self, X, y):\n        # Handle missing values\n        X_imputed = self.imputer.fit_transform(X)\n        \n        if hasattr(y, 'values'):\n            y = y.values\n            \n        # Create internal validation set\n        X_train, X_valid, y_train, y_valid = train_test_split(\n            X_imputed, \n            y, \n            test_size=0.2,\n            random_state=42\n        )\n        \n        # Train TabNet model\n        history = self.model.fit(\n            X_train=X_train,\n            y_train=y_train.reshape(-1, 1),\n            eval_set=[(X_valid, y_valid.reshape(-1, 1))],\n            eval_name=['valid'],\n            eval_metric=['mse'],\n            max_epochs=500,\n            patience=50,\n            batch_size=1024,\n            virtual_batch_size=128,\n            num_workers=0,\n            drop_last=False,\n            callbacks=[\n                TabNetPretrainedModelCheckpoint(\n                    filepath=self.best_model_path,\n                    monitor='valid_mse',\n                    mode='min',\n                    save_best_only=True,\n                    verbose=True\n                )\n            ]\n        )\n        \n        # Load the best model\n        if os.path.exists(self.best_model_path):\n            self.model.load_model(self.best_model_path)\n            os.remove(self.best_model_path)  # Remove temporary file\n        \n        return self\n    \n    def predict(self, X):\n        X_imputed = self.imputer.transform(X)\n        return self.model.predict(X_imputed).flatten()\n    \n    def __deepcopy__(self, memo):\n        # Add deepcopy support for scikit-learn\n        cls = self.__class__\n        result = cls.__new__(cls)\n        memo[id(self)] = result\n        for k, v in self.__dict__.items():\n            setattr(result, k, deepcopy(v, memo))\n        return result\n\n\nclass TabNetPretrainedModelCheckpoint(Callback):\n\n    def __init__(self, filepath, monitor='val_loss', mode='min', save_best_only=True, verbose=1):\n\n        super().__init__()  # Initialize parent class\n\n        self.filepath = filepath\n\n        self.monitor = monitor\n\n        self.mode = mode\n\n        self.save_best_only = save_best_only\n\n        self.verbose = verbose\n\n        self.best = float('inf') if mode == 'min' else -float('inf')\n\n        \n\n    def on_train_begin(self, logs=None):\n        self.model = self.trainer  # Use trainer itself as model\n\n    def on_epoch_end(self, epoch, logs=None):\n\n        logs = logs or {}\n\n        current = logs.get(self.monitor)\n\n        if current is None:\n\n            return\n\n\n        # Check if current metric is better than best\n\n        if (self.mode == 'min' and current < self.best) or (self.mode == 'max' and current > self.best):\n\n            if self.verbose:\n\n                print(f'\\nEpoch {epoch}: {self.monitor} improved from {self.best:.4f} to {current:.4f}')\n\n            self.best = current\n\n            if self.save_best_only:\n\n                self.model.save_model(self.filepath)  # Save the entire model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:09:23.476929Z","iopub.execute_input":"2025-06-28T15:09:23.477306Z","iopub.status.idle":"2025-06-28T15:09:23.493598Z","shell.execute_reply.started":"2025-06-28T15:09:23.477283Z","shell.execute_reply":"2025-06-28T15:09:23.492581Z"}},"outputs":[],"execution_count":81},{"cell_type":"code","source":"# Create model instances\n\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\n\nXGB_Model = XGBRegressor(**XGB_Params)\n\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n\nTabNet_Model = TabNetWrapper(**TabNet_Params) # New","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:09:42.50657Z","iopub.execute_input":"2025-06-28T15:09:42.507282Z","iopub.status.idle":"2025-06-28T15:09:42.515703Z","shell.execute_reply.started":"2025-06-28T15:09:42.507251Z","shell.execute_reply":"2025-06-28T15:09:42.514786Z"}},"outputs":[],"execution_count":82},{"cell_type":"code","source":"from xgboost import XGBRegressor\n\nXGB_Model = XGBRegressor(\n    n_estimators=100,\n    learning_rate=0.1,\n    max_depth=6,\n    subsample=0.8,\n    colsample_bytree=0.8,\n    tree_method='hist'  # ✅ Use CPU instead of 'gpu_hist'\n)\nfrom catboost import CatBoostRegressor\n\nCatBoost_Model = CatBoostRegressor(\n    iterations=1000,\n    learning_rate=0.05,\n    depth=6,\n    loss_function='RMSE',\n    verbose=0,              # or True if you want training logs\n    task_type='CPU'         # ✅ This forces CatBoost to use