{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":84493,"databundleVersionId":9871156,"sourceType":"competition"}],"dockerImageVersionId":30786,"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","execution":{"iopub.status.busy":"2024-11-26T08:27:38.0328Z","iopub.execute_input":"2024-11-26T08:27:38.033855Z","iopub.status.idle":"2024-11-26T08:27:39.263658Z","shell.execute_reply.started":"2024-11-26T08:27:38.033802Z","shell.execute_reply":"2024-11-26T08:27:39.262247Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/jane-street-real-time-market-data-forecasting/responders.csv\n/kaggle/input/jane-street-real-time-market-data-forecasting/sample_submission.csv\n/kaggle/input/jane-street-real-time-market-data-forecasting/features.csv\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=4/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=5/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=6/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=3/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=1/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=8/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=2/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=0/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=7/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=9/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/lags.parquet/date_id=0/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/test.parquet/date_id=0/part-0.parquet\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/jane_street_gateway.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/jane_street_inference_server.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/__init__.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/templates.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/base_gateway.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/relay.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/kaggle_evaluation.proto\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/__init__.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/generated/kaggle_evaluation_pb2.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/generated/kaggle_evaluation_pb2_grpc.py\n/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/generated/__init__.py\n","output_type":"stream"}],"execution_count":1},{"cell_type":"markdown","source":"- 比赛数据集由一组时间序列组成，有79个特征和9个响应者（都已匿名）\n- 目标，预测响应者responder_6","metadata":{}},{"cell_type":"code","source":"#读取文件\nfore_path='/kaggle/input/jane-street-real-time-market-data-forecasting/'\ntrain_path='/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/'\ncore_path='/kaggle/input/jane-street-real-time-market-data-forecasting/kaggle_evaluation/core/'\nresponders=pd.read_csv(fore_path+'responders.csv')\nsubmission=pd.read_csv(fore_path+'sample_submission.csv')\nfeatures=pd.read_csv(fore_path+'features.csv')\nlags=pd.read_parquet(fore_path+'lags.parquet/date_id=0/part-0.parquet')\ntest=pd.read_parquet(fore_path+'test.parquet/date_id=0/part-0.parquet')\ntrains={}\n\n#\nfor i in range(10):\n  train=pd.read_parquet(train_path+f\"partition_id={i}/part-0.parquet\")\n  trains[f'train_{i}'] = train","metadata":{"execution":{"iopub.status.busy":"2024-11-26T08:27:39.266043Z","iopub.execute_input":"2024-11-26T08:27:39.266663Z","iopub.status.idle":"2024-11-26T08:29:06.11062Z","shell.execute_reply.started":"2024-11-26T08:27:39.266613Z","shell.execute_reply":"2024-11-26T08:29:06.109636Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"# EDA","metadata":{}},{"cell_type":"code","source":"import polars as pl\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\nimport seaborn as sns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-26T08:29:06.112145Z","iopub.execute_input":"2024-11-26T08:29:06.112447Z","iopub.status.idle":"2024-11-26T08:29:07.855458Z","shell.execute_reply.started":"2024-11-26T08:29:06.112417Z","shell.execute_reply":"2024-11-26T08:29:07.854546Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"features.info()\nfeatures","metadata":{"execution":{"iopub.status.busy":"2024-11-26T08:29:07.857935Z","iopub.execute_input":"2024-11-26T08:29:07.858513Z","iopub.status.idle":"2024-11-26T08:29:07.918089Z","shell.execute_reply.started":"2024-11-26T08:29:07.858468Z","shell.execute_reply":"2024-11-26T08:29:07.916876Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 79 entries, 0 to 78\nData columns (total 18 columns):\n #   Column   Non-Null Count  Dtype \n---  ------   --------------  ----- \n 0   feature  79 non-null     object\n 1   tag_0    79 non-null     bool  \n 2   tag_1    79 non-null     bool  \n 3   tag_2    79 non-null     bool  \n 4   tag_3    79 non-null     bool  \n 5   tag_4    79 non-null     bool  \n 6   tag_5    79 non-null     bool  \n 7   tag_6    79 non-null     bool  \n 8   tag_7    79 non-null     bool  \n 9   tag_8    79 non-null     bool  \n 10  tag_9    79 non-null     bool  \n 11  tag_10   79 non-null     bool  \n 12  tag_11   79 non-null     bool  \n 13  tag_12   79 non-null     bool  \n 14  tag_13   79 non-null     bool  \n 15  tag_14   79 non-null     bool  \n 16  tag_15   79 non-null     bool  \n 17  tag_16   79 non-null     bool  \ndtypes: bool(17), object(1)\nmemory usage: 2.1+ KB\n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"       feature  tag_0  tag_1  tag_2  tag_3  tag_4  tag_5  tag_6  tag_7  tag_8  \\\n0   feature_00  False  False   True  False  False  False  False  False  False   \n1   feature_01  False  False   True  False  False  False  False  False  False   \n2   feature_02  False  False   True  False  False  False  False  False  False   \n3   feature_03  False  False   True  False  False  False  False  False  False   \n4   feature_04  False  False   True  False  False  False  False  False  False   \n..         ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n74  feature_74  False  False  False  False  False  False  False  False   True   \n75  feature_75  False  False  False  False  False  False  False  False   True   \n76  feature_76  False  False  False  False  False  False  False  False   True   \n77  feature_77  False  False  False  False  False  False  False  False   True   \n78  feature_78  False  False  False  False  False  False  False  False   True   \n\n    tag_9  tag_10  tag_11  tag_12  tag_13  tag_14  tag_15  tag_16  \n0   False   False   False   False   False    True   False    True  \n1   False   False   False   False    True    True   False    True  \n2   False   False   False    True   False   False   False    True  \n3   False   False   False   False    True   False   False    True  \n4   False   False   False    True    True   False   False    True  \n..    ...     ...     ...     ...     ...     ...     ...     ...  \n74  False   False   False   False   False    True   False   False  \n75  False   False   False    True   False   False   False   False  \n76  False   False   False    True   False   False   False   False  \n77  False   False   False   False    True   False   False   False  \n78  False   False   False   False    True   False   False   False  \n\n[79 rows x 18 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature</th>\n      <th>tag_0</th>\n      <th>tag_1</th>\n      <th>tag_2</th>\n      <th>tag_3</th>\n      <th>tag_4</th>\n      <th>tag_5</th>\n      <th>tag_6</th>\n      <th>tag_7</th>\n      <th>tag_8</th>\n      <th>tag_9</th>\n      <th>tag_10</th>\n      <th>tag_11</th>\n      <th>tag_12</th>\n      <th>tag_13</th>\n      <th>tag_14</th>\n      <th>tag_15</th>\n      <th>tag_16</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>feature_00</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>feature_01</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>feature_02</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>feature_03</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>feature_04</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>feature_74</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>feature_75</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>feature_76</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>feature_77</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>feature_78</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>79 rows × 18 columns</p>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"#灰度处理\nplt.figure(figsize=(20, 10))\nplt.imshow(features.iloc[:, 1:].T.values, cmap=\"gray\")\nplt.xlabel(\"feature_00  ~  feature_78\")\nplt.ylabel(\"tag_0  ~  tag_16\")\nplt.yticks(np.arange(17))\nplt.xticks(np.arange(79))\nplt.grid()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-11-26T08:29:28.472404Z","iopub.execute_input":"2024-11-26T08:29:28.4728Z","iopub.status.idle":"2024-11-26T08:29:29.198446Z","shell.execute_reply.started":"2024-11-26T08:29:28.472764Z","shell.execute_reply":"2024-11-26T08:29:29.197394Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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10))\nsns.heatmap(features[[ f\"tag_{no}\" for no in range(0,17,1) ] ].T.corr(), square=True, cmap=\"coolwarm\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-11-26T08:30:28.693758Z","iopub.execute_input":"2024-11-26T08:30:28.694208Z","iopub.status.idle":"2024-11-26T08:30:29.422876Z","shell.execute_reply.started":"2024-11-26T08:30:28.69417Z","shell.execute_reply":"2024-11-26T08:30:29.421827Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"responders.info()\nresponders","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:31:40.773325Z","iopub.execute_input":"2024-11-19T11:31:40.775451Z","iopub.status.idle":"2024-11-19T11:31:40.834413Z","shell.execute_reply.started":"2024-11-19T11:31:40.775371Z","shell.execute_reply":"2024-11-19T11:31:40.833243Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 9 entries, 0 to 8\nData columns (total 6 columns):\n #   Column     Non-Null Count  Dtype \n---  ------     --------------  ----- \n 0   responder  9 non-null      object\n 1   tag_0      9 non-null      bool  \n 2   tag_1      9 non-null      bool  \n 3   tag_2      9 non-null      bool  \n 4   tag_3      9 non-null      bool  \n 5   tag_4      9 non-null      bool  \ndtypes: bool(5), object(1)\nmemory usage: 245.0+ bytes\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"     responder  tag_0  tag_1  tag_2  tag_3  tag_4\n0  responder_0   True  False   True  False  False\n1  responder_1   True  False  False   True  False\n2  responder_2   True   True  False  False  False\n3  responder_3  False  False   True  False   True\n4  responder_4  False  False  False   True   True\n5  responder_5  False   True  False  False   True\n6  responder_6  False  False   True  False  False\n7  responder_7  False  False  False   True  False\n8  responder_8  False   True  False  False  False","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>responder</th>\n      <th>tag_0</th>\n      <th>tag_1</th>\n      <th>tag_2</th>\n      <th>tag_3</th>\n      <th>tag_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>responder_0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>responder_1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>responder_2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>responder_3</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>responder_4</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>responder_5</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>responder_6</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>responder_7</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>responder_8</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"# corr between responder_XX and responder_YY\nsns.heatmap(responders[[ f\"tag_{no}\" for no in range(0,5,1) ] ].T.corr(),  annot=True, square=True, cmap=\"coolwarm\")\nplt.xlabel(\"responder_0  ~  responder_8\")\nplt.ylabel(\"responder_0  ~  responder_8\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-11-26T08:30:54.439449Z","iopub.execute_input":"2024-11-26T08:30:54.439856Z","iopub.status.idle":"2024-11-26T08:30:54.942079Z","shell.execute_reply.started":"2024-11-26T08:30:54.439819Z","shell.execute_reply":"2024-11-26T08:30:54.940857Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAgcAAAG2CAYAAADr3yV2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAClxElEQVR4nOzdeVxU1f8/8NedYXZgAJFNRVQUJFEU1EQRM8TdXCozy+Wj+c3EUmxDLctUysz089Hyp+bHpM0SRf1YKppLJrmjlIKAsYjsO8wwM8yd3x/UwMQMMDjjMPB+Ph7zeDhn7rn3zZlx7nvOOfdcRqPRaEAIIYQQ8heOpQMghBBCSNtCyQEhhBBCdFByQAghhBAdlBwQQgghRAclB4QQQgjRQckBIYQQQnRQckAIIYQQHZQcEEIIIUQHJQeEEEII0UHJASGEEEJ0UHJACCGEPELnz5/H5MmT4eHhAYZhEBcX12yds2fPYtCgQRAIBPD29sbevXvNGiMlB4QQQsgjVF1djQEDBmD79u0t2v7PP//ExIkT8cQTTyAxMRHLli3DwoULceLECbPFyNCNlwghhBDLYBgGhw4dwtSpUw1u89Zbb+HYsWP4/ffftWXPPfccysrKcPz4cbPERT0HhBBCyENQKBSoqKjQeSgUCpPtPyEhAWFhYTplY8eORUJCgsmO8U82ZtuzBR3j+Vg6hGb1Tf7R0iGQR2jOsvuWDqFd+Cn8f5YOoVlRqtWWDqFd2BYpNfsxTHWuuLJqFt5//32dsjVr1uC9994zyf7z8vLg6uqqU+bq6oqKigrI5XKIRCKTHKehdpkcEEIIIc1heIxJ9hMVFYXIyEidMoFAYJJ9WwolB4QQQshDEAgEZk0G3NzckJ+fr1OWn58Pe3t7s/QaAJQcEEII6aA4NqbpOTC3YcOG4ccfdYei4+PjMWzYMLMdk5IDQgghHRLDs8yc/KqqKqSlpWmf//nnn0hMTISTkxM8PT0RFRWFnJwc7Nu3DwDw8ssvY9u2bXjzzTfxr3/9Cz///DO+//57HDt2zGwx0tUKhBBCyCN09epVDBw4EAMHDgQAREZGYuDAgXj33XcBALm5ucjKytJu36NHDxw7dgzx8fEYMGAAPvnkE+zevRtjx441W4zUc0AIIaRDstSwwqhRo9DUEkP6Vj8cNWoUbty4YcaodFFyQAghpEMy1dUK7RENKxBCCCFEB/UcEEII6ZCs5WoFS6DkgBBCSIdEwwqG0bACIYQQQnRQzwEhhJAOiYYVDKPkgBBCSIfEcCk5MISSA0IIIR0Sh5IDgyg5aILTiCD0XLEA0kH9IPRwwdUZryD/yOlHdvyjR4/iQGwsSktL0bNHDyxevBg+PvpvMZqZmYmYmBikpqWhoKAAixYtwrSpU3W2mTtvHgoKChrVnTRxIpYsWUIxWjBGAFgw2wuTw91gJ7FB0p0KbPosFfdz5U3WcXbiY/G8nng80AlCAQf3c+XYsDUFKWlVAIALR0P11tu+Jx3fHjL+NtJtPUaefzD4g0LBiO3AFuWi5nwc2PxswxX4QgiGjYdNr35ghGJoKkpR88sRqDOTAQBcjx7gDxoFTucu4NhKIT+2F7X3/jAqJn0mBgsQ3I8PkZDBvRw19p+Wo7CMbVHdMYMFeCpEiDPXFYg9W6MtH+7PQ5AvH11duBAJGLyxvRxyRfuOkZgPJQdN4ErEqLiVguy9sQg6sP2RHvvcuXPYuWsXlkZEwMfXF3FxcVj9zjvYtXMnHBwcGm1fo1DAzd0dI0JCsHPnTr373Lp1K1i1Wvs8MzMTK1etQkhICMVowRgBYPaMbnh6Uhes35KM3PwaLJzthc1r/fHCK1egVOlfSc1OYoPPNw7E9aQyvP5eEsoqVOjqIUJlVa12mykvXtSp83igE95+1QfnLha1uxhteg+AIGQyas7Egs3LAi8gBOIpC1H91UZo5NWNK3C4EE9dBI28CjU/xYCtKgfHzhEaZYNkh8eHuugBVLevQDRxrlHxGBI2mI/QAAFiTshQXM5iUrAQS6ZLsO7LStSqm67r6crF8P583C9svCHPhsHtjFrczqjFUyHCdh+jKTAc6jkwhK5WaELhifO4u2YL8g+feuTHPnToEMaPG4fw8HB09/TE0ogICAQCnDx5Uu/2Pn36YOGCBRgVGgoej6d3GwepFE5OTtrHpcuX4e7uDn9/f4rRgjECwDNTumDf95m4cKkY6RnVWPdpMjo5CRDyuLPBOrOf7oaCIgWit6bgTmolcvNrcOVGKR7k1f9SKylT6TxGPO6M60lleJBfY3C/1hojP2AkVH9cQu2dq2BLC6A4cxCaWhV4fkP0bs/zGwxGKIb82F6oczOgqSyF+sE9sEW52m3UmSlQ/nYCtfd+NyqWpjwxUIATl2qQlF6LB0Us9h2XQWrLYIC3/s+b9u/jAfMmiPBtvAzymsbJ2NkbSsRfUSAjt1ZP7fYXoykwXI5JHu2Rxf+qoqIibNy4EdOmTcOwYcMwbNgwTJs2DR9//DEKCwstHZ5FqFQqpKalISAgQFvG4XAQEBCAO8nJJjvGmTNnEB4eDoYxPnumGE0TIwB4uArh7CTAlcRSbVm1TI3bdyvQz9feYL3hQzohOa0SH7zlh6Mxw7BnyyBMDnczuL2jAw/BQU44Fp/X/mLkcMFx6QJ1dmqDQg3U2anguHXXW8Wmhx/UuZkQhE6DZMG7ED+/Avyg0UAr38eW6CRlILXlIDmr/uRYowQy8tTwcuc2WXfmaBF+v1eLlKxmfrp3gBiJ+Vk0Obhy5Qr69OmDf//735BKpRg5ciRGjhwJqVSKf//73/D19cXVq1ctGaJFVFRUgGVZODo66pQ7OjigtKTEJMdISEhAVVUVxoSFtao+xVjnYWMEACdHPgCgtEylU15aptS+po+HmwhTx3sg+4EckWuSEPdTLpYt8sa40a56tx8/2g0yuRrnLhqfdLf1GBmRBAyHC1ZWpVOukVWBI7bTX0faCTbe/gDDgfzIF1BeOQV+wEjwB7f+vWyOvbjuK7dSpvururKahb3EcFIS6MNDN1cujlwwvsfHWNYQo6lwuIxJHu2RReccLF26FM888wx27NjR6FeXRqPByy+/jKVLlyIhIcHgPhQKBRQK3RktKg0LHmPxTpE27cTJkwgKCkKnTp0sHYpB7TXGMaEueGNJH+3zN9cmterYHAZITqvEzpg/AQCp96rQo7sYU8d74PjP+Y22nzjGDSfPFhicH2BtMT4sBgw08ioozhwANBqwhTlQSKTgDwqF8nK8SY4R5MvDrDCR9vnncXrmPjTDwZbBjFFCbIutbna8vzWsIUZzoTkHhlk0Obh58yb27t2rtzuWYRgsX75ce79rQ6Kjo/H+++/rlM1inDCba3gctK2zt7cHh8NBaWmpTnlpWRkcnZweev/5+flITEzE6lWrWr0PirH1MV64XIzbd+t7xPi8ukTW0YGH4lKlttzRgY+0e1WN6v+tuFSJjGyZTllmtgyjgjs32ra/nxTdu4qx5qPb7SbGhjTyamhYNThiWzScT8+IbcHKKvXWYWUVAMsCDW6dy5bmgyOxBzhcgH34s1xSugoZefX7sfmrV95OzKCiuv64dhIO7hfoP56nKxf2Eg7eesFWW8blMOjVlYuRAXws21qBJu7+2y5iJI+eRZMDNzc3XL58Gb6+vnpfv3z5Mlxd9Xc//i0qKgqRkZE6ZT87BZosRkvg8Xjo7e2NxJs3ERwcDABgWRaJiYmYMnnyQ+8/Pj4eUqkUQ4bon6hFMZo3RrlcjRy57pdsUYkCQQMckfZn3a82sYgLvz72iPvxgcH9JN0ph2cXsU5Zty5i5BU07tadFO6G5NRKpGW07FehNcSog1WDLcgBt6t3g0sNGXC7eUN166LeKurcDPD6DATAAKg7c3EcOoOtKjdJYgAAChWg+Mflf+VVLHw8bZBTWJdkCfmAlxsXF24q9e0CKVm1WP+lboLzwlgR8ktYxF9RPPRJ1xpiNJf2OiRgChZNDl5//XUsWrQI165dw5NPPqlNBPLz83H69Gns2rULmzZtanIfAoEAAoFAp8xUQwpciRgSb0/tc3GPrrAf4AtlSTlqsnObqPnwpk2bhk82b0bv3r3h06cP4g4fhkKhwJgxYwAAmzZtQqdOnTB//nwAdRPjsrKyAAC1tbUoLi5Geno6RCIRPDw8tPtlWRbx8fEICwsDl9v05CKK8dHECAA/HMnB3JmeyH4gr7tM8AUvFJco8Mtv9ZfzbVnXH+cTinDwWN3JeP/hHOzYGIAXn/HEzxcK4NfHHlPGumPjtrs6+xaLuHhieGds+yK9XceoTDwPYdhMqAvug83PBi8gBIwNH6rbVwAAwjHPga0qhzLhJwCAKikB/P7DIRg5Bcpbv4Lj4Ax+0Giobl6o3ymPD460vheSsXcCx9kDmhoZNFVlrYrzzA0Fxg0VorCURXEFi4nBQpRXaXAzrX4+x9KnJbiZpsL5RCUUKiC3WPfkrVQB1TUanXI7MQN7CQNnh7rvPw9nLmqUGpRWaiDTc+WAtcdoCrRComEWTQ6WLFkCZ2dnfPrpp/jss8+g/uvacS6Xi8DAQOzduxfPPvusxeKTBvbDsNMx2ud+m1YCALL3HcStBVFmPXZoaCjKKyrwVUwMSkpL0atnT3ywdq12cl1BYSEYTn0SVFJSgoilS7XPY2NjERsbC39/f2z86CNt+Y3ERBQUFiL8r5MjxWj5GAHg69hsCIVcvBnRB7YSGyTdLseKNUk6Y+9d3ERwsK+/lCw5tRIrN/yB/5vTA/Oe647cfDn+vSsN8ed0F2gKG+kChgFOnW+8cFN7irE29SYUIgkEQ8eCkdiBLXwA2ZHd0Mjrhj0YWwdwGvyE1VSVQ3Z4N4QhkyGZFQlNdQVUNy9Aee2MdhuuS1eIpy/WPheGTAEAqO5cRc2p/a2K89QVJQQ8BrPGiCASMEjPUeOzg7pj9c5SDmxFxp24QgbwMWFY/doBy2fWdfHHHJfh0m2VoWpWGyMxL0ajaRsdPiqVCkVFdb9AnJ2dDV5j3hLHePpXv2tL+ib/aOkQyCM0Z5nxqxGSxn4K/5+lQ2hWlGq1pUNoF7ZFSs1+jGtPDDfJfgLP/GqS/bQlbWaFRB6PB3d3d0uHQQghpIOgqxUMazPJASGEEPIo0YREw2gxAEIIIYTooJ4DQgghHRINKxhGyQEhhJAOqeGVSkQXtQwhhBBCdFDPASGEkA6JhhUMo+SAEEJIh0RXKxhGwwqEEEII0UE9B4QQQjokGlYwjJIDQgghHRJdrWAYtQwhhBBCdFDPASGEkA6JhhUMo+SAEEJIh0TJgWE0rEAIIaRDYjiMSR6tsX37dnh5eUEoFGLo0KG4fPlyk9tv2bIFPj4+EIlE6NatG5YvX46amppWHbsl2mXPQd/kHy0dQrPu+E6wdAgtYg1tOWfZfUuH0Kx9W7paOoRmWUM7RqlWWzqEZkXz1lk6hGZZQzu2Z/v370dkZCR27NiBoUOHYsuWLRg7dixSUlLg4uLSaPtvvvkGb7/9Nvbs2YPg4GDcvXsX8+bNA8Mw2Lx5s1lipJ4DQgghHRLD4ZjkYazNmzfjpZdewvz58+Hn54cdO3ZALBZjz549ere/ePEihg8fjueffx5eXl4IDw/HrFmzmu1teBiUHBBCCOmQOFzGJA+FQoGKigqdh0Kh0HtMpVKJa9euISwsrD4ODgdhYWFISEjQWyc4OBjXrl3TJgP37t3Djz/+iAkTzNcDTckBIYQQ8hCio6MhlUp1HtHR0Xq3LSoqglqthqurq065q6sr8vLy9NZ5/vnnsXbtWowYMQI8Hg+9evXCqFGjsHLlSpP/LX+j5IAQQkiHZKoJiVFRUSgvL9d5REVFmSzOs2fPYsOGDfjss89w/fp1HDx4EMeOHcMHH3xgsmP8U7uckEgIIYQ0x1QrJAoEAggEghZt6+zsDC6Xi/z8fJ3y/Px8uLm56a3zzjvv4MUXX8TChQsBAP7+/qiursaiRYuwatUqcMyw0iP1HBBCCCGPCJ/PR2BgIE6fPq0tY1kWp0+fxrBhw/TWkclkjRIALpcLANBoNGaJk3oOCCGEdEiWWgQpMjISc+fORVBQEIYMGYItW7aguroa8+fPBwDMmTMHXbp00c5bmDx5MjZv3oyBAwdi6NChSEtLwzvvvIPJkydrkwRTo+SAEEJIh2Sp5GDmzJkoLCzEu+++i7y8PAQEBOD48ePaSYpZWVk6PQWrV68GwzBYvXo1cnJy0LlzZ0yePBnr1683W4yUHBBCCCGPWEREBCIiIvS+dvbsWZ3nNjY2WLNmDdasWfMIIvvrmI/sSIQQQkgbQrdsNoySA0IIIR0S3XjJMEoOCCGEdEjUc2AYtQwhhBBCdFDPASGEkI6JoWEFQzpkcnD06FEciI1FaWkpevbogcWLF8PHx0fvtpmZmYiJiUFqWhoKCgqwaNEiTJs6VWebufPmoaCgoFHdSRMnYsmSJeb4E7ScRgSh54oFkA7qB6GHC67OeAX5R043X9FErKUtF8z2wuRwN9hJbJB0pwKbPkvF/Vx5k3WcnfhYPK8nHg90glDAwf1cOTZsTUFKWhUA4MLRUL31tu9Jx7eHjLv9MbVjY61pRwCYGCxAcD8+REIG93LU2H9ajsIytkV1xwwW4KkQIc5cVyD2bI22fLg/D0G+fHR14UIkYPDG9nLI9d9Xp1k8/2DwB4WCEduBLcpFzfk4sPnZhivwhRAMGw+bXv3ACMXQVJSi5pcjUGcmAwC4Hj3AHzQKnM5dwLGVQn5sL2rv/dG64Bpo6+1oCjTnwLAOlxycO3cOO3ftwtKICPj4+iIuLg6r33kHu3buhIODQ6PtaxQKuLm7Y0RICHbu3Kl3n1u3bgWrVmufZ2ZmYuWqVQgJCTHXn6HFlYhRcSsF2XtjEXRgu9mP15C1tOXsGd3w9KQuWL8lGbn5NVg42wub1/rjhVeuQKnSv7qYncQGn28ciOtJZXj9vSSUVajQ1UOEyqpa7TZTXryoU+fxQCe8/aoPzl0sMio+akfTtCMAhA3mIzRAgJgTMhSXs5gULMSS6RKs+7ISteqm63q6cjG8Px/3CxtvyLNhcDujFrczavFUiNDouP5m03sABCGTUXMmFmxeFngBIRBPWYjqrzZCI69uXIHDhXjqImjkVaj5KQZsVTk4do7QKBskZDw+1EUPoLp9BaKJc1sdW0NtvR2J+XW4OQeHDh3C+HHjEB4eju6enlgaEQGBQICTJ0/q3d6nTx8sXLAAo0JDwePx9G7jIJXCyclJ+7h0+TLc3d3h7+9vzj8FAFB44jzurtmC/MOnzH6sf7KWtnxmShfs+z4TFy4VIz2jGus+TUYnJwFCHnc2WGf2091QUKRA9NYU3EmtRG5+Da7cKMWDvPpfQSVlKp3HiMedcT2pDA/yawzuVx9qR9O0IwA8MVCAE5dqkJReiwdFLPYdl0Fqy2CAt/52+hufB8ybIMK38TLIaxonOmdvKBF/RYGM3Fo9tVuOHzASqj8uofbOVbClBVCcOQhNrQo8vyF6t+f5DQYjFEN+bC/UuRnQVJZC/eAe2KJc7TbqzBQofzuB2nu/P1RsDbX1djQVhsMxyaM9ap9/lQEqlQqpaWkICAjQlnE4HAQEBOBOcrLJjnHmzBmEh4eDacfjWdbSlh6uQjg7CXAlsVRbVi1T4/bdCvTztTdYb/iQTkhOq8QHb/nhaMww7NkyCJPD9d8UBQAcHXgIDnLCsXj9t1w1hNpRV2vbEQA6SRlIbTlIzqo/8dQogYw8Nbzcm15iduZoEX6/V4uUrGZ+Fj8MDhccly5QZ6c2KNRAnZ0Kjlt3vVVsevhBnZsJQeg0SBa8C/HzK8APGm3WsfI2344mZKq7MrZHHSo5qKioAMuycHR01Cl3dHBAaUmJSY6RkJCAqqoqjAkLM8n+2ipraUsnRz4AoLRMpVNeWqbUvqaPh5sIU8d7IPuBHJFrkhD3Uy6WLfLGuNGuercfP9oNMrka5y4WGhUftaOu1rYjANiL677OKmW6v1grq1nYSwx/gQf68NDNlYsjF4zvqTAGI5KA4XDByqp0yjWyKnDEdvrrSDvBxtsfYDiQH/kCyiunwA8YCf5g832/tPV2JI9Gm55zkJ2djTVr1mDPnj0Gt1EoFFAoFI3KWnr7TFM7cfIkgoKC0KlTJ4scvz1pTVuOCXXBG0v6aJ+/uTapVcfmMEByWiV2xvwJAEi9V4Ue3cWYOt4Dx3/Ob7T9xDFuOHm2wODYuyW113YM8uVhVphI+/zzOD1j9s1wsGUwY5QQ22Krmx1LtwQGDDTyKijOHAA0GrCFOVBIpOAPCoXycrxJjtER2tGQ9jokYAptOjkoKSnBl19+2WRyEB0djffff1+n7NWlS/Haa6812tbe3h4cDgelpaU65aVlZXB0cnroePPz85GYmIjVq1Y99L7aurbalhcuF+P23ava53xe3X9+RwceikuV2nJHBz7S7lU1qv+34lIlMrJlOmWZ2TKMCu7caNv+flJ07yrGmo9uGxUrQO3YkLHtmJSuQkZe/ZnI5q8ebzsxg4rq+uTCTsLB/QL9ZyxPVy7sJRy89YKttozLYdCrKxcjA/hYtrUCprojrkZeDQ2rBkdsi4Zz/hmxLVhZpd46rKwCYFk0DIItzQdHYg9wuAD78Gdia2tHU2qvQwKmYNHk4MiRI02+fu/evWb3ERUVhcjISJ2ynPv6L3/i8Xjo7e2NxJs3ERwcDKDuPtqJiYmYMnlyC6M2LD4+HlKpFEOG6J9c1J601baUy9XIket+gRWVKBA0wBFpf9b9IhKLuPDrY4+4Hx8Y3E/SnXJ4dhHrlHXrIkZeQeMu00nhbkhOrURahvG/uKgd6xnbjgoVoPjHpXXlVSx8PG2QU1iXwAj5gJcbFxduKvXtAilZtVj/pe6J+YWxIuSXsIi/ojDtCY1Vgy3IAberd4NLDRlwu3lDdeui3irq3Azw+gwEwACoC4bj0BlsVblJEgPACtuRPBIWTQ6mTp0KhmGgaeKT09wEKoFA0GgIoaiJIYVp06bhk82b0bt3b/j06YO4w4ehUCgwZswYAMCmTZvQqVMn7X21VSoVsrKyAAC1tbUoLi5Geno6RCIRPDw8tPtlWRbx8fEICwsz2/219eFKxJB4e2qfi3t0hf0AXyhLylGTndtEzYdnLW35w5EczJ3piewH8rpL8F7wQnGJAr/8Vn+p3JZ1/XE+oQgHj9Wd6PYfzsGOjQF48RlP/HyhAH597DFlrDs2brurs2+xiIsnhnfGti/SWx0ftaNp2hEAztxQYNxQIQpLWRRXsJgYLER5lQY30+rnSix9WoKbaSqcT1RCoQJyi3VPjEoVUF2j0Sm3EzOwlzBwdqjrQfFw5qJGqUFppQYyPbPyDVEmnocwbCbUBffB5meDFxACxoYP1e0rAADhmOfAVpVDmfATAECVlAB+/+EQjJwC5a1fwXFwBj9oNFQ3L9TvlMcHR1p/xQhj7wSOswc0NTJoqspaHFtDbb0dTYV6DgyzaHLg7u6Ozz77DE899ZTe1xMTExEYGGjSY4aGhqK8ogJfxcSgpLQUvXr2xAdr12onhBUUFuqMQ5WUlCBi6VLt89jYWMTGxsLf3x8bP/pIW34jMREFhYUI/+sL/VGRBvbDsNMx2ud+m1YCALL3HcStBVFmPba1tOXXsdkQCrl4M6IPbCU2SLpdjhVrknTGtbu4ieBgX3+ZVnJqJVZu+AP/N6cH5j3XHbn5cvx7Vxriz+kuLBQ20gUMA5w633jBoZaidjRNOwLAqStKCHgMZo0RQSRgkJ6jxmcHdcfBnaUc2IqMOymEDOBjwrD66/KXz6zrPo85LsOl2ypD1RqpTb0JhUgCwdCxYCR2YAsfQHZkNzTyuqEZxtYBnAY/ljRV5ZAd3g1hyGRIZkVCU10B1c0LUF47o92G69IV4umLtc+FIVMAAKo7V1Fzar9Rf+ff2no7mgzNOTCI0TT1s93MpkyZgoCAAKxdu1bv6zdv3sTAgQPBsi1bletv99If7tfHo3DHd4KlQ2iRvsk/WjqEZs1ZZvwqeo/avi1dLR1Cs6yhHQOeCLB0CM2K5q2zdAjNilKttnQIzdoWKTX7MQpXzzfJfjqv+69J9tOWWLTn4I033kB1teHxRW9vb5w5c8bg64QQQggxPYsmB80t5SqRSBAaqn/ddUIIIeRh0KWMhrXpSxkJIYQQc6EJiYZR2kQIIYQQHdRzQAghpGOiYQWDKDkghBDSIdGwgmGUNhFCCCFEB/UcEEII6ZAYhn4fG0LJASGEkI6JhhUMorSJEEIIITqo54AQQkiHRIsgGUbJASGEkA6JrlYwjJIDQgghHRNNSDSIWoYQQgghOqjngBBCSIdEwwqGUXJgIX2Tf7R0CC1yx3eCpUNo3ridlo6gWXOW3bd0CM3at6WrpUNo1ubDlo6geVGq1ZYOoVnRvHWWDqEFPjb/IWhCokHUMoQQQgjRQT0HhBBCOiSGoWEFQ6jngBBCSMfE4Zjm0Qrbt2+Hl5cXhEIhhg4disuXLze5fVlZGZYsWQJ3d3cIBAL06dMHP/5ovuFpo3oOCgoK8PvvvyMwMBBSqRT5+fn48ssvwbIsJk6cCH9/f3PFSQghhLQL+/fvR2RkJHbs2IGhQ4diy5YtGDt2LFJSUuDi4tJoe6VSiTFjxsDFxQUHDhxAly5dkJmZCQcHB7PF2OLk4OzZs5g0aRJkMhlcXV1x/PhxTJo0CSKRCBwOB++99x6OHDmC8PBwswVLCCGEmIqlrlbYvHkzXnrpJcyfPx8AsGPHDhw7dgx79uzB22+/3Wj7PXv2oKSkBBcvXgSPxwMAeHl5mTXGFveHvPPOO5g3bx4qKiqwYsUKTJw4EU899RTu3r2L5ORkLF26FO+//745YyWEEEJMh+GY5KFQKFBRUaHzUCgUeg+pVCpx7do1hIWFacs4HA7CwsKQkJCgt86RI0cwbNgwLFmyBK6urujXrx82bNgAtVptlmYBjEgObt26heXLl8PW1hbLli1Dfn4+Fi5cqH190aJF+OOPP8wSJCGEEGJyHMYkj+joaEilUp1HdHS03kMWFRVBrVbD1dVVp9zV1RV5eXl669y7dw8HDhyAWq3Gjz/+iHfeeQeffPIJ1q0z3yWpLR5W4PP5qKmpAVCX+bAsq30OAHK5XNvdQQghhHQUUVFRiIyM1CkTCAQm2z/LsnBxccHOnTvB5XIRGBiInJwcfPzxx1izZo3JjtNQi3sOhg8fjrfffhu//vorli9fjkGDBmHdunWorq6GTCbDBx98gKCgILMESQghhJgaw3BM8hAIBLC3t9d5GEoOnJ2dweVykZ+fr1Oen58PNzc3vXXc3d3Rp08fcLlcbVnfvn2Rl5cHpVJpugZpoMXJwccff4yUlBSEhITgl19+QVxcHLhcLhwcHCCVSnHu3DmsX7/eLEESQgghJmeiYQVj8Pl8BAYG4vTp09oylmVx+vRpDBs2TG+d4cOHIy0tDSzLasvu3r0Ld3d38Pn81v3tzWjxsELv3r1x9+5dFBcXo1OnTgCAw4cP4/Tp05DL5Rg2bJi2nBBCCCH6RUZGYu7cuQgKCsKQIUOwZcsWVFdXa69emDNnDrp06aKdt7B48WJs27YNr732GpYuXYrU1FRs2LABr776qtliNHqFxH8mAE8++aTe7SZOnIjdu3fD3d29dZERQgghZsRY6N4KM2fORGFhId59913k5eUhICAAx48f105SzMrKAqdBbN26dcOJEyewfPly9O/fH126dMFrr72Gt956y2wxmm355PPnz0Mul5tr94QQQsjDseDyyREREYiIiND72tmzZxuVDRs2DL/99puZo6pHyycTQgghREeHvPHS0aNHcSA2FqWlpejZowcWL14MHx8fvdtmZmYiJiYGqWlpKCgowKJFizBt6lSdbebOm4eCgoJGdSdNnIglS5a0+zib4zQiCD1XLIB0UD8IPVxwdcYryD9yuvmKJrRgthcmh7vBTmKDpDsV2PRZKu7nNt2z5ezEx+J5PfF4oBOEAg7u58qxYWsKUtKqAAAXjobqrbd9Tzq+PWT8LZrbeozW8nmcGCxAcD8+REIG93LU2H9ajsIytvmKAMYMFuCpECHOXFcg9mz9pdrD/XkI8uWjqwsXIgGDN7aXQ65/jZt2ESPPPxj8QaFgxHZgi3JRcz4ObH624Qp8IQTDxsOmVz8wQjE0FaWo+eUI1JnJAACuRw/wB40Cp3MXcGylkB/bi9p7bWBdHLpls0EdLjk4d+4cdu7ahaUREfDx9UVcXBxWv/MOdu3cqXed6hqFAm7u7hgREoKdO3fq3efWrVvBNlipKjMzEytXrUJISEi7j7MluBIxKm6lIHtvLIIObDfrsfSZPaMbnp7UBeu3JCM3vwYLZ3th81p/vPDKFShVGr117CQ2+HzjQFxPKsPr7yWhrEKFrh4iVFbVareZ8uJFnTqPBzrh7Vd9cO5iUbuL0Vo+j2GD+QgNECDmhAzF5SwmBQuxZLoE676sRG0zi8l5unIxvD8f9wsbb8izYXA7oxa3M2rxVIiw1fFZQ4w2vQdAEDIZNWdiweZlgRcQAvGUhaj+aiM08urGFThciKcugkZehZqfYsBWlYNj5wiNskFiy+NDXfQAqttXIJo4t9WxmRzdldGgDpc2HTp0COPHjUN4eDi6e3piaUQEBAIBTp48qXd7nz59sHDBAowKDTW4yJODVAonJyft49Lly3B3d3+oG1FZS5wtUXjiPO6u2YL8w6fMehxDnpnSBfu+z8SFS8VIz6jGuk+T0clJgJDHnQ3Wmf10NxQUKRC9NQV3UiuRm1+DKzdK8SCv/pdaSZlK5zHicWdcTyrDg/wag/u11hit5fP4xEABTlyqQVJ6LR4Usdh3XAapLYMB3k0v0MbnAfMmiPBtvAzymsbJ2NkbSsRfUSAjt1ZP7fYVIz9gJFR/XELtnatgSwugOHMQmloVeH5D9G7P8xsMRiiG/NheqHMzoKkshfrBPbBFudpt1JkpUP52ArX3fn+o2Mij06GSA5VKhdS0NAQEBGjLOBwOAgICcCc52WTHOHPmDMLDw1t9r3BridMaeLgK4ewkwJXEUm1ZtUyN23cr0M/X3mC94UM6ITmtEh+85YejMcOwZ8sgTA7Xv0AJADg68BAc5IRj8fqXP7XmGK3l89hJykBqy0FyVv3JsUYJZOSp4eXObaImMHO0CL/fq0VKlvnWqreKGDlccFy6QJ2d2qBQA3V2Kjhu3fVWsenhB3VuJgSh0yBZ8C7Ez68AP2i0VfwqZzgckzzaI6P+KpVKhX/961/4888/m9125cqVcHJyanVg5lBRUQGWZeHo6KhT7ujggNKSEpMcIyEhAVVVVRjT4KYaxrKWOK2Bk2PdAiGlZSqd8tIypfY1fTzcRJg63gPZD+SIXJOEuJ9ysWyRN8aNdtW7/fjRbpDJ1Th3sbDdxWgtn0d7cd3XWaVM91d1ZTULe4nhE1WgDw/dXLk4csH4Hh9jtfUYGZEEDIcLVlalU66RVYEjttNfR9oJNt7+AMOB/MgXUF45BX7ASPAHW8F3i4luvNQeGTXngMfjITY2Fu+8806z20ZFRbVon3K5HNeuXYOTkxP8/Px0XqupqcH333+POXPmGKyvUCga3f1KoVCYdF1rY5w4eRJBQUFtfkEoa4nTWGNCXfDGkj7a52+uTWrVfjgMkJxWiZ0xdYlw6r0q9OguxtTxHjj+c36j7SeOccPJswUG5wdYW4yPWms+j0G+PMwKE2mffx6nZzy8GQ62DGaMEmJbbHWz4/2tYQ0xPiwGDDTyKijOHAA0GrCFOVBIpOAPCoXycrylw2uahW7ZbA2MnpA4depUxMXFYfny5Q998Lt37yI8PBxZWVlgGAYjRozAd999p104qby8HPPnz28yOYiOjm50q+hXly7Fa6+91mhbe3t7cDgclJaW6pSXlpXB0QS9HPn5+UhMTMTqVaseaj/WEmdbdOFyMW7fvap9zufVZfWODjwUl9avQe7owEfavapG9f9WXKpERrZMpywzW4ZRwZ0bbdvfT4ruXcVY89HtdhNjQ23185iUrkJGXv3Z0uavXnk7MYOK6voEyE7Cwf0C/WdVT1cu7CUcvPWCrbaMy2HQqysXIwP4WLa1ApqHyKWsIcaGNPJqaFg1OGJbNLx2ghHbgpVV6q3DyioAlkXDINjSfHAk9gCHC7BtMKMhzTI6OejduzfWrl2LX3/9FYGBgZBIJDqvG7Oc41tvvYV+/frh6tWrKCsrw7JlyzB8+HCcPXsWnp6eLdqHvrth5dzXf4kWj8dDb29vJN68ieDgYAB1a1onJiZiyuTJLY7bkPj4eEilUgwZon/iTktZS5xtkVyuRo5c98uoqESBoAGOSPuz7lebWMSFXx97xP34wOB+ku6Uw7OLWKesWxcx8goad+tOCndDcmol0jJa9qvQGmJsqK1+HhUqQPGPy//Kq1j4eNogp7AuyRLyAS83Li7c1H9zmpSsWqz/Uvek98JYEfJLWMRfUTz0SdcaYtTBqsEW5IDb1bvBpYYMuN28obp1UW8VdW4GeH0GAmAA1AXDcegMtqq8zScGTDsdEjAFo5ODL774Ag4ODrh27RquXbum8xrDMEYlBxcvXsSpU6fg7OwMZ2dnHD16FK+88gpCQkJw5syZRomHPgKBoNEQQlETQwrTpk3DJ5s3o3fv3vDp0wdxhw9DoVBgzJgxAIBNmzahU6dO2jWuVSoVsrKyAAC1tbUoLi5Geno6RCIRPDw8tPtlWRbx8fEICwvTuXNWa1lLnC3BlYgh8a5P9sQ9usJ+gC+UJeWoyc5toqZp/HAkB3NneiL7gbzuMsEXvFBcosAvv9VfzrdlXX+cTyjCwWN1J+P9h3OwY2MAXnzGEz9fKIBfH3tMGeuOjdvu6uxbLOLiieGdse2L9HYdo7V8Hs/cUGDcUCEKS1kUV7CYGCxEeZUGN9Pq53MsfVqCm2kqnE9UQqECcot1T95KFVBdo9EptxMzsJcwcHaoO5l4OHNRo9SgtFIDmZ4rB6w5RmXieQjDZkJdcB9sfjZ4ASFgbPhQ3b4CABCOeQ5sVTmUCT8BAFRJCeD3Hw7ByClQ3voVHAdn8INGQ3XzQv1OeXxwpPVX3jD2TuA4e0BTI4OmqqzFsZkcDSsYZHRy0JLJiC0ll8thY1MfAsMw+PzzzxEREYHQ0FB88803JjvW30JDQ1FeUYGvYmJQUlqKXj174oO1a7WTrQoKC3Vmn5aUlCBi6VLt89jYWMTGxsLf3x8bP/pIW34jMREFhYUI/+vLsqPE2RLSwH4YdjpG+9xv00oAQPa+g7i1oGVzUx7G17HZEAq5eDOiD2wlNki6XY4Va5J0xt67uIngYF9/KVlyaiVWbvgD/zenB+Y91x25+XL8e1ca4s/pLtoTNtIFDAOcOt94MZ/2FKO1fB5PXVFCwGMwa4wIIgGD9Bw1PjuoO1bvLOXAVmTcSSFkAB8ThtWvHbB8Zl0Xf8xxGS7dVhmqZpUx1qbehEIkgWDoWDASO7CFDyA7shsaed0QF2PrAE6D7gpNVTlkh3dDGDIZklmR0FRXQHXzApTXzmi34bp0hXj6Yu1zYcgUAIDqzlXUnNpv1N9JHg1Go2ldp5RSqcSff/6JXr166ZzgjTFkyBAsXboUL774YqPXIiIi8PXXX6OiogJqtXFdU/fSH+5XHKl3x3eCpUNoVvQ4/YvsEOPs29LV0iE0a/Nhw+s+kJaL5q2zdAjNslv6sdmPUfP9JpPsR/js6ybZT1ti9ICLTCbDggULIBaL8dhjj2m7DpcuXYoPP/zQqH1NmzYN3377rd7Xtm3bhlmzZqGVuQshhBDSNIYxzaMdMjo5iIqKws2bN3H27FkIhfVdWGFhYdi/37juoaioKPz4448GX//ss8/Asi1bb5wQQgghpmH0eEBcXBz279+Pxx9/XGcls8ceewzp1J1PCCHEWrTT1Q1NwejkoLCwEC4uLo3Kq6ur2/UyvIQQQtoZupTRIKNbJigoCMeOHdM+/zsh2L17N4YNG2a6yAghhBBiEUb3HGzYsAHjx4/H7du3UVtbi61bt+L27du4ePEizp07Z44YCSGEENOjdQ4MMrrnYMSIEUhMTERtbS38/f1x8uRJuLi4ICEhAYGBgeaIkRBCCDE9uvGSQa1aoKBXr17YtWuXqWMhhBBCHh2aJ2dQi5KDioqKFu/Q3t7w/ecJIYQQ0va1KDlwcHBo8ZUIxq5mSAghhFgEXcpoUIuSgzNn6tfIzsjIwNtvv4158+Zpr05ISEjAl19+iejoaPNESQghhJgaDSsY1KLkIDQ0VPvvtWvXYvPmzZg1a5a2bMqUKfD398fOnTsxd+5c00dJCCGEkEfG6D6VhIQEBAUFNSoPCgrC5cuXTRIUIYQQYnZ0tYJBRv9V3bp103ulwu7du9GtWzeTBEUIIYSYHYdjmkc7ZPSljJ9++ilmzJiBn376CUOHDgUAXL58GampqYiNjTV5gIQQQgh5tBhNK+6JfP/+fXz22WdITk4GAPTt2xcvv/xym+k5uGcFN4Cas+y+pUNoN6KOL7J0CM3qm2z47qNthcYKJme5HPvc0iE0K0q12tIhtAvbIqVmP0bNiS9Msh/h2AUm2U9b0qpFkLp27YoNGzaYOhZCCCHk0Wmn8wVMoVXJQVlZGS5fvoyCggKwLKvz2pw5c0wSGCGEEEIsw+jk4OjRo5g9ezaqqqpgb2+vszgSwzCUHBBCCLEOVjCUZilG96msWLEC//rXv1BVVYWysjKUlpZqHyUlJeaIkRBCCDE9ulrBIKN7DnJycvDqq69CLBabIx5CCCHkkbCGSbiWYnTKM3bsWFy9etUcsRBCCCEdwvbt2+Hl5QWhUIihQ4e2eBHB7777DgzDYOrUqWaNz+ieg4kTJ+KNN97A7du34e/vDx6Pp/P6lClTTBYcIYQQYjYWulph//79iIyMxI4dOzB06FBs2bIFY8eORUpKClxcXAzWy8jIwOuvv46QkBCzx2h0cvDSSy8BqLvHwj8xDEN3ZSSEEGIdLJQcbN68GS+99BLmz58PANixYweOHTuGPXv24O2339ZbR61WY/bs2Xj//ffxyy+/oKyszKwxGt0yLMsafFBiQAghpKNRKBSoqKjQeSgUCr3bKpVKXLt2DWFhYdoyDoeDsLAwJCQkGDzG2rVr4eLiggULHs2CS+1zmiUhhBDSDA3DmOQRHR0NqVSq84iOjtZ7zKKiIqjVari6uuqUu7q6Ii8vT2+dCxcu4IsvvtB7XyNzaVVycO7cOUyePBne3t7w9vbGlClT8Msvv5g6NkIIIcR8THRXxqioKJSXl+s8oqKiTBJiZWUlXnzxRezatQvOzs4m2WdLGD3n4KuvvsL8+fMxffp0vPrqqwCAX3/9FU8++ST27t2L559/3uRBEkIIIW2VQCCAQCBo0bbOzs7gcrnIz8/XKc/Pz4ebm1uj7dPT05GRkYHJkydry/5emdjGxgYpKSno1avXQ0Svn9HJwfr167Fx40YsX75cW/bqq69i8+bN+OCDDyg5IIQQYh0ssM4Bn89HYGAgTp8+rb0ckWVZnD59GhEREY229/X1RVJSkk7Z6tWrUVlZia1bt5rthodGJwf37t3TyWD+NmXKFKxcudIkQRFCCCFmZ6HVDSMjIzF37lwEBQVhyJAh2LJlC6qrq7VXL8yZMwddunRBdHQ0hEIh+vXrp1PfwcEBABqVm5LRyUG3bt1w+vRpeHt765SfOnWqzdyyuTlHjx7FgdhYlJaWomePHli8eDF8fHz0bpuZmYmYmBikpqWhoKAAixYtwrR/LD4xd948FBQUNKo7aeJELFmy5KFiXTDbC5PD3WAnsUHSnQps+iwV93PlTdZxduJj8byeeDzQCUIBB/dz5diwNQUpaVUAgAtHQ/XW274nHd8eMv5W0tYQY3OcRgSh54oFkA7qB6GHC67OeAX5R06b/DiGWMNn8ujRo4g9cAClpaXo0bNni2JMS03Vxjh12jSdbebNnas3xomTJrU6Rp5/MPiDQsGI7cAW5aLmfBzY/GzDFfhCCIaNh02vfmCEYmgqSlHzyxGoM+tuR8/16AH+oFHgdO4Cjq0U8mN7UXvvj1bF1tDEYAGC+/EhEjK4l6PG/tNyFJaxzVcEMGawAE+FCHHmugKxZ2u05cP9eQjy5aOrCxciAYM3tpdDrn/CfLuJ0VrNnDkThYWFePfdd5GXl4eAgAAcP35cO0kxKysLHAsvy2x0crBixQq8+uqrSExMRHBwMIC6OQd79+7F1q1bTR6gqZ07dw47d+3C0ogI+Pj6Ii4uDqvfeQe7du7UZmMN1SgUcHN3x4iQEOzcuVPvPrdu3Qq2wWWcmZmZWLlq1UMvVDF7Rjc8PakL1m9JRm5+DRbO9sLmtf544ZUrUKo0euvYSWzw+caBuJ5UhtffS0JZhQpdPUSorKrVbjPlxYs6dR4PdMLbr/rg3MWidhljS3AlYlTcSkH23lgEHdhulmMYYg2fyXPnzmHXzp2IWLoUvj4+iIuLwzurV2Pnrl16Y1TU1MDdzQ0hI0Y0GaO6wV1dMzMzsWrlylbHaNN7AAQhk1FzJhZsXhZ4ASEQT1mI6q82QiOvblyBw4V46iJo5FWo+SkGbFU5OHaO0CgbJLY8PtRFD6C6fQWiiXNbFdc/hQ3mIzRAgJgTMhSXs5gULMSS6RKs+7IStc1cDe7pysXw/nzcL2y8Ic+Gwe2MWtzOqMVTIcJ2H6MpWHL55IiICL3DCABw9uzZJuvu3bvX9AH9g9HJweLFi+Hm5oZPPvkE33//PQCgb9++2L9/P5566imTB2hqhw4dwvhx4xAeHg4AWBoRgStXruDkyZN49tlnG23v06cPfPr0AQD897//1btPB6lU5/n3P/wAd3d3+Pv7P1Ssz0zpgn3fZ+LCpWIAwLpPk3EkJhghjzvj9C+FeuvMfrobCooUiN6aoi3Lza/R2aakTKXzfMTjzrieVIYH/9iuvcTYEoUnzqPwxHmz7Ls51vCZPHToEMaNH6+NMWLp0iZj7OPjgz5/9SoYilH6j6Tih++/f6gY+QEjofrjEmrv1C3vrjhzEDZefcHzGwLltTONtuf5DQYjFEN2YBvwV5KirizV2UadmQJ1Zkqjug/jiYECnLhUg6T0umR433EZol+2xwBvHq6lqAzW4/OAeRNE+DZehnFDG59Yz95QAgB6d+V2iBhNwkKLIFmDVrXMtGnTcOHCBRQXF6O4uBgXLlywisRApVIhNS0NAQEB2jIOh4OAgADcSU422THOnDmD8PBwndtZG8vDVQhnJwGuJNZ/WVXL1Lh9twL9fO0N1hs+pBOS0yrxwVt+OBozDHu2DMLk8MYzYP/m6MBDcJATjsXrv77W2mNs66zhM6lSqZCWmqo3xuQ7d9pEjOBwwXHpAnV2aoNCDdTZqeC4dddbxaaHH9S5mRCEToNkwbsQP78C/KDRZp2k1knKQGrLQXJWfS9ZjRLIyFPDy73pE+bM0SL8fq8WKVnmXWzOGmI0FQ3DMcmjPTK65+BvV69exZ2/vhj8/PwQGBhosqDMpaKiAizLwtHRUafc0cEB97ObGJc0QkJCAqqqqjCmwepXreHkyAcAlP7jF3RpmVL7mj4ebiJMHS/C/rj72PdDFvr2tsOyRd5Q1Wpw/Of8RtuPH+0GmVyNcxf1/8q39hjbOmv4TBqK0cHREdn3TTP/4+8Yw8aMaVV9RiQBw+GClVXplGtkVeA66l+rnpF2ArerI1QpNyA/8gU4Ds4Qhk4DOFwoL8e3Ko7m2IvrTiSVMt0ht8pqFvYSw0lJoA8P3Vy52Ph1lcFtTMUaYiTmZ3RycP/+fcyaNQu//vqrdqyxrKwMwcHB+O6779C1a1ej9nfnzh389ttvGDZsGHx9fZGcnIytW7dCoVDghRdewOjRo5usr1AoGi1TqVAoWnzNqamdOHkSQUFB6NSpk1H1xoS64I0lfbTP31yb1MTWhnEYIDmtEjtj/gQApN6rQo/uYkwd76H3xDtxjBtOni0wOD/A2mIkjbX2M/konTxx4pHHyICBRl4FxZkDgEYDtjAHCokU/EGhJksOgnx5mBUm0j7/PE7P3IdmONgymDFKiG2x1c2O97eGNcRoNnTLZoOMTg4WLlwIlUqFO3fuaGcqp6SkYP78+Vi4cCGOHz/e4n0dP34cTz31FGxtbSGTyXDo0CHMmTMHAwYMAMuyCA8Px8mTJ5tMEKKjo/H+++/rlL26dClee+21Rtva29uDw+GgtFR3XLG0rAyOTk4tjtuQ/Px8JCYmYvWqVUbXvXC5GLfv1t8Km8+ry94dHXgoLlVqyx0d+Ei7ZzgzLy5VIiNbplOWmS3DqODOjbbt7ydF965irPnodruJ0dq05c/k3wzFWFZaCqd/9Ca0xt8xrlq9utX70MiroWHV4Iht0XA+PSO2BSur1FuHlVXUzTXQ1CedbGk+OBJ7gMMF2Ic/yyWlq5CRV78fm7965e3EDCqq649rJ+HgfoH+43m6cmEv4eCtF2y1ZVwOg15duRgZwMeyrRUN/4R2GaO5tNchAVMwOjk4d+4cLl68qHMJk4+PD/7zn/8YPct47dq1eOONN7Bu3Tp89913eP7557F48WKsX78eABAVFYUPP/ywyeQgKioKkZGROmU5Bro6eTweent7I/HmTe2VFizLIjExEVP0rN1grPj4eEilUgwZMsTounK5Gjly3f94RSUKBA1wRNqfdZm8WMSFXx97xP34wOB+ku6Uw7OLWKesWxcx8goaT+SbFO6G5NRKpGW07JeCNcRobdryZ7JhjN69e+NmgyuU/o5xsglu0W6KGMGqwRbkgNvVu8Glhgy43byhunVRbxV1bgZ4fQYCYADUnbk4Dp3BVpWbJDEAAIUKUPzj8r/yKhY+njbIKaxLqIV8wMuNiws3lfp2gZSsWqz/UjfBeWGsCPklLOKvKB76pGsNMZJHz+i0qVu3blCpGs9WVavV8PDwMGpff/zxB+bNmwcAePbZZ1FZWYmnn35a+/rs2bNx69atJvchEAhgb2+v82hqSGHatGk4fvw44k+dQlZWFrZt3w6FQoExf411btq0SWd2tUqlQnp6OtLT01FbW4vi4mKkp6fjwQPdkx/LsoiPj0dYWBi4XNPMxP3hSA7mzvTE8CGd0LO7BKsjfVFcosAvv9VfzrdlXX9Mn1jf7vsP5+AxHzu8+IwnurgLMSbUBVPGuuPgMd14xSIunhjeGUdP5rb7GFuCKxHDfoAv7Af41h27R1fYD/CFsJu72Y9tDZ/Jv2M8FR+PrKwsbN+2rc3FqEw8D95jQ2HjGwiOowsET0wHY8OH6vYVAIBwzHPgDxtfH2NSAhihGIKRU8A4OIPr5Qt+0GiokhokEzw+OM4e4DjXfX4ZeydwnD3A2Dq0Os4zNxQYN1QI/5428HDm4MVxYpRXaXAzrf57denTEowMqJu3o1ABucWszkOpAqprNMgtrj+p24kZdOnMgbND3de6hzMXXTpzIBYa33VuDTGaBMOY5tEOGd1z8PHHH2Pp0qXYvn07goKCANRNTnzttdewadMmowP4e2Yyh8OBUCiEtMElWHZ2digvLzd6n00JDQ1FeUUFvoqJQUlpKXr17IkP1q7VTrYqKCwE02DxiZKSEkQsXap9Hhsbi9jYWPj7+2PjRx9py28kJqKgsBDhrZxQpc/XsdkQCrl4M6IPbCU2SLpdjhVrknTG3ru4ieBgz9M+T06txMoNf+D/5vTAvOe6Izdfjn/vSkP8Od3FZsJGuoBhgFPnGy9C095ibAlpYD8MOx2jfe63qW61z+x9B3FrgWluoGKINXwmQ0NDUVFejpivvkJpSQl69uqFtR98oI2xsKAAnAZfkiUlJVja4BruhjF+tHGjtjzxxg0UFhRgzF+XSD6M2tSbUIgkEAwdC0ZiB7bwAWRHdkMjrxviYmwdwGnwE1ZTVQ7Z4d0QhkyGZFYkNNUVUN28oHPZI9elK8TTF2ufC0PqekpUd66i5tT+VsV56ooSAh6DWWNEEAkYpOeo8dlB3bF6ZykHtiLjTjohA/iYMKz+8sHlM+u6+GOOy3DptuHLD601RpOgYQWDGI3GuA4fR0dHyGQy1NbWwsamLrf4+98SiURn25KSkib3NWDAAHz00UcYN24cAOD333+Hr6+vdr+//PIL5s6di3v37hkTIu6lpxu1vSXMWWb6Vf46qqjjiywdQrP6Jv9o6RCaZckFYVrK5djnlg6hWVGq1s+dIPW2RUqb3+ghVV5t+Ry5ptgFjTPJftoSo3sOtmzZYrKDL168GOoGq7j9c53on376qdmrFQghhJDWsIaE2FKMTg7mzjXNEqIA8PLLLzf5+oYNG0x2LEIIIUQHDSsYZHTLXL9+Xef2kYcPH8bUqVOxcuVKKJX6Z7ISQgghxHoYnRz83//9H+7evQug7vbNM2fOhFgsxg8//IA333zT5AESQggh5qABY5JHe2R0cnD37l3tGus//PADQkND8c0332Dv3r2IjY01dXyEEEKIWdC9FQwzes6BRqMB+9cdzE6dOoVJkyYBqFv/oKjIPLfTJYQQQkyunZ7YTcHolgkKCsK6desQExODc+fOYeLEiQCAP//8E66uriYPkBBCCCGPVqsuZZw9ezbi4uKwatUqeHt7AwAOHDigXVqVEEIIaevoUkbDjE4O+vfvr3O1wt8+/vhjky0bTAghhJhbe50vYAqtapmysjLs3r0bUVFR2lUQb9++jYIC8y9zSwghhBDzMrrn4NatW3jyySfh4OCAjIwMvPTSS3BycsLBgweRlZWFffv2mSNOQgghxLRoWMEgo3sOIiMjMX/+fKSmpkIorL+BxoQJE3D+/HmTBkcIIYSYC13KaJjRf9WVK1fwf//3f43Ku3Tpgry8PJMERQghhBDLMXpYQSAQoKKiolH53bt30blzZ5MERQghhJhbe13d0BSM7jmYMmUK1q5dC5Wq7t7bDMMgKysLb731FmbMmGHyAAkhhBBzoGEFw4zuOfjkk0/w9NNPw8XFBXK5HKGhocjLy8OwYcOwfv16c8RotDnL7ls6hGbt29LV0iG0iDW0Zd/kHy0dQrPu+E6wdAjNsoZ2jFKttnQIzYrmrbN0CM2yhnYklmV0ciCVShEfH49ff/0VN2/eRFVVFQYNGoSwsDBzxEcIIYSYB12tYJBRyYFKpYJIJEJiYiKGDx+O4cOHmysuQgghxKw0rVvqp0MwKjng8Xjw9PSEWq02VzyEEELII0HLJxtmdNq0atUqrFy5UrsyIiGEEELaF6PnHGzbtg1paWnw8PBA9+7dIZFIdF6/fv26yYIjhBBCzKW9XmlgCkYnB1OnTjVDGIQQQsijRescGGZ0crBmzRpzxEEIIYSQNsLo5IAQQghpD2hYwTBqGUIIIR2ShmFM8miN7du3w8vLC0KhEEOHDsXly5cNbrtr1y6EhITA0dERjo6OCAsLa3J7U6DkgBBCCHmE9u/fj8jISKxZswbXr1/HgAEDMHbsWBQUFOjd/uzZs5g1axbOnDmDhIQEdOvWDeHh4cjJyTFbjJQcEEII6ZA0YEzyMNbmzZvx0ksvYf78+fDz88OOHTsgFouxZ88evdt//fXXeOWVVxAQEABfX1/s3r0bLMvi9OnTD9sEBtGcA0IIIR2SqeYcKBQKKBQKnTKBQACBQNBoW6VSiWvXriEqKkpbxuFwEBYWhoSEhBYdTyaTQaVSwcnJ6eECbwL1HBBCCCEPITo6GlKpVOcRHR2td9uioiKo1Wq4urrqlLu6uiIvL69Fx3vrrbfg4eFh1nsaUc8BIYSQDslU6xxERUUhMjJSp0xfr4EpfPjhh/juu+9w9uxZCIVCsxwD6ODJwYLZXpgc7gY7iQ2S7lRg02epuJ8rb7KOsxMfi+f1xOOBThAKOLifK8eGrSlISasCAFw4Gqq33vY96fj2kHG3Pz569CgOxMaitLQUPXv0wOLFi+Hj46N328zMTMTExCA1LQ0FBQVYtGgRpv1jwaq58+bpnfAyaeJELFmyxKjYGqJ2rPOw7dgcpxFB6LliAaSD+kHo4YKrM15B/hHzjTn+k7W048RgAYL78SESMriXo8b+03IUlrEtqjtmsABPhQhx5roCsWdrtOXD/XkI8uWjqwsXIgGDN7aXQ65oYkdN4PkHgz8oFIzYDmxRLmrOx4HNzzZcgS+EYNh42PTqB0YohqaiFDW/HIE6MxkAwPXoAf6gUeB07gKOrRTyY3tRe++P1gXXQFtvR1Mw1bCCoSEEfZydncHlcpGfn69Tnp+fDzc3tybrbtq0CR9++CFOnTqF/v37tzreluiwycHsGd3w9KQuWL8lGbn5NVg42wub1/rjhVeuQKnS6K1jJ7HB5xsH4npSGV5/LwllFSp09RChsqpWu82UFy/q1Hk80Alvv+qDcxeLjIrv3Llz2LlrF5ZGRMDH1xdxcXFY/c472LVzJxwcHBptX6NQwM3dHSNCQrBz5069+9y6dSvYBjfNyszMxMpVqxASEmJUbA1RO5qmHVuCKxGj4lYKsvfGIujAdrMe65+spR3DBvMRGiBAzAkZistZTAoWYsl0CdZ9WYnaZu4X5+nKxfD+fNwvbLwhz4bB7Yxa3M6oxVMhrf+1ZtN7AAQhk1FzJhZsXhZ4ASEQT1mI6q82QiOvblyBw4V46iJo5FWo+SkGbFU5OHaO0CgbJN88PtRFD6C6fQWiiXNbHVtDbb0dTcUSKyTy+XwEBgbi9OnT2hWH/55cGBERYbDexo0bsX79epw4cQJBQUFmj7PVaVNFRYUp43jknpnSBfu+z8SFS8VIz6jGuk+T0clJgJDHnQ3Wmf10NxQUKRC9NQV3UiuRm1+DKzdK8SCvPjMuKVPpPEY87ozrSWV4kF9jcL/6HDp0COPHjUN4eDi6e3piaUQEBAIBTp48qXd7nz59sHDBAowKDQWPx9O7jYNUCicnJ+3j0uXLcHd3h7+/v1GxNUTtaJp2bInCE+dxd80W5B8+Zdbj6GMt7fjEQAFOXKpBUnotHhSx2HdcBqktgwHe+mP4G58HzJsgwrfxMshrGie1Z28oEX9FgYzcWj21W44fMBKqPy6h9s5VsKUFUJw5CE2tCjy/IXq35/kNBiMUQ35sL9S5GdBUlkL94B7YolztNurMFCh/O4Hae78/VGwNtfV2tHaRkZHYtWsXvvzyS9y5cweLFy9GdXU15s+fDwCYM2eOzoTFjz76CO+88w727NkDLy8v5OXlIS8vD1VVVWaLsdXJwbx587BhwwZTxgIA0Gj0/9o0JQ9XIZydBLiSWKotq5apcftuBfr52husN3xIJySnVeKDt/xwNGYY9mwZhMnhhruBHB14CA5ywrH4lk0y+ZtKpUJqWhoCAgK0ZRwOBwEBAbiTnGzUvpo6xpkzZxAeHg6mlYt4UDuaph3bOmtpx05SBlJbDpKz6k88NUogI08NL3duk3Vnjhbh93u1SMky4+3oOVxwXLpAnZ3aoFADdXYqOG7d9Vax6eEHdW4mBKHTIFnwLsTPrwA/aDRgxs9am29HE9IwHJM8jDVz5kxs2rQJ7777LgICApCYmIjjx49rJylmZWUhN7c+Afz888+hVCrx9NNPw93dXfvYtGmTydrin1o9rPDJJ58gKCgIK1euNGU8EAgEuHnzJvr27WvS/Tbk5MgHAJSWqXTKS8uU2tf08XATYep4EfbH3ce+H7LQt7cdli3yhqpWg+M/5zfafvxoN8jkapy7WGhUfBUVFWBZFo6Ojjrljg4OuJ/dxNikERISElBVVYUxDzHbldrRNO3Y1llLO9qL676kK2W6PzAqq1nYSwyfTAN9eOjmysXGr833KwwAGJEEDIcLVqZ7HI2sClxHF/11pJ3A7eoIVcoNyI98AY6DM4Sh0wAOF8rL8WaJs623oylZ8sZLERERBocRzp49q/M8IyPD/AH9Q6uTAy8vL1RWViI/P7/RJRkt8c+ZnX9Tq9X48MMP0alTJwB1i0U0Rd/1paxaCQ63/uQ0JtQFbyzpo33+5toko+MFAA4DJKdVYmfMnwCA1HtV6NFdjKnjPfSe1CaOccPJswUGx94t6cTJkwgKCtK2c0tQOzbWmnYkjbWmHYN8eZgVJtI+/zxOz5h9MxxsGcwYJcS22Opmx9ItgQEDjbwKijMHAI0GbGEOFBIp+INCTZYcdIR2JMZrdXJw7do1SCQSdO7cuVX1t2zZggEDBjSazKTRaHDnzh1IJJIWdS9GR0fj/fff1ynr1nsuPH3ma59fuFyM23evap/zeXWZsaMDD8WlSm25owMfafcMZ73FpUpkZMt0yjKzZRgV3LgN+vtJ0b2rGGs+ut3s3/BP9vb24HA4KC0t1SkvLSuDowkWvcjPz0diYiJWr1plVD1qR12tbUdr01bbMSldhYy8+jORzV893nZiBhXV9YmknYSD+wX6z1ierlzYSzh46wVbbRmXw6BXVy5GBvCxbGsFTDXSqZFXQ8OqwRHbouGcf0ZsC1ZWqbcOK6sAWBYNg2BL88GR2AMcLsA+/JnY2trRlFp7X4SOoNXJwd69e/HMM8+Aw2ndtIUNGzZg586d+OSTTzB69GhtOY/Hw969e+Hn59ei/ei7vnTcc5d0nsvlauTIdT/URSUKBA1wRNqfdVmyWMSFXx97xP34wOCxku6Uw7OLWKesWxcx8goaT5KbFO6G5NRKpGUYn4XzeDz09vZG4s2bCA4OBlA3mzUxMRFTJk82en//FB8fD6lUiiFD9E+CMoTaUVdr29HatNV2VKgAxT8urSuvYuHjaYOcwrpkVcgHvNy4uHBTqW8XSMmqxfovdU/ML4wVIb+ERfwVhWlPaKwabEEOuF29G1xqyIDbzRuqWxf1VlHnZoDXZyAABkBdMByHzmCryk2SGABW2I4mpNFQcmBIqyck/vjjj3j22WdbfeC3334b+/fvx+LFi/H6669DpVI1X0kPgUAAe3t7nUfDIQVDfjiSg7kzPTF8SCf07C7B6khfFJco8Mtv9ZfKbVnXH9Mnemif7z+cg8d87PDiM57o4i7EmFAXTBnrjoPHdE+EYhEXTwzvjKMnc9Fa06ZNw/HjxxF/6hSysrKwbft2KBQKjBkzBkDd9a7//e9/tdurVCqkp6cjPT0dtbW1KC4uRnp6Oh480I2NZVnEx8cjLCwMXG7Tk4tagtrRNO3YElyJGPYDfGE/wBcAIO7RFfYDfCHs5m72Y1tLO565ocC4oUL497SBhzMHL44To7xKg5tp9d8vS5+WYGRA3XeEQgXkFrM6D6UKqK7RILe4/oRpJ2bQpTMHzg51X5kezlx06cyBWGjcyUWZeB68x4bCxjcQHEcXCJ6YDsaGD9XtKwAA4ZjnwB82Xru9KikBjFAMwcgpYBycwfXyBT9oNFRJDZIJHh8cZw9wnOv+jzH2TuA4e4CxdTAqtobaejsS82t1z4FCoYCLi/5JNC01ePBgXLt2DUuWLEFQUBC+/vrrRzbj++vYbAiFXLwZ0Qe2Ehsk3S7HijVJOuPaXdxEcLCvv3QnObUSKzf8gf+b0wPznuuO3Hw5/r0rDfHndBdyCRvpAoYBTp3Xf4etlggNDUV5RQW+iolBSWkpevXsiQ/WrtVOCisoLATToNempKQEEUuXap/HxsYiNjYW/v7+2PjRR9ryG4mJKCgsRPhfX+oPi9rRNO3YEtLAfhh2Okb73G9T3WTg7H0HcWtBlKFqJmEt7XjqihICHoNZY0QQCRik56jx2UHdcXBnKQe2IuO+Z0IG8DFhWP11+ctn1nWfxxyX4dLtlv+wqU29CYVIAsHQsWAkdmALH0B2ZDc08rphOMbWAZwGP7M1VeWQHd4NYchkSGZFQlNdAdXNC1BeO6PdhuvSFeLpi7XPhSFTAACqO1dRc2q/UX/n39p6O5qKhu4gYBCjaeW1gy+//DI6deqE9evXmySQ7777DsuWLUNhYSGSkpJaPKygz4jJ50wSkznt29LV0iG0yJxlxq1GaAnW0JZ3fCdYOoRm9U3+0dIhNGvzYcPrZ7QV0bx1lg6hWVGq1ZYOoVnbIqVmP8bd9CyT7KdPL0+T7KctaXXa9MEHH6C4uNhkiyE999xzuHr1Kg4ePIju3fVf80sIIYQQ82v1sELnzp2xY8cOU8aCrl27omvXtv8rkBBCiPWz5DoHbV2HvbcCIYSQjo2SA8MoOSCEENIhUXJgGE3VJIQQQogO6jkghBDSIdEiSIZRckAIIaRDomEFw2hYgRBCCCE6qOeAEEJIh0Q9B4ZRckAIIaRDouTAMBpWIIQQQoiOVvUclJeXIy8vDwDg5uYGqdT8a2ATQgghpkRXKxhmVM/B7t274efnBycnJ/j5+en8+4svvjBXjIQQQojJsWBM8miPWtxz8PHHH+O9997Dq6++irFjx8LV1RUAkJ+fj5MnT+K1115DaWkpXn/9dbMFSwghhBDza3FysG3bNvz3v//Fs88+q1Pet29fjBo1CgMGDMAbb7xByQEhhBCrQBMSDWtxclBQUAB/f3+Dr/v7+6OoqMgkQXUEc5bdt3QILbJvS9u/S6aGafv/wfsm/2jpEJp1x3eCpUNo3keXLR1Bs6JUqy0dQrOieessHUILfGz2I9CcA8NaPOdg8ODB+PDDD1FbW9voNbVajY8++giDBw82aXCEEEKIuWjAmOTRHhk1rDB27Fi4ublh5MiROnMOzp8/Dz6fj5MnT5otUEIIIYQ8Gi1ODvr374+7d+/iq6++wm+//YZ79+4BqLuUcd26dXj++edhb29vtkAJIYQQU6JhBcOMWufAzs4OixcvxuLFi5vd9sMPP8TLL78MBweH1sZGCCGEmE17HRIwBbOtkLhhwwaUlJSYa/eEEEIIMROz3VtBo9GYa9eEEELIQ6NhBcPoxkuEEEI6JNbSAbRhdOMlQgghhOigngNCCCEdEg0rGEbJASGEkA6JrlYwzKhhhdraWuzbtw/5+fnNbhsSEgKRSNTqwAghhBBiGUb1HNjY2ODll1/GnTt3mt32xx/b/lryhBBCOi4aVjDM6AmJQ4YMQWJiohlCIYQQQh4dS95bYfv27fDy8oJQKMTQoUNx+XLTNxX74Ycf4OvrC6FQCH9/f7P/ADd6zsErr7yCyMhIZGdnIzAwEBKJROf1/v37myw4QgghxFxYCy3Hs3//fkRGRmLHjh0YOnQotmzZgrFjxyIlJQUuLi6Ntr948SJmzZqF6OhoTJo0Cd988w2mTp2K69evo1+/fmaJkdEYuVoRh9O4s4FhGGg0GjAMA7VabbLgWmvE5HMt2m7BbC9MDneDncQGSXcqsOmzVNzPlTdZx9mJj8XzeuLxQCcIBRzcz5Vjw9YUpKRVAQAuHA3VW2/7nnR8e8j42zRbOsbmbtl89OhRHIiNRWlpKXr26IHFixfDx8dH77aZmZmIiYlBaloaCgoKsGjRIkybOlVnm7nz5qGgoKBR3UkTJ2LJkiV699vcLZuPHj2K2AMHUFpaih49e7YoxrTUVG2MU6dN09lm3ty5emOcOGmSwRiZZv6btYV2NMUtm51GBKHnigWQDuoHoYcLrs54BflHTj/0fv/2Uwtu2TwxWIDgfnyIhAzu5aix/7QchWUtu6J9zGABngoR4sx1BWLP1mjLh/vzEOTLR1cXLkQCBm9sL4dc0eo/w+IxNnfLZp5/MPiDQsGI7cAW5aLmfBzY/GzDFfhCCIaNh02vfmCEYmgqSlHzyxGoM5MBAFyPHuAPGgVO5y7g2EohP7YXtff+aDIGu6Xmv2Xz+T+qTbKfkY9Jmt+ogaFDh2Lw4MHYtm0bAIBlWXTr1g1Lly7F22+/3Wj7mTNnorq6Gv/73/+0ZY8//jgCAgKwY8eOhwveAKN7Dv78809zxPHIzZ7RDU9P6oL1W5KRm1+DhbO9sHmtP1545QqUKv1f5HYSG3y+cSCuJ5Xh9feSUFahQlcPESqr6m9jPeXFizp1Hg90wtuv+uDcxaJ2F+O5c+ewc9cuLI2IgI+vL+Li4rD6nXewa+dOvffUqFEo4ObujhEhIdi5c6fefW7duhVsgwQzMzMTK1etQkhIiFGxNYxx186diFi6FL4+PoiLi8M7q1dj565demNU1NTA3c0NISNGNBmjmq3/Is/MzMSqlSsfKsa23o4txZWIUXErBdl7YxF0YLtZj6VP2GA+QgMEiDkhQ3E5i0nBQiyZLsG6LytR28zvFk9XLob35+N+YeMNeTYMbmfU4nZGLZ4KEbbrGG16D4AgZDJqzsSCzcsCLyAE4ikLUf3VRmjkek6mHC7EUxdBI69CzU8xYKvKwbFzhEbZ4EcMjw910QOobl+BaOLcVsdmaqa6WkGhUECh0M3EBAIBBAJBo22VSiWuXbuGqKgobRmHw0FYWBgSEhL07j8hIQGRkZE6ZWPHjkVcXNzDB2+A0XMOunfv3uTDWjwzpQv2fZ+JC5eKkZ5RjXWfJqOTkwAhjzsbrDP76W4oKFIgemsK7qRWIje/BldulOJBXn32XlKm0nmMeNwZ15PK8CC/xuB+rTXGQ4cOYfy4cQgPD0d3T08sjYiAQCAweOtunz59sHDBAowKDQWPx9O7jYNUCicnJ+3j0uXLcHd3h7+/v1GxNYxx3PjxCA8Ph2f37ohYurTJGPv4+GDBwoUIHTXKYIxSBwedGC9fuvTQMbb1dmypwhPncXfNFuQfPmXW4xjyxEABTlyqQVJ6LR4Usdh3XAapLYMB3vrb6W98HjBvggjfxssgr2mceJ+9oUT8FQUycmv11G5fMfIDRkL1xyXU3rkKtrQAijMHoalVgec3RO/2PL/BYIRiyI/thTo3A5rKUqgf3ANblKvdRp2ZAuVvJ1B77/eHis3UNBrGJI/o6GhIpVKdR3R0tN5jFhUVQa1Ww9XVVafc1dUVeXl5euvk5eUZtb0ptGqFxJiYGAwfPhweHh7IzMwEAGzZsgWHDx82aXDm4uEqhLOTAFcSS7Vl1TI1bt+tQD9fw7edHj6kE5LTKvHBW344GjMMe7YMwuRwN4PbOzrwEBzkhGPxxr+BbT1GlUqF1LQ0BAQEaMs4HA4CAgJwJznZqH01dYwzZ84gPDwcTDNDB4bqp6Wm6o0xuQVX3DyqGNt6O1qLTlIGUlsOkrPqT441SiAjTw0vd26TdWeOFuH3e7VIyTLvsGibj5HDBcelC9TZqQ0KNVBnp4Ljpv/Hn00PP6hzMyEInQbJgnchfn4F+EGjgXb8WfunqKgolJeX6zwa9gxYI6OTg88//xyRkZGYMGECysrKtHMMHBwcsGXLFlPHZxZOjnwAQGmZSqe8tEypfU0fDzcRpo73QPYDOSLXJCHup1wsW+SNcaNd9W4/frQbZHI1zl0sbHcxVlRUgGVZODo66pQ7Ojig1ER340xISEBVVRXGhIW1qr6hGB0cHVFSWmqglnH+jjFszJhW1beGdrQW9uK6r7NKme6v6spqFvYSwyeqQB8eurlyceSC8b17xmrrMTIiCRgOF6ysSqdcI6sCR2ynv460E2y8/QGGA/mRL6C8cgr8gJHgD277nzeNxjQPgUAAe3t7nYe+IQUAcHZ2BpfLbbReUH5+Ptzc9P+Qc3NzM2p7UzB6zsF//vMf7Nq1C1OnTsWHH36oLQ8KCsLrr7/+UMFUV1fj+++/R1paGtzd3TFr1ix06tSpyTr6xnpYtRIcbv0JdEyoC95Y0kf7/M21Sa2Kj8MAyWmV2BlTN+8i9V4VenQXY+p4Dxz/ufHCUBPHuOHk2QKD8wMasoYYH7UTJ08iKCio2c+AJZ08caLNx2gN7dgaQb48zAqrX2jt8zjjJ5c52DKYMUqIbbHVzY73t4Y1xPiwGDDQyKugOHMA0GjAFuZAIZGCPygUysvxlg6vSawFVkjk8/kIDAzE6dOnMfWvicQsy+L06dOIiIjQW2fYsGE4ffo0li1bpi2Lj4/HsGHDzBZnqyYkDhw4sFG5QCBAdbVxH3w/Pz9cuHABTk5OyM7OxsiRI1FaWoo+ffogPT0dH3zwAX777Tf06NHD4D6io6Px/vvv65R16z0Xnj7ztc8vXC7G7btXtc/5vLrs3dGBh+JSpbbc0YGPtHu6GXNDxaVKZGTLdMoys2UYFdy50bb9/aTo3lWMNR/dNri/hqwhxobs7e3B4XBQ+o9f4KVlZXB0cjJ6f/+Un5+PxMRErF61qtX7MBRjWWkpnP7xS701/o5x1erVrd6HNbRjW5WUrkJGXv3Z0uavXnk7MYOK6vpk107Cwf0C/WdVT1cu7CUcvPWCrbaMy2HQqysXIwP4WLa1Ag9z93lriLEhjbwaGlYNjthW546FjNgWrKxSbx1WVgGwLBoGwZbmgyOxBzhcgG2DGY2FRUZGYu7cuQgKCsKQIUOwZcsWVFdXY/78uvPWnDlz0KVLF+28hddeew2hoaH45JNPMHHiRHz33Xe4evWqwQnJpmB0ctCjRw8kJiY2mnx4/Phx9O3b16h9JScno7a2buwtKioKHh4eSExMhFQqRVVVFaZNm4ZVq1bhm2++MbiPqKioRrM4xz13See5XK5Gjlz3A1pUokDQAEek/VmX0IhFXPj1sUfcjw8MHivpTjk8u4h1yrp1ESOvoHFX36RwNySnViIto2UJkzXE2BCPx0Nvb28k3ryJ4OBgAHXZb2JiIqZMnmz0/v4pPj4eUqkUQ4bonwTV0hi9e/fGzcTERjFOnjKlzcTY1tuxrVKoAMU/Lv8rr2Lh42mDnMK6hFrIB7zcuLhwU6lvF0jJqsX6L3VPei+MFSG/hEX8FcVDn3StIUYdrBpsQQ64Xb0bXGrIgNvNG6pbF/VWUedmgNdnIAAGQF0wHIfOYKvK23xiYKkVEmfOnInCwkK8++67yMvLQ0BAAI4fP66ddJiVlaWzbEBwcDC++eYbrF69GitXrkTv3r0RFxdntjUOgFYkB5GRkViyZAlqamqg0Whw+fJlfPvtt4iOjsbu3btbHUhCQgJ27NgBqVQKALC1tcX777+P5557rsl6+i4XaTikYMgPR3Iwd6Ynsh/I6y4TfMELxSUK/PJb/eV8W9b1x/mEIhw8Vncy3n84Bzs2BuDFZzzx84UC+PWxx5Sx7ti47a7OvsUiLp4Y3hnbvkhv0d9urTFOmzYNn2zejN69e8OnTx/EHT4MhUKBMX+Nv2/atAmdOnXSZsMqlQpZWVkA6u7TUVxcjPT0dIhEInh4eGj3y7Is4uPjERYWBi636UlaLYlx8yefoHfv3ujj44PDcXFtMsa23o4txZWIIfH21D4X9+gK+wG+UJaUoyY7t4mapnHmhgLjhgpRWMqiuILFxGAhyqs0uJlWP3dn6dMS3ExT4XyiEgoVkFuse/JWqoDqGo1OuZ2Ygb2EgbND3Re2hzMXNUoNSis1kOm5csCaY1QmnocwbCbUBffB5meDFxACxoYP1e0rAADhmOfAVpVDmfATAECVlAB+/+EQjJwC5a1fwXFwBj9oNFQ3L9TvlMcHR1p/lRVj7wSOswc0NTJoqspaHJupmTSxMlJERITBYYSzZ882KnvmmWfwzDPPmDmqekYnBwsXLoRIJMLq1ashk8nw/PPPw8PDA1u3bm32RK7P37Ona2pq4O7urvNaly5dUFho/GS+lvg6NhtCIRdvRvSBrcQGSbfLsWJNks7Yexc3ERzs6y8vSk6txMoNf+D/5vTAvOe6Izdfjn/vSkP8Od3FZsJGuoBhgFPnGy9C055iDA0NRXlFBb6KiUFJaSl69eyJD9au1U6uKygsBNMg+y0pKUHE0qXa57GxsYiNjYW/vz82fvSRtvxGYiIKCgsR3spJfv+MsaK8HDFffYXSkhL07NULaz/4QBtjYUEBOA1mVZeUlGBpg/+wDWP8aONGbXnijRsoLCjAmPBwk8TY1tuxpaSB/TDsdIz2ud+mlQCA7H0HcWuB+Wdvn7qihIDHYNYYEUQCBuk5anx2UHes3lnKga3IuF+MIQP4mDCsfu2A5TPruvhjjstw6bbKUDWrjLE29SYUIgkEQ8eCkdiBLXwA2ZHd0MjrhjMZWwdwGpxVNVXlkB3eDWHIZEhmRUJTXQHVzQtQXjuj3Ybr0hXi6Yu1z4UhdT13qjtXUXNqv1F/J3k0jF4hsSGZTIaqqiq9yz22BIfDQb9+/WBjY4PU1FTs3bsXM2bM0L5+/vx5PP/887h/37iVBVu6QiJpXnMrJLYFza2Q2BY0t0JiW2CKFRLNrSUrJJLmNbdCYlvwKFZIPGlgKMdY4QOa7622Nkb3HDQkFoshFoub39CANWvW6Dy3tbXVeX706FGzr+hGCCGkY7LUvRWsQYuSg4EDB7Z48ZTr16+3+OD/TA7+6eOPzZ85EkII6Zjols2GtSg5mNrgpi41NTX47LPP4Ofnp73G8rfffsMff/yBV155xSxBEkIIIeTRaVFy0PAX/sKFC/Hqq6/igw8+aLRNdnYTd+0ihBBC2hArmApkMUYvn/zDDz9gzpw5jcpfeOEFxMbGmiQoQgghxNxYMCZ5tEdGJwcikQi//vpro/Jff/0VQuHD3cqUEEIIIZZn9NUKy5Ytw+LFi3H9+nXtqmuXLl3Cnj178M4775g8QEIIIcQcaFjBMKOTg7fffhs9e/bE1q1b8dVXXwEA+vbti//+97949tlnTR4gIYQQYg50tYJhrVrn4Nlnn6VEgBBCCGmnWr0IklKpREFBAVhWd81vT09PAzUIIYSQtoMWQTLM6OQgNTUV//rXv3Dxou4dujQaDRiGgVrdtu/CRQghhAA056ApRicH8+bNg42NDf73v//B3d29xSsnEkIIIcQ6GJ0cJCYm4tq1a/D19TVHPIQQQsgjoWmnaxSYgtHJgZ+fH4qKiswRCyGEEPLI0JwDw4xeBOmjjz7Cm2++ibNnz6K4uBgVFRU6D0IIIcQaaDSmebRHjEZj3J/G4dTlE/+ca9CWJiRW/ucNS4fQrCjVakuH0G5Yw73p6f02jfFvDbF0CM1KOZBs6RDahcinzN/l/8NvbPMbtcAzjxv9O7vNM3pY4cyZM+aIgxBCCHmk2uuvflMwOjkIDQ01RxyEEELII8XSCokGtWoRpLKyMnzxxRe4c+cOAOCxxx7Dv/71L0ilUpMGRwghhJBHz+iBkqtXr6JXr1749NNPUVJSgpKSEmzevBm9evXC9evXzREjIYQQYnI0IdEwo3sOli9fjilTpmDXrl2wsamrXltbi4ULF2LZsmU4f/68yYMkhBBCTK29nthNwejk4OrVqzqJAQDY2NjgzTffRFBQkEmDI4QQQsijZ/Swgr29PbKyshqVZ2dnw87OziRBEUIIIebGakzzaI+MTg5mzpyJBQsWYP/+/cjOzkZ2dja+++47LFy4ELNmzTJHjIQQQojJaTSMSR7tkdHDCps2bQLDMJgzZw5qa2sBADweD4sXL8aHH35o8gAJIYQQ8mgZnRzw+Xxs3boV0dHRSE9PBwD06tULYrHY5MERQggh5kITEg1r1ToHACAWi+Hg4KD9NyGEEGJN2ut8AVMwes5BbW0t3nnnHUilUnh5ecHLywtSqRSrV6+GSqUyR4yEEEKIydE6B4YZ3XOwdOlSHDx4EBs3bsSwYcMAAAkJCXjvvfdQXFyMzz//3ORBEkIIIeTRMTo5+Oabb/Ddd99h/Pjx2rL+/fujW7dumDVrFiUHhBBCrEJ7/dVvCkYnBwKBAF5eXo3Ke/ToAT6fb4qYzI7nHwz+oFAwYjuwRbmoOR8HNj/bcAW+EIJh42HTqx8YoRiailLU/HIE6sy6W7NyPXqAP2gUOJ27gGMrhfzYXtTe+8MksU4MFiC4Hx8iIYN7OWrsPy1HYVnLbjM6ZrAAT4UIcea6ArFna7Tlw/15CPLlo6sLFyIBgze2l0OuaL8xWsv73dbb0VpibI7TiCD0XLEA0kH9IPRwwdUZryD/yGnzHfAfNBoNrp78D5Iv/wCFvAJuXoMQMm0NpJ29DNa58fP/w5+/x6Os4B64PCHcvAZi6PgVcHDpqd3myI4XkXvvik69vkNnYuSM99tljKZAcw4MM3rOQUREBD744AMoFPX/exUKBdavX4+IiAiTBmcONr0HQBAyGYrL8ZB9twXqogcQT1kIRiTRX4HDhXjqInDsHVHzUwyqYzai5ucD0FSX12/D40Nd9ACKc3EmjTVsMB+hAQJ8d1qOTd9UQanSYMl0CWy4zdf1dOVieH8+7heqG73Gs2FwO6MWJy8//DdwW4/RWt7vtt6O1hJjS3AlYlTcSsHvr1rmhHTz7G78/msMQqa/h2lLv4cNX4RjXyxErcrw3//g3hU8Fvw8pkbsx6SX9oBV1+LY7oVQKWU62/kOeQYvvvOL9vH4xDfabYwdQUlJCWbPng17e3s4ODhgwYIFqKqqanL7pUuXwsfHByKRCJ6ennj11VdRXl5usI4hRicHN27cwP/+9z907doVYWFhCAsLQ9euXXH06FHcvHkT06dP1z7aIn7ASKj+uITaO1fBlhZAceYgNLUq8PyG6N2e5zcYjFAM+bG9UOdmQFNZCvWDe2CLcrXbqDNToPztBGrv/W7SWJ8YKMCJSzVISq/FgyIW+47LILVlMMCb1/TfyAPmTRDh23gZ5DWNU+OzN5SIv6JARm5tu4/RWt7vtt6O1hJjSxSeOI+7a7Yg//CpR3K8hjQaDZIu7MOgJ1+G12NPopO7D56Y+RFkFQXI+MNwPBMX7oZP0HQ4ufVGJw9fjHo2GlVlD1B4X7fHyoYvgtius/bBF9q2yxhNpa1PSJw9ezb++OMPxMfH43//+x/Onz+PRYsWGdz+wYMHePDgATZt2oTff/8de/fuxfHjx7FgwQKjj230sIKDgwNmzJihU9atWzejD2wRHC44Ll2gvPZzg0IN1Nmp4Lh111vFpocf1LmZEIROg03Px6CRV6P27g0or50x66eik5SB1JaD5Kz6L8waJZCRp4aXOxfXUgxfGTJztAi/36tFSpYa44aaLcS2H6OVvN9tvh2tJEZrUFlyH7LKQnTpHawtE4js4NKtP/IzE+EdMLFF+1HWVAIAhGKpTnnajaNIu34EIrvO6O43CoOefAU8vqjdxWgqbMtGxCzizp07OH78OK5cuaK9b9F//vMfTJgwAZs2bYKHh0ejOv369UNsbKz2ea9evbB+/Xq88MILqK2t1bknUnOMTg7++9//GlulzWBEEjAcLliZbreMRlYFrqOL/jrSTuB2dYQq5QbkR74Ax8EZwtBpAIcL5eV4s8VqL67r1KmU6Z6QKqtZ2EsML9cZ6MNDN1cuNn5tuOvJVNp6jNbyfrf1dgSsI0ZrIKssBACIbDvplIvsnCGrLGrRPjQsi4tHNsDNaxCc3Ppoy70DJsHO0QNiexeU5N7FpZ82oawwA2Pn/KfdxdjWKBQKnaF2oG5+nkAgaPU+ExIS4ODgoHNDw7CwMHA4HFy6dAnTpk1r0X7Ky8thb29vVGIAtCI5kMvl0Gg02oWPMjMzcejQIfj5+SE8PNyofV2/fh2Ojo7o0aMHACAmJgY7duxAVlYWunfvjoiICDz33HNN7kPfm6JU1ULAa/X6TjoYMNDIq6A4cwDQaMAW5kAhkYI/KNSkJ4sgXx5mhdVnz5/HVRu9DwdbBjNGCbEtthq1jYd2H5o1xPiwHsX7bQ3taA0xWoPU60dx/uAa7fPx83c89D4vxK1FSX4qnlr8jU653+Mztf/u5O4DsX1n/G/nPJQXZ0HaydOqYzQXU3UGRkdH4/33deewrFmzBu+9916r95mXlwcXF90fMTY2NnByckJeXl6L9lFUVIQPPvigyaEIQ4w+gz711FOYPn06Xn75ZZSVlWHIkCHg8/koKirC5s2bsXjx4hbva/78+fjkk0/Qo0cP7N69G6+++ipeeuklvPjii0hJScFLL70EmUyGf/3rXwb3oe9NeXvcMKycMLzRthp5NTSsGhyxLRr2JjFiW7CySr37Z2UVdX1PDT5FbGk+OBJ7gMMFWNN86yWlq5CRV7+vvyd52YkZVFTXH9tOwsH9Av3H9HTlwl7CwVsv1I/hcTkMenXlYmQAH8u2VjzUfwZriLGhtvp+W0M7WkOM1qC73xN42rO/9rm6VgkAkFcVQ2Jf/8UvryxCJ4++ze7vQtxaZN45iymLv4Ktg1uT27r8ddyKoswmT7zWEKO5mOrzFxUVhcjISJ0yQ70Gb7/9Nj766KMm93fnzp2HjqmiogITJ06En59fq5IUo5OD69ev49NPPwUAHDhwAG5ubrhx4wZiY2Px7rvvGpUcpKamonfv3gCAzz77DFu3bsVLL72kfX3w4MFYv359k8mBvjdFuXuN/o1ZNdiCHHC7eje49IwBt5s3VLcu6q2izs0Ar89AAAyAuk8Sx6Ez2KpykyUGAKBQAYp/XBJWXsXCx9MGOYV1/1mFfMDLjYsLN5V695GSVYv1X+qe9F4YK0J+CYv4K4qH/o9gDTHqaKPvtzW0ozXEaA34QludCXcajQZiu87ISU2A818nWmVNFQqyb8FvmOG72mo0Gvx6+AP8+fspTPm/fbB36trssYsf1F16K7bXP4RmTTG2dcYMIaxYsQLz5s1rcpuePXvCzc0NBQUFOuW1tbUoKSmBm1vTSVdlZSXGjRsHOzs7HDp0CDxe05OG9TE6OZDJZLCzswMAnDx5EtOnTweHw8Hjjz+OzMxMo/YlFotRVFSE7t27IycnB0OG6M4gHzp0KP78888m96HvTalsYkhBmXgewrCZUBfcB5ufDV5ACBgbPlS36669FY55DmxVOZQJPwEAVEkJ4PcfDsHIKVDe+hUcB2fwg0ZDdfNC/U55fHCkztqnjL0TOM4e0NTIoKkqa0lT6HXmhgLjhgpRWMqiuILFxGAhyqs0uJlWP/lr6dMS3ExT4XyiEgoVkFus+4WuVAHVNRqdcjsxA3sJA2eHunFkD2cuapQalFZqINMzm9yaY7SW97utt6O1xNgSXIkYEu/6X6niHl1hP8AXypJy1GTnNlHz4TEMA/8Rc3D95x2QOnvBzqkLrp78N8T2LvB6LEy73dGd89DjsTD0G/4CgLpf42k3/oexc7eDJ5Ro5wXwhXaw4QlRXpyFtBv/g6fvSAjFDijOvYuEo9Fw7xGETu4+7S5GU7HEOgedO3dG586dm91u2LBhKCsrw7Vr1xAYGAgA+Pnnn8GyLIYONTyzt6KiAmPHjoVAIMCRI0cgFApbFafRyYG3tzfi4uIwbdo0nDhxAsuXLwcAFBQUwN7e3qh9jR8/Hp9//jl2796N0NBQHDhwAAMGDNC+/v3338Pb29vYEJtUm3oTCpEEgqFjwUjswBY+gOzIbmjkdROmGFsHcBr8pNFUlUN2eDeEIZMhmRUJTXUFVDcv1M1e/wvXpSvE0+t7TIQhUwAAqjtXUXNqf6tjPXVFCQGPwawxIogEDNJz1PjsoO74rbOUA1uRcfcTDxnAx4Rh9R+Y5TPrfjXEHJfh0m3j7o/R1mO0lve7rbejtcTYEtLAfhh2Okb73G/TSgBA9r6DuLUgyuTH+6cBoxZCpZTjfOy7UNZUwM0rEBMW7IINr/5HTkVxFmqqS7XPbyd8CwA4+v/m6Oxr1LMb4BM0HVwuDzmpF5F04UvUKuWQSN3Rwz8cg55seU+utcVoChqTdV8Z95lvib59+2LcuHF46aWXsGPHDqhUKu08vL+vVMjJycGTTz6Jffv2YciQIaioqEB4eDhkMhm