{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":53666,"databundleVersionId":6589269,"sourceType":"competition"}],"dockerImageVersionId":30918,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"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},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install optuna","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:10:00.607867Z","iopub.execute_input":"2025-04-15T11:10:00.608163Z","iopub.status.idle":"2025-04-15T11:10:05.32923Z","shell.execute_reply.started":"2025-04-15T11:10:00.608139Z","shell.execute_reply":"2025-04-15T11:10:05.328402Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: optuna in /usr/local/lib/python3.10/dist-packages (4.2.1)\nRequirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (1.14.1)\nRequirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna) (6.9.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.26.4)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (24.2)\nRequirement already satisfied: sqlalchemy>=1.4.2 in /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.36)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.67.1)\nRequirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.2)\nRequirement already satisfied: Mako in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (1.3.9)\nRequirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (4.12.2)\nRequirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.4.2->optuna) (3.1.1)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (2025.0.1)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (2022.0.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.10/dist-packages (from numpy->optuna) (2.4.1)\nRequirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (3.0.2)\nRequirement already satisfied: intel-openmp>=2024 in /usr/local/lib/python3.10/dist-packages (from mkl->numpy->optuna) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.10/dist-packages (from mkl->numpy->optuna) (2022.0.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.10/dist-packages (from tbb==2022.*->mkl->numpy->optuna) (1.2.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.10/dist-packages (from mkl_umath->numpy->optuna) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.10/dist-packages (from intel-openmp>=2024->mkl->numpy->optuna) (2024.2.0)\n","output_type":"stream"}],"execution_count":63},{"cell_type":"code","source":"import dask.dataframe as dd\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport polars as pl","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:31:45.793876Z","iopub.execute_input":"2025-04-15T10:31:45.794156Z","iopub.status.idle":"2025-04-15T10:31:49.773029Z","shell.execute_reply.started":"2025-04-15T10:31:45.794135Z","shell.execute_reply":"2025-04-15T10:31:49.772367Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"import seaborn as sns\nfrom sklearn.preprocessing import StandardScaler","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:31:49.774088Z","iopub.execute_input":"2025-04-15T10:31:49.774513Z","iopub.status.idle":"2025-04-15T10:31:50.236803Z","shell.execute_reply.started":"2025-04-15T10:31:49.774483Z","shell.execute_reply":"2025-04-15T10:31:50.235916Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"import numpy as np\nimport optuna","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:10:12.120517Z","iopub.execute_input":"2025-04-15T11:10:12.120828Z","iopub.status.idle":"2025-04-15T11:10:12.125125Z","shell.execute_reply.started":"2025-04-15T11:10:12.120804Z","shell.execute_reply":"2025-04-15T11:10:12.124265Z"}},"outputs":[],"execution_count":64},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import classification_report, accuracy_score\nimport xgboost as xgb","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:02:28.018566Z","iopub.execute_input":"2025-04-15T11:02:28.018897Z","iopub.status.idle":"2025-04-15T11:02:28.530048Z","shell.execute_reply.started":"2025-04-15T11:02:28.01887Z","shell.execute_reply":"2025-04-15T11:02:28.529158Z"}},"outputs":[],"execution_count":57},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:10:57.4623Z","iopub.execute_input":"2025-04-15T11:10:57.462613Z","iopub.status.idle":"2025-04-15T11:10:57.46625Z","shell.execute_reply.started":"2025-04-15T11:10:57.462588Z","shell.execute_reply":"2025-04-15T11:10:57.465565Z"}},"outputs":[],"execution_count":66},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:55:23.571035Z","iopub.execute_input":"2025-04-15T10:55:23.571397Z","iopub.status.idle":"2025-04-15T10:55:23.575036Z","shell.execute_reply.started":"2025-04-15T10:55:23.571366Z","shell.execute_reply":"2025-04-15T10:55:23.57421Z"}},"outputs":[],"execution_count":44},{"cell_type":"code","source":"# Load and preprocess train_series.parquet\ntrain_series = (\n pl.read_parquet(\"/kaggle/input/child-mind-institute-detect-sleep-states/train_series.parquet\")\n .with_columns([\n pl.col(\"timestamp\").str.to_datetime(),\n pl.col(\"timestamp\").str.to_datetime().dt.date().alias(\"date\"),\n pl.col(\"timestamp\").str.to_datetime().dt.time().alias(\"time\")\n ])\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:31:58.75387Z","iopub.execute_input":"2025-04-15T10:31:58.754176Z","iopub.status.idle":"2025-04-15T10:33:25.028439Z","shell.execute_reply.started":"2025-04-15T10:31:58.754152Z","shell.execute_reply":"2025-04-15T10:33:25.027499Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# Preview\nprint(train_series.head(3))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:33:25.02944Z","iopub.execute_input":"2025-04-15T10:33:25.029758Z","iopub.status.idle":"2025-04-15T10:33:25.040015Z","shell.execute_reply.started":"2025-04-15T10:33:25.029717Z","shell.execute_reply":"2025-04-15T10:33:25.03887Z"}},"outputs":[{"name":"stdout","text":"shape: (3, 7)\n┌──────────────┬──────┬─────────────────────────┬────────┬────────┬────────────┬──────────┐\n│ series_id ┆ step ┆ timestamp ┆ anglez ┆ enmo ┆ date ┆ time │\n│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n│ str ┆ u32 ┆ datetime[μs, UTC] ┆ f32 ┆ f32 ┆ date ┆ time │\n╞══════════════╪══════╪═════════════════════════╪════════╪════════╪════════════╪══════════╡\n│ 038441c925bb ┆ 0 ┆ 2018-08-14 19:30:00 UTC ┆ 2.6367 ┆ 0.0217 ┆ 2018-08-14 ┆ 19:30:00 │\n│ 038441c925bb ┆ 1 ┆ 2018-08-14 19:30:05 UTC ┆ 2.6368 ┆ 0.0215 ┆ 2018-08-14 ┆ 19:30:05 │\n│ 038441c925bb ┆ 2 ┆ 2018-08-14 19:30:10 UTC ┆ 2.637 ┆ 0.0216 ┆ 2018-08-14 ┆ 19:30:10 │\n└──────────────┴──────┴─────────────────────────┴────────┴────────┴────────────┴──────────┘\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"# Find rows with any nulls\nnan_rows = train_series.filter(\n pl.fold(acc=pl.lit(False), function=lambda acc, col: acc | col.is_null(), exprs=train_series.columns)\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:33:25.04155Z","iopub.execute_input":"2025-04-15T10:33:25.041815Z","iopub.status.idle":"2025-04-15T10:33:25.326067Z","shell.execute_reply.started":"2025-04-15T10:33:25.041793Z","shell.execute_reply":"2025-04-15T10:33:25.325421Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# Get unique series_ids that have NaN values\nnan_series_ids = nan_rows.select(pl.col(\"series_id\").unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:36:02.554764Z","iopub.execute_input":"2025-04-15T10:36:02.555107Z","iopub.status.idle":"2025-04-15T10:36:02.56002Z","shell.execute_reply.started":"2025-04-15T10:36:02.555079Z","shell.execute_reply":"2025-04-15T10:36:02.559307Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"# Show them\nprint(nan_series_ids)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:36:03.189821Z","iopub.execute_input":"2025-04-15T10:36:03.190081Z","iopub.status.idle":"2025-04-15T10:36:03.194189Z","shell.execute_reply.started":"2025-04-15T10:36:03.190061Z","shell.execute_reply":"2025-04-15T10:36:03.19338Z"}},"outputs":[{"name":"stdout","text":"shape: (0, 1)\n┌───────────┐\n│ series_id │\n│ --- │\n│ str │\n╞═══════════╡\n└───────────┘\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"# tf is already an eager Polars DataFrame (no need to collect)\ntf = train_series # if not already assigned\n\n# 1. Find rows with any NaN values\nnan_rows = tf.filter(\n pl.any_horizontal([pl.col(col).is_null() for col in tf.columns])\n)\n\n# 2. Get unique series_ids with NaNs\nbad_ids = nan_rows.select(\"series_id\").unique().to_series().to_list()\n\n# 3. Print results\nprint(f\"Found {len(bad_ids)} series_ids with NaN values:\")\nprint(bad_ids)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:36:04.429501Z","iopub.execute_input":"2025-04-15T10:36:04.429769Z","iopub.status.idle":"2025-04-15T10:36:04.47278Z","shell.execute_reply.started":"2025-04-15T10:36:04.42975Z","shell.execute_reply":"2025-04-15T10:36:04.472111Z"}},"outputs":[{"name":"stdout","text":"Found 0 series_ids with NaN values:\n[]\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"def optimize_dataframe(df):\n for col in df.select_dtypes(include=[\"int\", \"float\"]).columns:\n col_min = df[col].min()\n col_max = df[col].max()\n if pd.api.types.is_integer_dtype(df[col]):\n if col_min >= np.iinfo(np.int8).min and col_max <= np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif col_min >= np.iinfo(np.int16).min and col_max <= np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif pd.api.types.is_float_dtype(df[col]):\n if col_min >= np.finfo(np.float16).min and col_max <= np.finfo(np.float16).max:\n df[col] = df[col].astype(np.float16)\n elif col_min >= np.finfo(np.float32).min and col_max <= np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n return df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:44:03.425819Z","iopub.execute_input":"2025-04-15T10:44:03.426148Z","iopub.status.idle":"2025-04-15T10:44:03.43239Z","shell.execute_reply.started":"2025-04-15T10:44:03.426114Z","shell.execute_reply":"2025-04-15T10:44:03.431512Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"# Apply optimization\ntrain_series_pd = optimize_dataframe(train_series.to_pandas())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:44:04.426536Z","iopub.execute_input":"2025-04-15T10:44:04.426817Z","iopub.status.idle":"2025-04-15T10:44:22.342031Z","shell.execute_reply.started":"2025-04-15T10:44:04.426796Z","shell.execute_reply":"2025-04-15T10:44:22.341358Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"train_events = pd.read_csv(\"/kaggle/input/child-mind-institute-detect-sleep-states/train_events.csv\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:44:22.343044Z","iopub.execute_input":"2025-04-15T10:44:22.343355Z","iopub.status.idle":"2025-04-15T10:44:22.377458Z","shell.execute_reply.started":"2025-04-15T10:44:22.343326Z","shell.execute_reply":"2025-04-15T10:44:22.376841Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"# Join based on 'series_id' and 'step'\nmerged_data = pd.merge(train_series_pd, train_events, on=[\"series_id\", \"step\"], how=\"inner\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:44:45.250568Z","iopub.execute_input":"2025-04-15T10:44:45.250846Z","iopub.status.idle":"2025-04-15T10:45:35.673629Z","shell.execute_reply.started":"2025-04-15T10:44:45.250824Z","shell.execute_reply":"2025-04-15T10:45:35.672513Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"# Join based on 'series_id' and 'step'\nmerged_data1 = pd.merge(train_series_pd, train_events, on=[\"series_id\", \"step\"], how=\"left\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:45:35.675163Z","iopub.execute_input":"2025-04-15T10:45:35.675571Z","iopub.status.idle":"2025-04-15T10:46:37.388812Z","shell.execute_reply.started":"2025-04-15T10:45:35.675533Z","shell.execute_reply":"2025-04-15T10:46:37.388124Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"merged_data.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:46:48.229133Z","iopub.execute_input":"2025-04-15T10:46:48.229448Z","iopub.status.idle":"2025-04-15T10:46:48.234864Z","shell.execute_reply.started":"2025-04-15T10:46:48.22942Z","shell.execute_reply":"2025-04-15T10:46:48.234174Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(9585, 10)"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"merged_data1.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:46:56.614744Z","iopub.execute_input":"2025-04-15T10:46:56.615056Z","iopub.status.idle":"2025-04-15T10:46:56.619963Z","shell.execute_reply.started":"2025-04-15T10:46:56.615028Z","shell.execute_reply":"2025-04-15T10:46:56.61911Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"(127946340, 10)"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"merged_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:03.881882Z","iopub.execute_input":"2025-04-15T10:47:03.882202Z","iopub.status.idle":"2025-04-15T10:47:03.90856Z","shell.execute_reply.started":"2025-04-15T10:47:03.882173Z","shell.execute_reply":"2025-04-15T10:47:03.907825Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":" series_id step timestamp_x anglez enmo \\\n0 038441c925bb 4992 2018-08-15 02:26:00+00:00 -78.687500 0.009903 \n1 038441c925bb 10932 2018-08-15 10:41:00+00:00 -61.593750 0.026306 \n2 038441c925bb 20244 2018-08-15 23:37:00+00:00 -6.386719 0.018204 \n3 038441c925bb 27492 2018-08-16 09:41:00+00:00 -45.343750 0.016495 \n4 038441c925bb 39996 2018-08-17 03:03:00+00:00 -1.787109 0.000000 \n... ... ... ... ... ... \n9580 fe90110788d2 547152 2017-09-05 13:26:00+00:00 -56.781250 0.004200 \n9581 fe90110788d2 556560 2017-09-06 02:30:00+00:00 -16.593750 0.000000 \n9582 fe90110788d2 560604 2017-09-06 08:07:00+00:00 -43.062500 0.000000 \n9583 fe90110788d2 574620 2017-09-07 03:35:00+00:00 1.519531 0.006599 \n9584 fe90110788d2 581604 2017-09-07 13:17:00+00:00 79.062500 0.000000 \n\n date time night event timestamp_y \n0 2018-08-15 02:26:00 1 onset 2018-08-14T22:26:00-0400 \n1 2018-08-15 10:41:00 1 wakeup 2018-08-15T06:41:00-0400 \n2 2018-08-15 23:37:00 2 onset 2018-08-15T19:37:00-0400 \n3 2018-08-16 09:41:00 2 wakeup 2018-08-16T05:41:00-0400 \n4 2018-08-17 03:03:00 3 onset 2018-08-16T23:03:00-0400 \n... ... ... ... ... ... \n9580 2017-09-05 13:26:00 32 wakeup 2017-09-05T09:26:00-0400 \n9581 2017-09-06 02:30:00 33 onset 2017-09-05T22:30:00-0400 \n9582 2017-09-06 08:07:00 33 wakeup 2017-09-06T04:07:00-0400 \n9583 2017-09-07 03:35:00 34 onset 2017-09-06T23:35:00-0400 \n9584 2017-09-07 13:17:00 34 wakeup 2017-09-07T09:17:00-0400 \n\n[9585 rows x 10 columns]","text/html":"
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series_idsteptimestamp_xanglezenmodatetimenighteventtimestamp_y
0038441c925bb49922018-08-15 02:26:00+00:00-78.6875000.0099032018-08-1502:26:001onset2018-08-14T22:26:00-0400
1038441c925bb109322018-08-15 10:41:00+00:00-61.5937500.0263062018-08-1510:41:001wakeup2018-08-15T06:41:00-0400
2038441c925bb202442018-08-15 23:37:00+00:00-6.3867190.0182042018-08-1523:37:002onset2018-08-15T19:37:00-0400
3038441c925bb274922018-08-16 09:41:00+00:00-45.3437500.0164952018-08-1609:41:002wakeup2018-08-16T05:41:00-0400
4038441c925bb399962018-08-17 03:03:00+00:00-1.7871090.0000002018-08-1703:03:003onset2018-08-16T23:03:00-0400
.................................