CPU\n)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:14:14.010395Z","iopub.execute_input":"2025-06-28T15:14:14.011547Z","iopub.status.idle":"2025-06-28T15:14:14.017498Z","shell.execute_reply.started":"2025-06-28T15:14:14.011515Z","shell.execute_reply":"2025-06-28T15:14:14.016429Z"}},"outputs":[],"execution_count":86},{"cell_type":"code","source":"voting_model = VotingRegressor(estimators=[\n\n    ('lightgbm', Light),\n\n    ('xgboost', XGB_Model),\n\n    ('catboost', CatBoost_Model),\n\n    ('tabnet', TabNet_Model)\n\n])\n\n\n\nSubmission1 = TrainML(voting_model, test)\n\n\n\nSubmission1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:14:18.064777Z","iopub.execute_input":"2025-06-28T15:14:18.065131Z","iopub.status.idle":"2025-06-28T15:18:58.128541Z","shell.execute_reply.started":"2025-06-28T15:14:18.065106Z","shell.execute_reply":"2025-06-28T15:18:58.127307Z"}},"outputs":[{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [04:39<00:00, 55.98s/it]","output_type":"stream"},{"name":"stdout","text":"Mean Train QWK --> 0.8463\nMean Validation QWK ---> 0.4955\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.536\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    1\n1   000fd460    0\n2   00105258    1\n3   00115b9f    1\n4   0016bb22    1\n5   001f3379    1\n6   0038ba98    1\n7   0068a485    0\n8   0069fbed    1\n9   0083e397    1\n10  0087dd65    1\n11  00abe655    1\n12  00ae59c9    1\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    1\n16  00d56d4b    0\n17  00d9913d    1\n18  00e6167c    0\n19  00ebc35d    1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>id</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0038ba98</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>00af6387</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>00bd4359</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>00c0cd71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>00d56d4b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>00d9913d</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>00e6167c</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>00ebc35d</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":87},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\n\n\ndef process_file(filename, dirname):\n\n    df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n\n    df.drop('step', axis=1, inplace=True)\n\n    return df.describe().values.reshape(-1), filename.split('=')[1]\n\n\n\ndef load_time_series(dirname) -> pd.DataFrame:\n\n    ids = os.listdir(dirname)\n\n    \n\n    with ThreadPoolExecutor() as executor:\n\n        results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n\n    \n\n    stats, indexes = zip(*results)\n\n    \n\n    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n\n    df['id'] = indexes\n\n    return df\n\n        \n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\n\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\n\n\ntime_series_cols = train_ts.columns.tolist()\n\ntime_series_cols.remove(\"id\")\n\n\n\ntrain = pd.merge(train, train_ts, how=\"left\", on='id')\n\ntest = pd.merge(test, test_ts, how=\"left\", on='id')\n\n\n\ntrain = train.drop('id', axis=1)\n\ntest = test.drop('id', axis=1)   \n\n\n\nfeaturesCols = ['Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n\n                'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n\n                'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n\n                'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n\n                'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n\n                'PAQ_C-PAQ_C_Total', 'SDS-Season', 'SDS-SDS_Total_Raw',\n\n                'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n\n                'PreInt_EduHx-computerinternet_hoursday', 'sii']\n\n\n\nfeaturesCols += time_series_cols\n\n\n\ntrain = train[featuresCols]\n\ntrain = train.dropna(subset='sii')\n\n\n\ncat_c = ['Basic_Demos-Enroll_Season', 'CGAS-Season', 'Physical-Season', \n\n          'Fitness_Endurance-Season', 'FGC-Season', 'BIA-Season', \n\n          'PAQ_A-Season', 'PAQ_C-Season', 'SDS-Season', 'PreInt_EduHx-Season']\n\n\n\ndef update(df):\n\n    global cat_c\n\n    for c in cat_c: \n\n        df[c] = df[c].fillna('Missing')\n\n        df[c] = df[c].astype('category')\n\n    return df\n\n        \n\ntrain = update(train)\n\ntest = update(test)\n\n\n\ndef create_mapping(column, dataset):\n\n    unique_values = dataset[column].unique()\n\n    return {value: idx for idx, value in enumerate(unique_values)}\n\n\n\nfor col in cat_c:\n\n    mapping = create_mapping(col, train)\n\n    mappingTe = create_mapping(col, test)\n\n    \n\n    train[col] = train[col].replace(mapping).astype(int)\n\n    