++uorVFRUoKKiAkBdUsLltmBRkr//Io2RrXPgwAE8//zzUKvVGD16NOLj6yZpRUdH4/z58/jpp59avK8HDx5g+PDh8PT0RFBQED7//HMEBgaib9++SElJwW+//YZDhw5hwoQJxoSIyv+0/UU1olSrLR1CuxHNW2fpEJpF77dpjH9L//oUbUnKgWRLh9AuRD5l+hPuP/3nmGmSg6UTzRNrSUkJIiIicPToUXA4HMyYMQP//ve/YWtbl0BnZGSgR48eOHPmDEaNGoWzZ8/iiSee0LuvP//8U+/qxoYY3XPw9NNPY8SIEcjNzdX5lf/kk0+2+NKKv3l4eODGjRv48MMPcfToUWg0Gly+fBnZ2dkYPnw4fv31V53LOAghhJCOwsnJCd98843B1728vHR6P0aNGmWy3pBWXe/n5uaGqqoqxMfHY+TIkRCJRBg8eDAYxvjsycHBAR9++CE+/PDD1oRCCCGEtEpbXgTJ0oxePrm4uBhPPvkk+vTpgwkTJiA3t24Cz4IFC7BixQqTB0gIIYSYQ1tfPtmSjE4Oli9fDh6Ph6ysLO1CSAAwc+ZMHD9+3KTBEUIIIeTRM3pY4eTJkzhx4gS6dtW9hrV3795GX8pICCGEWArdstkwo5OD6upqnR6Dv5WUlDzUOtKEEELIo9RehwRMwehhhZCQEOzbt0/7nGEYsCyLjRs3GryEghBCCCHWw+ieg40bN+LJJ5/E1atXoVQq8eabb+KPP/5ASUkJfv31V3PESAghhJicxmTjCuZfk+FRM7rnoF+/frh79y5GjBiBp556CtXV1Zg+fTpu3LiBXr16mSNGQgghxORYjWke7ZFRPQcqlQrjxo3Djh07sGrVKnPFRAghhBALMio54PF4uHXrlrliIYQQQh4ZmpBomNHDCi+88AK++OILc8RCCCGEPDIsqzHJoz0yekJibW0t9uzZg1OnTiEwMBASiUTn9c2bN5ssOEIIIcRcqOfAMKOTg99//x2DBg0CANy9e1fntdbcW4EQQgghbYvRycGZM2ea34gQQghp46jnwLBW3ZWxrYtSrbZ0CM2K5q2zdAgtYg1taQ0xWsP7bQ3tmHIg2dIhNMvnaV9Lh9Asa2jHR4Gl7MAgoyckEkIIIaR9a5c9B4QQQkhzNKylI2i7KDkghBDSIWloWMEgGlYghBBCiA7qOSCEENIhsTSsYBAlB4QQQjokGlYwjIYVCCGEEKKDeg4IIYR0SO30tggmQckBIYSQDklD2YFBlBwQQgjpkGjKgWE054AQQgghOqjngBBCSIfE0rCCQZQcEEII6ZDoUkbDaFiBEEIIITqo54AQQkiHRDdeMqxDJwcTgwUI7seHSMjgXo4a+0/LUVjWsk/LmMECPBUixJnrCsSerdGWD/fnIciXj64uXIgEDN7YXg65wvjYeP7B4A8KBSO2A1uUi5rzcWDzsw1X4AshGDYeNr36gRGKoakoRc0vR6DOrLtvO9ejB/iDRoHTuQs4tlLIj+1F7b0/jA9Mj7bcjtYSo7W83229HTUaDa6e/A+SL/8AhbwCbl6DEDJtDaSdvQzWufHz/8Ofv8ejrOAeuDwh3LwGYuj4FXBw6and5siOF5F774pOvb5DZ2LkjPdbF2gLOI0IQs8VCyAd1A9CDxdcnfEK8o+cNtvxGmpP7dgUloYVDOqwwwphg/kIDRDgu9NybPqmCkqVBkumS2DDbb6upysXw/vzcb9Q3eg1ng2D2xm1OHm59Wcym94DIAiZDMXleMi+2wJ10QOIpywEI5Lor8DhQjx1ETj2jqj5KQbVMRtR8/MBaKrLGwTGh7roARTn4lodlz5tuR2tJUZreb/bejsCwM2zu/H7rzEImf4epi39HjZ8EY59sRC1KsP7fnDvCh4Lfh5TI/Zj0kt7wKprcWz3QqiUMp3tfIc8gxff+UX7eHziGw8db1O4EjEqbqXg91cf/YmzPbUjaZ0Omxw8MVCAE5dqkJReiwdFLPYdl0Fqy2CAN6/JenweMG+CCN/GyyCvaZx1nr2hRPwVBTJya1sdGz9gJFR/XELtnatgSwugOHMQmloVeH5D9G7P8xsMRiiG/NheqHMzoKkshfrBPbBFudpt1JkpUP52ArX3fm91XPq05Xa0lhit5f1u6+2o0WiQdGEfBj35MrweexKd3H3wxMyPIKsoQMYfpwzWm7hwN3yCpsPJrTc6efhi1LPRqCp7gML7uj0tNnwRxHadtQ++0Pah4m1O4YnzuLtmC/IPG47dHNpbOzZFo9GY5NEedcjkoJOUgdSWg+Ss+i+jGiWQkaeGl3vTP4Nmjhbh93u1SMlq/AvIJDhccFy6QJ2d2qBQA3V2Kjhu3fVWsenhB3VuJgSh0yBZ8C7Ez68AP2g0wDDmifEvbbodrSVGK3m/23w7AqgsuQ9ZZSG69A7WlglEdnDp1h/5mYkt3o+yphIAIBRLdcrTbhzFl+89ju8/mYxLP30ClVJukrjbmo7UjiyrMcnDXEpKSjB79mzY29vDwcEBCxYsQFVVVYvqajQajB8/HgzDIC4uzuhjW3TOwdKlS/Hss88iJCSk1ftQKBRQKHS7utS1CnBtBAbr2IvrcqJKme6bWlnNwl5i+As20IeHbq5cbPy6ZW9OazAiCRgOF6xM9xgaWRW4ji7660g7gdvVEaqUG5Af+QIcB2cIQ6cBHC6Ul+PNFmtbbse/tfUYreX9buvtCACyykIAgMi2k065yM4ZssqiFu1Dw7K4eGQD3LwGwcmtj7bcO2AS7Bw9ILZ3QUnuXVz6aRPKCjMwds5/TPcHtBHUjm3H7NmzkZubi/j4eKhUKsyfPx+LFi3CN99802zdLVu2gHmIHwwWTQ62b9+Ozz77DL169cKCBQswd+5cuLm5GbWP6OhovP++7pjc4PC3MGRslPZ5kC8Ps8JE2uefx1UbHauDLYMZo4TYFluNWvP+ADIaAwYaeRUUZw4AGg3YwhwoJFLwB4Wa9GRhDe1oDTE+rEfxfltDO6ZeP4rzB9don4+fv+Oh93khbi1K8lPx1GLdL1+/x2dq/93J3Qdi+8743855KC/OgrST50Mf15I6cju25RGBO3fu4Pjx47hy5QqCgoIAAP/5z38wYcIEbNq0CR4eHgbrJiYm4pNPPsHVq1fh7u7equNb/GqFkydP4ujRo9i0aRPeeecdjB8/Hi+99BImTJgADqf5UY+oqChERkbqlL21o0bneVK6Chl59d9Of0+gshMzqKiu/3TYSTi4X6D/W8zTlQt7CQdvvVA/PsblMOjVlYuRAXws21phkg+aRl4NDasGR2yLhnPAGbEtWFml3jqsrAJgWZ1POluaD47EHuBwAdY038zW0I7WEGNDbfX9toZ27O73BJ727K99rq5VAgDkVcWQ2Nf3usgri9DJo2+z+7sQtxaZd85iyuKvYOvQ9I8Ul7+OW1GUafXJQUduR1PdeElfD7ZAIIBAYLgHuzkJCQlwcHDQJgYAEBYWBg6Hg0uXLmHatGl668lkMjz//PPYvn270T+2G7J4cuDv748nn3wSH3/8MQ4dOoQ9e/Zg6tSpcHV1xbx58zB//nx4e3sbrK/vDeDa6L7hChWg+MflVuVVLHw8bZBTWPcfQcgHvNy4uHBTqfc4KVm1WP+l7pf1C2NFyC9hEX9FYboMlFWDLcgBt6t3g0vPGHC7eUN166LeKurcDPD6DATAAKgLhOPQGWxVuckSA8A62tEaYtTRRt9va2hHvtBWZzKbRqOB2K4zclIT4PzXSUxZU4WC7FvwGzbL4H40Gg1+PfwB/vz9FKb83z7YO3Vt9tjFD+ouGRXb6x/6sSYduR1NdSmjvh7sNWvW4L333mv1PvPy8uDiotsuNjY2cHJyQl5ensF6y5cvR3BwMJ566qlWHxtoA8nB33g8Hp599lk8++yzyMrKwp49e7B37158+OGHUKtN3yd55oYC44YKUVjKoriCxcRgIcqrNLiZptJus/RpCW6mqXA+UQmFCsgt1v2yVKqA6hqNTrmdmIG9hIGzQ12vh4czFzVKDUorNZDpmamtjzLxPIRhM6EuuA82Pxu8gBAwNnyobtddHywc8xzYqnIoE34CAKiSEsDvPxyCkVOgvPUrOA7O4AeNhurmhfqd8vjgSJ21Txl7J3CcPaCpkUFTVWZU2zXUltvRWmK0lve7rbcjwzDwHzEH13/eAamzF+ycuuDqyX9DbO8Cr8fCtNsd3TkPPR4LQ7/hLwCo+6WbduN/GDt3O3hCiXbMnS+0gw1PiPLiLKTd+B88fUdCKHZAce5dJByNhnuPIHRy9zG+IVuIKxFD4l3/a1rcoyvsB/hCWVKOmuzcJmo+nPbWjo+Cvh5sQ70Gb7/9Nj766KMm93fnzp1WxXHkyBH8/PPPuHHjRqvqN9RmkoOGPD098d5772HNmjU4dco8l/GcuqKEgMdg1hgRRAIG6TlqfHZQd2zUWcqBrci4CR0hA/iYMEyofb58Zl1GHnNchku3VYaq6ahNvQmFSALB0LFgJHZgCx9AdmQ3NPK6SV2MrQM4DTJeTVU5ZId3QxgyGZJZkdBUV0B18wKU185ot+G6dIV4+mLtc2HIFACA6s5V1Jzab9Tf2FBbbkdridFa3u+23o4AMGDUQqiUcpyPfRfKmgq4eQViwoJdsOHVf1FXFGehprpU+/x2wrcAgKP/b47OvkY9uwE+QdPB5fKQk3oRSRe+RK1SDonUHT38wzHoycUwJ2lgPww7HaN97rdpJQAge99B3FoQZaiaSbSndmyKqYYVjBlCWLFiBebNm9fkNj179oSbmxsKCgp0ymtra1FSUmJwuODnn39Geno6HBwcdMpnzJiBkJAQnD17tkUxAgCjseBFmj169MDVq1fRqVOn5jc2QsTm8uY3srBo3jpLh9AiUarVlg6hXbCG99sa3uuevewtHUKzfJ72tXQIzUo5kGzpEJoV+ZR5L8UGgCWbykyyn+2vO5hkPw3duXMHfn5+uHr1KgIDAwHUzdEbN24c7t+/r3dCYl5eHoqKdK8o8ff3x9atWzF58mT06NGjxce3aM/Bn3/+acnDE0IIIW1S3759MW7cOLz00kvYsWMHVCoVIiIi8Nxzz2kTg5ycHDz55JPYt28fhgwZAjc3N729Cp6enkYlBkAHXQSJEEIIYTWmeZjL119/DV9fXzz55JOYMGECRowYgZ07d2pfV6lUSElJgUwma2IvrdMm5xwQQggh5maqOQfm4uTk1OSCR15eXs0u39zamQPUc0AIIYQQHdRzQAghpENqrzdNMgVKDgghhHRI5rxpkrWj5IAQQkiHRD0HhtGcA0IIIYTooJ4DQgghHVJbv1rBkig5IIQQ0iFRcmAYDSsQQgghRAf1HBBCCOmQTHXL5vaIkgNCCCEdEg0rGEbDCoQQQgjRQT0HhBBCOiRa58AwSg4sJEq12tIhtEg0b52lQ2iWNbSlNcRoDe/1Lmy0dAjNSjmQbOkQmuXztK+lQ2ieKsXsh6AVEg2jYQVCCCGE6KCeA0IIIR0STUg0jJIDQgghHRLNOTCMkgNCCCEdkoZlLR1Cm0VzDgghhBCig3oOCCGEdEh0tYJhlBwQQgjpkGjOgWE0rEAIIYQQHdRzQAghpEOiSxkNo+SAEEJIh0TJgWE0rEAIIYQQHdRzQAghpENiNbTOgSGUHBBCCOmQaFjBMBpWIIQQQoiODt1zMDFYgOB+fIiEDO7lqLH/tByFZS3rZhozWICnQoQ4c12B2LM12vLh/jwE+fLR1YULkYDBG9vLIVe03xh5/sHgDwoFI7YDW5SLmvNxYPOzDVfgCyEYNh42vfqBEYqhqShFzS9HoM6su80t16MH+INGgdO5Czi2UsiP7UXtvT9aF1wDbb0drSFGa3ivNRoNrp78D5Iv/wCFvAJuXoMQMm0NpJ29DNa58fP/w5+/x6Os4B64PCHcvAZi6PgVcHDpqd3myI4XkXvvik69vkNnYuSM99tljC3lNCIIPVcsgHRQPwg9XHB1xivIP3LabMczNeo5MKzD9hyEDeYjNECA707LsembKihVGiyZLoENt/m6nq5cDO/Px/1CdaPXeDYMbmfU4uTlhzhLWEmMNr0HQBAyGYrL8ZB9twXqogcQT1kIRiTRX4HDhXjqInDsHVHzUwyqYzai5ucD0FSXNwiOD3XRAyjOxT1UbA219Xa0hhit5b2+eXY3fv81BiHT38O0pd/Dhi/CsS8WolZl+O9/cO8KHgt+HlMj9mPSS3vAqmtxbPdCqJQyne18hzyDF9/5Rft4fOIb7TbGluJKxKi4lYLfXzVfAmJOGo3GJI/2qMMmB08MFODEpRokpdfiQRGLfcdlkNoyGODNa7IenwfMmyDCt/EyyGsafyjO3lAi/ooCGbm17T5GfsBIqP64hNo7V8GWFkBx5iA0tSrw/Ibo3Z7nNxiMUAz5sb1Q52ZAU1kK9YN7YItytduoM1Og/O0Eau/9/lCxNdTW29EaYrSG91qj0SDpwj4MevJleD32JDq5++CJmR9BVlGAjD9OGaw3ceFu+ARNh5Nbb3Ty8MWoZ6NRVfYAhfd1ezFs+CKI7TprH3yhbbuM0RiFJ87j7potyD9sOPa2jGVZkzzaow6ZHHSSMpDacpCcVf+FWaMEMvLU8HJv+qfazNEi/H6vFilZjX+ldagYOVxwXLpAnZ3aoFADdXYqOG7d9Vax6eEHdW4mBKHTIFnwLsTPrwA/aDTAMGYLs823ozXEaCXvdWXJfcgqC9Gld7C2TCCyg0u3/sjPTGzxfpQ1lQAAoViqU5524yi+fO9xfP/JZFz66ROolPJ2GSMhQBuYc7Bt2zZcvnwZEyZMwHPPPYeYmBhER0eDZVlMnz4da9euhY2N4TAVCgUUCt3uOHWtAlwbgcE69uK6nKhSpvtLq7Kahb3E8JdXoA8P3Vy52Ph1VUv+tIfS1mNkRBIwHC5Yme5xNLIqcB1d9NeRdgK3qyNUKTcgP/IFOA7OEIZOAzhcKC/HmyXOtt6OQNuP0Vrea1llIQBAZNtJp1xk5wxZZVGL9qFhWVw8sgFuXoPg5NZHW+4dMAl2jh4Q27ugJPcuLv20CWWFGRg75z/tLsaOhOYcGGbR5GDdunXYuHEjwsPDsXz5cmRmZuLjjz/G8uXLweFw8Omnn4LH4+H99w2PZ0VHRzd6fXD4WxgyNkr7PMiXh1lhIu3zz+OqjY7VwZbBjFFCbIutRq0ZfqRZQ4wPiwEDjbwKijMHAI0GbGEOFBIp+INCTXbCsIZ2tIYYH9ajeK9Trx/F+YNrtM/Hz9/x0Pu8ELcWJfmpeGrxNzrlfo/P1P67k7sPxPad8b+d81BenAVpJ0+rjrEj09A6BwZZNDnYu3cv9u7di+nTp+PmzZsIDAzEl19+idmzZwMAfH198eabbzaZHERFRSEyMlKn7K0dNTrPk9JVyMir/wb9e5KXnZhBRXV95mgn4eB+gf5vWk9XLuwlHLz1Qv0YHpfDoFdXLkYG8LFsawUeZl6KNcTYkEZeDQ2rBkdsi4b/vRixLVhZpd46rKwCYFk0DIItzQdHYg9wuAD78Gc5a2hHa4ixobb6Xnf3ewJPe/bXPlfXKgEA8qpiSOzrezTklUXo5NG32f1diFuLzDtnMWXxV7B1cGtyW5e/jltRlNnkidcaYiREH4smBw8ePEBQUBAAYMCAAeBwOAgICNC+PmjQIDx48KDJfQgEAggEukMIXBvdb0WFClD845Kw8ioWPp42yCms+88q5ANeblxcuKnUe5yUrFqs/1L3i/CFsSLkl7CIv6J46C9ia4hRB6sGW5ADblfvBpefMeB284bq1kW9VdS5GeD1GQjg/7d352FV1fkfwN8XuJeLXK7IzlUBV0ASVEgCJCtxyxi1yZGiAkVtAREpCnQa9HFUNLNyzWWEKWWyyaU0TUk0c2NTSPZFVH5ulGwCssj9/P7w59HzYxEcmgP6eT3PfR7O/j7fLw/3w1llAO6G0TE2h7a6slO+LIDu0Y7dIaNIF+1rhVIluuCOiNDDyBxXCk7D7P++aBvqqlFa8iuGeLza6nqICCe/W4LizJ/wp7e+hNqkz0O3ffPq3dsxe6hbPq3SnTI+ybr6aYWysjLMnTsX+/btg46ODv785z/j888/h0rV9oWmp0+fxsKFC5GUlARdXV0MGzYMhw4dgoGBQZvLPUjS4sDKygrZ2dmwsbFBQUEBmpqakJ2dDScnJwBAVlYWLCz+mF/so+fqMcFdid/KtbhZpcUkTyUqqwkZhY3CPHNfMURGYSOOpzegvhG4dlP8B72hEaipI9F4ox4yqA1lMDO+ex5ZY6aLugZC+S1CbQtXk3fnjA3px6H0mY6m0v+B9kYJ5MO8IdNToDH77r3WyrF+0FZXouH0QQBA4/nTUDh7Qf/ZP6Hh15PQMTaDwu0FNGacuL9SuQI6Pc2EQZnaBDpmGlBdLai6ot3ZHtTV27E7ZOwOfS2TyTB01Js4m/gFeprZwcikN1IPr0EPtQXsnHyE+fZtDkQ/Jx885fU6gLv/jRee24/xAeshVxoK1wUolEbQkytRefMyCs/th43Ds1D2MMbNa/k4vW85rPu5wdTa/rHL2BG6hj1gOPD+UYke/fpA7eKAhrJK1JVca2PJrqGrFwf+/v64du0aEhIS0NjYiBkzZmDOnDmIj49vdZnTp09jwoQJiIqKwtq1a6Gnp4eMjAzo6HTs/gNJiwN/f3+8+eabmDx5Mo4cOYIPPvgA77//Pm7evAmZTIalS5filVde+UO2/VNKA/TlMrw61gAG+jIUXWnCht3i87dmPXWgMujY1dXeLgq86KEUhudPv1vhffVjLZKyG1tbrFtmvFOQgXoDQ+i7j4fM0Aja366i9vutoNt3L1yTqYyh88C/sFRdidrvtkLp7QvDV8NBNVVozDiBhrSjwjy6Fn3Q4+V3hGGl958AAI05qaj7aWeH9vOert6O3SFjd+lrl+dmobHhNo7v+hsa6qpgZeeKF4O2QE9+/+hi1c3LqKspF4azT/8LALBv05uidT33l2Wwd3sZurpyXCk4hfMn/ok7Dbdh2NMa/YaOw4gx7+BRdIeM7dXT9Sl4HPlKGB6yagEAoOTL3fg1KKq1xVg75OTk4Mcff0RKSopwhH3t2rV48cUXsWrVKmg0mhaXmz9/PkJDQxEZGSmMs7fveIEoIwmf4KDVahETE4PTp0/D09MTkZGR2LlzJz744APU1tbC19cX69atg6FhKw9aaUXI6sqHz8TaZbn871JHeKioxr9KHeGx0B36eovNSqkjPBbsX3GQOsJDTWrM+8O3MT4gvVPW8/1mx2Z3zbV0yrsjtm3bhvfeew/l5feLxDt37kCpVOLf//43pk6d2myZ0tJSWFpaYs2aNfjXv/6FoqIiODg4YOnSpRg1alSHti/pkQMdHR0sWLBANM7Pzw9+fn4SJWKMMfak6KzTCi3dNRcdHY1FixY98jqvX7/e7LS6np4eTExMcP369RaXuXDhAgBg0aJFWLVqFYYNG4Yvv/wSY8aMQWZmJgYNGtTu7T+RD0FijDHGOktUVBQqKytFn6iolk+rREZGQiaTtfnJzc19pBz3ntb41ltvYcaMGRg+fDg+/fRT2NvbY9u2bR1al+QPQWKMMcakQJ306OOOnEJ47733EBgY2OY8/fv3h5WVFUpLS0Xj79y5g7KyMlhZtXwbq7W1NQBgyJAhovGOjo64fPlyu/Ldw8UBY4yxJ5IUdyuYm5vD3Nz8ofN5eHigoqICaWlpcHV1BQAkJiZCq9XC3d29xWXs7Oyg0WiQlye+XiM/Px8TJ07sUE4+rcAYY+yJRKTtlM8fwdHRERMmTMDs2bORnJyMkydPIiQkBH5+fsKdCleuXIGDgwOSk5MB3L1VNiIiAmvWrMG3336LwsJCfPTRR8jNzUVQUFCHts9HDhhjjLEuaMeOHQgJCcGYMWOEhyCtWbNGmN7Y2Ii8vDzU1t5/dXdYWBjq6uowf/58lJWVwcXFBQkJCRgwYECHts3FAWOMsSeStos/BMnExKTNBx7Z2dmhpacRREZGip5z8Ci4OGCMMfZE6qwLEh9HfM0BY4wxxkT4yAFjjLEnUld/t4KUuDhgjDH2RPqj7jR4HPBpBcYYY4yJ8JEDxhhjTyQ+rdA6Lg4YY4w9kfhuhdbxaQXGGGOMiRF7qLq6OoqOjqa6ujqpo7SKM3YOztg5OGPn4IxMKjKiFh6vxESqqqrQs2dPVFZWQq1WSx2nRZyxc3DGzsEZOwdnZFLh0wqMMcYYE+HigDHGGGMiXBwwxhhjTISLg3bQ19dHdHQ09PX1pY7SKs7YOThj5+CMnYMzMqnwBYmMMcYYE+EjB4wxxhgT4eKAMcYYYyJcHDDGGGNMhIsDxhhjjIlwcfAQ69evh52dHZRKJdzd3ZGcnCx1JJHjx4/D19cXGo0GMpkMe/fulTpSM8uXL8fTTz8NIyMjWFhYYMqUKcjLy5M6lsjGjRvh7OwMtVoNtVoNDw8PHDx4UOpYbYqJiYFMJkNYWJjUUQSLFi2CTCYTfRwcHKSO1cyVK1fw+uuvw9TUFAYGBhg6dChSU1OljiWws7Nr1o4ymQzBwcFSRxM0NTXho48+Qr9+/WBgYIABAwZgyZIl4GvcHw9cHLRh586dCA8PR3R0NM6ePQsXFxeMHz8epaWlUkcT1NTUwMXFBevXr5c6Sqt+/vlnBAcH48yZM0hISEBjYyPGjRuHmpoaqaMJ+vTpg5iYGKSlpSE1NRUvvPACJk+ejKysLKmjtSglJQWbNm2Cs7Oz1FGacXJywrVr14TPiRMnpI4kUl5eDi8vL8jlchw8eBDZ2dn45JNP0KtXL6mjCVJSUkRtmJCQAACYNm2axMnuW7FiBTZu3Ih169YhJycHK1aswMqVK7F27Vqpo7HOIOmbHbq4kSNHUnBwsDDc1NREGo2Gli9fLmGq1gGgPXv2SB3joUpLSwkA/fzzz1JHaVOvXr1o69atUsdo5tatWzRo0CBKSEig0aNH07x586SOJIiOjiYXFxepY7Tpww8/pFGjRkkdo0PmzZtHAwYMIK1WK3UUwaRJk2jmzJmicS+//DL5+/tLlIh1Jj5y0IqGhgakpaXBx8dHGKejowMfHx+cPn1awmTdX2VlJQDAxMRE4iQta2pqwtdff42amhp4eHhIHaeZ4OBgTJo0SfS72ZUUFBRAo9Ggf//+8Pf3x+XLl6WOJPL999/Dzc0N06ZNg4WFBYYPH44tW7ZIHatVDQ0N2L59O2bOnAmZTCZ1HIGnpyeOHDmC/Px8AEBGRgZOnDiBiRMnSpyMdQY9qQN0Vb///juamppgaWkpGm9paYnc3FyJUnV/Wq0WYWFh8PLywlNPPSV1HJHz58/Dw8MDdXV1UKlU2LNnD4YMGSJ1LJGvv/4aZ8+eRUpKitRRWuTu7o64uDjY29vj2rVrWLx4Mby9vZGZmQkjIyOp4wEALly4gI0bNyI8PBwLFixASkoKQkNDoVAoEBAQIHW8Zvbu3YuKigoEBgZKHUUkMjISVVVVcHBwgK6uLpqamrB06VL4+/tLHY11Ai4O2H9VcHAwMjMzu9x5aACwt7dHeno6Kisr8e233yIgIAA///xzlykQSkpKMG/ePCQkJECpVEodp0UP/tfo7OwMd3d32Nra4ptvvkFQUJCEye7TarVwc3PDsmXLAADDhw9HZmYmvvjiiy5ZHPzjH//AxIkTodFopI4i8s0332DHjh2Ij4+Hk5MT0tPTERYWBo1G0yXbkXUMFwetMDMzg66uLm7cuCEaf+PGDVhZWUmUqnsLCQnB/v37cfz4cfTp00fqOM0oFAoMHDgQAODq6oqUlBR8/vnn2LRpk8TJ7kpLS0NpaSlGjBghjGtqasLx48exbt061NfXQ1dXV8KEzRkbG2Pw4MEoLCyUOorA2tq6WcHn6OiIXbt2SZSodZcuXcJPP/2E3bt3Sx2lmYiICERGRsLPzw8AMHToUFy6dAnLly/n4uAxwNcctEKhUMDV1RVHjhwRxmm1Whw5cqRLnofuyogIISEh2LNnDxITE9GvXz+pI7WLVqtFfX291DEEY8aMwfnz55Geni583Nzc4O/vj/T09C5XGABAdXU1ioqKYG1tLXUUgZeXV7NbafPz82FraytRotbFxsbCwsICkyZNkjpKM7W1tdDREX+F6OrqQqvVSpSIdSY+ctCG8PBwBAQEwM3NDSNHjsRnn32GmpoazJgxQ+pogurqatF/ZcXFxUhPT4eJiQlsbGwkTHZfcHAw4uPj8d1338HIyAjXr18HAPTs2RMGBgYSp7srKioKEydOhI2NDW7duoX4+HgcO3YMhw4dkjqawMjIqNl1GoaGhjA1Ne0y12+8//778PX1ha2tLa5evYro6Gjo6uri1VdflTqaYP78+fD09MSyZcvwl7/8BcnJydi8eTM2b94sdTQRrVaL2NhYBAQEQE+v6/2p9vX1xdKlS2FjYwMnJyecO3cOq1evxsyZM6WOxjqD1LdLdHVr164lGxsbUigUNHLkSDpz5ozUkUSOHj1KAJp9AgICpI4maCkfAIqNjZU6mmDmzJlka2tLCoWCzM3NacyYMXT48GGpYz1UV7uVcfr06WRtbU0KhYJ69+5N06dPp8LCQqljNbNv3z566qmnSF9fnxwcHGjz5s1SR2rm0KFDBIDy8vKkjtKiqqoqmjdvHtnY2JBSqaT+/fvTwoULqb6+XuporBPwK5sZY4wxJsLXHDDGGGNMhIsDxhhjjIlwccAYY4wxES4OGGOMMSbCxQFjjDHGRLg4YIwxxpgIFweMMcYYE+HigDHGGGMiXBywJ9KiRYswbNgwqWOwTnDs2DHIZDJUVFRIHYWxxwYXB4z9gX799Vd4e3tDqVSib9++WLlypdSR2CPKz8/H5MmTYWZmBrVajVGjRuHo0aNSx2LsD8HFAXskDQ0NUkeQXGNjY5vTq6qqMG7cONja2iItLQ0ff/wxFi1a1OVe8MN92b42eOmll3Dnzh0kJiYiLS0NLi4ueOmll4QXiTH2OOHigLXLc889h5CQEISFhcHMzAzjx48HAGRmZmLixIlQqVSwtLTEG2+8gd9//11Y7ttvv8XQoUNhYGAAU1NT+Pj4oKamBgAQGBiIKVOmYPHixTA3N4darcbbb78t+kNdX1+P0NBQWFhYQKlUYtSoUUhJSRGm3zukfOTIEbi5uaFHjx7w9PRs9kremJgYWFpawsjICEFBQairq2u2j1u3boWjoyOUSiUcHBywYcMGYdrFixchk8mwc+dOjB49GkqlEjt27GizzXbs2IGGhgZs27YNTk5O8PPzQ2hoKFavXt2Blu98906pbN26Ff369YNSqQQAVFRUYNasWUJfvPDCC8jIyBCWy8jIwPPPPw8jIyOo1Wq4uroiNTUVABAXFwdjY2Ps3bsXgwYNglKpxPjx41FSUiLa9saNGzFgwAAoFArY29vjq6++Ek2XyWTYunUrpk6dih49emDQoEH4/vvvRfMcOHAAgwcPhoGBAZ5//nlcvHix2T6eOHEC3t7eMDAwQN++fREaGir83gGAnZ0dlixZgjfffBNqtRpz5sxps81+//13FBQUIDIyEs7Ozhg0aBBiYmJQW1uLzMzMhzc6Y92N1G9+Yt3D6NGjSaVSUUREBOXm5lJubi6Vl5eTubk5RUVFUU5ODp09e5bGjh1Lzz//PBERXb16lfT09Gj16tVUXFxMv/76K61fv55u3bpFREQBAQGkUqlo+vTplJmZSfv37ydzc3NasGCBsN3Q0FDSaDR04MABysrKooCAAOrVqxfdvHmTiO6/ldLd3Z2OHTtGWVlZ5O3tTZ6ensI6du7cSfr6+rR161bKzc2lhQsXkpGREbm4uAjzbN++naytrWnXrl104cIF2rVrF5mYmFBcXBwRERUXFxMAsrOzE+a5evVqm232xhtv0OTJk0XjEhMTCQCVlZU9cl+0RKvVtnve6OhoMjQ0pAkTJtDZs2cpIyODiIh8fHzI19eXUlJSKD8/n9577z0yNTUV2trJyYlef/11ysnJofz8fPrmm28oPT2diIhiY2NJLpeTm5sbnTp1ilJTU2nkyJGifti9ezfJ5XJav3495eXl0SeffEK6urqUmJgozAOA+vTpQ/Hx8VRQUEChoaGkUqmEDJcvXyZ9fX0KDw+n3Nxc2r59O1laWhIAKi8vJyKiwsJCMjQ0pE8//ZTy8/Pp5MmTNHz4cAoMDBS2Y2trS2q1mlatWkWFhYUPfXOkVqsle3t7mjVrFlVXV1NjYyN9/PHHZGFh0el9yVhXwMUBa5fRo0fT8OHDReOWLFlC48aNE40rKSkRXjOblpZGAOjixYstrjMgIIBMTEyopqZGGLdx40ZSqVTU1NRE1dXVJJfLaceOHcL0hoYG0mg0tHLlSiK6Xxz89NNPwjw//PADAaDbt28TEZGHhwe9++67om27u7uLioMBAwZQfHx8s/3z8PAgovvFwWeffdZmOz1o7NixNGfOHNG4rKwsAkDZ2dntXs+NGzdozpw5ZG1tTXZ2dvTuu+9ScnIyNTU10YULF2jKlCnCF2N7REdHk1wup9LSUmHcL7/8Qmq1murq6kTzDhgwgDZt2kREREZGRkKx9P/FxsYSANErzXNycggAJSUlERGRp6cnzZ49W7TctGnT6MUXXxSGAdBf//pXYbi6upoA0MGDB4mIKCoqioYMGSJax4cffigqDoKCgpq1+y+//EI6OjrC74StrS1NmTKllRZqWUlJCbm6upJMJiNdXV2ytrams2fPdmgdjHUXfFqBtZurq6toOCMjA0ePHoVKpRI+Dg4OAICioiK4uLhgzJgxGDp0KKZNm4YtW7agvLxctA4XFxf06NFDGPbw8EB1dTVKSkpQVFSExsZGeHl5CdPlcjlGjhyJnJwc0XqcnZ2Fn62trQEApaWlAICcnBy4u7uL5vfw8BB+rqmpQVFREYKCgkT78ve//x1FRUWi5dzc3NrXWJ3oo48+gomJCQ4cOIBNmzahvr4eY8eOhb6+PkaMGIERI0ZArVZ3aJ22trYwNzcXhjMyMlBdXQ1TU1NRGxQXFwttEB4ejlmzZsHHxwcxMTHN2kZPTw9PP/20MOzg4ABjY2Ohr3JyckR9CQBeXl5t9qWhoSHUanW7+/LevsTFxYn2Y/z48dBqtSguLhbm60hfEhGCg4NhYWGBX375BcnJyZgyZQp8fX1x7dq1dq+Hse5CT+oArPswNDQUDVdXV8PX1xcrVqxoNq+1tTV0dXWRkJCAU6dO4fDhw1i7di0WLlyIpKQk9OvXr1OzyeVy4WeZTAYA0Gq17Vq2uroaALBly5ZmXzy6urqi4f/fBm2xsrLCjRs3ROPuDVtZWbV7PStWrICxsbEwPG7cOHzxxRf47bffYGVlJexvR7TUl9bW1jh27Fizee9te9GiRXjttdfwww8/4ODBg4iOjsbXX3+NqVOndnj7bXmwL4G7/dnevgTu7stbb72F0NDQZtNsbGyEnzvSl4mJidi/fz/Ky8uFQmzDhg1ISEjAP//5T0RGRrZ7XYx1B3zkgD2yESNGICsrC3Z2dhg4cKDoc+8Pr0wmg5eXFxYvXoxz585BoVBgz549wjoyMjJw+/ZtYfjMmTNQqVTo27evcOHayZMnhemNjY1ISUnBkCFD2p3T0dERSUlJonFnzpwRfra0tIRGo8GFCxea7cd/UsR4eHjg+PHjorsaEhISYG9vj169erV7PQ8WBvfo6enB2tr6kQqDlowYMQLXr1+Hnp5eszYwMzMT5hs8eDDmz5+Pw4cP4+WXX0ZsbKww7c6dO8IFigCQl5eHiooKODo6ArjbDw/2JQCcPHmyw32ZnJwsGvdgX97bl+zs7Gb7MXDgQCgUinZv60G1tbUAAB0d8Z9MHR2dDhUujHUXXBywRxYcHIyysjK8+uqrSElJQVFREQ4dOoQZM2agqakJSUlJWLZsGVJTU3H58mXs3r0bv/32m/BlAdy9hSwoKAjZ2dk4cOAAoqOjERISAh0dHRgaGuKdd95BREQEfvzxR2RnZ2P27Nmora1FUFBQu3POmzcP27ZtQ2xsLPLz8xEdHY2srCzRPIsXL8by5cuxZs0a5Ofn4/z584iNjf2P7ix47bXXoFAoEBQUhKysLOzcuROff/45wsPDH3mdfxQfHx94eHhgypQpOHz4MC5evIhTp05h4cKFSE1Nxe3btxESEoJjx47h0qVLOHnyJFJSUkR9KZfLMXfuXCQlJSEtLQ2BgYF45plnMHLkSABAREQE4uLisHHjRhQUFGD16tXYvXs33n///XbnfPvtt1FQUICIiAjk5eUhPj4ecXFxonk+/PBDnDp1CiEhIUhPT0dBQQG+++47hISEPHL7eHh4oFevXggICEBGRgby8/MRERGB4uJiTJo06ZHXy1iXJfVFD6x7GD16NM2bN6/Z+Pz8fJo6dSoZGxuTgYEBOTg4UFhYGGm1WsrOzqbx48eTubk56evr0+DBg2nt2rXCsgEBATR58mT629/+RqampqRSqWj27Nmii+Ju375Nc+fOJTMzM9LX1ycvLy9KTk4Wpt+7IPHBC/LOnTtHAKi4uFgYt3TpUjIzMyOVSkUBAQH0wQcfiC5IJCLasWMHDRs2jBQKBfXq1YueffZZ2r17NxHdvyDx3LlzHWq3jIwMGjVqFOnr61Pv3r0pJiamQ8v/EaKjo5vtOxFRVVUVzZ07lzQaDcnlcurbty/5+/vT5cuXqb6+nvz8/Khv376kUChIo9FQSEiIcIFfbGws9ezZk3bt2kX9+/cnfX198vHxoUuXLom2sWHDBurfvz/J5XIaPHgwffnll6LpAGjPnj2icT179qTY2FhheN++fTRw4EDS19cnb29v2rZtW7PfgeTkZBo7diypVCoyNDQkZ2dnWrp0qTDd1taWPv300w61W0pKCo0bN45MTEzIyMiInnnmGTpw4ECH1sFYdyEjIpK0OmFPrMDAQFRUVGDv3r1SR2H/obi4OISFhfEjjBl7TPBpBcYYY4yJcHHA2CO692TIlj7Lli2TOh7rgGXLlrXalxMnTpQ6HmP/dXxagbFHdOXKFdGdFg8yMTGBiYnJfzkRe1RlZWUoKytrcZqBgQF69+79X07EmLS4OGCMMcaYCJ9WYIwxxpgIFweMMcYYE+HigDHGGGMiXBwwxhhjTISLA8YYY4yJcHHAGGOMMREuDhhjjDEm8r+HcrzXAxnIeAAAAABJRU5ErkJggg=="},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"submission.info()\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:31:41.550989Z","iopub.execute_input":"2024-11-19T11:31:41.551382Z","iopub.status.idle":"2024-11-19T11:31:41.575009Z","shell.execute_reply.started":"2024-11-19T11:31:41.551344Z","shell.execute_reply":"2024-11-19T11:31:41.573494Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 39 entries, 0 to 38\nData columns (total 2 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   row_id       39 non-null     int64  \n 1   responder_6  39 non-null     float64\ndtypes: float64(1), int64(1)\nmemory usage: 752.0 bytes\n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"    row_id  responder_6\n0        0          0.0\n1        1          0.0\n2        2          0.0\n3        3          0.0\n4        4          0.0\n5        5          0.0\n6        6          0.0\n7        7          0.0\n8        8          0.0\n9        9          0.0\n10      10          0.0\n11      11          0.0\n12      12          0.0\n13      13          0.0\n14      14          0.0\n15      15          0.0\n16      16          0.0\n17      17          0.0\n18      18          0.0\n19      19          0.0\n20      20          0.0\n21      21          0.0\n22      22          0.0\n23      23          0.0\n24      24          0.0\n25      25          0.0\n26      26          0.0\n27      27          0.0\n28      28          0.0\n29      29          0.0\n30      30          0.0\n31      31          0.0\n32      32          0.0\n33      33          0.0\n34      34          0.0\n35      35          0.0\n36      36          0.0\n37      37          0.0\n38      38          0.