9580fe90110788d25471522017-09-05 13:26:00+00:00-56.7812500.0042002017-09-0513:26:0032wakeup2017-09-05T09:26:00-0400
9581fe90110788d25565602017-09-06 02:30:00+00:00-16.5937500.0000002017-09-0602:30:0033onset2017-09-05T22:30:00-0400
9582fe90110788d25606042017-09-06 08:07:00+00:00-43.0625000.0000002017-09-0608:07:0033wakeup2017-09-06T04:07:00-0400
9583fe90110788d25746202017-09-07 03:35:00+00:001.5195310.0065992017-09-0703:35:0034onset2017-09-06T23:35:00-0400
9584fe90110788d25816042017-09-07 13:17:00+00:0079.0625000.0000002017-09-0713:17:0034wakeup2017-09-07T09:17:00-0400
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9585 rows × 10 columns

\n
"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"# Step 1: Summary Statistics\nprint(\"Summary statistics:\")\nprint(merged_data.describe())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:09.954355Z","iopub.execute_input":"2025-04-15T10:47:09.954647Z","iopub.status.idle":"2025-04-15T10:47:10.028332Z","shell.execute_reply.started":"2025-04-15T10:47:09.954628Z","shell.execute_reply":"2025-04-15T10:47:10.027505Z"}},"outputs":[{"name":"stdout","text":"Summary statistics:\n step anglez enmo date \\\ncount 9585.000000 9585.000000 9585.000000 9585 \nmean 214352.123944 -inf 0.015076 2018-07-01 02:17:09.859000 \nmin 936.000000 -89.750000 0.000000 2017-08-06 00:00:00 \n25% 95436.000000 -48.500000 0.000000 2018-01-26 00:00:00 \n50% 200604.000000 -16.015625 0.001300 2018-06-17 00:00:00 \n75% 317520.000000 28.468750 0.017395 2018-12-17 00:00:00 \nmax 739392.000000 89.750000 0.858398 2019-07-05 00:00:00 \nstd 141268.408192 46.187500 0.037140 NaN \n\n night \ncount 9585.000000 \nmean 12.861555 \nmin 1.000000 \n25% 6.000000 \n50% 12.000000 \n75% 19.000000 \nmax 43.000000 \nstd 8.168517 \n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:49: RuntimeWarning: overflow encountered in reduce\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n","output_type":"stream"}],"execution_count":19},{"cell_type":"code","source":"# Check for missing values\nprint(\"\\nMissing values per column:\")\nprint(merged_data.isnull().sum())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:14.549002Z","iopub.execute_input":"2025-04-15T10:47:14.549418Z","iopub.status.idle":"2025-04-15T10:47:14.559893Z","shell.execute_reply.started":"2025-04-15T10:47:14.549385Z","shell.execute_reply":"2025-04-15T10:47:14.559056Z"}},"outputs":[{"name":"stdout","text":"\nMissing values per column:\nseries_id 0\nstep 0\ntimestamp_x 0\nanglez 0\nenmo 0\ndate 0\ntime 0\nnight 0\nevent 0\ntimestamp_y 0\ndtype: int64\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"merged_data.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:23.927743Z","iopub.execute_input":"2025-04-15T10:47:23.928027Z","iopub.status.idle":"2025-04-15T10:47:23.933028Z","shell.execute_reply.started":"2025-04-15T10:47:23.928007Z","shell.execute_reply":"2025-04-15T10:47:23.93238Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"Index(['series_id', 'step', 'timestamp_x', 'anglez', 'enmo', 'date', 'time',\n 'night', 'event', 'timestamp_y'],\n dtype='object')"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"sns.countplot(x='event', data=merged_data)\nplt.title('Distribution of Target Label (event)')\nplt.xlabel('Event')\nplt.ylabel('Count')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:25.743602Z","iopub.execute_input":"2025-04-15T10:47:25.74388Z","iopub.status.idle":"2025-04-15T10:47:25.977855Z","shell.execute_reply.started":"2025-04-15T10:47:25.743859Z","shell.execute_reply":"2025-04-15T10:47:25.97709Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"missing_values = merged_data.isna().sum()\nprint(\"Missing values:\\n\", missing_values)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:29.089951Z","iopub.execute_input":"2025-04-15T10:47:29.090271Z","iopub.status.idle":"2025-04-15T10:47:29.098445Z","shell.execute_reply.started":"2025-04-15T10:47:29.090236Z","shell.execute_reply":"2025-04-15T10:47:29.097682Z"}},"outputs":[{"name":"stdout","text":"Missing values:\n series_id 0\nstep 0\ntimestamp_x 0\nanglez 0\nenmo 0\ndate 0\ntime 0\nnight 0\nevent 0\ntimestamp_y 0\ndtype: int64\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"sample_series = merged_data['series_id'].unique()[0] # Just pick the first one\nsample_df = merged_data[merged_data['series_id'] == sample_series]\n\nplt.figure(figsize=(15, 4))\nplt.plot(sample_df['timestamp_x'], sample_df['event'], marker='o', linestyle='-', alpha=0.6)\nplt.title(f\"Event distribution over time for Series ID: {sample_series}\")\nplt.xlabel(\"Timestamp\")\nplt.ylabel(\"Event\")\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:32.947525Z","iopub.execute_input":"2025-04-15T10:47:32.947796Z","iopub.status.idle":"2025-04-15T10:47:33.222885Z","shell.execute_reply.started":"2025-04-15T10:47:32.947775Z","shell.execute_reply":"2025-04-15T10:47:33.222091Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"plt.figure(figsize=(14, 5))\n\n# ENMO vs Event\nplt.subplot(1, 2, 1)\nsns.boxplot(x='event', y='enmo', data=merged_data)\nplt.title('ENMO vs Event')\n\n# ANGLEZ vs Event\nplt.subplot(1, 2, 2)\nsns.boxplot(x='event', y='anglez', data=merged_data)\nplt.title('ANGLEZ vs Event')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:47.931648Z","iopub.execute_input":"2025-04-15T10:47:47.93193Z","iopub.status.idle":"2025-04-15T10:47:48.239873Z","shell.execute_reply.started":"2025-04-15T10:47:47.931908Z","shell.execute_reply":"2025-04-15T10:47:48.239012Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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x=\"enmo\", hue=\"event\", common_norm=False)\nplt.title(\"ENMO Distribution by Event\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:47:54.467591Z","iopub.execute_input":"2025-04-15T10:47:54.467887Z","iopub.status.idle":"2025-04-15T10:47:54.787615Z","shell.execute_reply.started":"2025-04-15T10:47:54.46786Z","shell.execute_reply":"2025-04-15T10:47:54.786853Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"sample_df['enmo_mean_rolling'] = sample_df['enmo'].rolling(window=100, min_periods=1).mean()\n\nplt.figure(figsize=(15, 4))\nplt.plot(sample_df['timestamp_x'], sample_df['enmo_mean_rolling'], label='Rolling Mean of ENMO')\nplt.title(f\"Rolling Mean ENMO over Time ({sample_series})\")\nplt.xlabel(\"Timestamp\")\nplt.ylabel(\"ENMO (rolling mean)\")\nplt.legend()\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:04.771959Z","iopub.execute_input":"2025-04-15T10:48:04.772256Z","iopub.status.idle":"2025-04-15T10:48:05.015224Z","shell.execute_reply.started":"2025-04-15T10:48:04.772233Z","shell.execute_reply":"2025-04-15T10:48:05.014434Z"}},"outputs":[{"name":"stderr","text":":1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n sample_df['enmo_mean_rolling'] = sample_df['enmo'].rolling(window=100, min_periods=1).mean()\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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Skewness:\", merged_data['enmo'].skew())\nprint(\"ANGLEZ Skewness:\", merged_data['anglez'].skew())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:08.1467Z","iopub.execute_input":"2025-04-15T10:48:08.146994Z","iopub.status.idle":"2025-04-15T10:48:08.157813Z","shell.execute_reply.started":"2025-04-15T10:48:08.146972Z","shell.execute_reply":"2025-04-15T10:48:08.156906Z"}},"outputs":[{"name":"stdout","text":"ENMO Skewness: 7.348\nANGLEZ Skewness: nan\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py:1260: RuntimeWarning: overflow encountered in multiply\n adjusted3 = adjusted2 * adjusted\n/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:49: RuntimeWarning: invalid value encountered in reduce\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n","output_type":"stream"}],"execution_count":28},{"cell_type":"code","source":"print(\"ANGLEZ Unique Values:\", merged_data['anglez'].nunique())\nprint(\"ANGLEZ Missing Values:\", merged_data['anglez'].isna().sum())\nprint(\"ANGLEZ Data Type:\", merged_data['anglez'].dtype)\nprint(\"ANGLEZ Sample Values:\\n\", merged_data['anglez'].head(10))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:10.000104Z","iopub.execute_input":"2025-04-15T10:48:10.000433Z","iopub.status.idle":"2025-04-15T10:48:10.00805Z","shell.execute_reply.started":"2025-04-15T10:48:10.000404Z","shell.execute_reply":"2025-04-15T10:48:10.007351Z"}},"outputs":[{"name":"stdout","text":"ANGLEZ Unique Values: 5183\nANGLEZ Missing Values: 0\nANGLEZ Data Type: float16\nANGLEZ Sample Values:\n 0 -78.687500\n1 -61.593750\n2 -6.386719\n3 -45.343750\n4 -1.787109\n5 -64.687500\n6 -71.625000\n7 37.781250\n8 -71.125000\n9 -45.031250\nName: anglez, dtype: float16\n","output_type":"stream"}],"execution_count":29},{"cell_type":"code","source":"merged_data['anglez'] = merged_data['anglez'].astype('float32')\nprint(\"ANGLEZ Skewness (float32):\", merged_data['anglez'].skew())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:12.873666Z","iopub.execute_input":"2025-04-15T10:48:12.873952Z","iopub.status.idle":"2025-04-15T10:48:12.893652Z","shell.execute_reply.started":"2025-04-15T10:48:12.87393Z","shell.execute_reply":"2025-04-15T10:48:12.892413Z"}},"outputs":[{"name":"stdout","text":"ANGLEZ Skewness (float32): 0.26211095\n","output_type":"stream"}],"execution_count":30},{"cell_type":"code","source":"sns.histplot(merged_data['anglez'], kde=True, bins=50)\nplt.title(\"Distribution of ANGLEZ\")\nplt.xlabel(\"AngleZ\")\nplt.ylabel(\"Frequency\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:21.988538Z","iopub.execute_input":"2025-04-15T10:48:21.988819Z","iopub.status.idle":"2025-04-15T10:48:22.254084Z","shell.execute_reply.started":"2025-04-15T10:48:21.988798Z","shell.execute_reply":"2025-04-15T10:48:22.253365Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"# Map categorical 'event' values to numerical labels\nmerged_data['event_numeric'] = merged_data['event'].map({'awake': 0, 'sleep': 1})\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:25.453643Z","iopub.execute_input":"2025-04-15T10:48:25.453955Z","iopub.status.idle":"2025-04-15T10:48:25.459738Z","shell.execute_reply.started":"2025-04-15T10:48:25.453932Z","shell.execute_reply":"2025-04-15T10:48:25.458809Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"# Now compute correlation\ncorr_features = ['enmo', 'anglez', 'event_numeric']\ncorr_matrix = merged_data[corr_features].corr()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:26.885956Z","iopub.execute_input":"2025-04-15T10:48:26.886293Z","iopub.status.idle":"2025-04-15T10:48:26.891885Z","shell.execute_reply.started":"2025-04-15T10:48:26.886246Z","shell.execute_reply":"2025-04-15T10:48:26.891168Z"}},"outputs":[],"execution_count":33},{"cell_type":"code","source":"# Plot heatmap\nsns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\")\nplt.title(\"Feature Correlation with Target (Event)\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:27.166395Z","iopub.execute_input":"2025-04-15T10:48:27.166602Z","iopub.status.idle":"2025-04-15T10:48:27.343297Z","shell.execute_reply.started":"2025-04-15T10:48:27.166584Z","shell.execute_reply":"2025-04-15T10:48:27.342513Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n xa[xa < 0] = -1\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"# Set style\nsns.set(style=\"whitegrid\")\n\n# ENMO Distribution\nplt.figure(figsize=(12, 5))\nsns.kdeplot(data=merged_data, x=\"enmo\", hue=\"event\", fill=True, common_norm=False)\nplt.title(\"ENMO Distribution by Sleep State\")\nplt.xlabel(\"ENMO\")\nplt.ylabel(\"Density\")\nplt.show()\n\n# ANGLEZ Distribution\nplt.figure(figsize=(12, 5))\nsns.kdeplot(data=merged_data, x=\"anglez\", hue=\"event\", fill=True, common_norm=False)\nplt.title(\"ANGLEZ Distribution by Sleep State\")\nplt.xlabel(\"ANGLEZ\")\nplt.ylabel(\"Density\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:32.187313Z","iopub.execute_input":"2025-04-15T10:48:32.187622Z","iopub.status.idle":"2025-04-15T10:48:32.768633Z","shell.execute_reply.started":"2025-04-15T10:48:32.187596Z","shell.execute_reply":"2025-04-15T10:48:32.767802Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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rfn5+fro48+Urt27dStW7dK27Zu3VqXXHKJLrnkEh08eFDnnXeennrqKW9wr034qsi+ffvKjGyXjuyWrvsv/aIkNzfX57m7d+8uc73q1lb6Jcnvv/9e5lxaWppatGhRZQitieq8T0k6/vjj1atXL61YsUJt27bVnj179Le//a3WrxsbG6tOnTp5v7QpT2JiokzTVMeOHcudgVCqdNZFVFSUTjnllGq9/vbt231ma2zfvl1ut7tGszKO1qdPH7399tvav3+/pIp/35s3b1Z6eroeeOABn40f169fX6ZtRddITEzUl19+qZNOOqnev4wCgGDHGncAQIMpXev+xhtvVNk2MTFRd955p26++eYyO20fbeTIkerevbuWLl1aZnM8t9utu+++W9nZ2ZoxY0at6y4sLNStt96qQ4cOafr06RUGFZfLVSYct2zZUq1bt/ZZIx0eHl6mXW2VlJTo9ddf9z4uLi7W66+/rvj4eO9GZImJiZKkb775xqfW8n4P1a2tdevW6tmzp9555x3l5OR4j2/evFnr16+v9uyC6qrO+yw1fvx4rV+/Xi+88ILi4uJ8NqqryG+//VbmTgmS58uNbdu2VRrI//SnP8lqtWrhwoVlZg+YpuldytGnTx8lJibqueeeK3efhPJev/RWa6VeeuklSar0PTkcDn3//fflnitdG1/6fsLDwyXJ53coHZkdcfT7MU1TL774YplrVnSNUaNGyeVy6cknnyzznJKSkjLtAQDVx4g7AKBKn332mdLS0socP+mkkypdy1066v7f//63Wq9z2WWXVdnGbrdr/vz5uuyyy3TxxRdrwoQJ6tOnj3Jzc7Vy5Ur98ssvuvLKKzVmzJhqvWZGRobeffddSZ4NubZt26Y1a9Zo//79uvLKKytdo5+fn6/TTz9dI0eO1AknnKCIiAh98cUX+umnn3x2me/du7dWr16t+++/X8nJyYqIiPDeL72mWrduraVLl2r37t1KSkrS6tWrtXHjRt13333eHeWPO+44paSk6NFHH1V2drZiY2O1evVqlZSUlLleTWq79dZbNW3aNE2aNEnnn3++93Zw0dHRdfqipLbvs9TYsWP10EMP6cMPP9RFF11UrZ31169frwULFmjYsGE68cQTFRERoV27dumtt95ScXGxZs6cWeFzExMTNWvWLD3yyCPavXu3RowYocjISO3atUsfffSRLrjgAk2dOlUWi0Vz587VtGnTNHbsWE2YMEFt2rRRRkaGvv76a0VFRempp57yufauXbs0ffp0nXbaafrhhx+0fPlyjR07tsId5SVPcL/wwguVkpKi0047TW3btlVubq4++ugjffvttxoxYoR69erlrT0mJkavvfaaIiMjFRERob59+6pr165KTEzUAw88oIyMDEVFRen9998vN2yXfnEyd+5cnXrqqbJarRozZowGDhyoSZMm6emnn9bGjRu9U/XT09O1Zs0a/fWvf63W3gMAgLII7gCAKpXew/lY999/f5WbsM2YMUNTpkyp13q6deum5cuXa8mSJVq7dq2WLVumsLAw9enTR4sXL65RKN64caNuvfVWGYahyMhItWvXTmeeeaYmTpxY6ci/5FmbftFFF2n9+vX64IMPZJqmEhMTdffdd+viiy/2trv44ou1ceNGLVu2TM8//7w6dOhQ6+AeGxurefPmae7cuXrjjTfUqlUrzZkzRxdccIFPu4cfflhz5szRkiVLFBMTo/PPP1+pqane26vVprZTTjlFzzzzjObPn6/58+crJCREAwYM0C233FKjzfjq831KntucDRkyROvWravWbe0kz6h5fn6+1q9fr6+++krZ2dmKiYlR3759dcUVV1S4OWKpq6++WklJSXr++ee1aNEiSVLbtm01ZMgQn88vNTVVr7/+up588km99NJLKigoUEJCgvr27euza36pxx9/XE888YQeeeQRhYSE6NJLL9Wtt95aaS0xMTGaO3euPv30Uy1btkz79++