test[col] = test[col].replace(mappingTe).astype(int)\n\n\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\n\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n\n    return np.where(oof_non_rounded < thresholds[0], 0,\n\n                    np.where(oof_non_rounded < thresholds[1], 1,\n\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\n\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n\n\n\ndef TrainML(model_class, test_data):\n\n    X = train.drop(['sii'], axis=1)\n\n    y = train['sii']\n\n\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n\n    \n\n    train_S = []\n\n    test_S = []\n\n    \n\n    oof_non_rounded = np.zeros(len(y), dtype=float) \n\n    oof_rounded = np.zeros(len(y), dtype=int) \n\n    test_preds = np.zeros((len(test_data), n_splits))\n\n\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n\n\n        model = clone(model_class)\n\n        model.fit(X_train, y_train)\n\n\n\n        y_train_pred = model.predict(X_train)\n\n        y_val_pred = model.predict(X_val)\n\n\n\n        oof_non_rounded[test_idx] = y_val_pred\n\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n\n\n        train_S.append(train_kappa)\n\n        test_S.append(val_kappa)\n\n        \n\n        test_preds[:, fold] = model.predict(test_data)\n\n        \n\n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n\n        clear_output(wait=True)\n\n\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n\n                              method='Nelder-Mead')\n\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n\n    \n\n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n\n\n    tpm = test_preds.mean(axis=1)\n\n    tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n\n    \n\n    submission = pd.DataFrame({\n\n        'id': sample['id'],\n\n        'sii': tpTuned\n\n    })\n\n\n\n    return submission\n\n\n\n# Model parameters for LightGBM\n\nParams = {\n\n    'learning_rate': 0.046,\n\n    'max_depth': 12,\n\n    'num_leaves': 478,\n\n    'min_data_in_leaf': 13,\n\n    'feature_fraction': 0.893,\n\n    'bagging_fraction': 0.784,\n\n    'bagging_freq': 4,\n\n    'lambda_l1': 10,  # Increased from 6.59\n\n    'lambda_l2': 0.01  # Increased from 2.68e-06\n\n}\n\n\n\n\n\n# XGBoost parameters\n\nXGB_Params = {\n\n    'learning_rate': 0.05,\n\n    'max_depth': 6,\n\n    'n_estimators': 200,\n\n    'subsample': 0.8,\n\n    'colsample_bytree': 0.8,\n\n    'reg_alpha': 1,  # Increased from 0.1\n\n    'reg_lambda': 5,  # Increased from 1\n\n    'random_state': SEED\n\n}\n\n\n\n\n\nCatBoost_Params = {\n\n    'learning_rate': 0.05,\n\n    'depth': 6,\n\n    'iterations': 200,\n\n    'random_seed': SEED,\n\n    'cat_features': cat_c,\n\n    'verbose': 0,\n\n    'l2_leaf_reg': 10  # Increase this value\n\n}\n\n\n\n# Create model instances\n\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\n\nXGB_Model = XGBRegressor(**XGB_Params)\n\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n\nTabNet_Model = TabNetWrapper(**TabNet_Params)  # New:TAbNet\n\n\n\n# Combine models using Voting Regressor\n\nvoting_model = VotingRegressor(estimators=[\n\n    ('lightgbm', Light),\n\n    ('xgboost', XGB_Model),\n\n    ('catboost', CatBoost_Model),\n\n    ('tabnet', TabNet_Model)  # New:TabNet\n\n])\n\n\n\n# Train the ensemble model\n\nSubmission2 = TrainML(voting_model, test)\n\n\n\n# Save submission\n\n#Submission2.to_csv('submission.csv', index=False)\n\nSubmission2\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:20:37.181343Z","iopub.execute_input":"2025-06-28T15:20:37.182539Z","iopub.status.idle":"2025-06-28T15:25:16.147461Z","shell.execute_reply.started":"2025-06-28T15:20:37.182507Z","shell.execute_reply":"2025-06-28T15:25:16.146393Z"}},"outputs":[{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [03:25<00:00, 41.09s/it]","output_type":"stream"},{"name":"stdout","text":"Mean Train QWK --> 0.6756\nMean Validation QWK ---> 0.3708\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.466\u001b[0m\n","output_type":"stream"},{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    1\n1   000fd460    0\n2   00105258    0\n3   00115b9f    0\n4   0016bb22    0\n5   001f3379    1\n6   0038ba98    0\n7   0068a485    0\n8   0069fbed    1\n9   0083e397    0\n10  0087dd65    0\n11  00abe655    0\n12  00ae59c9    1\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    1\n16  00d56d4b    0\n17  00d9913d    0\n18  00e6167c    0\n19  00ebc35d    