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>row_id</th>\n      <th>responder_6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>10</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>11</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>12</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>13</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>14</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>15</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>16</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>17</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>18</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>20</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>21</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>22</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>23</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>24</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>25</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>26</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>27</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>28</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>29</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>30</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>31</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>32</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>33</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>34</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>35</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>36</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>37</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>38</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"trains['train_1'].info()\ntrains['train_1']","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:31:47.768597Z","iopub.execute_input":"2024-11-19T11:31:47.769005Z","iopub.status.idle":"2024-11-19T11:31:48.260904Z","shell.execute_reply.started":"2024-11-19T11:31:47.76897Z","shell.execute_reply":"2024-11-19T11:31:48.259812Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2804247 entries, 0 to 2804246\nData columns (total 92 columns):\n #   Column       Dtype  \n---  ------       -----  \n 0   date_id      int16  \n 1   time_id      int16  \n 2   symbol_id    int8   \n 3   weight       float32\n 4   feature_00   float32\n 5   feature_01   float32\n 6   feature_02   float32\n 7   feature_03   float32\n 8   feature_04   float32\n 9   feature_05   float32\n 10  feature_06   float32\n 11  feature_07   float32\n 12  feature_08   float32\n 13  feature_09   int8   \n 14  feature_10   int8   \n 15  feature_11   int16  \n 16  feature_12   float32\n 17  feature_13   float32\n 18  feature_14   float32\n 19  feature_15   float32\n 20  feature_16   float32\n 21  feature_17   float32\n 22  feature_18   float32\n 23  feature_19   float32\n 24  feature_20   float32\n 25  feature_21   float32\n 26  feature_22   float32\n 27  feature_23   float32\n 28  feature_24   float32\n 29  feature_25   float32\n 30  feature_26   float32\n 31  feature_27   float32\n 32  feature_28   float32\n 33  feature_29   float32\n 34  feature_30   float32\n 35  feature_31   float32\n 36  feature_32   float32\n 37  feature_33   float32\n 38  feature_34   float32\n 39  feature_35   float32\n 40  feature_36   float32\n 41  feature_37   float32\n 42  feature_38   float32\n 43  feature_39   float32\n 44  feature_40   float32\n 45  feature_41   float32\n 46  feature_42   float32\n 47  feature_43   float32\n 48  feature_44   float32\n 49  feature_45   float32\n 50  feature_46   float32\n 51  feature_47   float32\n 52  feature_48   float32\n 53  feature_49   float32\n 54  feature_50   float32\n 55  feature_51   float32\n 56  feature_52   float32\n 57  feature_53   float32\n 58  feature_54   float32\n 59  feature_55   float32\n 60  feature_56   float32\n 61  feature_57   float32\n 62  feature_58   float32\n 63  feature_59   float32\n 64  feature_60   float32\n 65  feature_61   float32\n 66  feature_62   float32\n 67  feature_63   float32\n 68  feature_64   float32\n 69  feature_65   float32\n 70  feature_66   float32\n 71  feature_67   float32\n 72  feature_68   float32\n 73  feature_69   float32\n 74  feature_70   float32\n 75  feature_71   float32\n 76  feature_72   float32\n 77  feature_73   float32\n 78  feature_74   float32\n 79  feature_75   float32\n 80  feature_76   float32\n 81  feature_77   float32\n 82  feature_78   float32\n 83  responder_0  float32\n 84  responder_1  float32\n 85  responder_2  float32\n 86  responder_3  float32\n 87  responder_4  float32\n 88  responder_5  float32\n 89  responder_6  float32\n 90  responder_7  float32\n 91  responder_8  float32\ndtypes: float32(86), int16(3), int8(3)\nmemory usage: 944.0 MB\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"         date_id  time_id  symbol_id    weight  feature_00  feature_01  \\\n0            170        0          0  2.112212         NaN         NaN   \n1            170        0          1  2.760715         NaN         NaN   \n2            170        0          2  1.813596         NaN         NaN   \n3            170        0          3  0.926893         NaN         NaN   \n4            170        0          7  1.665231         NaN         NaN   \n...          ...      ...        ...       ...         ...         ...   \n2804242      339      848         19  4.384988   -0.357850   -1.135620   \n2804243      339      848         30  0.913199    0.013418   -1.095379   \n2804244      339      848         33  1.226038   -0.378911   -1.521467   \n2804245      339      848         34  1.267464   -0.343313   -1.548126   \n2804246      339      848         38  2.266878   -0.414920   -1.261129   \n\n         feature_02  feature_03  feature_04  feature_05  ...  feature_78  \\\n0               NaN         NaN         NaN    1.060330  ...   -0.421823   \n1               NaN         NaN         NaN    0.482468  ...    3.111076   \n2               NaN         NaN         NaN    1.020798  ...    0.458474   \n3               NaN         NaN         NaN    0.510098  ...   17.805511   \n4               NaN         NaN         NaN    0.547458  ...   -0.249322   \n...             ...         ...         ...         ...  ...         ...   \n2804242   -0.375377    0.005456   -1.241660   -0.139730  ...   -0.118423   \n2804243   -0.315413   -0.227001   -1.131748   -0.119023  ...   -0.094272   \n2804244    0.209378   -0.241914   -1.597019   -0.082501  ...   -0.196051   \n2804245   -0.188658    0.174160   -1.160716   -0.150091  ...   -0.221749   \n2804246    0.046846   -0.117544   -1.687853   -0.096446  ...    0.178854   \n\n         responder_0  responder_1  responder_2  responder_3  responder_4  \\\n0          -0.293646    -0.061842    -0.305413    -0.419151    -0.111796   \n1          -0.075267    -0.359360    -1.270054    -0.018332    -0.040286   \n2          -5.000000    -5.000000     0.194658    -5.000000    -5.000000   \n3           3.336086     2.051951     2.400644     0.962730    -0.939277   \n4          -0.707027    -0.344866    -1.248052    -0.129645    -3.145927   \n...              ...          ...          ...          ...          ...   \n2804242     0.163901     0.134067     1.124346     0.484879     0.255225   \n2804243     0.637513    -0.011480     1.779785     0.757132     0.274228   \n2804244    -3.343947    -1.012429    -2.968987    -1.112956    -0.598563   \n2804245     0.966054     0.551264     0.175104    -0.035818    -0.015150   \n2804246     0.344730     0.556149     2.404575     0.893058     0.378054   \n\n         responder_5  responder_6  responder_7  responder_8  \n0          -0.535104    -0.044332    -0.039061    -0.744789  \n1          -1.417509     0.085840     0.487232    -0.124533  \n2          -5.000000     1.583400     0.018712    -1.055035  \n3           1.845870    -2.372452    -1.663179    -4.585349  \n4          -0.452708     0.300044     0.489202     0.242737  \n...              ...          ...          ...          ...  \n2804242     0.479602     0.008540     0.027126     0.000452  \n2804243     0.741818     1.014812     0.573904     2.359970  \n2804244    -1.537665    -0.259630    -0.105986    -0.480081  \n2804245    -0.425369    -0.115926    -0.012685    -0.144224  \n2804246     1.023353     0.276847     0.197048     0.424032  \n\n[2804247 rows x 92 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date_id</th>\n      <th>time_id</th>\n      <th>symbol_id</th>\n      <th>weight</th>\n      <th>feature_00</th>\n      <th>feature_01</th>\n      <th>feature_02</th>\n      <th>feature_03</th>\n      <th>feature_04</th>\n      <th>feature_05</th>\n      <th>...</th>\n      <th>feature_78</th>\n      <th>responder_0</th>\n      <th>responder_1</th>\n      <th>responder_2</th>\n      <th>responder_3</th>\n      <th>responder_4</th>\n      <th>responder_5</th>\n      <th>responder_6</th>\n      <th>responder_7</th>\n      <th>responder_8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>170</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.112212</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.060330</td>\n      <td>...</td>\n      <td>-0.421823</td>\n      <td>-0.293646</td>\n      <td>-0.061842</td>\n      <td>-0.305413</td>\n      <td>-0.419151</td>\n      <td>-0.111796</td>\n      <td>-0.535104</td>\n      <td>-0.044332</td>\n      <td>-0.039061</td>\n      <td>-0.744789</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>170</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2.760715</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.482468</td>\n      <td>...</td>\n      <td>3.111076</td>\n      <td>-0.075267</td>\n      <td>-0.359360</td>\n      <td>-1.270054</td>\n      <td>-0.018332</td>\n      <td>-0.040286</td>\n      <td>-1.417509</td>\n      <td>0.085840</td>\n      <td>0.487232</td>\n      <td>-0.124533</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>170</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1.813596</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.020798</td>\n      <td>...</td>\n      <td>0.458474</td>\n      <td>-5.000000</td>\n      <td>-5.000000</td>\n      <td>0.194658</td>\n      <td>-5.000000</td>\n      <td>-5.000000</td>\n      <td>-5.000000</td>\n      <td>1.583400</td>\n      <td>0.018712</td>\n      <td>-1.055035</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>170</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0.926893</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.510098</td>\n      <td>...</td>\n      <td>17.805511</td>\n      <td>3.336086</td>\n      <td>2.051951</td>\n      <td>2.400644</td>\n      <td>0.962730</td>\n      <td>-0.939277</td>\n      <td>1.845870</td>\n      <td>-2.372452</td>\n      <td>-1.663179</td>\n      <td>-4.585349</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>170</td>\n      <td>0</td>\n      <td>7</td>\n      <td>1.665231</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.547458</td>\n      <td>...</td>\n      <td>-0.249322</td>\n      <td>-0.707027</td>\n      <td>-0.344866</td>\n      <td>-1.248052</td>\n      <td>-0.129645</td>\n      <td>-3.145927</td>\n      <td>-0.452708</td>\n      <td>0.300044</td>\n      <td>0.489202</td>\n      <td>0.242737</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2804242</th>\n      <td>339</td>\n      <td>848</td>\n      <td>19</td>\n      <td>4.384988</td>\n      <td>-0.357850</td>\n      <td>-1.135620</td>\n      <td>-0.375377</td>\n      <td>0.005456</td>\n      <td>-1.241660</td>\n      <td>-0.139730</td>\n      <td>...</td>\n      <td>-0.118423</td>\n      <td>0.163901</td>\n      <td>0.134067</td>\n      <td>1.124346</td>\n      <td>0.484879</td>\n      <td>0.255225</td>\n      <td>0.479602</td>\n      <td>0.008540</td>\n      <td>0.027126</td>\n      <td>0.000452</td>\n    </tr>\n    <tr>\n      <th>2804243</th>\n      <td>339</td>\n      <td>848</td>\n      <td>30</td>\n      <td>0.913199</td>\n      <td>0.013418</td>\n      <td>-1.095379</td>\n      <td>-0.315413</td>\n      <td>-0.227001</td>\n      <td>-1.131748</td>\n      <td>-0.119023</td>\n      <td>...</td>\n      <td>-0.094272</td>\n      <td>0.637513</td>\n      <td>-0.011480</td>\n      <td>1.779785</td>\n      <td>0.757132</td>\n      <td>0.274228</td>\n      <td>0.741818</td>\n      <td>1.014812</td>\n      <td>0.573904</td>\n      <td>2.359970</td>\n    </tr>\n    <tr>\n      <th>2804244</th>\n      <td>339</td>\n      <td>848</td>\n      <td>33</td>\n      <td>1.226038</td>\n      <td>-0.378911</td>\n      <td>-1.521467</td>\n      <td>0.209378</td>\n      <td>-0.241914</td>\n      <td>-1.597019</td>\n      <td>-0.082501</td>\n      <td>...</td>\n      <td>-0.196051</td>\n      <td>-3.343947</td>\n      <td>-1.012429</td>\n      <td>-2.968987</td>\n      <td>-1.112956</td>\n      <td>-0.598563</td>\n      <td>-1.537665</td>\n      <td>-0.259630</td>\n      <td>-0.105986</td>\n      <td>-0.480081</td>\n    </tr>\n    <tr>\n      <th>2804245</th>\n      <td>339</td>\n      <td>848</td>\n      <td>34</td>\n      <td>1.267464</td>\n      <td>-0.343313</td>\n      <td>-1.548126</td>\n      <td>-0.188658</td>\n      <td>0.174160</td>\n      <td>-1.160716</td>\n      <td>-0.150091</td>\n      <td>...</td>\n      <td>-0.221749</td>\n      <td>0.966054</td>\n      <td>0.551264</td>\n      <td>0.175104</td>\n      <td>-0.035818</td>\n      <td>-0.015150</td>\n      <td>-0.425369</td>\n      <td>-0.115926</td>\n      <td>-0.012685</td>\n      <td>-0.144224</td>\n    </tr>\n    <tr>\n      <th>2804246</th>\n      <td>339</td>\n      <td>848</td>\n      <td>38</td>\n      <td>2.266878</td>\n      <td>-0.414920</td>\n      <td>-1.261129</td>\n      <td>0.046846</td>\n      <td>-0.117544</td>\n      <td>-1.687853</td>\n      <td>-0.096446</td>\n      <td>...</td>\n      <td>0.178854</td>\n      <td>0.344730</td>\n      <td>0.556149</td>\n      <td>2.404575</td>\n      <td>0.893058</td>\n      <td>0.378054</td>\n      <td>1.023353</td>\n      <td>0.276847</td>\n      <td>0.197048</td>\n      <td>0.424032</td>\n    </tr>\n  </tbody>\n</table>\n<p>2804247 rows × 92 columns</p>\n</div>"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"train_6=trains['train_9']\ntrain_6","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:48:51.920945Z","iopub.execute_input":"2024-11-19T11:48:51.921349Z","iopub.status.idle":"2024-11-19T11:48:52.717037Z","shell.execute_reply.started":"2024-11-19T11:48:51.921313Z","shell.execute_reply":"2024-11-19T11:48:52.715767Z"},"trusted":true},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"         date_id  time_id  symbol_id    weight  feature_00  feature_01  \\\n0           1530        0          0  3.084694    1.153571    1.563784   \n1           1530        0          1  2.232906    0.553354    1.730064   \n2           1530        0          2  2.404948    1.532503    2.095852   \n3           1530        0          3  1.986533    0.647099    1.687460   \n4           1530        0          4  2.742601    1.096778    1.551411   \n...          ...      ...        ...       ...         ...         ...   \n6274571     1698      967         34  3.242493    2.525160   -0.721981   \n6274572     1698      967         35  1.079139    1.857906   -0.790646   \n6274573     1698      967         36  1.033172    2.515527   -0.672298   \n6274574     1698      967         37  1.243116    2.663298   -0.889112   \n6274575     1698      967         38  3.193685    2.728506   -0.745238   \n\n         feature_02  feature_03  feature_04  feature_05  ...  