X1WpVly5ddOutt2ry5MnetjabTfPmzdOjjz6qe+65RyUlJbr//vs1YcIEPfXUU5o7d66efvpphYaG6qyzztIll1xS5jP905/+pMmTJ2vVqlVavny5TNP0flF27733qk+fPnrttdf02GOPyWq1qkOHDjrnnHO894sHANScYdbnDjEAAAB+cN1112nz5s368MMP/V1KrSxYsEALFy7Ul19+ySZuAIAyWOMOAACatH379tVotB0AgKaGqfIAAKBJ2rlzp7777jv95z//UUhISLlTzwEAaA4YcQcAAE3SN998o1tvvVW7du3SvHnzlJCQ4O+SAABoEKxxBwAAAAAggDHiDgAAAABAACO4AwAAAAAQwNicTtL3338v0zRls9n8XQoAAAAAIAg4nU4ZhqF+/fpV2ZYRd0mmaYql/h6maaq4uJjPA80WfRzBgH6OYEA/R3NHH2/+apJDGXGXvCPtycnJfq7E/woKCrRx40Z1795dERER/i4HqHf0cQQD+jmCAf0czR19vPn76aefqt2WEXcAAAAAAAIYwR0AAAAAgABGcAcAAAAAIIAR3AEAAAAACGAEdwAAAAAAAhi7ygMAAABAgHK5XCosLPR3Gaghm80mq9Vab9cjuAMAAABAgCm9v/eOHTtksTBRuimKi4tT27ZtZRhGna9FcAcAAACAAHPw4EHZbDa1bt1aUVFR9RL+0DhM01RBQYH27dsnSWrXrl2dr0lwBwAAAIAA4nK5lJubq1atWik+Pr5ep1yjcYSHh0uS9u3bp9atW9f5d8icCwAAAAAIIE6nU5IUGhrq50pQFxEREZKO/D7rIuCC+8cff6yJEyeqX79+OvXUU3XDDTdo586dZdq9+eabGjlypJKTk3XOOefok08+8UO1AAAAANAwmB7ftNXn7y+ggvvXX3+tGTNmqHv37lq0aJHuvPNO/fbbb7ryyit9dlJctWqV7rrrLo0aNUpLly5VSkqKZsyYoR9++MF/xQMAAAAA0AACao37qlWr1L59e/3zn//0fjsRHx+vyy67TD///LP69+8vSZo/f77GjBmjWbNmSZIGDRqkzZs3a9GiRVq6dKm/ygcAAAAAoN4F1Ih7SUmJIiMjfaYUREdHSzpyO4SdO3cqPT1do0aN8nnu6NGj9eWXX6q4uLjxCgYAAAAA+N26deu0YMECf5fRYAIquE+YMEHbtm3Tyy+/rNzcXO3cuVOPPvqoevXqpZNOOkmSlJaWJknq0qWLz3O7desmp9NZ7np4AAAAAEDztW7dOi1cuNDfZTSYgJoq379/fy1cuFA333yz7r33XklSz5499cwzz3i3z8/OzpYkxcTE+Dy39HHp+ZoqvddesHM4HD4/geaGPo5gQD9HMKCfozkrKiryzjg2TVMul8vPFQW+0s8rkD4rl8slt9sth8Mht9td5rxpmtXewC6ggvt3332nW2+9VRdccIHOOOMMHTp0SE8++aSuvvpqvfLKKwoLC2uw13Y6ndq4cWODXb9JMU39sfZVFXfoKzM00t/VAA0iPT3d3yUADY5+jmBAP0dzFRLiiWpFRUV+ruSIffv26cknn9Tnn3+u3NxcderUSZdeeqnOPfdcHTx4UGeffbauuuoqXXPNNT7PS09P14QJE3TrrbfqwgsvlCTl5ubqqaee0tq1a5WZmak2bdpowoQJmjJliiwWz8TwPXv2aOzYsZo1a5YiIyP1wgsvKCMjQ8cdd5zuuOMO9e7dW5J09913a8WKFZKkXr16eV/3u+++a4yPpUJFRUUqKSnxzhovj91ur9a1Aiq4z507V4MGDdLtt9/uPZaSkqIzzjhD7777riZNmqTY2FhJnl90QkKCt11OTo4kec/XlM1mU/fu3etQffPgcDi067t1itj8idp166nwE3r6uySgXjkcDqWnpyspKUnh4eH+LgdoEPRzBAP6OZqzoqIi7d69W5LnXu6BcFu4AwcO6PLLL5ckXXLJJWrRooX+7//+T/fee6+Ki4s1ZcoU9e/fXx999JFuuOEGn+euXbtWVqtVY8eOVVhYmBwOh66++mplZGToggsuULt27fTDDz9owYIFysrK0h133CHpyH3s33//feXn52vSpEmSpOeee0633HKL3n//fdlsNl100UU6ePCgvvjiC82bN8/7ug058FtdISEhSkxM9L6Xo23durX616nPoupq27ZtGj58uM+xtm3bqkWLFtqxY4ckqWvXrpI8a91L/1762GazqVOnTrV6bcMwFBERUcvKmxf7nl88P0OsfCZotsLDw+nfaPbo5wgG9HM0RxaLxRvWDcPwLhv2p/nz58vlcmnFihVq0aKFJE+Av+mmm7Ro0SJddNFFGjNmjObMmaNt27bp+OOP9z53zZo1GjBggNq0aSNJevHFF7Vz5069/fbbSkpKkiRdfPHFatOmjZ599llNnTpV7dq18468//HHH/rggw+8g7TdunXTtddeqy+//FJnnnmmTj75ZHXp0kVffPGFzjvvvEb8VCpntVplsVgUHh5e7pcINflCJqA2p2vfvr1+/fVXn2O7d+9WVlaWOnToIEnq1KmTkpKStGbNGp92q1ev1uDBg6s91QDlM10lsu39zfN3d4mfqwEAAADgb6Zp6oMPPtCwYcNkmqYyMzO9f0499VTl5ubql19+0VlnnaWQkBCtXr3a+9zNmzdr69atGj16tPfYmjVrdPLJJysmJsbnWqeccopcLpe++eYbn9cfPXq0z8zq0tuEB9PG5AE14n7hhRfqn//8p+bOnathw4bp0KFDWrx4sVq2bOlz+7eZM2dq9uzZSkxMVGpqqlavXq0NGzbopZde8mP1zUNR+o+ylBR6HrgI7gAAAECwy8zMVE5Ojl5//XW9/vrrFbY5+eSTNWjQIL333nuaNWuWJM8Aa0hIiM466yxv2+3bt2vTpk0aPHhwhdc6Wrt27Xwel4b40uXSwSCggvuUKVNkt9v16quv6q233lJkZKRSUlL0+OOPe6djSNLYsWPlcDi0dOlSLVmyRF26dNHChQvVr18/P1bfPDg2fiFXRAtZi/JkugNnR0YAAAAA/lG6I/o555xT4VT0Hj16SJLGjBmjO+64Qxs3blTPnj313nvvadCgQYqPj/e53pAhQ3TVVVeVe63S6fOlKloqULqTfDAIqOBuGIYuuugiXXTRRVW2nThxoiZOnNgIVQUXV+YeuaJaKcTpkMmIOwAAABD04uPjFRkZKbfbrVNOOaXStiNGjNCcOXO80+XT09PL7DKfmJiogoKCKq9VE4GwgV9DCqg17vA/0+2SDKtkscoMoHsgAgAAAPAPq9WqkSNH6v3339fmzZvLnD96antMTIxOPfVUvffee1q1apVsNptGjBjh037UqFH6/vvv9X//939lrpWTk6OSkpoPIJbeXaK5Tp8PqBF3BAC3SzIMyWJhjTsAAAAASdLNN9+sr7/+WhdccIEmTpyo7t27Kzs7W7/88ou+/PJL/fe///W2HT16tG655Ra98sorOvXUUxUTE+NzralTp2rt2rWaPn26zjvvPPXu3VsOh0ObN2/W+++/r48//thnan11lN7Tfe7cuTr11FNltVo1ZsyYur/xAEFwhw/T7ZJpWGRYrDJdTn+XAwAAACAAtGrVSm+++aYWLVqkDz/8UK+++qri4uLUvXt3zZ4926ftsGHDFBYWpvz8fJ/d5EuFh4fr3//+t55++mmtWbNG77zzjqKiopSUlKSZM2cqOjq6xvX96U9/0uTJk7Vq1SotX75cpmk2q+BumMG0or8CP/30kyQpOTnZz5X4X/rjU+WI7aTw3N2K7n2aWg6f4u+SgHpVUFDg3SyF+/6iuaKfIxjQz9GcFRYWatu2bWrXrp1iY2MD4j7uqLnCwkL9/vvv6tKlS7n3ca9JDmWNO3yYh6fKe0bcmSoPAAAAAP5GcIcvt0syLJ4N6rgdHAAAAAD4HcEdvtxumYZFslgYcQcAAACAAEBwhw/z8Ii7YbHKdBPcAQAAAMDfCO7wVXo7OIMRdwAAAAAIBAR3eJmmKZlu74g793EHAAAAAP8juOOIw5vRHVnjzuZ0AAAAAOBvBHd4maW7yBuGZLHKdDn9WxAAAAAAgOCOo5ROjTcsnjXu3A4OAAAAAPyO4A4v0+32/KV0V3nWuAMAAACA3xHc4VV6+zfz8FR5NqcDAAAAAP8L8XcBCCDeNe6lm9MR3AEAAIBAsi+rQDn5xX557ZhIu1q3iPDLa9eXnJwcvfDCCxo1apS6d+/u73KqjeAOL/OoNe6GxSq3m+AOAAAABIp9WQW69oG1KnL6Zy+qUJtVT942rEmH95ycHC1cuFDHHXccwR1N05E17oZksMYdAAAACCQ5+cUqcrp0wfDjlNDI4Xl/VoHe+HiLcvKLm3Rwb6oI7jjCu8bdM+LOGncAAAAg8CS0iFCHhCh/l1EjH3zwgRYtWqS0tDTFxsZqzJgxuummmxQaGqqvv/5aU6ZM0XPPPadly5Zp7dq1iouL08UXX6xp06Z5r7FlyxY9+OCD2rBhgwoLC9W2bVudf/75Pm2+//57PfbYY9qwYYOsVqvOOOMM3XnnnWrZsqV27dql4cOHS5JuuOEG73M+/vhjdezYsfE+jFpgczp4ma5j1rhzOzgAAAAAdfTxxx/r+uuvV/fu3bVo0SJdddVVeu2113TLLbf4tLv77ruVlJSkRYsW6cwzz9TDDz+szz77zHt++vTpysnJ0T/+8Q89/fTTmjp1qhwOh/f8999/r8mTJys6OlqPPfaY7rvvPv3000+69tprJUmtW7fWwoULJUk33XSTXn/9db3++utq3bp1I3wKdcOIO7y8QZ2p8gAAAADqycKFC5WSkqJHHnlEkjR06FCFh4drzpw52rRpk7fdn/70J82cOVOSNHjwYH366ad6//33NXToUGVmZmrXrl3661//qmHDhkmSBg0a5PM6jzzyiPr06aOFCxfKMAxJ0vHHH6+xY8dq3bp1Ov3009WzZ09JUufOnZWSktLQb73eMOKOI47aVd6wWI48BgAAAIBayM/P18aNGzVy5Eif46NHj5Yk/e9///MeO/XUU71/NwxD3bp10969eyVJLVq0UIcOHfToo4/q7bff9h4v5XA49N133+nss8+Wy+VSSUmJSkpKlJSUpHbt2umnn35qqLfYKAju8DKPWuMuCyPuAAAAAOomNzdXpmmqZcuWPsejo6Nlt9uVnZ3tc+xoNptNxcWeW98ZhqFnn31WXbt21b333qvTTz9dEyZM0DfffCPJs1u8y+XS/fffr969e/v82bNnj/74448GfqcNi6nyOOLoNe4Ga9wBAAAA1E10dLQMw1BmZqbP8dzcXBUXFys2Nrba1+rSpYvmz58vp9Op77//Xo8++qimT5+uzz77zPs611xzjUaMGFHmuS1atKjze/EnRtzhdSSoG95d5U3T9GtNAAAAAJquyMhI9ezZU2vWrPE5/t5770mSTj755Bpf02azaeDAgbr66quVl5enffv2KSIiQikpKUpLS1NycnKZP6W7xttsNklSUVFRHd9Z42LEHV7e4G7xTJWX5FnnbqWbAAAAAKidGTNm6LrrrtPs2bN1zjnn6Pfff9djjz2mkSNHqkePHvr666+rvMZvv/2mBx54QKNHj1anTp2Ul5enp59+Wh06dFBiYqIk6dZbb9Vll12mWbNmacyYMYqJidHevXv1xRdfaMKECUpNTVVCQoJiYmK0atUqdezYUXa7XT169JDdbm/oj6FOSGQ44vBUee993OUJ8wbBHQAAAAgY+7MKmtRrDh8+XE888YQWLVqka6+9VnFxcbrgggt08803V/saCQkJatWqlZ5++mllZGQoOjpa/fv310MPPSSr1ZNdTjrpJL3yyitasGCB7rjjDjmdTrVt21aDBg1S586dJUkWi0X333+/Hn30UV1++eUqLi5uEvdxJ5HBq3RzOsnwjLpLng3qbKH+KwoAAACAJCkm0q5Qm1VvfLzFL68farMqJrJ2I9MjR44ss7N8qdTUVJ/bwpV68sknvX9v2bKlHnrooSpfJzk5WUuWLKm0zYgRI8pdBx/ICO44wu32/DSOmirPzvIAAABAQGjdIkJP3jZMOfnFfnn9mEi7WreI8MtrBzuCO7y8I+6GRYZx+BjBHQAAAAgYrVtEEJ6DELvKw8v03g7uqKny3BIOAAAAAPyKEXcc4S45fA/3Y9a4AwAAAAD8hhF3eJlutzewG4d/yk1wBwAAAAB/CqjgPnnyZPXo0aPcP6tWrfK2e/PNNzVy5EglJyfrnHPO0SeffOLHqpsP010iwzi8KV3p7eAYcQcAAAAAvwqoqfJ333238vLyfI698MIL+uCDDzR48GBJ0qpVq3TXXXdp+vTpGjRokFavXq0ZM2bo5ZdfVkpKih+qbkZcLu+IuwyCOwAAAAAEgoAK7t27dy9z7Oabb9aQIUMUHx8vSZo/f77GjBmjWbNmSZIGDRqkzZs3a9GiRVq6dGljltvsmG5XOVPl2ZwOAAAAAPwpoKbKH+u7777Trl27NG7cOEnSzp07lZ6erlGjRvm0Gz16tL788ksVF/vnfobNhel2MVUeAAAAAAJMQAf3lStXKiIiQsOHD5ckpaWlSZK6dOni065bt25yOp3auXNno9fYrLhKjhpxJ7gDAAAACEyTJ0/WNddc4+8yGk1ATZU/WklJid577z0NGzZMERERkqTs7GxJUkxMjE/b0sel52vDNE0VFBTU+vnNgbOoUKYMz99LPIG9sCBfCvLPBc2Lw+Hw+Qk0R/RzBAP6OZqzoqIimaYpyZNTXK4jy1dLsg/I5cjxS13W8BiFxLbyy2sfyzTNMp9NoHG5XHK73XI4HHK73WXOm6YpwzCqda2ADe7r169XZmamxo4d2yiv53Q6tXHjxkZ5rUAVfmC/bIc7VMb+A4qWtGvHdjmLw/1bGNAA0tPT/V0C0ODo5wgG9HM0VyEhnqhWVFTkPebKOaADL94ulfhpiXCIXa2mzJM1xv/h3e12y+VyqbCw0N+lVKioqEglJSXemePlsdvt1bpWwAb3lStXKi4uTqeeeqr3WGxsrCQpNzdXCQkJ3uM5OTk+52vDZrOVuzleMMnZ+40Kszwdp03bdiqQ1KF9W4X36OnfwoB65HA4lJ6erqSkJIWH86UUmif6OYIB/RzNWVFRkXbv3i1JCg0N9Y7KFh0qlkqKFTP4PIXEJFR2iXpXkrNfOV++rRB3sULDwqr9vG+//VZTpkzRmjVrlJiYKEm69tpr9emnn+rdd9/VcccdJ0maPXu28vLy9NRTT+nRRx/VunXrtGvXLkVHR6t///667bbbfDKgxWKR1WpV2OFaCgsLNWvWLKWlpenZZ59Vp06dtHfvXj366KP6/PPP5XA41KdPH91+++3q3bu39zq9evXS7NmzdeWVV3qPvfjii5o3b55+/fVXSdJ///tfXX755Vq8eLHeeustrV+/XpGRkbrkkkuqnK4fEhKixMREhYaGljm3devWan+OARncCwsL9dFHH+mcc86RzWbzHu/ataskz1r30r+XPrbZbOrUqVOtX9MwDO+U/GCVbxgyrJ4uYQ8LV4Eku9Ua9J8Lmqfw8HD6Npo9+jmCAf0czZHFYvGGdcMwZLV69p+yHt6Hyh7XWraW7Ru5JsNbQ2k91dGvXz+Fhobqu+++U5cuXeR2u/Xdd995j51wwgmSPAF/8uTJslqtyszM1PTp09W6dWtlZmbqX//6ly677DKtWrXKOxPBMAzvZ5Ofn6/p06dr//79euWVV9SmTRtlZ2dr8uTJioiI0F133aXo6Gj9+9//1hVXXKEPPvhALVu2POq9WXzeU+lnX3rMcngfsHvuuUdjxozRhRdeqC+++EJPPPGEWrRooYsuuqjc9261WmWxWBQeHu79guFo1Z0mLwVocF+7dq0KCgq8u8mX6tSpk5KSkrRmzRqNGDHCe3z16tUaPHhwtacZoALuI5vTeXeVd7M5HQAAAIDasdvt6tu3r7799lv9+c9/1qZNm+RwODRhwgR98803uvjii7V9+3bt27dPAwYMkCTdf//93ue7XC7169dPQ4cO1VdffeUzI1vy7HM2bdo0FRUV6eWXX/YG8hdeeEE5OTl68803vccGDx6skSNH6tlnn9Wtt95a4/cyaNAg3XbbbZKk0047TQcPHtTixYs1adIkb7hvKAG5q/yKFSvUvn17nXzyyWXOzZw5UytXrtT8+fP19ddf6+6779aGDRt07bXX+qHS5sV0u2UYh3eVNwzJsHh2mgcAAACAWurfv7+++eYbSdI333yjPn36aOjQoT7HwsPD1adPH0nSunXrdOGFF+rkk09Wr169NHToUEll97TIysrSlClTJHmmtx89ir5+/XqlpqYqNjZWJSUlKikpkcVi0YABA/TTTz/V6n2cddZZPo9HjhypjIwM7d27t1bXq4mAG3HPzs7W//3f/+myyy4rd+rA2LFj5XA4tHTpUi1ZskRdunTRwoUL1a9fPz9U27yY7hJPWD/MsFhlugN3l0YAAAAAgW/gwIFavHixMjIy9O2336p///7q37+/Dhw4oPT0dH377bc68cQTZbPZvIOyw4cP17Rp09SyZUsZhqELLrjAZ6M+yRPks7Ozdeedd5bZ7ywrK0s//PCDz3r2UqVr7WsqPj7e53GrVp5N+vbv36/27Rt26ULABffY2Fj9/PPPlbaZOHGiJk6c2EgVBRGXyztFXpJksXIfdwAAAAB1kpKSIpvNpm+++cY7ZT4uLk7HHXecvvnmG33zzTc699xzJUkfffSRoqKi9Pjjj3unn5du1Hesfv36afDgwZo3b57i4uI0fvx477nY2FiddtppuuGGG8o87+gl1na7XU6n0+d86ebnx8rMzPR5fODAAUny2TSvoQRccIf/mO4SGUetzTCsVpkBfF9EAAAAAIEvIiJCvXr10uuvv65Dhw55l0QPGDBAy5cv165du9S/f39Jno3KbTabz+zrFStWVHjtyy+/XEVFRbrjjjsUGhqqs88+W5J0yimnaPny5erWrVulG1i2bdtW27Zt8zn2xRdflNv2ww8/9Jku//7776t169Zq27ZtFZ9A3RHc4WW6XT5T5WWxssYdAAAAQJ31799fzz77rHr37q2oqCjvsZdfflk2m8279HnIkCF64YUXdN999+mss87S999/r3fffbfSa19zzTUqLCzU7NmzFRoaqjPPPFOXX365VqxYoUsvvVRTpkxR+/btlZmZqR9//FFt2rTR5ZdfLsmzTv2FF15QcnKyunTpouXLlysjI6Pc1/nqq6/0wAMPaMiQIVq/fr3effddzZkzp8E3ppMI7jiay1XOGneCOwAAABBISnIONLnXHDhwoJ599lnvyLok7y7yffr08d4u7fTTT9fs2bP10ksvadmyZTrppJP09NNPa+TIkZVe/4YbblBhYaGuv/56Pf300zrllFP0+uuv6/HHH9fDDz+sQ4cOqWXLljrxxBN9Rs2vvfZaHTx4UIsWLZJhGJo0aZKmTJmiefPmlXmNe++9V6+//rpeffVVRUZG6oYbbtAll1xSp8+lugju8DKPvh2cxBp3AAAAIIBYI6JlhNh1aP1bfnl9I8Qua0R0rZ57xhlnaNOmTT7HEhISyhyTpGnTpmnatGk+x45t9+9//7vM82677Tbv7dpKr/+Pf/yj0roiIiJ8bj9X6oorrihzrGXLllq8eHGl12soBHd4mS6XDMuRLmFYLAR3AAAAIECExCao0/T5chXk+uX1rRHRColt+I3YUBbBHUe4XVJI6JHHFqvnGAAAAICAEBKbQHgOQgR3eJlul89UecNgxB0AAABAcEtNTS13Sn9javjt79BkmK4SGWxOBwAAAAABheCOI8q5HRwj7gAAAADgXwR3eHmmyluPHLBYPLeIAwAAANDoTNP0dwmog/r8/RHccYTbJcNyzFR5RtwBAACARmWz2SRJRUVFfq4EdVFQUCDpyO+zLticDl6mq6TsVHnWuAMAAACNymq1Kjo6WgcOHJDNZlNUVJQMw/B3Wagm0zRVUFCgffv2KS4uTlarteonVYHgDi/T7fbdVZ4RdwAAAMAvWrZsqf3792vfvn06cOCAv8tBLcTFxalt27b1ci2CO45wl8gwjnwbZFgscpc4/VgQAAAAEJxKR9gTExPrZao1GpfNZquXkfZSBHd4HXsfd1msksvhv4IAAACAIGe1WhUWFubvMuBnbE6HI1wupsoDAAAAQIAhuMPLNN0y2JwOAAAAAAIKwR2SDt9j8Jj7uBsWCyPuAAAAAOBnBHd4uF2en8axa9xd/qkHAAAAACCJ4I7DzMPB3fAZcWeqPAAAAAD4G8EdHt4Rd+PIMTanAwAAAAC/I7hDkmSWTok/ZsTdG+gBAAAAAH5BcIckHZkS77PGnc3pAAAAAMDfCO7wqHCNOyPuAAAAAOBPBHdIOrI53dFr3A2LVXKVeG4VBwAAAADwC4I7JJW/xt37d9Pd+AUBAAAAACQR3FHq8Bp3w3KkS5ROm2edOwAAAAD4D8EdkiTTfXhU3ShnxJ3gDgAAAAB+Q3CHR2k49xlx9/ydEXcAAAAA8B+COyQd2ZzOOPp2cIdH39lZHgAAAAD8h+AOSUfdx72cEXcR3AEAAADAbwju8PCucT+qS5ROlSe4AwAAAIDfENwh6cg6duPo28EZBHcAAAAA8LeADO5vv/22zj33XCUnJys1NVVXXXWVCgsLvefXrl2rc845R8nJyRo5cqTeeustP1bbPHjDeTm3g2OqPAAAAAD4T4i/CzjW4sWLtXTpUk2fPl0pKSnKysrSl19+KZfLEx6//fZbzZgxQ+eff77uvPNOffXVV/rrX/+qyMhInX322X6uvgk7/Pn6TJX3jri7/VAQAAAAAEAKsOCelpamhQsX6sknn9Tpp5/uPT5y5Ejv3xcvXqy+ffvq3nvvlSQNGjRIO3fu1Pz58wnudWCah3eVL29zOm4HBwAAAAB+E1BT5ZctW6aOHTv6hPajFRcX6+uvvy4T0EePHq1t27Zp165djVFm8+QdcT9qjXvp5nQmI+4AAAAA4C8BNeL+448/6vjjj9eTTz6pf//738rNzVWfPn10xx136MQTT9SOHTvkdDrVtWtXn+d169ZNkmfEvmPHjrV6bdM0VVBQUOf30FQVOTzvvdjp9PwsKpK12DPSXliQL3cQfzZoXhwOh89PoDminyMY0M/R3NHHmz/TNGUYRrXaBlRw379/v37++Wdt3rxZd999t8LDw/XUU0/pyiuv1AcffKDs7GxJUkxMjM/zSh+Xnq8Np9OpjRs31r74Js6+e5ciJWVk7JMMQ3v37pVRmKsoSdt/T1NJDqPuaF7S09P9XQLQ4OjnCAb0czR39PHmzW63V6tdQAX30lHvJ554QieccIIk6cQTT9SwYcP00ksv6dRTT22w17bZbOrevXuDXT/QFRTvUc6vFrVt10579+5V27ZtZS2JU85PUmKnjgrt3NPfJQL1wuFwKD09XUlJSQoPD/d3OUCDoJ8jGNDP0dzRx5u/rVu3VrttQAX3mJgYxcXFeUO7JMXFxalXr17aunWrxowZI0nKzc31eV5OTo4kKTY2ttavbRiGIiIiav38ps4ZYpUsFtlDQyVJ9tBQ2WxSjqRQmy2oPxs0T+Hh4fRrNHv0cwQD+jmaO/p481XdafJSgG1OV9mId1FRkRITE2Wz2ZSWluZzrvTxsWvfUX2mu+TIfdtLeW8Hx33cAQAAAMBfAiq4n3nmmTp06JDPWvOsrCz98ssv6t27t+x2u1JTU/X+++/7PG/16tXq1q1brTemgyS3WzomuHtvB0dwBwAAAAC/Caip8iNGjFBycrKuv/563XjjjQoNDdWSJUtkt9t18cUXS5L+8pe/aMqUKbrnnns0atQoff3111q5cqUee+wxP1fftJmuEhnGMd/jMOIOAAAAAH4XUCPuFotFS5YsUUpKiubMmaObbrpJUVFRevnll5WQkCBJ6t+/vxYsWKD//e9/mjp1qlauXKm5c+dq1KhRfq6+aTPdrgpH3AnuAAAAAOA/ATXiLknx8fF66KGHKm0zfPhwDR8+vJEqChKukiNT40uVBnmCOwAAAAD4TUCNuMN/TLPsGncd3uWQEXcAAAAA8B+COzzKWeNuGBZPeHcR3AEAAADAXwjukCSZbrd07FR5SbJYGXEHAAAAAD8iuMPD7Sq7q7wOj7qbbj8UBAAAAACQCO44zDTd3jXtPiwWma6Sxi8IAAAAACCJ4I5S5dwOTpIMi9UzjR4AAAAA4BcEd0jyrHE3yhtxNyzcDg4AAAAA/IjgDg/T7QnpxzAMi0w3U+UBAAAAwF8I7pB0+F7t5a5xt0pMlQcAAAAAvyG4w6OCXeVlsXA7OAAAAADwI4I7JB0ecS/nPu6eqfIEdwAAAADwF4I7PNzlr3GXhc3pAAAAAMCfCO6Q5BlxL29XeUbcAQAAAMC/CO6QVPFUeVkskovgDgAAAAD+QnCHR2W3gzMJ7gAAAADgLwR3SJJMt1tGRSPu3A4OAAAAAPyG4A4Pt6v8zekMi0xXSePXAwAAAACQRHBHqQqCu8F93AEAAADArwjukFTJVHmDqfIAAAAA4E8Ed0g6vKt8ObeDk8Ui081UeQAAAADwF4I7PNwuGeXuKm9lqjwAAAAA+BHBHZIks4LbwXl2lSe4AwAAAIC/ENzh4XZ7QvoxDMMi00VwBwAAAAB/IbhDkmeNe3lT5RlxBwAAAAD/IrjDo4Kp8obB7eAAAAAAwJ8I7vCoZFd5RtwBAAAAwH8I7pBUyX3cLewqDwAAAAD+RHCHB1PlAQAAACAgEdwhybM5XYW3g2NXeQAAAADwG4I7PCqYKm8YFpkmwR0AAAAA/IXgDkmSWcFUec/mdO7GLwgAAAAAIIngjlIV7SpvWGS6Shq/HgAAAACAJII7dHi03TTLnypvsXo2rgMAAAAA+EVABfdly5apR48eZf48/PDDPu3efPNNjRw5UsnJyTrnnHP0ySef+KniZqJ0KrxhLXuOXeUBAAAAwK9C/F1AeZ555hlFR0d7H7dp08b791WrVumuu+7S9OnTNWjQIK1evVozZszQyy+/rJSUFD9U2/SZh0fUyx9xZ1d5AAAAAPCngAzuvXv3Vnx8fLnn5s+frzFjxmjWrFmSpEGDBmnz5s1atGiRli5d2ohVNiOlI+rlrXG3WLzBHgAAAADQ+AJqqnxVdu7cqfT0dI0aNcrn+OjRo/Xll1+quLjYT5U1baZ3qnz5t4OT2yXTNBu5KgAAAACAFKAj7mPHjlVWVpbat2+vCy64QFdddZWsVqvS0tIkSV26dPFp361bNzmdTu3cuVPdunWr1WuapqmCgoI6194UuR15kiRniUsqKpIkFR/+6XR5Qn1Bfp5nozqgiXM4HD4/geaIfo5gQD9Hc0cfb/5M05RR3qzncgRUcE9ISNDMmTN14oknyjAMrV27Vo8//rgyMjI0Z84cZWdnS5JiYmJ8nlf6uPR8bTidTm3cuLH2xTdhRlGe4iTtP3BArpIwSdLevXslSSEHsxQu6bdff5WsAdVdgDpJT0/3dwlAg6OfIxjQz9Hc0cebN7vdXq12AZXETjvtNJ122mnex6eeeqpCQ0P1wgsvaPr06Q362jabTd27d2/Q1whUrrxM7f9Eat26rcz4ttq7d6/atm0re2ioii15yv9d6nF8d1ns4f4uFagzh8Oh9PR0JSUlKTycPo3miX6OYEA/R3NHH2/+tm7dWu22ARXcyzNq1Cg999xz2rhxo2JjYyVJubm5SkhI8LbJycmRJO/52jAMQxEREXUrtokqceZLkuxhoTJDQz1/Dw1VWFioTLvncXhoqKzhwfn5oHkKDw8P2n/zCB70cwQD+jmaO/p481XdafJSE9ucrmvXrpLkXeteKi0tTTabTZ06dfJHWU2eWcmu8t517dzLHQAAAAD8IuCD++rVq2W1WtWrVy916tRJSUlJWrNmTZk2gwcPrvb6APiqbFf50mMmwR0AAAAA/CKgpspPnTpVqamp6tGjhyTp448/1htvvKEpU6Z4p8bPnDlTs2fPVmJiolJTU7V69Wpt2LBBL730kj9Lb9oO36fdsFh07E3fDMvhME9wBwAAAAC/CKjg3qVLF7311lvau3ev3G63kpKSdOedd2ry5MneNmPHjpXD4dDSpUu1ZMkSdenSRQsXLlS/fv38WHkT550qX86Iu4URdwAAAADwp4AK7n/729+q1W7ixImaOHFiA1cTPEpDucFUeQAAAAAIOAG/xh0Nz7vG3VK2OzBVHgAAAAD8i+AO7xr38naVl2E93MTdiAUBAAAAAErVKbhfddVVWrFihQoLC+urHvhDJVPlvSPurpLGrAgAAAAAcFid1rjv3LlTt9xyiyIiInTWWWdp/PjxGjx4cI1uJA//865fL2eqvHeNu8mIOwAAAAD4Q52C+/vvv68NGzZo+fLlWrNmjZYvX65WrVpp7NixOuecc9SzZ8/6qhMNqZL7uLPGHQAAAAD8q867yvft21d9+/bVnXfeqfXr12v58uV6/fXX9fzzz6tbt24aP368xo0bp7Zt29ZHvWgAle4qX3o7OKbKAwAAAIBf1NvmdBaLRaeddpoeeughffrppxo5cqS2bt2qRx55RMOGDdPll1+uTz/9tL5eDvWpkhH3I7eDY6o8AAAAAPhDvd7H/dtvv9Xy5cv1/vvvKzs7W8cdd5zOPfdchYSE6K233tJf/vIXTZ8+XTfccEN9vizqyLt+vdzbwXl2lWeqPAAAAAD4R52D+9atW7V8+XKtXLlSf/zxh1q2bKnzzjtP48eP91njftlll+muu+7SK6+8QnAPNN6p8oZkHnPOO+JOcAcAAAAAf6hTcB8/frw2b94su92u4cOH6+6779Zpp50mS3m7k0tKTU3Vm2++WZeXRAPwhnLDUia4szkdAAAAAPhXnYJ7TEyM7r33Xo0aNUpRUVFVth8+fLg+/vjjurwkGsLhqfKGxSK5yyT3w00I7gAAAADgD3UK7g888IDi4+MVFhZW7vnCwkJlZmaqffv2kqTw8HB16NChLi+JBuAz4i7fgF464k5wBwAAAAD/qNOu8sOHD9eHH35Y4fm1a9dq+PDhdXkJNIZq7CrPVHkAAAAA8I86BXfTPHYnM19Op7PC9e4IHEdG3I2yJxlxBwAAAAC/qvFU+by8POXk5HgfHzp0SHv27CnTLicnR6tXr1ZCQkLdKkTDc7slw+LZVf4YhmHxBHoXwR0AAAAA/KHGwf3555/XokWLJHluH/bPf/5T//znP8tta5qmZs2aVacC0fBMt6vce7h7GRZG3AEAAADAT2oc3IcMGaKIiAiZpqmHHnpIY8aMUe/evX3aGIah8PBw9e7dW8nJyfVWLBqI6faMrFfAsFi9O88DAAAAABpXjYN7v3791K9fP0mSw+HQWWedpR49etR7YWg85uGp8hWyWGS6ShqvIAAAAACAV51uBzdjxoz6qgP+VMVUecOweMI9AAAAAKDR1Si4L1y4UIZh6C9/+YssFosWLlxY5XMMw9B1111X6wLR8Ey3q9Kp8rJYuR0cAAAAAPhJrYL7tGnTZLfbCe7Nhemuxog7U+UBAAAAwB9qFNx/++23Sh+jafKMuJdzD/dSFovnlnEAAAAAgEZXyfxoBA23WzKsFZ+3WLkdHAAAAAD4SZ02pyuPw+HQqlWrVFxcrNNPP10dOnSo75dAPavqPu4G93EHAAAAAL+pU3C/8847tWHDBq1cuVKSVFxcrAsuuEBbtmyRJEVHR+uFF15Qr1696l4pGo7prsZUeYI7AAAAAPhDnabKf/311zrrrLO8j1euXKktW7bo4Ycf1sqVK9WqVatqbWAH/zLdrkrv486IOwAAAAD4T52C+4EDB3ymwn/00Ufq06ePxo4dq+7du+uCCy7Qhg0b6lwkGpi78l3lZbFILoI7AAAAAPhDnYJ7eHi4cnNzJUklJSX673//q1NPPdV7PjIy0nsegct0VzFV3rDINAnuAAAAAOAPdVrj3rt3b73xxhtKTU3V2rVrlZ+fr2HDhnnP79ixQy1btqxzkWhgblelu8ob3A4OAAAAAPymTsF91qxZuuqqq/TnP/9Zpmlq5MiR6tu3r/f8hx9+qJNOOqnORaJhmaZLRiVr3GVYZLpKGq8gAAAAAIBXnYJ7cnKy3nvvPX333XeKiYnRwIEDvedycnJ08cUX+xxDgHK7JUvFU+XZnA4AAAAA/KfO93GPj4/XiBEjyhyPiYnRZZddVtfLoxFUtau8mCoPAAAAAH5T5+AuSXl5edqzZ49ycnJkmmaZ8wMGDKjxNfPz8zVq1ChlZGToP//5j5KTk73n3nzzTT3zzDPas2ePunTpohtvvFFnnnlmnd5DUHO7pSru4266mSoPAAAAAP5Qp+CelZWl++67Tx988IFc5dwuzDRNGYahjRs31vjaTz75ZLnXXLVqle666y5Nnz5dgwYN0urVqzVjxgy9/PLLSklJqc3bCHqm6a50jTtT5QEAAADAf+oU3O+66y598sknmjx5svr376+YmJh6KWrbtm165ZVXdNttt+nuu+/2OTd//nyNGTNGs2bNkiQNGjRImzdv1qJFi7R06dJ6ef2gU+VUeaunDQAAAACg0dUpuK9fv16XXXaZbr311vqqR5I0d+5cXXjhherSpYvP8Z07dyo9PV233HKLz/HRo0frwQcfVHFxsex2e73WEgxMt8uzjr0ChmGRu8TZiBUBAAAAAEpVMsxatbCwMHXo0KG+apEkrVmzRps3b9Z1111X5lxaWpoklQn03bp1k9Pp1M6dO+u1lmBhul0yqljjzog7AAAAAPhHnUbczznnHH300Ue65JJL6qUYh8OhefPm6cYbb1RUVFSZ89nZ2ZJUZkp+6ePS87VhmqYKCgpq/fymzO0skWlIhYVFKi4qkiTvT0lyuU25S5xB+/mgeXE4HD4/geaIfo5gQD9Hc0cfb/5K94SrjjoF95EjR+qbb77R1KlTNWnSJLVt21ZWq7VMu969e1freosXL1bLli315z//uS5l1YrT6azVJnrNQVR+rmSxKnP7du+xvXv3ev8emp8vi6MgaD8fNE/p6en+LgFocPRzBAP6OZo7+njzVt2l3nUK7hdffLH371988UWZ8zXZVX737t167rnntGjRIuXm5kqSd4S3oKBA+fn5io2NlSTl5uYqISHB+9ycnBxJ8p6vDZvNpu7du9f6+U3ZwR9DZdgj1LpzZxUXFWnv3r1q27at7KGhkqSCrM0qKclXx549/VwpUHcOh0Pp6elKSkpSeHi4v8sBGgT9HMGAfo7mjj7e/G3durXabesU3O+///66PN3Hrl275HQ6dfXVV5c5N2XKFJ144ol65JFHJHnWunft2tV7Pi0tTTabTZ06dar16xuGoYiIiFo/vynLMiRLSIjCwkK9x+yhod7HxTabXKYZtJ8Pmqfw8HD6NJo9+jmCAf0czR19vPmq7jR5qY7B/bzzzqvL03307NlTL774os+xjRs36v7779ff//53JScnq1OnTkpKStKaNWs0YsQIb7vVq1dr8ODB7ChfS6bbLaOSXeU9t4MrabyCAAAAAABedQruR9u3b58yMzOVmJhYq2+EYmJilJqaWu653r17e9fJz5w5U7Nnz1ZiYqJSU1O1evVqbdiwQS+99FKd6g9qVdzH3bBYZboI7gAAAADgD3W6HZwkffTRRzr77LN1+umn67zzztOPP/4oScrMzNS5556rDz/8sM5FHm3s2LG67777tHLlSk2dOlXfffedFi5cqH79+tXr6wQT0+2WKpmmYVgJ7gAAAADgL3UacV+7dq1mzpyplJQUjR07VgsXLvSei4+PV5s2bbRs2TKdddZZtbp+amqqNm3aVOb4xIkTNXHixFrXjWO4XVVOlTe5jzsAAAAA+EWdRtwXLVqk/v3769VXXy33Xu4pKSncQqwJMKsxVV6MuAMAAACAX9QpuG/ZskWjRo2q8HyrVq108ODBurwEGoPprjS4izXuAAAAAOA3dQru4eHhcjgcFZ7fuXOn4uLi6vISaARV7SpvWKyS6Wa6PAAAAAD4QZ2Ce2pqqt555x2VlJQdjd2/f7/eeOMNnXrqqXV5CTSGKqbKy2KVJII7AAAAAPhBnYL7rFmztHfvXp1//vl6/fXXZRiGPv/8cz322GMaN26cTNPUddddV1+1ooF41rhXvqu8JNa5AwAAAIAf1Cm4d+3aVa+88ori4uL0xBNPyDRNPfvss3r66ad1/PHH65VXXlHHjh3rq1Y0FLMaU+Ul1rkDAAAAgB/U6XZwknTcccfp+eefV3Z2trZv3y7TNNWpUyfFx8fXR31oBJ77uFdjqjzBHQAAAAAaXa2De3Fxsd59912tX79eO3bsUH5+viIjI9W5c2eddtppGjt2rOx2e33Wiobidsmo6nZwIrgDAAAAgD/UKrhv2rRJ1157rfbs2SPTNBUdHa2IiAhlZmbq119/1Zo1a/TUU09p8eLF6tatW33XjHpmmm6pkqnyjLgDAAAAgP/UOLjn5+frL3/5izIzM3XjjTdq/PjxatOmjfd8RkaG3nnnHS1evFjTp0/Xu+++q4iIiHotGvWsiqnypSPubE4HAAAAAI2vxpvTLVu2TH/88YeefvppXX311T6hXZLatGmja665RosXL9auXbv09ttv11uxqH+maXo2p2OqPAAAAAAEpBoH908//VRDhgxRampqpe0GDx6sU045RWvXrq11cWgEptvzs7Kp8tbS+7gT3AEAAACgsdU4uG/evFkDBw6sVttBgwZp8+bNNS4Kjch9OLhXa8Td2RgVAQAAAACOUuPgnp2drYSEhGq1bdWqlbKzs2tcFBqP6XZJUqVT5dmcDgAAAAD8p8bBvbi4WCEh1dvTzmq1yulklDagHQ7ushgVNmFzOgAAAADwn1rdDm737t365Zdfqmy3a9eu2lwejcg0azJVnuAOAAAAAI2tVsH9iSee0BNPPFFlO9M0ZRgVj+QiABxe485UeQAAAAAITDUO7vfff39D1AE/KV3jXumIu5XgDgAAAAD+UuPgft555zVEHfCXw1PljcpuB2cQ3AEAAADAX2q8OR2al2qNuFssnvMEdwAAAABodAT3YOcN7pXvRWBYrDLdBHcAAAAAaGwE9yBnuqsxVV6SrFamygMAAACAHxDcg5276tvBeU4T3AEAAADAHwjuQa46a9wlSQR3AAAAAPALgnuwq86u8pIMSwib0wEAAACAHxDcg1x1R9yZKg8AAAAA/kFwD3beNe6V7yrP5nQAAAAA4B8E9yBXOuJe9VR5C8EdAAAAAPyA4B7szOrtKi9LCMEdAAAAAPyA4B7kajLiLjfBHQAAAAAaG8E92HE7OAAAAAAIaAT3IGe6qzdVnl3lAQAAAMA/CO7B7nBwN6raVZ7gDgAAAAB+EVDBfd26dbr00ks1aNAg9enTR8OHD9f999+v3Nxcn3Zr167VOeeco+TkZI0cOVJvvfWWnypu+szSdesWa6XtPCPuzkaoCAAAAABwtBB/F3C0Q4cOqW/fvpo8ebLi4uK0ZcsWLViwQFu2bNFzzz0nSfr22281Y8YMnX/++brzzjv11Vdf6a9//asiIyN19tln+/kdND2lo+hGdYJ7SXFjlAQAAAAAOEpABffx48f7PE5NTZXdbtddd92ljIwMtWnTRosXL1bfvn117733SpIGDRqknTt3av78+QT32ijdnK6KXeWZKg8AAAAA/hFQU+XLExcXJ0lyOp0qLi7W119/XSagjx49Wtu2bdOuXbv8UGHT5g3jVW1OZyW4AwAAAIA/BGRwd7lcKioq0i+//KJFixZp2LBh6tixo3bs2CGn06muXbv6tO/WrZskKS0tzR/lNmmm2yVZrGxOBwAAAAABKqCmypc688wzlZGRIUk67bTT9Mgjj0iSsrOzJUkxMTE+7Usfl56vDdM0VVBQUOvnN1XFhQ7JsKiwsMjzuMj3ZymXW3KXFAflZ4TmxeFw+PwEmiP6OYIB/RzNHX28+TNNs+oB1MMCMrgvWbJEDodDW7du1eLFizV9+nT961//atDXdDqd2rhxY4O+RiAK3fuHwmVox/btPsf37t3r89iem6uQQkdQfkZontLT0/1dAtDg6OcIBvRzNHf08ebNbrdXq11ABvcTTjhBktSvXz8lJydr/Pjx+vDDD9W9e3dJKnN7uJycHElSbGxsrV/TZrN5rx9M8nI2Kz/dpsTOnSV5Rtr37t2rtm3byh4a6m3nyN+h4pzd6tmzp79KBeqFw+FQenq6kpKSFB4e7u9ygAZBP0cwoJ+juaOPN39bt26tdtuADO5H69Gjh2w2m3bs2KFhw4bJZrMpLS1Np512mrdN6dr2Y9e+14RhGIqIiKhzvU1NodUiw2pVWFioz3F7aKjPsRJ7qIrdrqD8jNA8hYeH05/R7NHPEQzo52ju6OPNV3WnyUsBujnd0X788Uc5nU517NhRdrtdqampev/9933arF69Wt26dVPHjh39VGUT5iqp8h7ukticDgAAAAD8JKBG3GfMmKE+ffqoR48eCgsL02+//aZnn31WPXr00IgRIyRJf/nLXzRlyhTdc889GjVqlL7++mutXLlSjz32mJ+rb5pMt6vKW8FJntvBieAOAAAAAI0uoIJ73759tXr1ai1ZskSmaapDhw6aOHGipk6d6l20379/fy1YsECPP/64/vOf/6h9+/aaO3euRo0a5efqmybT7arWiLthscp0E9wBAAAAoLEFVHC/+uqrdfXVV1fZbvjw4Ro+fHgjVBQEXCWSpRorJixWyTSrHfQBAAAAAPUj4Ne4o2F5gng1psofDuuscwcAAACAxkVwD3Kmq0SyVGPiRekoO8EdAAAAABoVwT3YMeIOAAAAAAGN4B7kTFdJtXaVF8EdAAAAAPyC4B7karzGnZ3lAQAAAKBREdyDnatEMqpxOzgrI+4AAAAA4A8E9yBX3RF3NqcDAAAAAP8guAc5z67y1RhxZ407AAAAAPgFwT3Ima4SbyivFMEdAAAAAPyC4B7s3CUSt4MDAAAAgIBFcA9ypsvFiDsAAAAABDCCe5AzKxhxLy4xfR4bbE4HAAAAAH4R4u8C4Gcul4xjbgeXdlC66/3d6psYrpF9Y3VSUgS3gwMAAAAAPyG4BznT7Soz4r4t01CozdCB3BI9uHKvWkZZNaJXpFJNwzNCDwAAAABoNAT3IGe6y+4qvyvbUKd4my4e0kp7sor1bVqB3vhvjiyR3TTO5fRTpQAAAAAQnFjjHuyOuY+7aZranS21jfV8p9O+hV3nnByntnEh2upsw1R5AAAAAGhkBPcgZ7pdMo6aKr8vx6XCEkPt43wnYyS2DNXWkjZsTgcAAAAAjYzgHuRMl8tnxH3b/mJJUttjg3sru7LcUTqQy1R5AAAAAGhMBPdgd8yI++/7ihUTairC7ts1ElvaJUmbMlyNWh4AAAAABDuCe5Az3S7pqNvBbdtXrJaRZpl2UWFWtbAW6Lf97sYsDwAAAACCHsE9iJmm6TPiXuIytf2gUwmR5bdvF5KjTQeMRqwQAAAAAEBwD2buw9PeD69x35VZLKfLVKtyRtwlqUNornbnWpRbUNxYFQIAAABA0CO4B7HSW7uV3sd9a0aRLIbUMqL89h3s+ZKkjb9nNkp9AAAAAACCe1Azjxlx35pRqFbRVtms5bePsTkVbXPrl7SDjVQhAAAAAIDgHsy8I+6ebrB1b5HaxYZU2NxisahDpFM/px1olPIAAAAAAAT3oOYdcTesKnK6tSuzWO3ibBW2N6w2dYwo1LZd2SosKmmkKgEAAAAguBHcg5jpPjLi/vv+IrlNqX1cxSPusoaoQ2i+XG5Tm3ZkNVKVAAAAABDcCO7BzHVkjfvWjCKFWKVW0RUscJdkWEMUb8lTRGiIfmWdOwAAAAA0CoJ7ECudKm9YLNqaUaR2cTZZLRXfp92w2iRXsRLbRetngjsAAAAANAqCexArvR2cLFZt3Vuo9nH2StsbISEyS4qV1DZGm7ZnqcTlboQqAQAAACC4EdyD2eHgnue0aF9OiTrEV7wxneQZcXc7i5XULlZFTpfSdmc3RpUAAAAAENQI7kGsdKr875mmJKl9iypG3K0hUkmx2idEyhZi0c/bmC4PAAAAAA2N4B7ESneVTztoKsxmKD6y4o3pJE9wN0ucCrFa1LF1lDbtyGyMMgEAAAAgqAVUcH/vvff0l7/8RUOHDlVKSorGjx+v//znPzJN06fdm2++qZEjRyo5OVnnnHOOPvnkEz9V3MQd3lX+j1xTrWNsMoyKN6aTJIXYZLpLZJoudUiI0padhxq+RgAAAAAIcgEV3J9//nmFh4fr9ttv1+LFizV06FDdddddWrRokbfNqlWrdNddd2nUqFFaunSpUlJSNGPGDP3www/+K7yJKp0qvy/PrbiIykfbpcO7yksyncXqkBCl/VkO5eQXN2iNAAAAABDsQvxdwNEWL16s+Ph47+PBgwfr0KFD+te//qVrr71WFotF8+fP15gxYzRr1ixJ0qBBg7R582YtWrRIS5cu9VPlTVPprvL7c93q06k6wd3TxixxqkNClCRp665DOqlH64YrEgAAAACCXECNuB8d2kv17NlTeXl5Kigo0M6dO5Wenq5Ro0b5tBk9erS+/PJLFRcz+lsTprtEJaZFhwrciqtifbt0ZMTdXVKs+NgwhYVatZXp8gAAAADQoAIquJfnf//7n9q0aaOoqCilpaVJkrp06eLTplu3bnI6ndq5c6c/Smy6XC5luSNlSoqLqHryhWH1tDGdxbIYhtq3itLWXYcatkYAAAAACHIBNVX+WN9++61Wr16t2267TZKUne25b3hMTIxPu9LHpedrwzRNFRQU1Pr5TVGRo0CZbs+U90ibW8XFxXI6nZLk/Xk0t9vzs9iRJ3dRodrEh2nzjsyg+9zQtDkcDp+fQHNEP0cwoJ+juaOPN3+maVa9QfhhARvc9+7dqxtvvFGpqamaMmVKg7+e0+nUxo0bG/x1Aol99y5luiIlmXLkHFBx3pFzmZllb/VmOIsULmnvnt1yFZiym0U6mF2kb7//WZFhVU+1BwJJenq6v0sAGhz9HMGAfo7mjj7evNnt9mq1C8jgnpOTo2nTpikuLk4LFiyQxeKZ0R8bGytJys3NVUJCgk/7o8/Xhs1mU/fu3etQddNT4PxDn7r3KCbcqvbtPJ+n0+lUZmam4uPjZbPZfNqbJcXK2ya1jo+TvVNnRbco0rqff1ZIVFv1PK6VP94CUGMOh0Pp6elKSkpSeHi4v8sBGgT9HMGAfo7mjj7e/G3durXabQMuuBcWFuqaa65Rbm6uXn/9dUVHR3vPde3aVZKUlpbm/XvpY5vNpk6dOtX6dQ3DUERERO0Lb4KcVosOmlFqERVS5psem81W5pgZ4hlVDzFMhYWGqV2rUIWHhmjnPodOOTG4Pjs0feHh4UH3bx7Bh36OYEA/R3NHH2++qjtNXgqwzelKSko0a9YspaWl6ZlnnlGbNm18znfq1ElJSUlas2aNz/HVq1dr8ODB1Z5mAA/T7VKmK0qx1biHuyQZFqtkscrt9OzebxiGOiREags7ywMAAABAgwmoEfe///3v+uSTT3T77bcrLy9PP/zwg/dcr169ZLfbNXPmTM2ePVuJiYlKTU3V6tWrtWHDBr300kv+K7ypcpXooDtKnaoZ3CXJYrVJriO33WufEKVf0g42RHUAAAAAAAVYcF+/fr0kad68eWXOffzxx+rYsaPGjh0rh8OhpUuXasmSJerSpYsWLlyofv36NXa5TV6x06Ucd3i1bgXnFWLzjrhLUoeEKH32/W5l5RSqRUxYA1QJAAAAAMEtoIL72rVrq9Vu4sSJmjhxYgNX0/wdyHNJsimuBiPuhjVEZolvcJekrbsOaUCvtvVdIgAAAAAEvYBa447GdSDflCTFRdY0uB+5x3uL6FBFhIVo667seq8PAAAAAEBwD2r7800ZMhUTXpPgbpPbWXTksWGoQ0KUtu7MaogSAQAAACDoEdyD2IECKdpaLKul+rchkNUqHTVVXpLat4piZ3kAAAAAaCAE9yB2oMCiaGtR1Q2P4hlx9w3uHVpHKSu3SAezHfVZHgAAAABABPegtq/Aohirs+qGRzGsITJdvsG94+EN6raxzh0AAAAA6h3BPYgdKLQqxlbT4G6TecyIe2yUXZHhNm3ddageqwMAAAAASAT3oFXsdCmn2KrYkOKqGx/FsIbIfcwad88GdZGscwcAAACABkBwD1L7D3nWo8fYSmr0PCMkpMzmdJLUMSFav6Vnyu0266U+AAAAAIAHwT1IZWQWSJJiQmoY3K02uUvKTq/v2iFWeQ6n0v/IqZf6AAAAAAAeBPcgtS+zQIZMRdtrFtxltUlul0y37/M6tYlWiNXQhq0H6rFKAAAAAADBPUjtyypQTEiJrBZrjZ5nWEMkSW6n76i7LcSixLYx+mnr/nqrEQAAAABAcA9aGQcLFB3ilCw16wKlwV2usuvcu3aI1U/bDsrlctdHiQAAAAAAEdyD1t7MAsWEFMswjBo978iIe9ng3q19rBxFJdq2m/u5AwAAAEB9IbgHqX2ZBYqxFklGTafK2yRJZjk7y3doHSV7iEU/sc4dAAAAAOoNwT0IFTldOpRXpBhrkQxL7UbcywvuIVaLOreLYYM6AAAAAKhHBPcgtD/Lcyu4aEuRZNR0jfvhEfdypspLnnXuv/x+UCWscwcAAACAekFwD0L7Mh2SpBiLo8ab0ymkdMS9qNzT3TrEqqjYpS07DtWlRAAAAADAYQT3IJSRVSCLYSjKcNRqxN2whsjlyC33fLtWUQqzW7VhG7eFAwAAAID6QHAPQhkH8xUXbZcht4ya3g7OMGQJjZIrP6fc81aLoaR2MfpxC+vcAQAAAKA+ENyD0L4sh+KiwySXS7XpApawCLkKKr7lW9cOsfotPVPOElcdqgQAAAAASAT3oJSRma+4qFDJdNV4xF2SLGGRcuVXHtydJW79tj2rLmUCAAAAAERwD0p7DxYoPiZMpttd4zXuUtXBvW3LSEWEhWgD0+UBAAAAoM4I7kGmoNCpnPxixceESu6ar3GXPMHdXZgv011S/nnDUJf2sdqwlQ3qAAAAAKCuCO5BJiPTcw/3+JgwmaZbMowaX8MaGinJlKug/A3qJKlr+1ht2pElR1H54R4AAAAAUD0E9yCz92C+JKlFTKhkuiXDWuNrGOGRklTpBnUndG4hl8vUVz//UbtCAQAAAACSCO5B548DBbLbLIq0H/7V12aqfGiEJFV4SzhJahETpqR2Mfrk2521qhMAAAAA4EFwDzJ7M/PVMiZckluSZNRmc7oQu4yQULkrGXGXpJTjE/TDlv3KyimsTakAAAAAABHcg84fB/LVIiZUpssT3Gsz4i5JlvDISkfcJSm5WytZLYY++2F3rV4DAAAAAEBwDzp7D+YrPiZMchVLkgxLzde4S5LFHlHpGndJCg8NUY/O8UyXBwAAAIA6ILgHEZfb1P4sh+JjwuQucUqSDGtIra5lDY+UK6/y4C5JKcclaNvubO3MyK3V6wAAAABAsCO4B5EDhxxyuU3PreBKPCPuqmVwt4RFyuWoOrj36NxC4aEh+vS7XbV6HQAAAAAIdgT3IFJ6K7j4mDCpjiPuRmiU3EUOuUu/AKhAiNWiPt1a6pNvd8rtNmv1WgAAAAAQzAjuQWTvwQIZkuKiQ+V2lQZ3W62uFRIVJ0kq3r+jyrYpxyVo/yGHNqZn1uq1AAAAACCYBVRw3759u+bMmaPx48erV69eGjt2bLnt3nzzTY0cOVLJyck655xz9MknnzRypU3T3oP5iosOVYjVItN5eHM6a+02p7NGx8sSESPH7z9X2bZzuxi1iA7VJ/9jkzoAAAAAqKmACu5btmzRunXr1LlzZ3Xr1q3cNqtWrdJdd92lUaNGaenSpUpJSdGMGTP0ww8/NG6xTdDeg/lqERMmSd417rUdcTcMQ6Ftu6hw568yXSWVtrUYhk48LkGf/7BbzhJXrV4PAAAAAIJVQAX3YcOGad26dZo/f7569+5dbpv58+drzJgxmjVrlgYNGqR7771XycnJWrRoUSNX2/T8cSBf8dGhkiTT5ZRkSLW8HZwk2dt0keksVOGeLVW2TTk+QfmFJfrqp721fj0AAAAACEYBFdwtlsrL2blzp9LT0zVq1Cif46NHj9aXX36p4uLKN0oLdnszCxQfGy7JM+JuhITIMIxaXy8kqoWs0fFypP1QZdvWLSLUrUOs3vpki0yTTeoAAAAAoLoCKrhXJS0tTZLUpUsXn+PdunWT0+nUzp2soa5IXkGx8h1OxcccHnF3Omu9o/zRwjqeoMKdG5X9zSqZZuXT4If266Btu7O1YeuBOr8uAAAAAASLuie3RpSd7blveExMjM/x0sel52vDNE0VFBTUvrgAl747R5IUGWZRYVGhnEUOyRJSZpaC0+n0+VkVS+suCispUf7mb1R8aL+iBp0rwx5WbtuOCWFq2zJCb3y0Scd1iKzDuwFqz+Fw+PwEmiP6OYIB/RzNHX28+TNNs9ozoJtUcG9ITqdTGzdu9HcZDeaXHZ4vJfKz92l7gUWhWQcV4jaVkZFRbvvMzBrcus0aI2uHFJl//KSDa5bI0WO43GFR5TY9rq1V//fLQa394ke1a2Gv8fsA6kt6erq/SwAaHP0cwYB+juaOPt682e3Vy0RNKrjHxsZKknJzc5WQkOA9npOT43O+Nmw2m7p37163AgPYpv2/K8yereO6JckwDOXv+1lOR5jatGnj087pdCozM1Px8fGy2Wqw43ybNnK37yTHho8V+8cPij794nK/PerUydRPO37Wz7stGnZKz7q+LaDGHA6H0tPTlZSUpPDwcH+XAzQI+jmCAf0czR19vPnbunVrtds2qeDetWtXSZ617qV/L31ss9nUqVOnWl/bMAxFRETUucZAlZnrVMvYcIWHef7RF5guWW22Cr/hsVVyrkL2eFl7DVHOd+/LvetXRXY/udxmp53YUavW/64rxiWrTXzz/cwR2MLDw5v1v3lAop8jONDP0dzRx5uvmmwU3qQ2p+vUqZOSkpK0Zs0an+OrV6/W4MGDax40g8gfB/LV4vCt4CRJJcWSpf6/t7G1bK/Qdt2V+7/35Soqf8+Ak09orfBQq975tPrfMAEAAABAsAqoEXeHw6F169ZJknbv3q28vDxvSB84cKDi4+M1c+ZMzZ49W4mJiUpNTdXq1au1YcMGvfTSS/4sPeD9cTBfPTvHex+7S4rrZVf58kQc319ZGb+rYNN/Fd33jDLn7TarBvVppw/+u10X/qmHYqNCy14EAAAAACApwIL7wYMHdcMNN/gcK3384osvKjU1VWPHjpXD4dDSpUu1ZMkSdenSRQsXLlS/fv38UXKTUOJy68Ahh+L7Htnt3SwpliW0/A3k6spiD1dY++OUv+krRfY6RZaQsjMhBie30//9sFsrPk/TpWez1h0AAAAAKhJQwb1jx47atGlTle0mTpyoiRMnNkJFzcO+rAKZphQfc3Rwd8qIsDbYa4Z17q3C3ZvkSPtBkccPLHM+Isym1N5t9e66bRo7pKviohl1BwAAAIDyNKk17qidvQc9a82PDe6y1mDX+BqyRsTI3jpJeb+ul2m6ym1z+kkdJUN6/aOqv6wBAAAAgGBFcA8CGQfzZTEMn7XkZgOucS8VltRbrrwsFe7YWO75iDCbTk/pqPe+TNfeg/kNWgsAAAAANFUE9yDwx8ECtYgJldVy5HYDZolTlgYO7raYBNni2yvvl89lmma5bQYnt1NkmE0vvVd+uAcAAACAYEdwDwJ7D+arRfRR0+RNt0yXU2rg4C5J4Z37yJm5R8UZv5d73m6zalj/Tlr3/W5t23WowesBAAAAgKaG4B4E/jiQr/iYo6bJu0okqcGnyktSSMv2skbHK+fHtRWOup98QhslxIXrhVW/Nng9AAAAANDUENybOdM0lZFZcMzGdMWSGie4G4ahyOMGyLl/hwq3/1RuG6vF0FkDE/X95v3asHV/g9cEAAAAAE0Jwb2Zy8wplKOoRC1jw73HjgT3httV/mi2lu1lb52k7P99IHdJUbltendtqU6to/Tc8l/kcpc/Mg8AAAAAwYjg3sxt2XlIktSxdZT3mFni9PzF2nD3cT9WxPH9ZRYVKPu/q8qdMm8YhsYM6aJtu7O15sv0RqsLAAAAAAIdwb2Z27wjSzGRdsVE2r3HSoN7Y424S5I1PFoRPU+RI+0H5W/6utw2iW1jNKBnG72w6ldl5RQ2Wm0AAAAAEMgI7s3c5h1Z6tA6SoZx9K3gSqfKN96IuySFte+msM69lfO/NSqqYJf5kYM6y2Ix9Mzynxu1NgAAAAAIVAT3ZsztNrV5xyF1SojyPV464m5pvBH3UhHdT5atRVtlffaGXAXZZc+H2TRqcJI++363vt+0r9HrAwAAAIBAQ3Bvxnbvz5OjqEQd20T7nnB5Rtwb4z7uxzIsVkUlD5UMQ5nrXpdpusu06Xd8grq0j9Hit35UsdPV6DUCAAAAQCAhuDdjW3ZmSZI6HDvi7my828GVx2IPV1Sf0+Q8uEuOtB/KnDcMQ+OHdtO+LIf+s3ZL4xcIAAAAAAGE4N6Mbd5xSAktwhUe6hvQzRKnZLHKsPjv129r0Vb2tl2V892HcjvLbkTXukWETkvpoDc/3qxtuw41foEAAAAAECAI7s3Yph1Z6njMaLskyVXcqDvKVyTyuJNlOouU9/Pn5Z4/8+ROahMfoQde/Eb5DmcjVwcAAAAAgYHg3kw5S1z6fU+2OraOLnPO7XQ2+o7y5bGERSk0safyN30ld7GjzHlbiEUXntVDWblFmv/G9+Xe/x0AAAAAmjuCezP1+54cuVymOrYuO+JulgTGiLskhXfqJdNVooIt35Z7vmVsuCac0V1fbPhDKz8v/xZyAAAAANCcEdybqc07smS1GGrXKrLMOdPllGHxz8Z0x7KERSi0fXflbfxSpquk3DZ9urXSKcnt9OyKn7V5R1YjVwgAAAAA/kVwb6Y278hSu1aRCrGW/RWbJYExVb5UeOc+chfmqyDtxwrbnD04Se1bRmreC98oJ7+4EasDAAAAAP8iuDdTm3ZklTtNXvJMlffHPdwrYo2Mla11ovJ/XV/ufd0lKcRq0YV/6qGCohLd9dR65RYQ3gEAAAAEB4J7M5TncGrP/vxyN6aTAmuNe6nwzn1UkntAhTt/q7BNi+gwXTmutzKyHPrbYsI7AAAAgOBAcG+Gtu70rAPvVMGIu7ukOKCmykuSLa61bHFtlffL55XuHt+uZSThHQAAAEBQIbg3Q5t3HFJYqFUt48LLb+AMvBF3SQrr0kfOg7tU9MfWStsR3gEAAAAEE4J7M7R5R5Y6JkTLYhjlnnc5cmWERjRyVVWztewoW1wb5X73gUzTVWnbo8P7TY+v09adhxqnSAAAAABoZAT3ZsY0zUo3pnM7i+QuKpA1vPzz/mQYhsKP6y/noQw5fv+pyvbtWkbqLxP6KsRq0ewFn2n5/22rdJo9AAAAADRFBPdm5sChQh3KLaowuJfkeda/W8LL37jO32xxrWVvnaSc7z+Su9hRZfv4mDBdfW6yBvVuq6Xv/Kx//Ou/ymPqPAAAAIBmhODezHy3aZ8MqcId5V15mZIka4AGd0mKOH6ATKdDOd9/WK32IVaLxgzpqkvPPkEbth7QtQ+t1Wff72L0HQAAAECzQHBvRtxuU29/ulU9u8QrJtJebhtXXpYMa4gMe1gjV1d91vAoRXQ/WQVbvlVhFRvVHa1Xl5aaOTFF7VtG6qGX/qe/Lv5C2/fmNGClAAAAANDwCO7NyDe/7tXu/XkamtKhwjauvEOyhEfLqGDjukAR2rGHbPHtdeiLt+UqzKv28+KiQ3XJ2T11+Zhe+uNgvm545FM98+7Pys4rasBqAQAAAKDhENybkbc+2aKkdjFKbBtTYZuS3CxZwgJvY7pjGYZFUb1PlVxOHVr/lkzTXaPnH5/YQtdfkKIRAxK15st0XfWPD/XSextZ/w4AAACgySG4NxO//n5QG9OzdFolo+2S5MrNlDUicNe3H80SFqmo3kNV9Eeacn9YW+Pnh1gtOv2kjpp9ycka0Kutln26VVP/8aFe/WCTcvIJ8AAAAACahhB/F4D68dbarWrdIlw9OreosI1puuXKP6TQ9l0bsbK6sbXqoIjjTlbeL5/JGhWryOMG1PgakeE2jRqcpFNPbK/Pvt+lNz7apDc/3qxT+rbTqMFd1KtLfMAvHQAAAAAQvAjuzcDOjFz999e9+vOZ3WWpJIC6HXky3SWyhFc8lT4QhXXuI7cjT9n/XSkZFkV2P7lW14mOsGvMkK46/aRO+u63DH27MUPrvtutTq2jNPSkjurbvZWO69RCthAmogAAAAAIHE0yuG/btk1z587V999/r8jISI0fP16zZs2S3V7+TurN3dufblVMpF0nHpdQaTuX9x7ugb/G/WiGYSiiR6okKfurd1WSe1AxfYfJsNau+0aF2zS0X0edmtJBabuz9c2ve/XW2i16ec1vCrVb1SspXickxat1iwgltAhX6xYRahUXJluItT7fFgAAAABUS5ML7tnZ2brsssuUlJSkBQsWKCMjQ/PmzVNhYaHmzJnj7/Ia3cFsh9b+b6fOGpCoEGvlI8UlTTS4S5JhsSjihEGyhEcp/9cvVLTjN0X3G6Gwjj1kWGoXqC2Goe4d49S9Y5xcblN/HMhT2u5sbdudrZWfpym3wOnT3moxZLdZFWa3KtRulS3EqhCrIVuIVbYQi+whFtltVoXarN52EeE2RYSGKCLcpqgwm2Kj7YqNClVcVKiiIuyyWpiiDwAAAKByTS64v/baa8rPz9fChQsVFxcnSXK5XPr73/+ua665Rm3atPFvgY0oO69Ij7/6vWxWiwb2altle1deliyh4bJYbY1QXf0zDEPhScmyteqggt/+q6zPXpMlNFL2tl1kj28rS0SsrOHRskTEKCQyrkYj8laLoY6to9WxdbSG9usoSXKWuJWdV6RDeUXKyS9WsdMlZ4lbxU6XikvccrncKnGZcrndcrlMFZe4VVBYIqfLLWeJW84Sl4qKXXIUl6ioyCWzzPuRWkSHqVVcmFrFhatVbLhaxoYrIS5cLePC1Co2XC1iwpi6DwAAAAS5JhfcP/vsMw0ePNgb2iVp1KhRuvvuu7V+/XpNmDDBf8U1op+2HtBDL3+rYqdbF/2ph8JCq/5VuoscMuzhjVBdwwqJildM/7NVkpupoj+2yZW9X7m7N8ssOWqneMOikJhWsrXsKHtCB9ladpAtrk2NRudtIRZPoI6r+2dmmqaKil3KL3Qq3+FU3uE/OXnFys4v0v4sh7buPKTsvGIVOV0+z40IC1FMpF0xkaGKjbQrLDTEO+ofZg+R1WooxGqR1WLIarEoxGrIevhxyOFzNpvVMyMg5PBsgFDPc8PsVoWFhijUZpWF0X8AAAAgIDW54J6WlqY///nPPsdiYmKUkJCgtLQ0P1XVeFwut179cJPe+HCzunSI1QXDj1NMZKi/y/KLkOh4hUTHex+bLqfcRQ65C/Plyj8kV26mnPu3y/H7D5LplmEJUUjLdrK3SpQ9oZPsrTrIEh7TKDvKG4bhCdyhIWoZW/kXAYVFJcrOL1Z2XpFyC4qV73Aqv7BE+Q6ncguKlZlT6Bn5L/HMAHC5Tblcptxuz9/dbtNzzH3sGH9l9UmhNqvCD9cYfvjLgTB7iMJCj0z/tx/+AsAWYlVIiCGbtfSnRVarxfMlgdVy5MsEq6EQy5HHFovh+YLh8BcLpY8thiGrtfTxUe0Ot7UYYud/AAAABK0mF9xzcnIUE1N2V/TY2FhlZ2fX6ppOp1OmaWrDhg11La/B5eQXq224S7ee30Fhdqvk2q+inOo9NzSpu+ydkqRK9vCz2exqn9ReFotVamI5yZBN1nCbrIqRTe28x01TkumW3C6ZbpfkdkumS8WHdkjZFslikWEcno5uGIefcOzFDUnG4Z868vOoV/d+Xubh/+N54SOPvW0OX8cwDodRy1HPNWVIipOp2ChTijy6FqtkWA8/56jXN02ZptvzeqU/S2s8fH3TsHgfe8s7/NmYpnn4j+eEedQ5z+VNmSqRVHLkmPe1PS8ptySn50dR2U+vXnk/wtLP4KiP/uhwf/SvyDj6L4ff87c//HLkGj6N6r/rV/QVSpledEz9xuG/HHnP5R/zPj7quZVX49vee7GjqzWPOXz4xYxyKz/mUJk37DlgmmZpp5Onr5lHnnz0v42j/63VUKXPqsEljTKPjn5TR+o+8hlV/+Le/ySUf6aKI75VVfgah/9db968+ZgvvRrgP+xGg1y1SSnv/22UwedU70r/G7Jlyxa+3EWzRB9v/pxOZ7V/t00uuDeE0g+rKfyDKHF5/gEXFrtUWOyqovWxrJIsUkF1nlfTazcFlsN/jmLKkzZr7Nj/lVZVNDu2b5lHPae6n3V1/pdheevhzRq8Rn1o2H9HR754OPZbhDIPKueqQVugyWqO/y0HjlXi7wKABkYfrw2LYSihRWAvEza8A3lVa3LBPSYmRrm5uWWOZ2dnKzY2tlbX7NevX13LAgAAAACgQTS57aq7du1aZi17bm6u9u/fr65du/qpKgAAAAAAGkaTC+5Dhw7VF198oZycIwu716xZI4vFoiFDhvixMgAAAAAA6p9hmtXaUiVgZGdna8yYMerSpYuuueYaZWRkaN68eRo3bpzmzJnj7/IAAAAAAKhXTS64S9K2bdt033336fvvv1dkZKTGjx+vG2+8UXZ7JdulAwAAAADQBDXJ4A4AAAAAQLBocmvcAQAAAAAIJgR3AAAAAAACGMEdAAAAAIAARnAHAAAAACCAEdwBAAAAAAhgBHcAAAAAAAIYwR0AAAAAgABGcA8i27Zt0xVXXKGUlBQNGTJEDz74oIqLi6t8nmmaWrJkic444wz17dtXkyZN0g8//NDwBQM1VJs+vm/fPj344IMaP368+vXrp6FDh+rmm2/W7t27G6lqoGZq+9/yoz3//PPq0aOHrrnmmgaqEqi9uvTxjIwM3XbbbRo0aJD69u2rUaNGafny5Q1cMVBzte3nWVlZmjNnjs444wylpKRo7NixevXVVxuhYvhbiL8LQOPIzs7WZZddpqSkJC1YsEAZGRmaN2+eCgsLNWfOnEqfu3TpUs2fP1+zZ89Wjx499PLLL+vKK6/Uu+++q06dOjXSOwAqV9s+/ssvv+jDDz/Un//8Z5144onKysrS4sWLNXHiRK1cuVLx8fGN+C6AytXlv+Wl9u/fr0WLFqlly5YNXC1Qc3Xp4/v27dOkSZPUpUsX3XfffYqKitKWLVtq/MUW0NDq0s9vuOEGpaWl6aabblK7du302Wef6Z577pHVatUFF1zQSO8AfmEiKDz11FNmSkqKmZWV5T322muvmT179jT37t1b4fMKCwvNk046yXzkkUe8x4qKiswzzzzTvPvuuxuwYqBmatvHs7OzTafT6XPsjz/+MHv06GE+++yzDVUuUCu17edHu+WWW8xbb73VvPTSS82rr766gSoFaqcufXz27NnmpEmTzJKSkgauEqib2vbzffv2mccff7z51ltv+Ry/5JJLzClTpjRUuQgQTJUPEp999pkGDx6suLg477FRo0bJ7XZr/fr1FT7vu+++U15enkaNGuU9ZrfbddZZZ+mzzz5ryJKBGqltH4+JiVFIiO/ko7Zt2yo+Pl779u1rqHKBWqltPy/17bff6qOPPtLNN9/cgFUCtVfbPp6Xl6f33ntPF198saxWayNUCtRebft5SUmJJCk6OtrneFRUlEzTbJBaETgI7kEiLS1NXbt29TkWExOjhIQEpaWlVfo8SWWe261bN+3Zs0eFhYX1XyxQC7Xt4+X5/fffdfDgQXXr1q0+SwTqrC793OVy6b777tP06dPVunXrhiwTqLXa9vFffvlFTqdTISEhuvTSS9W7d28NGTJEDz30kJxOZ0OXDdRIbft5u3btdOqpp+qpp57S1q1blZeXp9WrV2v9+vW65JJLGrps+Blr3INETk6OYmJiyhyPjY1VdnZ2pc+z2+0KDQ31OR4TEyPTNJWdna2wsLB6rxeoqdr28WOZpqm5c+eqdevWGjNmTH2WCNRZXfr5K6+8IofDocsvv7yBqgPqrrZ9/MCBA5Kkv/3tb7rgggs0Y8YMbdiwQfPnz5fFYmGWCQJKXf5bvmDBAt14443e/41itVr1t7/9TSNHjmyQWhE4CO4AcJQFCxboq6++0jPPPKOIiAh/lwPUi4MHD2r+/Pl64IEHZLfb/V0OUO/cbrck6ZRTTtHtt98uSRo0aJDy8/P13HPP6brrrmOgAU2eaZq64447lJ6erkceeUQJCQn64osv9M9//lOxsbEMODRzBPcgERMTo9zc3DLHs7OzFRsbW+nziouLVVRU5DPqnpOTI8MwKn0u0Jhq28eP9sYbb2jRokX6xz/+ocGDB9d3iUCd1bafP/HEE+rRo4f69++vnJwcSZ61kiUlJcrJyVFERESZvR4Af6jL/16RPGH9aIMHD9ZTTz2l7du3q0ePHvVbLFBLte3nn376qdasWaPly5d7+3NqaqoOHjyoefPmEdybOda4B4muXbuWWTOTm5ur/fv3l1ljc+zzJM+a36OlpaWpffv2fHuNgFHbPl7qww8/1D333KPrr79e559/fkOVCdRJbfv577//rm+++UYDBgzw/vnuu+/0+eefa8CAAfriiy8aunSgWmrbx7t3717pdYuKiuqlPqA+1Lafb926VVarVccff7zP8Z49e2rfvn1yOBwNUi8CA8E9SAwdOlRffPGFd6RFktasWSOLxaIhQ4ZU+LyTTjpJUVFReu+997zHnE6nPvjgAw0dOrRBawZqorZ9XJK+/vpr3XTTTZo4caKuu+66hi4VqLXa9vM777xTL774os+fE044QSkpKXrxxRfVt2/fxigfqFJt+3iHDh10/PHHl/kS6osvvlBYWFiVwR5oTHXp5y6XS5s2bfI5/ssvv6hly5YKDw9vsJrhf4bJvQOCQnZ2tsaMGaMuXbrommuuUUZGhubNm6dx48Zpzpw53naXXXaZ9uzZow8//NB7bMmSJVqwYIFmz56t448/Xq+++qo+//xzvfvuu+rUqZM/3g5QRm37+LZt2zRp0iS1a9dOf//732WxHPk+Mz4+XomJiY3+XoCK1OW/5ceaPHmyIiIi9PTTTzdG6UC11KWPr127Vtdee60mT56sM844Qz/99JMWLlyoqVOn6sYbb/TH2wHKVdt+npeXp3Hjxslms+m6665T69at9fnnn+u5557TzJkzde211/rrLaERsKAtSMTGxuqFF17Qfffdp+uuu06RkZE6//zzy/w/MrfbLZfL5XNs2rRpMk1Tzz33nDIzM9WzZ089++yzhHYElNr28R9//FG5ubnKzc3VRRdd5NP2vPPO07x58xqlfqA66vLfcqApqEsfHzZsmB599FE9+eSTevXVV9W6dWvNnDlTV199dWO+BaBKte3nUVFRev755/XYY4/p4YcfVm5urjp27Kjbb79dl156aWO/DTQyRtwBAAAAAAhgrHEHAAAAACCAEdwBAAAAAAhgBHcAAAAAAAIYwR0AAAAAgABGcAcAAAAAIIAR3AEAAAAACGAEdwAAAAAAAhjBHQAAAACAAEZwBwAgCCxbtkw9evSo8M8PP/wgSd7Hzz33XIXX+Omnn7zHFixYoB49euiEE07QH3/8UeY5eXl56tu3r3r06KF77723zPmsrCw98MADGjlypJKTkzVw4EBNnTpVn3zySf29eQAAmrgQfxcAAAAaz/XXX6+OHTuWOZ6YmOjz+Nlnn9VFF12k8PDwal3Xbrdr5cqVmjZtms/xDz74oMLnpKWl6fLLL1dmZqYmTJig5ORk5eTkaMWKFZo+fbquvPJK3XbbbdV6fQAAmjOCOwAAQWTo0KFKTk6utE3Pnj21ceNGvfbaa7riiiuqdd3TTz9dq1atKhPcV65cqTPOOEPvv/++z3Gn06kbbrhBOTk5evnll3XiiSd6z11++eWaPXu2nnvuOSUnJ2v06NHVfHcAADRPTJUHAAA+TjrpJA0aNEjPPPOMCgsLq/WcsWPHauPGjdq2bZv32P79+/XVV19p7NixZdp/8MEH2rx5s6ZNm+YT2iXJarXq3nvvVUxMjBYsWFC3NwMAQDNAcAcAIIjk5eUpMzPT509WVlaZdjNnztSBAwf06quvVuu6AwYMUNu2bbVy5UrvsdWrVysiIkJnnHFGmfZr166VJJ177rnlXi86OlrDhw9XWlqatm/fXq0aAABorpgqDwBAELn88svLHLPb7T4bzklS//79lZqa6l3rHhYWVuW1R48erVWrVumGG26QJK1YsUJnnXWW7HZ7mbbbtm1TdHS0OnToUOH1TjjhBG/bzp07V/n6AAA0VwR3AACCyJw5c9SlSxefYxZL+RPwZs6cqUsvvVSvvfZauYH/WOPGjdNzzz2nDRs2KDY2Vj/99JNuuummctvm5+crMjKy0uuVns/Ly6vytQEAaM4I7gAABJG+fftWuTldqQEDBig1NVXPPPOMLrzwwirb9+rVS127dtXKlSsVExOjhIQEDRo0qNy2kZGR5U7RP1p+fr63LQAAwYw17gAAoEIzZszQ/v379dprr1Wr/dixY7V69WqtWLFCo0aNqnA0v1u3bsrNzdWePXsqvNamTZskSd27d6954QAANCMEdwAAUKGBAwdq4MCB1d5hfty4cdq/f7/S09M1bty4CtuVblj3zjvvlHs+Ly9PH3/8sbp27cr6dgBA0CO4AwCASs2cOVP79+/XG2+8UWXbxMRE3Xnnnbr55pvVt2/fCtuNHDlS3bt319KlS8tsjOd2u3X33XcrOztbM2bMqHP9AAA0daxxBwAgiHz22WdKS0src/ykk05Sp06dyn1O6aj7f//732q9xmWXXVZlG7vdrvnz5+uyyy7TxRdfrAkTJqhPnz7Kzc3VypUr9csvv+jKK6/UmDFjqvWaAAA0ZwR3AACCyPz588s9fv/991cY3CXPWvcpU6bUay3dunXT8uXLtWTJEq1du1bLli1TWFiY+vTpo8WLF2vYsGH1+noAADRVhmmapr+LAAAAAAAA5WONOwAAAAAAAYzgDgAAAABAACO4AwAAAAAQwAjuAAAAAAAEMII7AAAAAAABjOAOAAAAAEAAI7gDAAAAABDACO4AAAAAAAQwgjsAAAAAAAGM4A4AAAAAQAAjuAMAAAAAEMAI7gAAAAAABDCCOwAAAAAAAez/AYrThcdQRRT6AAAAAElFTkSuQmCC\n"},"metadata":{}},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n data_subset = grouped_data.get_group(pd_key)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"# Grouped statistics\nsummary_stats = merged_data.groupby(\"event\")[[\"enmo\", \"anglez\"]].describe()\nprint(summary_stats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:47.895757Z","iopub.execute_input":"2025-04-15T10:48:47.896084Z","iopub.status.idle":"2025-04-15T10:48:47.930189Z","shell.execute_reply.started":"2025-04-15T10:48:47.896056Z","shell.execute_reply":"2025-04-15T10:48:47.929354Z"}},"outputs":[{"name":"stdout","text":" enmo \\\n count mean std min 25% 50% 75% max \nevent \nonset 4791.0 0.022903 0.048004 0.0 0.0 0.006302 0.025848 0.858398 \nwakeup 4794.0 0.007240 0.018219 0.0 0.0 0.000000 0.007801 0.355957 \n\n anglez \\\n count mean std min 25% 50% \nevent \nonset 4791.0 -12.669401 43.311123 -89.5625 -48.343750 -18.890625 \nwakeup 4794.0 -6.712762 48.692459 -89.7500 -48.820312 -11.707031 \n\n \n 75% max \nevent \nonset 20.320312 88.625 \nwakeup 36.328125 89.750 \n","output_type":"stream"}],"execution_count":36},{"cell_type":"code","source":"# Extract time-based features from the 'date' column\nmerged_data['day_of_week'] = merged_data['date'].dt.dayofweek\nmerged_data['month'] = merged_data['date'].dt.month\nmerged_data['hour_of_day'] = merged_data['date'].dt.hour\nmerged_data['is_weekend'] = merged_data['day_of_week'].isin([5, 6]).astype(int)\n\n# Check the first few rows to see the new features\nprint(merged_data[['date', 'day_of_week', 'month', 'hour_of_day', 'is_weekend']].head())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:49.318083Z","iopub.execute_input":"2025-04-15T10:48:49.318521Z","iopub.status.idle":"2025-04-15T10:48:49.333285Z","shell.execute_reply.started":"2025-04-15T10:48:49.318484Z","shell.execute_reply":"2025-04-15T10:48:49.33238Z"}},"outputs":[{"name":"stdout","text":" date day_of_week month hour_of_day is_weekend\n0 2018-08-15 2 8 0 0\n1 2018-08-15 2 8 0 0\n2 2018-08-15 2 8 0 0\n3 2018-08-16 3 8 0 0\n4 2018-08-17 4 8 0 0\n","output_type":"stream"}],"execution_count":37},{"cell_type":"code","source":"# Compute rolling mean and rolling standard deviation for 'enmo' and 'anglez'\nmerged_data['enmo_rolling_mean'] = merged_data['enmo'].rolling(window=5).mean()\nmerged_data['anglez_rolling_mean'] = merged_data['anglez'].rolling(window=5).mean()\n\n# Check the new features\nprint(merged_data[['enmo_rolling_mean', 'anglez_rolling_mean']].head(10))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:51.625658Z","iopub.execute_input":"2025-04-15T10:48:51.625947Z","iopub.status.idle":"2025-04-15T10:48:51.637354Z","shell.execute_reply.started":"2025-04-15T10:48:51.625925Z","shell.execute_reply":"2025-04-15T10:48:51.636207Z"}},"outputs":[{"name":"stdout","text":" enmo_rolling_mean anglez_rolling_mean\n0 NaN NaN\n1 NaN NaN\n2 NaN NaN\n3 NaN NaN\n4 0.014182 -38.759766\n5 0.017221 -35.959766\n6 0.014059 -37.966016\n7 0.010419 -29.132422\n8 0.009000 -34.288672\n9 0.011119 -42.937500\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1458: RuntimeWarning: invalid value encountered in greater\n has_large_values = (abs_vals > 1e6).