1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>id</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0038ba98</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>00af6387</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>00bd4359</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>00c0cd71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>00d56d4b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>00d9913d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>00e6167c</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>00ebc35d</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":88},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\n\n\nfeaturesCols = ['Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n\n                'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n\n                'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n\n                'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n\n                'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n\n                'PAQ_C-PAQ_C_Total', 'SDS-Season', 'SDS-SDS_Total_Raw',\n\n                'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n\n                'PreInt_EduHx-computerinternet_hoursday', 'sii']\n\n\n\ncat_c = ['Basic_Demos-Enroll_Season', 'CGAS-Season', 'Physical-Season', \n\n          'Fitness_Endurance-Season', 'FGC-Season', 'BIA-Season', \n\n          'PAQ_A-Season', 'PAQ_C-Season', 'SDS-Season', 'PreInt_EduHx-Season']\n\n\n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\n\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\n\n\ntime_series_cols = train_ts.columns.tolist()\n\ntime_series_cols.remove(\"id\")\n\n\n\ntrain = pd.merge(train, train_ts, how=\"left\", on='id')\n\ntest = pd.merge(test, test_ts, how=\"left\", on='id')\n\n\n\ntrain = train.drop('id', axis=1)\n\ntest = test.drop('id', axis=1)\n\n\n\nfeaturesCols += time_series_cols\n\n\n\ntrain = train[featuresCols]\n\ntrain = train.dropna(subset='sii')\n\n\n\ndef update(df):\n\n    global cat_c\n\n    for c in cat_c: \n\n        df[c] = df[c].fillna('Missing')\n\n        df[c] = df[c].astype('category')\n\n    return df\n\n\n\ntrain = update(train)\n\ntest = update(test)\n\n\n\ndef create_mapping(column, dataset):\n\n    unique_values = dataset[column].unique()\n\n    return {value: idx for idx, value in enumerate(unique_values)}\n\n\n\nfor col in cat_c:\n\n    mapping = create_mapping(col, train)\n\n    mappingTe = create_mapping(col, test)\n\n    \n\n    train[col] = train[col].replace(mapping).astype(int)\n\n    test[col] = test[col].replace(mappingTe).astype(int)\n\n\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\n\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n\n    return np.where(oof_non_rounded < thresholds[0], 0,\n\n                    np.where(oof_non_rounded < thresholds[1], 1,\n\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\n\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n\n\n\ndef TrainML(model_class, test_data):\n\n    X = train.drop(['sii'], axis=1)\n\n    y = train['sii']\n\n\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n\n    \n\n    train_S = []\n\n    test_S = []\n\n    \n\n    oof_non_rounded = np.zeros(len(y), dtype=float) \n\n    oof_rounded = np.zeros(len(y), dtype=int) \n\n    test_preds = np.zeros((len(test_data), n_splits))\n\n\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n\n\n        model = clone(model_class)\n\n        model.fit(X_train, y_train)\n\n\n\n        y_train_pred = model.predict(X_train)\n\n        y_val_pred = model.predict(X_val)\n\n\n\n        oof_non_rounded[test_idx] = y_val_pred\n\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n\n\n        train_S.append(train_kappa)\n\n        test_S.append(val_kappa)\n\n        \n\n        test_preds[:, fold] = model.predict(test_data)\n\n        \n\n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n\n        clear_output(wait=True)\n\n\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n\n                              method='Nelder-Mead')\n\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n\n    \n\n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n\n\n    tpm = test_preds.mean(axis=1)\n\n    tp_rounded = threshold_Rounder(tpm, KappaOPtimizer.x)\n\n\n\n    return tp_rounded\n\n\n\nimputer = SimpleImputer(strategy='median')\n\n\n\nensemble = VotingRegressor(estimators=[\n\n    ('lgb', Pipeline(steps=[('imputer', imputer), ('regressor', LGBMRegressor(random_state=SEED))])),\n\n    ('xgb', Pipeline(steps=[('imputer', imputer), ('regressor', XGBRegressor(random_state=SEED))])),\n\n    ('cat', Pipeline(steps=[('imputer', imputer), ('regressor', CatBoostRegressor(random_state=SEED, silent=True))])),\n\n    ('rf', Pipeline(steps=[('imputer', imputer), ('regressor', RandomForestRegressor(random_state=SEED))])),\n\n    ('gb', Pipeline(steps=[('imputer', imputer), ('regressor', GradientBoostingRegressor(random_state=SEED))])),\n\n    ('tabnet', Pipeline(steps=[('imputer', imputer), ('regressor', TabNetWrapper(**TabNet_Params))]))  # New:TabNet\n\n])\n\n\n\nSubmission3 = TrainML(ensemble, test)\n\n\n\nSubmission3 = TrainML(ensemble, test)\n\nSubmission3 = pd.DataFrame({\n\n    'id': sample['id'],\n\n    'sii': Submission3\n\n})\n\n\n\nSubmission3\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:26:20.039662Z","iopub.execute_input":"2025-06-28T15:26:20.040085Z","iopub.status.idle":"2025-06-28T15:37:24.799983Z","shell.execute_reply.started":"2025-06-28T15:26:20.040055Z","shell.execute_reply":"2025-06-28T15:37:24.798845Z"}},"outputs":[{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [04:54<00:00, 58.91s/it]","output_type":"stream"},{"name":"stdout","text":"Mean Train QWK --> 0.8563\nMean Validation QWK ---> 0.3671\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.444\u001b[0m\n","output_type":"stream"},{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    1\n1   000fd460    0\n2   00105258    0\n3   00115b9f    0\n4   0016bb22    1\n5   001f3379    1\n6   0038ba98    0\n7   0068a485    0\n8   0069fbed    2\n9   0083e397    1\n10  0087dd65    1\n11  00abe655    1\n12  00ae59c9    1\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    2\n16  00d56d4b    0\n17  00d9913d    0\n18  00e6167c    0\n19  00ebc35d    1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>id</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0038ba98</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>00af6387</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>00bd4359</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>00c0cd71</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>00d56d4b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>00d9913d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>00e6167c</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>00ebc35d</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":89},{"cell_type":"code","source":"sub1 = Submission1\n\nsub2 = Submission2\n\nsub3 = Submission3\n\n\n\nsub1 = sub1.sort_values(by='id').reset_index(drop=True)\n\nsub2 = sub2.sort_values(by='id').reset_index(drop=True)\n\nsub3 = sub3.sort_values(by='id').reset_index(drop=True)\n\n\n\ncombined = pd.DataFrame({\n\n    'id': sub1['id'],\n\n    'sii_1': sub1['sii'],\n\n    'sii_2': sub2['sii'],\n\n    'sii_3': sub3['sii']\n\n})\n\n\n\ndef majority_vote(row):\n\n    return row.mode()[0]\n\n\n\ncombined['final_sii'] = combined[['sii_1', 'sii_2', 'sii_3']].apply(majority_vote, axis=1)\n\n\n\nfinal_submission = combined[['id', 'final_sii']].rename(columns={'final_sii': 'sii'})\n\n\n\nfinal_submission.to_csv('submission.csv', index=False)\n\n\n\nprint(\"Majority voting completed and saved to 'Final_Submission.csv'\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:40:25.684548Z","iopub.execute_input":"2025-06-28T15:40:25.684878Z","iopub.status.idle":"2025-06-28T15:40:25.711008Z","shell.execute_reply.started":"2025-06-28T15:40:25.684852Z","shell.execute_reply":"2025-06-28T15:40:25.710202Z"}},"outputs":[{"name":"stdout","text":"Majority voting completed and saved to 'Final_Submission.csv'\n","output_type":"stream"}],"execution_count":91},{"cell_type":"code","source":"final_submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-28T15:40:42.907831Z","iopub.execute_input":"2025-06-28T15:40:42.908187Z","iopub.status.idle":"2025-06-28T15:40:42.917147Z","shell.execute_reply.started":"2025-06-28T15:40:42.908164Z","shell.execute_reply":"2025-06-28T15:40:42.916083Z"}},"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    1\n1   000fd460    0\n2   00105258    0\n3   00115b9f    0\n4   0016bb22    1\n5   001f3379    1\n6   0038ba98    0\n7   0068a485    0\n8   0069fbed    1\n9   0083e397    1\n10  0087dd65    1\n11  00abe655    1\n12  00ae59c9    1\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    1\n16  00d56d4b    0\n17  00d9913d    0\n18  00e6167c    0\n19  00ebc35d    1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>id</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0038ba98</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>00af6387</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>00bd4359</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>00c0cd71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>00d56d4b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>00d9913d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>00e6167c</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>00ebc35d</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":92},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}