feature_78  \\\n0          0.697396    0.756759    2.580965    0.171311  ...    0.999516   \n1          0.990195    0.611490    2.023031    0.319015  ...    0.160609   \n2          0.919688    0.583715    2.330047    0.337096  ...   -0.065761   \n3          0.569406    1.061679    2.444131    0.150487  ...    0.526284   \n4          0.632113    0.368218    2.181873    0.214604  ...   -0.965623   \n...             ...         ...         ...         ...  ...         ...   \n6274571    2.544025    2.477615    0.417557    0.785812  ...    0.016936   \n6274572    2.745439    2.339877    0.845065    0.651370  ...    0.050860   \n6274573    2.289250    2.521592    0.255077    0.919892  ...    0.152333   \n6274574    2.313155    3.101428    0.324454    0.618944  ...   -0.029483   \n6274575    2.788789    2.343393    0.454731    0.862839  ...   -0.247774   \n\n         responder_0  responder_1  responder_2  responder_3  responder_4  \\\n0           0.417462     0.323897     0.601499     2.074103     0.746552   \n1          -0.318671    -0.399384    -0.635306     2.092151     0.342582   \n2           0.200878    -0.006571     0.518870    -0.344441     0.641694   \n3          -0.349773    -0.235901    -0.428956    -1.903627    -1.214619   \n4          -0.373938    -0.209282    -0.095182    -1.598217     0.968505   \n...              ...          ...          ...          ...          ...   \n6274571     0.243475     0.166927     0.384940    -0.174297    -0.066046   \n6274572     0.850152     0.909382     1.015314     0.235962     0.122539   \n6274573     0.395684    -0.292574    -3.215846    -0.535129    -0.178484   \n6274574     1.925987     0.479394     3.621867    -0.107114    -0.063599   \n6274575     1.228778     0.512562    -0.050865     0.160883     0.080756   \n\n         responder_5  responder_6  responder_7  responder_8  \n0           0.552013     3.071231     0.914794     0.997124  \n1           0.757289     1.979042     0.967537     1.219739  \n2          -0.646040    -0.506260     0.739797    -2.041514  \n3          -0.469500    -2.590589    -0.946317    -0.390001  \n4          -0.705594    -1.579623     0.954296    -1.805623  \n...              ...          ...          ...          ...  \n6274571    -0.038767    -0.132337    -0.022426    -0.252461  \n6274572     0.099559    -0.249584    -0.123571    -0.460630  \n6274573    -1.808150    -0.065355    -0.000367    -0.125170  \n6274574     1.204755    -0.148711    -0.026583    -0.256395  \n6274575    -0.078237    -0.138548    -0.038771    -0.211940  \n\n[6274576 rows x 92 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date_id</th>\n      <th>time_id</th>\n      <th>symbol_id</th>\n      <th>weight</th>\n      <th>feature_00</th>\n      <th>feature_01</th>\n      <th>feature_02</th>\n      <th>feature_03</th>\n      <th>feature_04</th>\n      <th>feature_05</th>\n      <th>...</th>\n      <th>feature_78</th>\n      <th>responder_0</th>\n      <th>responder_1</th>\n      <th>responder_2</th>\n      <th>responder_3</th>\n      <th>responder_4</th>\n      <th>responder_5</th>\n      <th>responder_6</th>\n      <th>responder_7</th>\n      <th>responder_8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1530</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.084694</td>\n      <td>1.153571</td>\n      <td>1.563784</td>\n      <td>0.697396</td>\n      <td>0.756759</td>\n      <td>2.580965</td>\n      <td>0.171311</td>\n      <td>...</td>\n      <td>0.999516</td>\n      <td>0.417462</td>\n      <td>0.323897</td>\n      <td>0.601499</td>\n      <td>2.074103</td>\n      <td>0.746552</td>\n      <td>0.552013</td>\n      <td>3.071231</td>\n      <td>0.914794</td>\n      <td>0.997124</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1530</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2.232906</td>\n      <td>0.553354</td>\n      <td>1.730064</td>\n      <td>0.990195</td>\n      <td>0.611490</td>\n      <td>2.023031</td>\n      <td>0.319015</td>\n      <td>...</td>\n      <td>0.160609</td>\n      <td>-0.318671</td>\n      <td>-0.399384</td>\n      <td>-0.635306</td>\n      <td>2.092151</td>\n      <td>0.342582</td>\n      <td>0.757289</td>\n      <td>1.979042</td>\n      <td>0.967537</td>\n      <td>1.219739</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1530</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2.404948</td>\n      <td>1.532503</td>\n      <td>2.095852</td>\n      <td>0.919688</td>\n      <td>0.583715</td>\n      <td>2.330047</td>\n      <td>0.337096</td>\n      <td>...</td>\n      <td>-0.065761</td>\n      <td>0.200878</td>\n      <td>-0.006571</td>\n      <td>0.518870</td>\n      <td>-0.344441</td>\n      <td>0.641694</td>\n      <td>-0.646040</td>\n      <td>-0.506260</td>\n      <td>0.739797</td>\n      <td>-2.041514</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1530</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1.986533</td>\n      <td>0.647099</td>\n      <td>1.687460</td>\n      <td>0.569406</td>\n      <td>1.061679</td>\n      <td>2.444131</td>\n      <td>0.150487</td>\n      <td>...</td>\n      <td>0.526284</td>\n      <td>-0.349773</td>\n      <td>-0.235901</td>\n      <td>-0.428956</td>\n      <td>-1.903627</td>\n      <td>-1.214619</td>\n      <td>-0.469500</td>\n      <td>-2.590589</td>\n      <td>-0.946317</td>\n      <td>-0.390001</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1530</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2.742601</td>\n      <td>1.096778</td>\n      <td>1.551411</td>\n      <td>0.632113</td>\n      <td>0.368218</td>\n      <td>2.181873</td>\n      <td>0.214604</td>\n      <td>...</td>\n      <td>-0.965623</td>\n      <td>-0.373938</td>\n      <td>-0.209282</td>\n      <td>-0.095182</td>\n      <td>-1.598217</td>\n      <td>0.968505</td>\n      <td>-0.705594</td>\n      <td>-1.579623</td>\n      <td>0.954296</td>\n      <td>-1.805623</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6274571</th>\n      <td>1698</td>\n      <td>967</td>\n      <td>34</td>\n      <td>3.242493</td>\n      <td>2.525160</td>\n      <td>-0.721981</td>\n      <td>2.544025</td>\n      <td>2.477615</td>\n      <td>0.417557</td>\n      <td>0.785812</td>\n      <td>...</td>\n      <td>0.016936</td>\n      <td>0.243475</td>\n      <td>0.166927</td>\n      <td>0.384940</td>\n      <td>-0.174297</td>\n      <td>-0.066046</td>\n      <td>-0.038767</td>\n      <td>-0.132337</td>\n      <td>-0.022426</td>\n      <td>-0.252461</td>\n    </tr>\n    <tr>\n      <th>6274572</th>\n      <td>1698</td>\n      <td>967</td>\n      <td>35</td>\n      <td>1.079139</td>\n      <td>1.857906</td>\n      <td>-0.790646</td>\n      <td>2.745439</td>\n      <td>2.339877</td>\n      <td>0.845065</td>\n      <td>0.651370</td>\n      <td>...</td>\n      <td>0.050860</td>\n      <td>0.850152</td>\n      <td>0.909382</td>\n      <td>1.015314</td>\n      <td>0.235962</td>\n      <td>0.122539</td>\n      <td>0.099559</td>\n      <td>-0.249584</td>\n      <td>-0.123571</td>\n      <td>-0.460630</td>\n    </tr>\n    <tr>\n      <th>6274573</th>\n      <td>1698</td>\n      <td>967</td>\n      <td>36</td>\n      <td>1.033172</td>\n      <td>2.515527</td>\n      <td>-0.672298</td>\n      <td>2.289250</td>\n      <td>2.521592</td>\n      <td>0.255077</td>\n      <td>0.919892</td>\n      <td>...</td>\n      <td>0.152333</td>\n      <td>0.395684</td>\n      <td>-0.292574</td>\n      <td>-3.215846</td>\n      <td>-0.535129</td>\n      <td>-0.178484</td>\n      <td>-1.808150</td>\n      <td>-0.065355</td>\n      <td>-0.000367</td>\n      <td>-0.125170</td>\n    </tr>\n    <tr>\n      <th>6274574</th>\n      <td>1698</td>\n      <td>967</td>\n      <td>37</td>\n      <td>1.243116</td>\n      <td>2.663298</td>\n      <td>-0.889112</td>\n      <td>2.313155</td>\n      <td>3.101428</td>\n      <td>0.324454</td>\n      <td>0.618944</td>\n      <td>...</td>\n      <td>-0.029483</td>\n      <td>1.925987</td>\n      <td>0.479394</td>\n      <td>3.621867</td>\n      <td>-0.107114</td>\n      <td>-0.063599</td>\n      <td>1.204755</td>\n      <td>-0.148711</td>\n      <td>-0.026583</td>\n      <td>-0.256395</td>\n    </tr>\n    <tr>\n      <th>6274575</th>\n      <td>1698</td>\n      <td>967</td>\n      <td>38</td>\n      <td>3.193685</td>\n      <td>2.728506</td>\n      <td>-0.745238</td>\n      <td>2.788789</td>\n      <td>2.343393</td>\n      <td>0.454731</td>\n      <td>0.862839</td>\n      <td>...</td>\n      <td>-0.247774</td>\n      <td>1.228778</td>\n      <td>0.512562</td>\n      <td>-0.050865</td>\n      <td>0.160883</td>\n      <td>0.080756</td>\n      <td>-0.078237</td>\n      <td>-0.138548</td>\n      <td>-0.038771</td>\n      <td>-0.211940</td>\n    </tr>\n  </tbody>\n</table>\n<p>6274576 rows × 92 columns</p>\n</div>"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"- data_id和time_id，提供时间顺序，但值之间的实际时间间隔可能不同，增加了趋势分析难度\n- symbol_id 标识唯一的金融工具，指在金融市场中可交易的金融资产，值代表39种金融资产\n- weight 计算评分函数的权重，值用来进行评估\n- feature_{00...78} 匿名市场数据，里面的值是什么意思\n- responder_{0...8} 匿名响应者，范围**\\[-5,5\\]** ,值代表购买行为\n- 尝试预测respondder_6\n- 每个symbol_id并不保证出现在所有的time_id和date_id组合中。此外，新的symbol_id值可能会出现在未来的测试集中。","metadata":{}},{"cell_type":"markdown","source":"评估函数：\n\n![image.png](attachment:fc32da76-d66e-496c-ad54-54a226e8b09d.png)\n\n- $w_i$是权重向量","metadata":{},"attachments":{"fc32da76-d66e-496c-ad54-54a226e8b09d.png":{"image/png":"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"}}},{"cell_type":"code","source":"train = (\n    pl.read_parquet(f\"{fore_path}/train.parquet/partition_id=0/part-0.parquet\")\n)\ntrain.shape","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:52:15.347882Z","iopub.execute_input":"2024-11-19T11:52:15.34829Z","iopub.status.idle":"2024-11-19T11:52:18.20967Z","shell.execute_reply.started":"2024-11-19T11:52:15.348254Z","shell.execute_reply":"2024-11-19T11:52:18.208508Z"},"trusted":true},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"(1944210, 92)"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"supervised_usable = (\n    train\n    .filter(pl.col('responder_6').is_not_null())\n)\n\nmissing_count = (\n    supervised_usable\n    .null_count()\n    .transpose(include_header=True,\n               header_name='feature',\n               column_names=['null_count'])\n    .sort('null_count', descending=True)\n    .with_columns((pl.col('null_count') / len(supervised_usable)).alias('null_ratio'))\n)\n\nplt.figure(figsize=(6, 20))\nplt.title(f'Missing values over the {len(supervised_usable)} samples which have a target')\nplt.barh(np.arange(len(missing_count)), missing_count.get_column('null_ratio'), color='coral', label='missing')\nplt.barh(np.arange(len(missing_count)), \n         1 - missing_count.get_column('null_ratio'),\n         left=missing_count.get_column('null_ratio'),\n         color='darkseagreen', label='available')\nplt.yticks(np.arange(len(missing_count)), missing_count.get_column('feature'))\nplt.gca().xaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\nplt.xlim(0, 1)\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:52:20.439941Z","iopub.execute_input":"2024-11-19T11:52:20.440297Z","iopub.status.idle":"2024-11-19T11:52:21.698952Z","shell.execute_reply.started":"2024-11-19T11:52:20.440266Z","shell.execute_reply":"2024-11-19T11:52:21.697661Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x2000 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lags.info()\nlags","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:31:43.590893Z","iopub.execute_input":"2024-11-19T11:31:43.591294Z","iopub.status.idle":"2024-11-19T11:31:43.625479Z","shell.execute_reply.started":"2024-11-19T11:31:43.591257Z","shell.execute_reply":"2024-11-19T11:31:43.62416Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 39 entries, 0 to 38\nData columns (total 12 columns):\n #   Column             Non-Null Count  Dtype  \n---  ------             --------------  -----  \n 0   date_id            39 non-null     int16  \n 1   time_id            39 non-null     int16  \n 2   symbol_id          39 non-null     int8   \n 3   responder_0_lag_1  39 non-null     float32\n 4   responder_1_lag_1  39 non-null     float32\n 5   responder_2_lag_1  39 non-null     float32\n 6   responder_3_lag_1  39 non-null     float32\n 7   responder_4_lag_1  39 non-null     float32\n 8   responder_5_lag_1  39 non-null     float32\n 9   responder_6_lag_1  39 non-null     float32\n 10  responder_7_lag_1  39 non-null     float32\n 11  responder_8_lag_1  39 non-null     float32\ndtypes: float32(9), int16(2), int8(1)\nmemory usage: 1.7 KB\n","output_type":"stream"},{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"    date_id  time_id  symbol_id  responder_0_lag_1  responder_1_lag_1  \\\n0         0        0          0          -0.442215          -0.322407   \n1         0        0          1          -0.651829          -1.707840   \n2         0        0          2          -0.656373          -0.264575   \n3         0        0          3          -0.188186          -0.190970   \n4         0        0          4          -0.257462          -0.471325   \n5         0        0          5           0.027579          -0.020169   \n6         0        0          6          -0.419646          -0.181228   \n7         0        0          7          -0.114118          -0.198511   \n8         0        0          8          -0.374147           0.092127   \n9         0        0          9          -0.529529           0.040104   \n10        0        0         10          -0.709064          -0.137431   \n11        0        0         11          -0.182779          -0.262493   \n12        0        0         12          -0.409564          -0.210898   \n13        0        0         13           0.254306           0.114433   \n14        0        0         14           0.464961           0.077041   \n15        0        0         15           0.059957           0.173762   \n16        0        0         16           0.138667           0.062221   \n17        0        0         17          -0.235209          -0.201598   \n18        0        0         18          -1.760321          -0.488708   \n19        0        0         19          -0.120426          -0.257001   \n20        0        0         20           0.100019          -0.064951   \n21        0        0         21          -0.118341          -0.156263   \n22        0        0         22           0.077620          -0.095140   \n23        0        0         23           0.049998          -0.024269   \n24        0        0         24           0.053630           0.338161   \n25        0        0         25           0.859229          -0.752454   \n26        0        0         26          -0.727778          -0.085832   \n27        0        0         27          -0.947047           0.345270   \n28        0        0         28           0.185609           0.019040   \n29        0        0         29           0.410091           0.073472   \n30        0        0         30          -0.165723           0.038975   \n31        0        0         31          -0.434564          -2.683184   \n32        0        0         32          -0.221083           0.319112   \n33        0        0         33          -0.322945          -0.100357   \n34        0        0         34          -0.185392          -0.187891   \n35        0        0         35          -0.308923          -0.434147   \n36        0        0         36          -0.074661          -0.261698   \n37        0        0         37          -0.658366          -0.282258   \n38        0        0         38           0.572666           0.066861   \n\n    responder_2_lag_1  responder_3_lag_1  responder_4_lag_1  \\\n0            0.143594          -0.926890          -0.782236   \n1           -0.893942          -1.065488          -1.871338   \n2           -0.892879          -1.511886          -1.033480   \n3           -0.701490           0.098453          -1.015506   \n4           -0.297420           0.074018          -0.324194   \n5            0.640348          -0.948373          -0.374251   \n6           -0.194079           0.667993           0.936857   \n7           -0.200027          -0.410021          -0.135167   \n8            0.294723           0.402989           2.060188   \n9           -0.333090          -0.959040          -1.318411   \n10          -0.475960          -0.506644          -0.297788   \n11          -0.349921          -0.725857          -0.469289   \n12          -0.097313           0.420984          -1.611198   \n13           0.064752          -0.685130          -0.384532   \n14           0.601805          -0.178444           1.127965   \n15          -0.248479          -0.187606           0.539572   \n16          -0.140365          -2.740061          -1.360370   \n17           0.406477           4.062799           1.399957   \n18          -0.883805           0.771329           1.164359   \n19           0.461233           0.334276          -0.430230   \n20           0.103129           0.194796           5.000000   \n21          -0.315895          -1.117113          -0.318234   \n22          -0.195245           2.259837          -0.049306   \n23           0.399543           1.664860           1.774516   \n24           0.828120           0.061310           