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in less\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in greater\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n","output_type":"stream"}],"execution_count":38},{"cell_type":"code","source":"# Check missing values across columns\nprint(merged_data.isna().sum())\n\n# Fill missing values with the mean (or median, depending on distribution)\nmerged_data['enmo'] = merged_data['enmo'].fillna(merged_data['enmo'].mean())\nmerged_data['anglez'] = merged_data['anglez'].fillna(merged_data['anglez'].mean())\n\n# For categorical columns like 'night', you can fill missing values with the most frequent value\nmerged_data['night'] = merged_data['night'].fillna(merged_data['night'].mode()[0])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:48:54.544972Z","iopub.execute_input":"2025-04-15T10:48:54.545328Z","iopub.status.idle":"2025-04-15T10:48:54.557633Z","shell.execute_reply.started":"2025-04-15T10:48:54.545266Z","shell.execute_reply":"2025-04-15T10:48:54.556877Z"}},"outputs":[{"name":"stdout","text":"series_id 0\nstep 0\ntimestamp_x 0\nanglez 0\nenmo 0\ndate 0\ntime 0\nnight 0\nevent 0\ntimestamp_y 0\nevent_numeric 9585\nday_of_week 0\nmonth 0\nhour_of_day 0\nis_weekend 0\nenmo_rolling_mean 4\nanglez_rolling_mean 4\ndtype: int64\n","output_type":"stream"}],"execution_count":39},{"cell_type":"code","source":"# Fill missing values in rolling mean columns (forward-fill or fill with the mean)\nmerged_data['enmo_rolling_mean'] = merged_data['enmo_rolling_mean'].fillna(method='ffill')\nmerged_data['anglez_rolling_mean'] = merged_data['anglez_rolling_mean'].fillna(method='ffill')\n\n# Alternatively, you can use the column mean to fill missing values\n# merged_data['enmo_rolling_mean'] = merged_data['enmo_rolling_mean'].fillna(merged_data['enmo_rolling_mean'].mean())\n# merged_data['anglez_rolling_mean'] = merged_data['anglez_rolling_mean'].fillna(merged_data['anglez_rolling_mean'].mean())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:49:23.399521Z","iopub.execute_input":"2025-04-15T10:49:23.399815Z","iopub.status.idle":"2025-04-15T10:49:23.407975Z","shell.execute_reply.started":"2025-04-15T10:49:23.399793Z","shell.execute_reply":"2025-04-15T10:49:23.407219Z"}},"outputs":[{"name":"stderr","text":":2: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n merged_data['enmo_rolling_mean'] = merged_data['enmo_rolling_mean'].fillna(method='ffill')\n:3: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n merged_data['anglez_rolling_mean'] = merged_data['anglez_rolling_mean'].fillna(method='ffill')\n","output_type":"stream"}],"execution_count":40},{"cell_type":"code","source":"# Forward-fill missing values in rolling mean columns\nmerged_data['enmo_rolling_mean'] = merged_data['enmo_rolling_mean'].ffill()\nmerged_data['anglez_rolling_mean'] = merged_data['anglez_rolling_mean'].ffill()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T08:53:30.668805Z","iopub.execute_input":"2025-04-15T08:53:30.669128Z","iopub.status.idle":"2025-04-15T08:53:30.673658Z","shell.execute_reply.started":"2025-04-15T08:53:30.669102Z","shell.execute_reply":"2025-04-15T08:53:30.67274Z"}},"outputs":[],"execution_count":92},{"cell_type":"code","source":"# Check for any remaining missing values\nprint(merged_data.isna().sum())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:49:33.033518Z","iopub.execute_input":"2025-04-15T10:49:33.033797Z","iopub.status.idle":"2025-04-15T10:49:33.042337Z","shell.execute_reply.started":"2025-04-15T10:49:33.033775Z","shell.execute_reply":"2025-04-15T10:49:33.041535Z"}},"outputs":[{"name":"stdout","text":"series_id 0\nstep 0\ntimestamp_x 0\nanglez 0\nenmo 0\ndate 0\ntime 0\nnight 0\nevent 0\ntimestamp_y 0\nevent_numeric 9585\nday_of_week 0\nmonth 0\nhour_of_day 0\nis_weekend 0\nenmo_rolling_mean 4\nanglez_rolling_mean 4\ndtype: int64\n","output_type":"stream"}],"execution_count":41},{"cell_type":"code","source":"\n\n# Encode non-null event column\nlabel_encoder = LabelEncoder()\nmerged_data = merged_data[merged_data['event'].notnull()].copy()\nmerged_data['event_numeric'] = label_encoder.fit_transform(merged_data['event'])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:55:34.3426Z","iopub.execute_input":"2025-04-15T10:55:34.342886Z","iopub.status.idle":"2025-04-15T10:55:34.352472Z","shell.execute_reply.started":"2025-04-15T10:55:34.342856Z","shell.execute_reply":"2025-04-15T10:55:34.351691Z"}},"outputs":[],"execution_count":45},{"cell_type":"code","source":"# Now define X and y\nX = merged_data.drop(columns=['series_id', 'timestamp_x', 'timestamp_y', 'event', 'event_numeric'])\ny = merged_data['event_numeric'].astype(int)\n\nprint(merged_data.shape)\nprint(label_encoder.classes_) # This shows the mapping\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:55:42.327215Z","iopub.execute_input":"2025-04-15T10:55:42.32762Z","iopub.status.idle":"2025-04-15T10:55:42.334953Z","shell.execute_reply.started":"2025-04-15T10:55:42.327589Z","shell.execute_reply":"2025-04-15T10:55:42.334229Z"}},"outputs":[{"name":"stdout","text":"(9585, 17)\n['onset' 'wakeup']\n","output_type":"stream"}],"execution_count":46},{"cell_type":"code","source":"# Check for any remaining missing values\nprint(merged_data.isna().sum())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:55:59.806192Z","iopub.execute_input":"2025-04-15T10:55:59.806541Z","iopub.status.idle":"2025-04-15T10:55:59.815317Z","shell.execute_reply.started":"2025-04-15T10:55:59.806512Z","shell.execute_reply":"2025-04-15T10:55:59.814364Z"}},"outputs":[{"name":"stdout","text":"series_id 0\nstep 0\ntimestamp_x 0\nanglez 0\nenmo 0\ndate 0\ntime 0\nnight 0\nevent 0\ntimestamp_y 0\nevent_numeric 0\nday_of_week 0\nmonth 0\nhour_of_day 0\nis_weekend 0\nenmo_rolling_mean 4\nanglez_rolling_mean 4\ndtype: int64\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"merged_data = merged_data.dropna(subset=['enmo_rolling_mean', 'anglez_rolling_mean'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:56:57.441898Z","iopub.execute_input":"2025-04-15T10:56:57.442222Z","iopub.status.idle":"2025-04-15T10:56:57.449403Z","shell.execute_reply.started":"2025-04-15T10:56:57.442194Z","shell.execute_reply":"2025-04-15T10:56:57.448747Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"# Drop any non-numeric columns\nX = X.drop(columns=['date', 'time']) # if not already done","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:59:51.213293Z","iopub.execute_input":"2025-04-15T10:59:51.213678Z","iopub.status.idle":"2025-04-15T10:59:51.219406Z","shell.execute_reply.started":"2025-04-15T10:59:51.213646Z","shell.execute_reply":"2025-04-15T10:59:51.218549Z"}},"outputs":[],"execution_count":53},{"cell_type":"code","source":"# Drop non-numeric columns from X\nX = merged_data.drop(columns=[\n 'series_id', 'timestamp_x', 'timestamp_y', 'event', 'event_numeric', 'date', 'time'\n])\n\ny = merged_data['event_numeric'].astype(int)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:00:19.76992Z","iopub.execute_input":"2025-04-15T11:00:19.770253Z","iopub.status.idle":"2025-04-15T11:00:19.775732Z","shell.execute_reply.started":"2025-04-15T11:00:19.770223Z","shell.execute_reply":"2025-04-15T11:00:19.775027Z"}},"outputs":[],"execution_count":54},{"cell_type":"code","source":"# Re-split data\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.2, random_state=42, stratify=y\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:00:25.472797Z","iopub.execute_input":"2025-04-15T11:00:25.473105Z","iopub.status.idle":"2025-04-15T11:00:25.483765Z","shell.execute_reply.started":"2025-04-15T11:00:25.473081Z","shell.execute_reply":"2025-04-15T11:00:25.482946Z"}},"outputs":[],"execution_count":55},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T10:57:52.772018Z","iopub.execute_input":"2025-04-15T10:57:52.772343Z","iopub.status.idle":"2025-04-15T10:57:52.866886Z","shell.execute_reply.started":"2025-04-15T10:57:52.772314Z","shell.execute_reply":"2025-04-15T10:57:52.866256Z"}},"outputs":[],"execution_count":50},{"cell_type":"code","source":"scaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:00:50.23296Z","iopub.execute_input":"2025-04-15T11:00:50.233244Z","iopub.status.idle":"2025-04-15T11:00:50.243054Z","shell.execute_reply.started":"2025-04-15T11:00:50.233221Z","shell.execute_reply":"2025-04-15T11:00:50.242438Z"}},"outputs":[],"execution_count":56},{"cell_type":"code","source":"models = {\n \"Logistic Regression\": LogisticRegression(max_iter=1000),\n \"K-Nearest Neighbors\": KNeighborsClassifier(),\n \"Support Vector Machine\": SVC(),\n \"Decision Tree\": DecisionTreeClassifier(),\n \"Random Forest\": RandomForestClassifier(),\n \"XGBoost\": xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n}\n\nfor name, model in models.items():\n model.fit(X_train_scaled, y_train)\n y_pred = model.predict(X_test_scaled)\n print(f\"\\n{name}\")\n print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:02:39.406343Z","iopub.execute_input":"2025-04-15T11:02:39.406637Z","iopub.status.idle":"2025-04-15T11:02:46.075461Z","shell.execute_reply.started":"2025-04-15T11:02:39.406616Z","shell.execute_reply":"2025-04-15T11:02:46.074708Z"}},"outputs":[{"name":"stdout","text":"\nLogistic Regression\nAccuracy: 0.9123630672926447\n precision recall f1-score support\n\n 0 0.93 0.89 0.91 958\n 1 0.90 0.93 0.91 959\n\n accuracy 0.91 1917\n macro avg 0.91 0.91 0.91 1917\nweighted avg 0.91 0.91 0.91 1917\n\n\nK-Nearest Neighbors\nAccuracy: 0.5164319248826291\n precision recall f1-score support\n\n 0 0.52 0.49 0.50 958\n 1 0.52 0.54 0.53 959\n\n accuracy 0.52 1917\n macro avg 0.52 0.52 0.52 1917\nweighted avg 0.52 0.52 0.52 1917\n\n\nSupport Vector Machine\nAccuracy: 0.6395409494001043\n precision recall f1-score support\n\n 0 0.73 0.44 0.55 958\n 1 0.60 0.83 0.70 959\n\n accuracy 0.64 1917\n macro avg 0.66 0.64 0.63 1917\nweighted avg 0.66 0.64 0.63 1917\n\n\nDecision Tree\nAccuracy: 0.8753260302556077\n precision recall f1-score support\n\n 0 0.86 0.89 0.88 958\n 1 0.89 0.86 0.87 959\n\n accuracy 0.88 1917\n macro avg 0.88 0.88 0.88 1917\nweighted avg 0.88 0.88 0.88 1917\n\n\nRandom Forest\nAccuracy: 0.7052686489306208\n precision recall f1-score support\n\n 0 0.72 0.68 0.70 958\n 1 0.69 0.73 0.71 959\n\n accuracy 0.71 1917\n macro avg 0.71 0.71 0.71 1917\nweighted avg 0.71 0.71 0.71 1917\n\n\nXGBoost\nAccuracy: 0.9134063641105895\n precision recall f1-score support\n\n 0 0.92 0.91 0.91 958\n 1 0.91 0.92 0.91 959\n\n accuracy 0.91 1917\n macro avg 0.91 0.91 0.91 1917\nweighted avg 0.91 0.91 0.91 1917\n\n","output_type":"stream"}],"execution_count":58},{"cell_type":"code","source":"# hyper parameter tuning using optuna\n\ndef objective(trial, model_name):\n if model_name == \"XGBoost\":\n params = {\n \"max_depth\": trial.suggest_int(\"max_depth\", 3, 10),\n \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.3),\n \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 500),\n \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0),\n \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.5, 1.0),\n \"gamma\": trial.suggest_float(\"gamma\", 0, 5),\n \"reg_alpha\": trial.suggest_float(\"reg_alpha\", 0, 5),\n \"reg_lambda\": trial.suggest_float(\"reg_lambda\", 0, 5),\n }\n\n model = XGBClassifier(\n **params, use_label_encoder=False, eval_metric='logloss', verbosity=0\n )\n\n elif model_name == \"Logistic Regression\":\n params = {\n \"C\": trial.suggest_float(\"C\", 0.01, 10),\n \"penalty\": trial.suggest_categorical(\"penalty\", [\"l1\", \"l2\"]),\n \"solver\": trial.suggest_categorical(\"solver\", [\"liblinear\", \"saga\"]),\n }\n\n model = LogisticRegression(max_iter=1000, **params)\n\n elif model_name == \"Decision Tree\":\n params = {\n \"criterion\": trial.suggest_categorical(\"criterion\", [\"gini\", \"entropy\", \"log_loss\"]),\n \"max_depth\": trial.suggest_int(\"max_depth\", 2, 20),\n \"min_samples_split\": trial.suggest_int(\"min_samples_split\", 2, 10),\n \"min_samples_leaf\": trial.suggest_int(\"min_samples_leaf\", 1, 5),\n }\n\n model = DecisionTreeClassifier(**params)\n\n elif model_name == \"Random Forest\":\n params = {\n \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 300),\n \"max_depth\": trial.suggest_int(\"max_depth\", 5, 20),\n \"min_samples_split\": trial.suggest_int(\"min_samples_split\", 2, 10),\n \"min_samples_leaf\": trial.suggest_int(\"min_samples_leaf\", 1, 5),\n \"bootstrap\": trial.suggest_categorical(\"bootstrap\", [True, False]),\n }\n\n model = RandomForestClassifier(**params)\n\n else:\n raise ValueError(f\"Unknown model: {model_name}\")\n\n score = cross_val_score(model, X_train_scaled, y_train, cv=3, scoring=\"accuracy\").mean()\n return score\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:29:44.70067Z","iopub.execute_input":"2025-04-15T11:29:44.70101Z","iopub.status.idle":"2025-04-15T11:29:44.708751Z","shell.execute_reply.started":"2025-04-15T11:29:44.700983Z","shell.execute_reply":"2025-04-15T11:29:44.707814Z"}},"outputs":[],"execution_count":76},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nimport xgboost as xgb\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\ndef objective(trial, model_name):\n # Split train data into training and validation\n X_t, X_val, y_t, y_val = train_test_split(\n X_train_scaled, y_train, test_size=0.2, stratify=y_train, random_state=42\n )\n\n try:\n if model_name == \"Logistic Regression\":\n C = trial.suggest_float(\"C\", 1e-4, 10, log=True)\n solver = trial.suggest_categorical(\"solver\", [\"liblinear\", \"lbfgs\", \"saga\"])\n\n if solver == \"liblinear\":\n penalty = trial.suggest_categorical(\"penalty\", [\"l1\", \"l2\"])\n model = LogisticRegression(C=C, solver=solver, penalty=penalty, max_iter=1000)\n elif solver == \"saga\":\n penalty = trial.suggest_categorical(\"penalty\", [\"l1\", \"l2\", \"elasticnet\"])\n if penalty == \"elasticnet\":\n l1_ratio = trial.suggest_float(\"l1_ratio\", 0.0, 1.0)\n model = LogisticRegression(C=C, solver=solver, penalty=penalty, l1_ratio=l1_ratio, max_iter=1000)\n else:\n model = LogisticRegression(C=C, solver=solver, penalty=penalty, max_iter=1000)\n else: # lbfgs\n model = LogisticRegression(C=C, solver=solver, penalty=\"l2\", max_iter=1000)\n\n \n\n elif model_name == \"Support Vector Machine\":\n params = {\n \"C\": trial.suggest_float(\"C\", 1e-4, 10, log=True),\n \"kernel\": trial.suggest_categorical(\"kernel\", [\"linear\", \"rbf\", \"poly\"]),\n \"gamma\": trial.suggest_categorical(\"gamma\", [\"scale\", \"auto\"]),\n }\n model = SVC(**params)\n\n elif model_name == \"XGBoost\":\n params = {\n \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000),\n \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-4, 0.3, log=True),\n \"max_depth\": trial.suggest_int(\"max_depth\", 3, 10),\n \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0),\n \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.5, 1.0),\n \"gamma\": trial.suggest_float(\"gamma\", 0, 5),\n \"reg_alpha\": trial.suggest_float(\"reg_alpha\", 0, 5),\n \"reg_lambda\": trial.suggest_float(\"reg_lambda\", 0, 5),\n }\n model = xgb.XGBClassifier(**params, use_label_encoder=False, eval_metric=\"logloss\")\n model.fit(X_t, y_t, eval_set=[(X_val, y_val)], early_stopping_rounds=20, verbose=False)\n preds = model.predict(X_val)\n return accuracy_score(y_val, preds)\n\n else:\n raise ValueError(f\"Unsupported model: {model_name}\")\n\n # For models that don’t need early stopping\n model.fit(X_t, y_t)\n preds = model.predict(X_val)\n return accuracy_score(y_val, preds)\n\n except Exception as e:\n print(f\"[Trial Failed for {model_name}] Error: {e}\")\n return float(\"nan\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:38:00.67473Z","iopub.execute_input":"2025-04-15T11:38:00.675059Z","iopub.status.idle":"2025-04-15T11:38:00.685935Z","shell.execute_reply.started":"2025-04-15T11:38:00.675035Z","shell.execute_reply":"2025-04-15T11:38:00.684897Z"}},"outputs":[],"execution_count":79},{"cell_type":"code","source":"def run_optuna(model_name, n_trials=25):\n # Create an Optuna study for hyperparameter optimization\n study = optuna.create_study(direction=\"maximize\")\n \n # Optimize the study by passing the objective function and number of trials\n study.optimize(lambda trial: objective(trial, model_name), n_trials=n_trials)\n \n # Print the best parameters found by Optuna for the current model\n print(f\"\\n🔧 Best parameters for {model_name}:\")\n print(study.best_params)\n \n # Print the best accuracy score achieved during the optimizationa\n print(f\"✅ Best accuracy: {study.best_value:.4f}\")\n \n # Return the study object for further analysis if needed\n return study","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:38:00.892807Z","iopub.execute_input":"2025-04-15T11:38:00.893191Z","iopub.status.idle":"2025-04-15T11:38:00.897847Z","shell.execute_reply.started":"2025-04-15T11:38:00.893157Z","shell.execute_reply":"2025-04-15T11:38:00.896886Z"}},"outputs":[],"execution_count":80},{"cell_type":"code","source":"# Running Optuna optimization for top 4 models\nmodels_to_optimize = [\n \"Logistic Regression\", \n \"Support Vector Machine\", \n \"XGBoost\"\n]\n\n# Run optimization for each model and store the study objects\nstudies = {}\nfor model_name in models_to_optimize:\n studies[model_name] = run_optuna(model_name, n_trials=25)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:38:01.022831Z","iopub.execute_input":"2025-04-15T11:38:01.023032Z","iopub.status.idle":"2025-04-15T11:39:22.665491Z","shell.execute_reply.started":"2025-04-15T11:38:01.023014Z","shell.execute_reply":"2025-04-15T11:39:22.661352Z"}},"outputs":[{"name":"stderr","text":"[I 2025-04-15 11:38:01,024] A new study created in memory with name: no-name-219e59c9-fa02-4ec2-9746-de86aa36ad58\n[I 2025-04-15 11:38:01,051] Trial 0 finished with value: 0.868232224396608 and parameters: {'C': 0.7904937587512633, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,213] Trial 1 finished with value: 0.6314416177429876 and parameters: {'C': 0.07941211166956426, 'solver': 'saga', 'penalty': 'l2'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,225] Trial 2 finished with value: 0.49967384213959554 and parameters: {'C': 0.0002107592931636561, 'solver': 'saga', 'penalty': 'l1'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,242] Trial 3 finished with value: 0.6470971950424005 and parameters: {'C': 0.11382164065961756, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,254] Trial 4 finished with value: 0.5851272015655578 and parameters: {'C': 0.0004851278233477057, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,268] Trial 5 finished with value: 0.6125244618395304 and parameters: {'C': 0.0372154707435988, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,279] Trial 6 finished with value: 0.5831702544031311 and parameters: {'C': 0.000368692523760751, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,297] Trial 7 finished with value: 0.6562296151337247 and parameters: {'C': 0.13654267356961838, 'solver': 'lbfgs'}. Best is trial 0 with value: 0.868232224396608.\n[I 2025-04-15 11:38:01,309] Trial 8 finished with value: 0.5003261578604045 and parameters: {'C': 0.00046796661957986034, 'solver': 'saga', 'penalty': 'elasticnet', 'l1_ratio': 0.4570854314670373}. Best is trial 0 with value: 0.868232224396608.\n/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n warnings.warn(\n[I 2025-04-15 11:38:02,555] Trial 9 finished with value: 0.9360730593607306 and parameters: {'C': 6.120572865720077, 'solver': 'saga', 'penalty': 'elasticnet', 'l1_ratio': 0.042362809739345364}. Best is trial 9 with value: 0.9360730593607306.\n[W 2025-04-15 11:38:02,565] Trial 10 failed with parameters: {'C': 7.428332799812172, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,566] Trial 10 failed with value nan.\n[W 2025-04-15 11:38:02,576] Trial 11 failed with parameters: {'C': 7.547992744693891, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,576] Trial 11 failed with value nan.\n[W 2025-04-15 11:38:02,585] Trial 12 failed with parameters: {'C': 8.092780774389723, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,586] Trial 12 failed with value nan.\n[W 2025-04-15 11:38:02,595] Trial 13 failed with parameters: {'C': 9.863356925162382, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,596] Trial 13 failed with value nan.\n[W 2025-04-15 11:38:02,605] Trial 14 failed with parameters: {'C': 8.65187853137688, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,606] Trial 14 failed with value nan.\n[W 2025-04-15 11:38:02,614] Trial 15 failed with parameters: {'C': 9.43849668758932, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,614] Trial 15 failed with value nan.\n[W 2025-04-15 11:38:02,623] Trial 16 failed with parameters: {'C': 3.405734481262767, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,624] Trial 16 failed with value nan.\n[W 2025-04-15 11:38:02,633] Trial 17 failed with parameters: {'C': 9.699651162335686, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,634] Trial 17 failed with value nan.\n[W 2025-04-15 11:38:02,642] Trial 18 failed with parameters: {'C': 7.9156738187662885, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,643] Trial 18 failed with value nan.\n[W 2025-04-15 11:38:02,652] Trial 19 failed with parameters: {'C': 6.475436448783142, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,652] Trial 19 failed with value nan.\n[W 2025-04-15 11:38:02,661] Trial 20 failed with parameters: {'C': 7.589321315916321, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,662] Trial 20 failed with value nan.\n[W 2025-04-15 11:38:02,670] Trial 21 failed with parameters: {'C': 9.943324546566163, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,671] Trial 21 failed with value nan.\n[W 2025-04-15 11:38:02,680] Trial 22 failed with parameters: {'C': 9.855097488385532, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,681] Trial 22 failed with value nan.\n[W 2025-04-15 11:38:02,690] Trial 23 failed with parameters: {'C': 8.366485299423701, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,690] Trial 23 failed with value nan.\n[W 2025-04-15 11:38:02,699] Trial 24 failed with parameters: {'C': 6.4243343391005805, 'solver': 'liblinear'} because of the following error: The value nan is not acceptable.\n[W 2025-04-15 11:38:02,700] Trial 24 failed with value nan.\n[I 2025-04-15 11:38:02,701] A new study created in memory with name: no-name-1dd4328d-69c5-440b-b0ed-fbed59e02466\n","output_type":"stream"},{"name":"stdout","text":"[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n[Trial Failed for Logistic Regression] Error: index 2 is out of bounds for axis 1 with size 2\n\n🔧 Best parameters for Logistic Regression:\n{'C': 6.120572865720077, 'solver': 'saga', 'penalty': 'elasticnet', 'l1_ratio': 0.042362809739345364}\n✅ Best accuracy: 0.9361\n","output_type":"stream"},{"name":"stderr","text":"[I 2025-04-15 11:38:05,293] Trial 0 finished with value: 0.502283105022831 and parameters: {'C': 0.0003230100406119382, 'kernel': 'poly', 'gamma': 'auto'}. Best is trial 0 with value: 0.502283105022831.\n[I 2025-04-15 11:38:08,027] Trial 1 finished with value: 0.5870841487279843 and parameters: {'C': 0.1370871213073774, 'kernel': 'rbf', 'gamma': 'auto'}. Best is trial 1 with value: 0.5870841487279843.\n[I 2025-04-15 11:38:10,602] Trial 2 finished with value: 0.54337899543379 and parameters: {'C': 0.01798231500330935, 'kernel': 'poly', 'gamma': 'scale'}. Best is trial 1 with value: 0.5870841487279843.\n[I 2025-04-15 11:38:12,995] Trial 3 finished with value: 0.5603392041748206 and parameters: {'C': 0.04633051208932027, 'kernel': 'poly', 'gamma': 'scale'}. Best is trial 1 with value: 0.5870841487279843.\n[I 2025-04-15 11:38:15,099] Trial 4 finished with value: 0.5335942596216569 and parameters: {'C': 0.00027468365228355547, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 1 with value: 0.5870841487279843.\n[I 2025-04-15 11:38:17,181] Trial 5 finished with value: 0.5714285714285714 and parameters: {'C': 0.005546227712512956, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 1 with value: 0.5870841487279843.\n[I 2025-04-15 11:38:19,711] Trial 6 finished with value: 0.91324200913242 and parameters: {'C': 0.5023380386218469, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:21,669] Trial 7 finished with value: 0.5649054142204827 and parameters: {'C': 0.0033714424018181457, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:24,071] Trial 8 finished with value: 0.5609915198956295 and parameters: {'C': 0.10282348240656405, 'kernel': 'poly', 'gamma': 'auto'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:26,708] Trial 9 finished with value: 0.5766470971950424 and parameters: {'C': 0.3059339994742012, 'kernel': 'poly', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:30,032] Trial 10 finished with value: 0.7893020221787345 and parameters: {'C': 7.61290681318659, 'kernel': 'rbf', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:33,227] Trial 11 finished with value: 0.7475538160469667 and parameters: {'C': 5.551390782123767, 'kernel': 'rbf', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:36,456] Trial 12 finished with value: 0.7808219178082192 and parameters: {'C': 7.069819102912283, 'kernel': 'rbf', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:39,197] Trial 13 finished with value: 0.6099151989562949 and parameters: {'C': 1.3524070695543182, 'kernel': 'rbf', 'gamma': 'scale'}. Best is trial 6 with value: 0.91324200913242.\n[I 2025-04-15 11:38:41,783] Trial 14 finished with value: 0.9373776908023483 and parameters: {'C': 1.065717104603647, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 14 with value: 0.9373776908023483.\n[I 2025-04-15 11:38:44,316] Trial 15 finished with value: 0.9328114807566862 and parameters: {'C': 0.8324019643302246, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 14 with value: 0.9373776908023483.\n[I 2025-04-15 11:38:46,916] Trial 16 finished with value: 0.9373776908023483 and parameters: {'C': 1.397532255541558, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 14 with value: 0.9373776908023483.\n[I 2025-04-15 11:38:49,472] Trial 17 finished with value: 0.9399869536855838 and parameters: {'C': 2.030368323939648, 'kernel': 'linear', 'gamma': 'scale'}. Best is trial 17 with value: 0.9399869536855838.\n[I 2025-04-15 11:38:52,042] Trial 18 finished with value: 0.9399869536855838 and parameters: {'C': 2.508039677537151, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 17 with value: 0.9399869536855838.\n[I 2025-04-15 11:38:54,128] Trial 19 finished with value: 0.5766470971950424 and parameters: {'C': 0.011483473013859774, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 17 with value: 0.9399869536855838.\n[I 2025-04-15 11:38:56,879] Trial 20 finished with value: 0.9406392694063926 and parameters: {'C': 3.1242191062193596, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 20 with value: 0.9406392694063926.\n[I 2025-04-15 11:38:59,636] Trial 21 finished with value: 0.9412915851272016 and parameters: {'C': 2.808687631148116, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 21 with value: 0.9412915851272016.\n[I 2025-04-15 11:39:02,666] Trial 22 finished with value: 0.9412915851272016 and parameters: {'C': 2.8661855784858874, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 21 with value: 0.9412915851272016.\n[I 2025-04-15 11:39:05,080] Trial 23 finished with value: 0.7508153946510111 and parameters: {'C': 0.2771512906261351, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 21 with value: 0.9412915851272016.\n[I 2025-04-15 11:39:07,995] Trial 24 finished with value: 0.9412915851272016 and parameters: {'C': 3.693114198612509, 'kernel': 'linear', 'gamma': 'auto'}. Best is trial 21 with value: 0.9412915851272016.\n[I 2025-04-15 11:39:07,997] A new study created in memory with name: no-name-44d82deb-8e9e-4dd4-b96f-8ba901f7e2da\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"\n🔧 Best parameters for Support Vector Machine:\n{'C': 2.808687631148116, 'kernel': 'linear', 'gamma': 'auto'}\n✅ Best accuracy: 0.9413\n","output_type":"stream"},{"name":"stderr","text":"[I 2025-04-15 11:39:08,216] Trial 0 finished with value: 0.9119373776908023 and parameters: {'n_estimators': 545, 'learning_rate': 0.27686314689346964, 'max_depth': 4, 'subsample': 0.9624919378650283, 'colsample_bytree': 0.6906424494619248, 'gamma': 0.2583508266023665, 'reg_alpha': 3.7709047383383956, 'reg_lambda': 1.5997045375786534}. Best is trial 0 with value: 0.9119373776908023.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:09,138] Trial 1 finished with value: 0.6464448793215917 and parameters: {'n_estimators': 549, 'learning_rate': 0.00015250399328427405, 'max_depth': 9, 'subsample': 0.5681219501457553, 'colsample_bytree': 0.962710843020312, 'gamma': 1.4010061831028264, 'reg_alpha': 4.609195250793619, 'reg_lambda': 1.7268545121342056}. Best is trial 0 with value: 0.9119373776908023.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:09,445] Trial 2 finished with value: 0.7945205479452054 and parameters: {'n_estimators': 566, 'learning_rate': 0.04217641220153731, 'max_depth': 3, 'subsample': 0.9274841245994827, 'colsample_bytree': 0.5313035202753646, 'gamma': 2.4253511969083235, 'reg_alpha': 4.436430365595291, 'reg_lambda': 4.871604659033195}. Best is trial 0 with value: 0.9119373776908023.