0.594190   \n25           0.372965           1.137440          -0.969744   \n26          -0.500547           0.080261          -0.168391   \n27          -0.416790           0.068557           2.012803   \n28           0.928451           0.699763           0.094604   \n29           0.327349           1.020221          -0.262787   \n30          -0.088357          -0.745973          -0.318136   \n31          -0.439510           2.700525           2.373121   \n32          -0.359562          -1.037934           1.451325   \n33           0.044535          -0.853970          -1.932011   \n34          -0.206658          -0.634903          -0.643175   \n35          -1.354941           0.300540          -0.830827   \n36          -0.007051          -2.600390          -1.146709   \n37          -0.438998          -0.709998          -1.143526   \n38          -0.552490           0.107840           0.535348   \n\n    responder_5_lag_1  responder_6_lag_1  responder_7_lag_1  responder_8_lag_1  \n0           -0.036595          -1.305746          -0.795677          -0.143724  \n1           -0.615652          -1.162801          -1.205924          -1.245934  \n2           -0.378265          -1.574290          -1.863071          -0.027343  \n3           -0.054984           0.329152          -0.965471           0.576635  \n4           -0.597093           0.219856          -0.276356          -0.904790  \n5           -0.240350          -0.913801          -0.548867          -1.283726  \n6            0.517728           0.896325           1.068884           1.579290  \n7           -0.182887          -0.492168          -0.142915          -0.202081  \n8           -0.225042           0.956460           2.185598          -0.435856  \n9           -0.774299          -0.716492          -1.471419          -1.107083  \n10          -0.530738          -0.263427          -0.169489          -0.410877  \n11          -1.125309          -0.832106          -0.240194          -0.760374  \n12           1.065879           0.798224          -3.035606           1.810822  \n13          -0.765541          -1.385921          -0.441037          -1.359048  \n14          -0.445524          -0.507432           0.985169          -1.497043  \n15           0.244086          -0.256192           0.974333           0.636512  \n16          -1.297678          -2.992689          -2.387136          -1.834790  \n17           2.418881           5.000000           1.615073           3.689315  \n18           2.030865           2.570583           2.326662           2.364794  \n19          -0.053538           0.386924          -0.633187          -0.465996  \n20           0.019647           0.360535           5.000000           0.000687  \n21          -1.938428          -1.851255          -0.355483          -2.953523  \n22           0.815069           2.036146           0.032317           1.591067  \n23           0.849245           2.734162           2.179242           2.438320  \n24          -2.695299           0.086643           0.464238          -5.000000  \n25           0.149047           0.522117          -0.690022          -0.084661  \n26          -0.017894           0.453380          -0.151904           0.455708  \n27           1.190820           0.641240           2.143041           2.033262  \n28          -0.093131           0.815095           0.101383          -0.993829  \n29           0.182656           0.794698          -0.455156           0.103020  \n30          -0.699026          -1.051536          -0.334043          -1.380436  \n31           0.748074           2.698507           3.004424           1.457855  \n32          -1.596154          -0.922532           1.574045          -3.389474  \n33          -1.108600          -1.377528          -2.624511          -0.798179  \n34          -0.443875          -0.556474          -1.122211          -0.884185  \n35           0.424937           0.518839          -0.687369           1.440577  \n36          -1.601274          -3.216254          -1.249338          -2.868875  \n37          -1.562932          -0.506418          -1.355508          -2.630985  \n38          -0.501347          -0.169114           0.457801          -0.136777  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date_id</th>\n      <th>time_id</th>\n      <th>symbol_id</th>\n      <th>responder_0_lag_1</th>\n      <th>responder_1_lag_1</th>\n      <th>responder_2_lag_1</th>\n      <th>responder_3_lag_1</th>\n      <th>responder_4_lag_1</th>\n      <th>responder_5_lag_1</th>\n      <th>responder_6_lag_1</th>\n      <th>responder_7_lag_1</th>\n      <th>responder_8_lag_1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.442215</td>\n      <td>-0.322407</td>\n      <td>0.143594</td>\n      <td>-0.926890</td>\n      <td>-0.782236</td>\n      <td>-0.036595</td>\n      <td>-1.305746</td>\n      <td>-0.795677</td>\n      <td>-0.143724</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.651829</td>\n      <td>-1.707840</td>\n      <td>-0.893942</td>\n      <td>-1.065488</td>\n      <td>-1.871338</td>\n      <td>-0.615652</td>\n      <td>-1.162801</td>\n      <td>-1.205924</td>\n      <td>-1.245934</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>-0.656373</td>\n      <td>-0.264575</td>\n      <td>-0.892879</td>\n      <td>-1.511886</td>\n      <td>-1.033480</td>\n      <td>-0.378265</td>\n      <td>-1.574290</td>\n      <td>-1.863071</td>\n      <td>-0.027343</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>-0.188186</td>\n      <td>-0.190970</td>\n      <td>-0.701490</td>\n      <td>0.098453</td>\n      <td>-1.015506</td>\n      <td>-0.054984</td>\n      <td>0.329152</td>\n      <td>-0.965471</td>\n      <td>0.576635</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>-0.257462</td>\n      <td>-0.471325</td>\n      <td>-0.297420</td>\n      <td>0.074018</td>\n      <td>-0.324194</td>\n      <td>-0.597093</td>\n      <td>0.219856</td>\n      <td>-0.276356</td>\n      <td>-0.904790</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0.027579</td>\n      <td>-0.020169</td>\n      <td>0.640348</td>\n      <td>-0.948373</td>\n      <td>-0.374251</td>\n      <td>-0.240350</td>\n      <td>-0.913801</td>\n      <td>-0.548867</td>\n      <td>-1.283726</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>-0.419646</td>\n      <td>-0.181228</td>\n      <td>-0.194079</td>\n      <td>0.667993</td>\n      <td>0.936857</td>\n      <td>0.517728</td>\n      <td>0.896325</td>\n      <td>1.068884</td>\n      <td>1.579290</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>-0.114118</td>\n      <td>-0.198511</td>\n      <td>-0.200027</td>\n      <td>-0.410021</td>\n      <td>-0.135167</td>\n      <td>-0.182887</td>\n      <td>-0.492168</td>\n      <td>-0.142915</td>\n      <td>-0.202081</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>-0.374147</td>\n      <td>0.092127</td>\n      <td>0.294723</td>\n      <td>0.402989</td>\n      <td>2.060188</td>\n      <td>-0.225042</td>\n      <td>0.956460</td>\n      <td>2.185598</td>\n      <td>-0.435856</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>-0.529529</td>\n      <td>0.040104</td>\n      <td>-0.333090</td>\n      <td>-0.959040</td>\n      <td>-1.318411</td>\n      <td>-0.774299</td>\n      <td>-0.716492</td>\n      <td>-1.471419</td>\n      <td>-1.107083</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>-0.709064</td>\n      <td>-0.137431</td>\n      <td>-0.475960</td>\n      <td>-0.506644</td>\n      <td>-0.297788</td>\n      <td>-0.530738</td>\n      <td>-0.263427</td>\n      <td>-0.169489</td>\n      <td>-0.410877</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>-0.182779</td>\n      <td>-0.262493</td>\n      <td>-0.349921</td>\n      <td>-0.725857</td>\n      <td>-0.469289</td>\n      <td>-1.125309</td>\n      <td>-0.832106</td>\n      <td>-0.240194</td>\n      <td>-0.760374</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>-0.409564</td>\n      <td>-0.210898</td>\n      <td>-0.097313</td>\n      <td>0.420984</td>\n      <td>-1.611198</td>\n      <td>1.065879</td>\n      <td>0.798224</td>\n      <td>-3.035606</td>\n      <td>1.810822</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0.254306</td>\n      <td>0.114433</td>\n      <td>0.064752</td>\n      <td>-0.685130</td>\n      <td>-0.384532</td>\n      <td>-0.765541</td>\n      <td>-1.385921</td>\n      <td>-0.441037</td>\n      <td>-1.359048</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>0</td>\n      <td>14</td>\n      <td>0.464961</td>\n      <td>0.077041</td>\n      <td>0.601805</td>\n      <td>-0.178444</td>\n      <td>1.127965</td>\n      <td>-0.445524</td>\n      <td>-0.507432</td>\n      <td>0.985169</td>\n      <td>-1.497043</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0</td>\n      <td>0</td>\n      <td>15</td>\n      <td>0.059957</td>\n      <td>0.173762</td>\n      <td>-0.248479</td>\n      <td>-0.187606</td>\n      <td>0.539572</td>\n      <td>0.244086</td>\n      <td>-0.256192</td>\n      <td>0.974333</td>\n      <td>0.636512</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0</td>\n      <td>0</td>\n      <td>16</td>\n      <td>0.138667</td>\n      <td>0.062221</td>\n      <td>-0.140365</td>\n      <td>-2.740061</td>\n      <td>-1.360370</td>\n      <td>-1.297678</td>\n      <td>-2.992689</td>\n      <td>-2.387136</td>\n      <td>-1.834790</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n      <td>-0.235209</td>\n      <td>-0.201598</td>\n      <td>0.406477</td>\n      <td>4.062799</td>\n      <td>1.399957</td>\n      <td>2.418881</td>\n      <td>5.000000</td>\n      <td>1.615073</td>\n      <td>3.689315</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>0</td>\n      <td>0</td>\n      <td>18</td>\n      <td>-1.760321</td>\n      <td>-0.488708</td>\n      <td>-0.883805</td>\n      <td>0.771329</td>\n      <td>1.164359</td>\n      <td>2.030865</td>\n      <td>2.570583</td>\n      <td>2.326662</td>\n      <td>2.364794</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>0</td>\n      <td>19</td>\n      <td>-0.120426</td>\n      <td>-0.257001</td>\n      <td>0.461233</td>\n      <td>0.334276</td>\n      <td>-0.430230</td>\n      <td>-0.053538</td>\n      <td>0.386924</td>\n      <td>-0.633187</td>\n      <td>-0.465996</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>0.100019</td>\n      <td>-0.064951</td>\n      <td>0.103129</td>\n      <td>0.194796</td>\n      <td>5.000000</td>\n      <td>0.019647</td>\n      <td>0.360535</td>\n      <td>5.000000</td>\n      <td>0.000687</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>-0.118341</td>\n      <td>-0.156263</td>\n      <td>-0.315895</td>\n      <td>-1.117113</td>\n      <td>-0.318234</td>\n      <td>-1.938428</td>\n      <td>-1.851255</td>\n      <td>-0.355483</td>\n      <td>-2.953523</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>0.077620</td>\n      <td>-0.095140</td>\n      <td>-0.195245</td>\n      <td>2.259837</td>\n      <td>-0.049306</td>\n      <td>0.815069</td>\n      <td>2.036146</td>\n      <td>0.032317</td>\n      <td>1.591067</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>0</td>\n      <td>0</td>\n      <td>23</td>\n      <td>0.049998</td>\n      <td>-0.024269</td>\n      <td>0.399543</td>\n      <td>1.664860</td>\n      <td>1.774516</td>\n      <td>0.849245</td>\n      <td>2.734162</td>\n      <td>2.179242</td>\n      <td>2.438320</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>0.053630</td>\n      <td>0.338161</td>\n      <td>0.828120</td>\n      <td>0.061310</td>\n      <td>0.594190</td>\n      <td>-2.695299</td>\n      <td>0.086643</td>\n      <td>0.464238</td>\n      <td>-5.000000</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>0.859229</td>\n      <td>-0.752454</td>\n      <td>0.372965</td>\n      <td>1.137440</td>\n      <td>-0.969744</td>\n      <td>0.149047</td>\n      <td>0.522117</td>\n      <td>-0.690022</td>\n      <td>-0.084661</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>-0.727778</td>\n      <td>-0.085832</td>\n      <td>-0.500547</td>\n      <td>0.080261</td>\n      <td>-0.168391</td>\n      <td>-0.017894</td>\n      <td>0.453380</td>\n      <td>-0.151904</td>\n      <td>0.455708</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>0</td>\n      <td>0</td>\n      <td>27</td>\n      <td>-0.947047</td>\n      <td>0.345270</td>\n      <td>-0.416790</td>\n      <td>0.068557</td>\n      <td>2.012803</td>\n      <td>1.190820</td>\n      <td>0.641240</td>\n      <td>2.143041</td>\n      <td>2.033262</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>0</td>\n      <td>0</td>\n      <td>28</td>\n      <td>0.185609</td>\n      <td>0.019040</td>\n      <td>0.928451</td>\n      <td>0.699763</td>\n      <td>0.094604</td>\n      <td>-0.093131</td>\n      <td>0.815095</td>\n      <td>0.101383</td>\n      <td>-0.993829</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>0</td>\n      <td>0</td>\n      <td>29</td>\n      <td>0.410091</td>\n      <td>0.073472</td>\n      <td>0.327349</td>\n      <td>1.020221</td>\n      <td>-0.262787</td>\n      <td>0.182656</td>\n      <td>0.794698</td>\n      <td>-0.455156</td>\n      <td>0.103020</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>-0.165723</td>\n      <td>0.038975</td>\n      <td>-0.088357</td>\n      <td>-0.745973</td>\n      <td>-0.318136</td>\n      <td>-0.699026</td>\n      <td>-1.051536</td>\n      <td>-0.334043</td>\n      <td>-1.380436</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>0</td>\n      <td>0</td>\n      <td>31</td>\n      <td>-0.434564</td>\n      <td>-2.683184</td>\n      <td>-0.439510</td>\n      <td>2.700525</td>\n      <td>2.373121</td>\n      <td>0.748074</td>\n      <td>2.698507</td>\n      <td>3.004424</td>\n      <td>1.457855</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>-0.221083</td>\n      <td>0.319112</td>\n      <td>-0.359562</td>\n      <td>-1.037934</td>\n      <td>1.451325</td>\n      <td>-1.596154</td>\n      <td>-0.922532</td>\n      <td>1.574045</td>\n      <td>-3.389474</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>-0.322945</td>\n      <td>-0.100357</td>\n      <td>0.044535</td>\n      <td>-0.853970</td>\n      <td>-1.932011</td>\n      <td>-1.108600</td>\n      <td>-1.377528</td>\n      <td>-2.624511</td>\n      <td>-0.798179</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>0</td>\n      <td>0</td>\n      <td>34</td>\n      <td>-0.185392</td>\n      <td>-0.187891</td>\n      <td>-0.206658</td>\n      <td>-0.634903</td>\n      <td>-0.643175</td>\n      <td>-0.443875</td>\n      <td>-0.556474</td>\n      <td>-1.122211</td>\n      <td>-0.884185</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>0</td>\n      <td>0</td>\n      <td>35</td>\n      <td>-0.308923</td>\n      <td>-0.434147</td>\n      <td>-1.354941</td>\n      <td>0.300540</td>\n      <td>-0.830827</td>\n      <td>0.424937</td>\n      <td>0.518839</td>\n      <td>-0.687369</td>\n      <td>1.440577</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>0</td>\n      <td>0</td>\n      <td>36</td>\n      <td>-0.074661</td>\n      <td>-0.261698</td>\n      <td>-0.007051</td>\n      <td>-2.600390</td>\n      <td>-1.146709</td>\n      <td>-1.601274</td>\n      <td>-3.216254</td>\n      <td>-1.249338</td>\n      <td>-2.868875</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>-0.658366</td>\n      <td>-0.282258</td>\n      <td>-0.438998</td>\n      <td>-0.709998</td>\n      <td>-1.143526</td>\n      <td>-1.562932</td>\n      <td>-0.506418</td>\n      <td>-1.355508</td>\n      <td>-2.630985</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>0.572666</td>\n      <td>0.066861</td>\n      <td>-0.552490</td>\n      <td>0.107840</td>\n      <td>0.535348</td>\n      <td>-0.501347</td>\n      <td>-0.169114</td>\n      <td>0.457801</td>\n      <td>-0.136777</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"- 上表格针对0日0时，9个响应者对39种资产的滞后响应，也就是说响应者在0日0时前一刻做出的响应，范围在【-5,5】","metadata":{}},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test.info()\ntest","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:31:46.068654Z","iopub.execute_input":"2024-11-19T11:31:46.069087Z","iopub.status.idle":"2024-11-19T11:31:46.13834Z","shell.execute_reply.started":"2024-11-19T11:31:46.069049Z","shell.execute_reply":"2024-11-19T11:31:46.137151Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 39 entries, 0 to 38\nData columns (total 85 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   row_id      39 non-null     int64  \n 1   date_id     39 non-null     int16  \n 2   time_id     39 non-null     int16  \n 3   symbol_id   39 non-null     int8   \n 4   weight      39 non-null     float32\n 5   is_scored   39 non-null     bool   \n 6   feature_00  39 non-null     float32\n 7   feature_01  39 non-null     float32\n 8   feature_02  39 non-null     float32\n 9   feature_03  39 non-null     float32\n 10  feature_04  39 non-null     float32\n 11  feature_05  39 non-null     float32\n 12  feature_06  39 non-null     float32\n 13  feature_07  39 non-null     float32\n 14  feature_08  39 non-null     float32\n 15  feature_09  39 non-null     float64\n 16  feature_10  39 non-null     float64\n 17  feature_11  39 non-null     float64\n 18  feature_12  39 non-null     float32\n 