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:10,122] Trial 3 finished with value: 0.9125896934116112 and parameters: {'n_estimators': 825, 'learning_rate': 0.05386963534085739, 'max_depth': 5, 'subsample': 0.5291930328448645, 'colsample_bytree': 0.7555608863090757, 'gamma': 2.3075348880330004, 'reg_alpha': 0.2523607735918576, 'reg_lambda': 4.515836893900243}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:10,628] Trial 4 finished with value: 0.9015003261578604 and parameters: {'n_estimators': 315, 'learning_rate': 0.06368331425474359, 'max_depth': 9, 'subsample': 0.7034498401751685, 'colsample_bytree': 0.518582436985956, 'gamma': 0.1791239948511969, 'reg_alpha': 2.0963893273953444, 'reg_lambda': 4.413655022134552}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:10,993] Trial 5 finished with value: 0.6236138290932811 and parameters: {'n_estimators': 412, 'learning_rate': 0.0017268202846968536, 'max_depth': 3, 'subsample': 0.7550865706043492, 'colsample_bytree': 0.906371395716099, 'gamma': 3.2213682743320504, 'reg_alpha': 4.180500881713225, 'reg_lambda': 4.937995000038969}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:11,146] Trial 6 finished with value: 0.6797129810828441 and parameters: {'n_estimators': 106, 'learning_rate': 0.0053958130899297374, 'max_depth': 8, 'subsample': 0.891483262119285, 'colsample_bytree': 0.5524471289913759, 'gamma': 1.5222392551603148, 'reg_alpha': 2.435297991327703, 'reg_lambda': 3.6449721223966076}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:12,242] Trial 7 finished with value: 0.6588388780169602 and parameters: {'n_estimators': 824, 'learning_rate': 0.0006676338639668028, 'max_depth': 9, 'subsample': 0.9398904553808055, 'colsample_bytree': 0.9575404398841945, 'gamma': 3.2765540947450633, 'reg_alpha': 4.641826360465329, 'reg_lambda': 4.4740542531297525}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:13,667] Trial 8 finished with value: 0.8858447488584474 and parameters: {'n_estimators': 889, 'learning_rate': 0.010869594477094053, 'max_depth': 9, 'subsample': 0.6270684628914456, 'colsample_bytree': 0.6263073022803771, 'gamma': 0.06107292602983261, 'reg_alpha': 2.9632772580155784, 'reg_lambda': 4.187085753751095}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:13,987] Trial 9 finished with value: 0.6731898238747553 and parameters: {'n_estimators': 282, 'learning_rate': 0.001134651295999966, 'max_depth': 9, 'subsample': 0.8536400544650319, 'colsample_bytree': 0.5774656733130558, 'gamma': 3.5372594587112123, 'reg_alpha': 3.6109836057608002, 'reg_lambda': 4.225032635897081}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:14,101] Trial 10 finished with value: 0.8773646444879322 and parameters: {'n_estimators': 973, 'learning_rate': 0.21497697756840606, 'max_depth': 6, 'subsample': 0.5226566289914663, 'colsample_bytree': 0.8133775197735547, 'gamma': 4.960920651943554, 'reg_alpha': 0.019715910540311743, 'reg_lambda': 0.5164097069519324}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:14,358] Trial 11 finished with value: 0.9099804305283757 and parameters: {'n_estimators': 671, 'learning_rate': 0.28405229354280453, 'max_depth': 5, 'subsample': 0.7934455203227869, 'colsample_bytree': 0.7008506279460069, 'gamma': 1.255385520170323, 'reg_alpha': 0.036664269410043776, 'reg_lambda': 2.561781284928422}. Best is trial 3 with value: 0.9125896934116112.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:14,784] Trial 12 finished with value: 0.913894324853229 and parameters: {'n_estimators': 759, 'learning_rate': 0.05162257672171009, 'max_depth': 5, 'subsample': 0.6722597739372492, 'colsample_bytree': 0.7740774926799256, 'gamma': 2.234512483867308, 'reg_alpha': 1.3945830777462753, 'reg_lambda': 1.852047616616936}. Best is trial 12 with value: 0.913894324853229.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:15,480] Trial 13 finished with value: 0.91324200913242 and parameters: {'n_estimators': 753, 'learning_rate': 0.02772246946242773, 'max_depth': 6, 'subsample': 0.6532491745774264, 'colsample_bytree': 0.8120661375742385, 'gamma': 2.1979424856711414, 'reg_alpha': 1.1947618365274935, 'reg_lambda': 2.993036699111272}. Best is trial 12 with value: 0.913894324853229.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:16,260] Trial 14 finished with value: 0.9060665362035225 and parameters: {'n_estimators': 697, 'learning_rate': 0.015941121287455767, 'max_depth': 7, 'subsample': 0.6664599254059084, 'colsample_bytree': 0.8404138835358245, 'gamma': 1.9223513763568658, 'reg_alpha': 1.2735994482430064, 'reg_lambda': 2.8468854550664227}. Best is trial 12 with value: 0.913894324853229.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:16,861] Trial 15 finished with value: 0.8734507501630789 and parameters: {'n_estimators': 720, 'learning_rate': 0.02562582954708738, 'max_depth': 6, 'subsample': 0.6008572164269905, 'colsample_bytree': 0.8210591215609793, 'gamma': 4.448236269187982, 'reg_alpha': 1.2958147881278252, 'reg_lambda': 1.7643140663976657}. Best is trial 12 with value: 0.913894324853229.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:19,288] Trial 16 finished with value: 0.857795172863666 and parameters: {'n_estimators': 988, 'learning_rate': 0.007049444985728578, 'max_depth': 7, 'subsample': 0.6923885535423193, 'colsample_bytree': 0.7549504171857209, 'gamma': 2.815865048008675, 'reg_alpha': 1.1622447880162419, 'reg_lambda': 3.2633003736785398}. Best is trial 12 with value: 0.913894324853229.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:19,688] Trial 17 finished with value: 0.9217221135029354 and parameters: {'n_estimators': 803, 'learning_rate': 0.10610521225459299, 'max_depth': 5, 'subsample': 0.801122610205077, 'colsample_bytree': 0.8981861124334332, 'gamma': 0.8495003432388222, 'reg_alpha': 1.9046047676248357, 'reg_lambda': 0.7983614062945747}. Best is trial 17 with value: 0.9217221135029354.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:20,196] Trial 18 finished with value: 0.9204174820613177 and parameters: {'n_estimators': 624, 'learning_rate': 0.10688891336017815, 'max_depth': 4, 'subsample': 0.7779220226674763, 'colsample_bytree': 0.9065808448012767, 'gamma': 0.8298895526390054, 'reg_alpha': 2.0331874430714363, 'reg_lambda': 0.06381241345635547}. Best is trial 17 with value: 0.9217221135029354.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:20,571] Trial 19 finished with value: 0.9269406392694064 and parameters: {'n_estimators': 589, 'learning_rate': 0.13454850180798378, 'max_depth': 4, 'subsample': 0.8387076645185811, 'colsample_bytree': 0.8921190555928924, 'gamma': 0.7963950546813319, 'reg_alpha': 1.9641628422837936, 'reg_lambda': 0.04406347087278217}. Best is trial 19 with value: 0.9269406392694064.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:20,953] Trial 20 finished with value: 0.9204174820613177 and parameters: {'n_estimators': 452, 'learning_rate': 0.11616558386869416, 'max_depth': 4, 'subsample': 0.838204293709104, 'colsample_bytree': 0.8943471790753951, 'gamma': 0.6089797638100537, 'reg_alpha': 3.053319271069458, 'reg_lambda': 0.9480552345866949}. Best is trial 19 with value: 0.9269406392694064.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:21,323] Trial 21 finished with value: 0.923679060665362 and parameters: {'n_estimators': 625, 'learning_rate': 0.11987783721773816, 'max_depth': 4, 'subsample': 0.7855469499453085, 'colsample_bytree': 0.8975209838983887, 'gamma': 0.8225331985052231, 'reg_alpha': 1.9656922891801019, 'reg_lambda': 0.02446432820431678}. Best is trial 19 with value: 0.9269406392694064.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:21,701] Trial 22 finished with value: 0.9217221135029354 and parameters: {'n_estimators': 445, 'learning_rate': 0.12138765485903705, 'max_depth': 4, 'subsample': 0.8252139616191283, 'colsample_bytree': 0.999018483074145, 'gamma': 0.8444596243028419, 'reg_alpha': 1.9368689120546185, 'reg_lambda': 0.027927513024779672}. Best is trial 19 with value: 0.9269406392694064.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:22,109] Trial 23 finished with value: 0.9223744292237442 and parameters: {'n_estimators': 627, 'learning_rate': 0.11751393246205008, 'max_depth': 5, 'subsample': 0.7389695290627332, 'colsample_bytree': 0.8617699941311455, 'gamma': 1.1190243946441418, 'reg_alpha': 0.7044925348517896, 'reg_lambda': 0.8772493330333634}. Best is trial 19 with value: 0.9269406392694064.\n/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n warnings.warn(\n[I 2025-04-15 11:39:22,656] Trial 24 finished with value: 0.6373124592302675 and parameters: {'n_estimators': 607, 'learning_rate': 0.0036962842515378324, 'max_depth': 3, 'subsample': 0.8832556714562555, 'colsample_bytree': 0.8591462488560592, 'gamma': 1.7059101829385839, 'reg_alpha': 0.6052894595206415, 'reg_lambda': 0.4873760377539639}. Best is trial 19 with value: 0.9269406392694064.\n","output_type":"stream"},{"name":"stdout","text":"\n🔧 Best parameters for XGBoost:\n{'n_estimators': 589, 'learning_rate': 0.13454850180798378, 'max_depth': 4, 'subsample': 0.8387076645185811, 'colsample_bytree': 0.8921190555928924, 'gamma': 0.7963950546813319, 'reg_alpha': 1.9641628422837936, 'reg_lambda': 0.04406347087278217}\n✅ Best accuracy: 0.9269\n","output_type":"stream"}],"execution_count":81},{"cell_type":"code","source":"# Re-train and evaluate the models with optimized hyperparameters\nfor model_name, study in studies.items():\n print(f\"\\nEvaluating {model_name} with optimized parameters...\")\n \n # Get the best hyperparameters from the Optuna study\n best_params = study.best_params\n \n # Initialize the model with the optimized parameters\n if model_name == \"Logistic Regression\":\n model = LogisticRegression(**best_params, max_iter=1000)\n elif model_name == \"K-Nearest Neighbors\":\n model = KNeighborsClassifier(**best_params)\n elif model_name == \"Support Vector Machine\":\n model = SVC(**best_params)\n elif model_name == \"XGBoost\":\n model = xgb.XGBClassifier(**best_params, use_label_encoder=False, eval_metric='mlogloss')\n \n # Train the model on the scaled training data\n model.fit(X_train_scaled, y_train)\n \n # Make predictions on the test data\n y_pred = model.predict(X_test_scaled)\n \n # Evaluate the model performance\n accuracy = accuracy_score(y_test, y_pred)\n print(f\"✅ Test accuracy for {model_name}: {accuracy:.4f}\")\n print(classification_report(y_test, y_pred))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:39:44.506528Z","iopub.execute_input":"2025-04-15T11:39:44.506822Z","iopub.status.idle":"2025-04-15T11:39:50.750906Z","shell.execute_reply.started":"2025-04-15T11:39:44.506799Z","shell.execute_reply":"2025-04-15T11:39:50.750199Z"}},"outputs":[{"name":"stdout","text":"\nEvaluating Logistic Regression with optimized parameters...\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"✅ Test accuracy for Logistic Regression: 0.9395\n precision recall f1-score support\n\n 0 0.95 0.93 0.94 958\n 1 0.93 0.95 0.94 959\n\n accuracy 0.94 1917\n macro avg 0.94 0.94 0.94 1917\nweighted avg 0.94 0.94 0.94 1917\n\n\nEvaluating Support Vector Machine with optimized parameters...\n✅ Test accuracy for Support Vector Machine: 0.9390\n precision recall f1-score support\n\n 0 0.94 0.94 0.94 958\n 1 0.94 0.94 0.94 959\n\n accuracy 0.94 1917\n macro avg 0.94 0.94 0.94 1917\nweighted avg 0.94 0.94 0.94 1917\n\n\nEvaluating XGBoost with optimized parameters...\n✅ Test accuracy for XGBoost: 0.9207\n precision recall f1-score support\n\n 0 0.93 0.92 0.92 958\n 1 0.92 0.93 0.92 959\n\n accuracy 0.92 1917\n macro avg 0.92 0.92 0.92 1917\nweighted avg 0.92 0.92 0.92 1917\n\n","output_type":"stream"}],"execution_count":82},{"cell_type":"code","source":"study.best_params","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:42:12.907864Z","iopub.execute_input":"2025-04-15T11:42:12.908219Z","iopub.status.idle":"2025-04-15T11:42:12.914621Z","shell.execute_reply.started":"2025-04-15T11:42:12.908192Z","shell.execute_reply":"2025-04-15T11:42:12.913698Z"}},"outputs":[{"execution_count":83,"output_type":"execute_result","data":{"text/plain":"{'n_estimators': 589,\n 'learning_rate': 0.13454850180798378,\n 'max_depth': 4,\n 'subsample': 0.8387076645185811,\n 'colsample_bytree': 0.8921190555928924,\n 'gamma': 0.7963950546813319,\n 'reg_alpha': 1.9641628422837936,\n 'reg_lambda': 0.04406347087278217}"},"metadata":{}}],"execution_count":83},{"cell_type":"code","source":"import xgboost as xgb\nimport joblib\nimport pandas as pd\n\n# Final model parameters from Optuna\nfinal_params = {\n 'n_estimators': 589,\n 'learning_rate': 0.13454850180798378,\n 'max_depth': 4,\n 'subsample': 0.8387076645185811,\n 'colsample_bytree': 0.8921190555928924,\n 'gamma': 0.7963950546813319,\n 'reg_alpha': 1.9641628422837936,\n 'reg_lambda': 0.04406347087278217\n}\n\n# Initialize and train the final XGBoost model\nfinal_model = xgb.XGBClassifier(\n **final_params,\n use_label_encoder=False,\n eval_metric='logloss'\n)\n\n# Train on the entire training set\nfinal_model.fit(X_train_scaled, y_train)\n\n# Save the trained model\njoblib.dump(final_model, \"xgboost_sleep_detection_model.pkl\")\nprint(\" Final model saved as 'xgboost_sleep_detection_model.pkl'\")\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:43:36.02921Z","iopub.execute_input":"2025-04-15T11:43:36.029569Z","iopub.status.idle":"2025-04-15T11:43:36.328607Z","shell.execute_reply.started":"2025-04-15T11:43:36.029541Z","shell.execute_reply":"2025-04-15T11:43:36.32742Z"}},"outputs":[{"name":"stdout","text":" Final model saved as 'xgboost_sleep_detection_model.pkl'\n","output_type":"stream"}],"execution_count":84},{"cell_type":"code","source":"# Save the merged dataframe to CSV (assuming it's named `merged_df`)\nmerged_data.to_csv(\"preprocessed_accelerometer_data.csv\", index=False)\nprint(\" Merged dataframe saved as 'preprocessed_accelerometer_data.csv'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:43:44.88132Z","iopub.execute_input":"2025-04-15T11:43:44.881609Z","iopub.status.idle":"2025-04-15T11:43:45.036899Z","shell.execute_reply.started":"2025-04-15T11:43:44.881586Z","shell.execute_reply":"2025-04-15T11:43:45.036209Z"}},"outputs":[{"name":"stdout","text":" Merged dataframe saved as 'preprocessed_accelerometer_data.csv'\n","output_type":"stream"}],"execution_count":86},{"cell_type":"code","source":"# Save the merged dataframe to CSV (assuming it's named `merged_df`)\nmerged_data1.to_csv(\"preprocessed_accelerometer_data_left_join.csv\", index=False)\nprint(\" Merged dataframe saved as 'preprocessed_accelerometer_data.csv'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T11:44:02.974411Z","iopub.execute_input":"2025-04-15T11:44:02.974705Z","iopub.status.idle":"2025-04-15T12:04:13.459805Z","shell.execute_reply.started":"2025-04-15T11:44:02.974683Z","shell.execute_reply":"2025-04-15T12:04:13.459022Z"}},"outputs":[{"name":"stdout","text":" Merged dataframe saved as 'preprocessed_accelerometer_data.csv'\n","output_type":"stream"}],"execution_count":87},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}