19  feature_13  39 non-null     float32\n 20  feature_14  39 non-null     float32\n 21  feature_15  0 non-null      float32\n 22  feature_16  39 non-null     float32\n 23  feature_17  0 non-null      float32\n 24  feature_18  39 non-null     float32\n 25  feature_19  39 non-null     float32\n 26  feature_20  39 non-null     float32\n 27  feature_21  38 non-null     float32\n 28  feature_22  39 non-null     float32\n 29  feature_23  39 non-null     float32\n 30  feature_24  39 non-null     float32\n 31  feature_25  39 non-null     float32\n 32  feature_26  38 non-null     float32\n 33  feature_27  38 non-null     float32\n 34  feature_28  39 non-null     float32\n 35  feature_29  39 non-null     float32\n 36  feature_30  39 non-null     float32\n 37  feature_31  38 non-null     float32\n 38  feature_32  0 non-null      float32\n 39  feature_33  0 non-null      float32\n 40  feature_34  39 non-null     float32\n 41  feature_35  39 non-null     float32\n 42  feature_36  39 non-null     float32\n 43  feature_37  39 non-null     float32\n 44  feature_38  39 non-null     float32\n 45  feature_39  0 non-null      float32\n 46  feature_40  39 non-null     float32\n 47  feature_41  0 non-null      float32\n 48  feature_42  0 non-null      float32\n 49  feature_43  39 non-null     float32\n 50  feature_44  0 non-null      float32\n 51  feature_45  39 non-null     float32\n 52  feature_46  39 non-null     float32\n 53  feature_47  39 non-null     float32\n 54  feature_48  39 non-null     float32\n 55  feature_49  39 non-null     float32\n 56  feature_50  0 non-null      float32\n 57  feature_51  39 non-null     float32\n 58  feature_52  0 non-null      float32\n 59  feature_53  0 non-null      float32\n 60  feature_54  39 non-null     float32\n 61  feature_55  0 non-null      float32\n 62  feature_56  39 non-null     float32\n 63  feature_57  39 non-null     float32\n 64  feature_58  0 non-null      float32\n 65  feature_59  39 non-null     float32\n 66  feature_60  39 non-null     float32\n 67  feature_61  39 non-null     float32\n 68  feature_62  39 non-null     float32\n 69  feature_63  39 non-null     float32\n 70  feature_64  39 non-null     float32\n 71  feature_65  39 non-null     float32\n 72  feature_66  39 non-null     float32\n 73  feature_67  39 non-null     float32\n 74  feature_68  39 non-null     float32\n 75  feature_69  39 non-null     float32\n 76  feature_70  39 non-null     float32\n 77  feature_71  39 non-null     float32\n 78  feature_72  39 non-null     float32\n 79  feature_73  0 non-null      float32\n 80  feature_74  0 non-null      float32\n 81  feature_75  39 non-null     float32\n 82  feature_76  39 non-null     float32\n 83  feature_77  39 non-null     float32\n 84  feature_78  39 non-null     float32\ndtypes: bool(1), float32(77), float64(3), int16(2), int64(1), int8(1)\nmemory usage: 13.3 KB\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"    row_id  date_id  time_id  symbol_id    weight  is_scored  feature_00  \\\n0        0        0        0          0  3.169998       True         0.0   \n1        1        0        0          1  2.165993       True         0.0   \n2        2        0        0          2  3.065550       True         0.0   \n3        3        0        0          3  2.698642       True         0.0   \n4        4        0        0          4  1.803330       True         0.0   \n5        5        0        0          5  2.605776       True         0.0   \n6        6        0        0          6  1.047993       True         0.0   \n7        7        0        0          7  4.231289       True         0.0   \n8        8        0        0          8  2.600524       True         0.0   \n9        9        0        0          9  1.256275       True         0.0   \n10      10        0        0         10  2.453041       True         0.0   \n11      11        0        0         11  1.866914       True         0.0   \n12      12        0        0         12  3.204243       True         0.0   \n13      13        0        0         13  2.641490       True         0.0   \n14      14        0        0         14  1.244236       True         0.0   \n15      15        0        0         15  1.347678       True         0.0   \n16      16        0        0         16  4.124031       True         0.0   \n17      17        0        0         17  3.356702       True         0.0   \n18      18        0        0         18  2.020030       True         0.0   \n19      19        0        0         19  5.524168       True         0.0   \n20      20        0        0         20  0.973524       True         0.0   \n21      21        0        0         21  1.362301       True         0.0   \n22      22        0        0         22  1.266906       True         0.0   \n23      23        0        0         23  1.836982       True         0.0   \n24      24        0        0         24  1.741244       True         0.0   \n25      25        0        0         25  1.893547       True         0.0   \n26      26        0        0         26  1.139385       True         0.0   \n27      27        0        0         27  3.306752       True         0.0   \n28      28        0        0         28  1.637218       True         0.0   \n29      29        0        0         29  1.271961       True         0.0   \n30      30        0        0         30  1.696184       True         0.0   \n31      31        0        0         31  1.300182       True         0.0   \n32      32        0        0         32  1.291041       True         0.0   \n33      33        0        0         33  1.164155       True         0.0   \n34      34        0        0         34  3.240565       True         0.0   \n35      35        0        0         35  1.057221       True         0.0   \n36      36        0        0         36  0.907022       True         0.0   \n37      37        0        0         37  1.393967       True         0.0   \n38      38        0        0         38  4.100645       True         0.0   \n\n    feature_01  feature_02  feature_03  ...  feature_69  feature_70  \\\n0          0.0         0.0         0.0  ...        -0.0        -0.0   \n1         -0.0         0.0         0.0  ...        -0.0        -0.0   \n2         -0.0         0.0         0.0  ...         0.0        -0.0   \n3          0.0         0.0         0.0  ...         0.0        -0.0   \n4         -0.0         0.0         0.0  ...        -0.0        -0.0   \n5         -0.0         0.0         0.0  ...        -0.0        -0.0   \n6         -0.0         0.0         0.0  ...        -0.0        -0.0   \n7          0.0         0.0         0.0  ...         0.0        -0.0   \n8          0.0         0.0         0.0  ...        -0.0        -0.0   \n9         -0.0         0.0         0.0  ...        -0.0        -0.0   \n10        -0.0         0.0         0.0  ...         0.0        -0.0   \n11        -0.0         0.0         0.0  ...         0.0        -0.0   \n12        -0.0         0.0         0.0  ...        -0.0        -0.0   \n13        -0.0         0.0         0.0  ...         0.0        -0.0   \n14        -0.0         0.0         0.0  ...        -0.0        -0.0   \n15        -0.0         0.0         0.0  ...         0.0        -0.0   \n16         0.0         0.0         0.0  ...        -0.0        -0.0   \n17        -0.0         0.0         0.0  ...         0.0        -0.0   \n18        -0.0         0.0         0.0  ...        -0.0        -0.0   \n19        -0.0         0.0         0.0  ...         0.0        -0.0   \n20        -0.0         0.0         0.0  ...        -0.0        -0.0   \n21        -0.0         0.0         0.0  ...        -0.0        -0.0   \n22        -0.0         0.0         0.0  ...        -0.0        -0.0   \n23        -0.0         0.0         0.0  ...         0.0        -0.0   \n24        -0.0         0.0         0.0  ...        -0.0        -0.0   \n25        -0.0         0.0         0.0  ...         0.0        -0.0   \n26        -0.0         0.0         0.0  ...        -0.0        -0.0   \n27        -0.0         0.0         0.0  ...        -0.0        -0.0   \n28        -0.0         0.0         0.0  ...         0.0        -0.0   \n29        -0.0         0.0         0.0  ...        -0.0        -0.0   \n30        -0.0         0.0         0.0  ...        -0.0        -0.0   \n31        -0.0         0.0         0.0  ...         0.0        -0.0   \n32        -0.0         0.0         0.0  ...        -0.0        -0.0   \n33        -0.0         0.0         0.0  ...        -0.0        -0.0   \n34        -0.0         0.0         0.0  ...         0.0        -0.0   \n35        -0.0         0.0         0.0  ...        -0.0        -0.0   \n36        -0.0         0.0         0.0  ...        -0.0        -0.0   \n37        -0.0         0.0         0.0  ...        -0.0        -0.0   \n38         0.0         0.0         0.0  ...         0.0        -0.0   \n\n    feature_71  feature_72  feature_73  feature_74  feature_75  feature_76  \\\n0          0.0         0.0         NaN         NaN         0.0   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<td>0</td>\n      <td>38</td>\n      <td>4.100645</td>\n      <td>True</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.0</td>\n      <td>0.0</td>\n      <td>-0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>-0.0</td>\n      <td>-0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>39 rows × 85 columns</p>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"markdown","source":"- test数据集增加了一列  **is_scored** ,如果为true表示这个日期计分，如果为False表示日期不计分\n\n- if is_scored == 0, 提交一个常量作为预测, else 继续推理","metadata":{}},{"cell_type":"markdown","source":"# 数据处理（lags）","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:35:17.723228Z","iopub.execute_input":"2024-11-19T11:35:17.723639Z","iopub.status.idle":"2024-11-19T11:35:18.472986Z","shell.execute_reply.started":"2024-11-19T11:35:17.723602Z","shell.execute_reply":"2024-11-19T11:35:18.471521Z"}}},{"cell_type":"code","source":"import pandas as pd\nimport polars as pl\nimport numpy as np\nimport gc\nfrom matplotlib import pyplot as plt\nimport matplotlib.cm as cm\nfrom sklearn.model_selection import StratifiedGroupKFold","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:53:59.697693Z","iopub.execute_input":"2024-11-19T11:53:59.698392Z","iopub.status.idle":"2024-11-19T11:53:59.986492Z","shell.execute_reply.started":"2024-11-19T11:53:59.698346Z","shell.execute_reply":"2024-11-19T11:53:59.985131Z"},"trusted":true},"outputs":[],"execution_count":21},{"cell_type":"code","source":"class CONFIG:\n    target_col = \"responder_6\"\n    lag_cols_original = [\"date_id\", \"symbol_id\"] + [f\"responder_{idx}\" for idx in range(9)]\n    lag_cols_rename = { f\"responder_{idx}\" : f\"responder_{idx}_lag_1\" for idx in range(9)}\n    valid_ratio = 0.05\n    start_dt = 1100","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:00.9411Z","iopub.execute_input":"2024-11-19T11:54:00.941622Z","iopub.status.idle":"2024-11-19T11:54:00.949735Z","shell.execute_reply.started":"2024-11-19T11:54:00.941571Z","shell.execute_reply":"2024-11-19T11:54:00.948181Z"},"trusted":true},"outputs":[],"execution_count":22},{"cell_type":"code","source":"# Use last 2 parquets\ntrain = pl.scan_parquet(\n    f\"/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet\"\n).select(\n    pl.int_range(pl.len(), dtype=pl.UInt32).alias(\"id\"),\n    pl.all(),\n).with_columns(\n    (pl.col(CONFIG.target_col)*2).cast(pl.Int32).alias(\"label\"),\n).filter(\n    pl.col(\"date_id\").gt(CONFIG.start_dt)\n)","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:07.980364Z","iopub.execute_input":"2024-11-19T11:54:07.980887Z","iopub.status.idle":"2024-11-19T11:54:07.988714Z","shell.execute_reply.started":"2024-11-19T11:54:07.980795Z","shell.execute_reply":"2024-11-19T11:54:07.987345Z"},"trusted":true},"outputs":[],"execution_count":23},{"cell_type":"code","source":"lags = train.select(pl.col(CONFIG.lag_cols_original))\nlags = lags.rename(CONFIG.lag_cols_rename)\nlags = lags.with_columns(\n    date_id = pl.col('date_id') + 1,  # lagged by 1 day\n    )\nlags = lags.group_by([\"date_id\", \"symbol_id\"], maintain_order=True).last()  # pick up last record of previous date\nlags","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:13.401215Z","iopub.execute_input":"2024-11-19T11:54:13.402594Z","iopub.status.idle":"2024-11-19T11:54:13.750264Z","shell.execute_reply.started":"2024-11-19T11:54:13.402551Z","shell.execute_reply":"2024-11-19T11:54:13.748908Z"},"trusted":true},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<LazyFrame at 0x7D0F340AD4B0>","text/html":"<h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimized version</p><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n -->\n<!-- Title: polars_query Pages: 1 -->\n<svg width=\"2108pt\" height=\"578pt\"\n viewBox=\"0.00 0.00 2107.50 578.25\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" 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1190.5,-222.5 1190.5,-258.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-235.82\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p4&#45;&#45;p5 -->\n<g id=\"edge4\" class=\"edge\">\n<title>p4&#45;&#45;p5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-294.2C1049.75,-283.35 1049.75,-269.42 1049.75,-258.6\"/>\n</g>\n<!-- p6 -->\n<g id=\"node6\" class=\"node\">\n<title>p6</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1339.75,-186.5 759.75,-186.5 759.75,-150.5 1339.75,-150.5 1339.75,-186.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-163.82\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p5&#45;&#45;p6 -->\n<g id=\"edge5\" class=\"edge\">\n<title>p5&#45;&#45;p6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-222.2C1049.75,-211.35 1049.75,-197.42 1049.75,-186.6\"/>\n</g>\n<!-- p7 -->\n<g id=\"node7\" class=\"node\">\n<title>p7</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1084.75,-114.5 1014.75,-114.5 1014.75,-78.5 1084.75,-78.5 1084.75,-114.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-91.83\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p6&#45;&#45;p7 -->\n<g id=\"edge6\" class=\"edge\">\n<title>p6&#45;&#45;p7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-150.2C1049.75,-139.35 1049.75,-125.42 1049.75,-114.6\"/>\n</g>\n<!-- p8 -->\n<g id=\"node8\" class=\"node\">\n<title>p8</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1554.25,-42.5 545.25,-42.5 545.25,0 1554.25,0 1554.25,-42.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-25.2\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-7.95\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p7&#45;&#45;p8 -->\n<g id=\"edge7\" class=\"edge\">\n<title>p7&#45;&#45;p8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-78.14C1049.75,-67.56 1049.75,-53.99 1049.75,-42.87\"/>\n</g>\n</g>\n</svg>\n"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"train = train.join(lags, on=[\"date_id\", \"symbol_id\"],  how=\"left\")\ntrain","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:19.92002Z","iopub.execute_input":"2024-11-19T11:54:19.920447Z","iopub.status.idle":"2024-11-19T11:54:19.957902Z","shell.execute_reply.started":"2024-11-19T11:54:19.920409Z","shell.execute_reply":"2024-11-19T11:54:19.956208Z"},"trusted":true},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<LazyFrame at 0x7D103E2FEDD0>","text/html":"<h4>NAIVE QUERY PLAN</h4><p>run 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font-size=\"14.00\">JOIN LEFT</text>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-637.95\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)];</text>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-620.7\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)]</text>\n</g>\n<!-- p2 -->\n<g id=\"node2\" class=\"node\">\n<title>p2</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"645.25,-564.88 363.75,-564.88 363.75,-528.88 645.25,-528.88 645.25,-564.88\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-542.2\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p1&#45;&#45;p2 -->\n<g id=\"edge1\" class=\"edge\">\n<title>p1&#45;&#45;p2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M962.54,-622.59C873.39,-610.51 757.17,-593.96 654.5,-576.75 633.97,-573.31 611.92,-569.26 591.23,-565.31\"/>\n</g>\n<!-- p6 -->\n<g id=\"node6\" class=\"node\">\n<title>p6</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2763.25,-576.75 663.75,-576.75 663.75,-517 2763.25,-517 2763.25,-576.75\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-559.45\" font-family=\"Times,serif\" font-size=\"14.00\">AGG [col(&quot;responder_0_lag_1&quot;).last(), col(&quot;responder_1_lag_1&quot;).last(), col(&quot;responder_2_lag_1&quot;).last(), col(&quot;responder_3_lag_1&quot;).last(), col(&quot;responder_4_lag_1&quot;).last(), col(&quot;responder_5_lag_1&quot;).last(), col(&quot;responder_6_lag_1&quot;).last(), col(&quot;responder_7_lag_1&quot;).last(), col(&quot;responder_8_lag_1&quot;).last()]</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-542.2\" font-family=\"Times,serif\" font-size=\"14.00\">BY</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-524.95\" font-family=\"Times,serif\" font-size=\"14.00\">[col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)]</text>\n</g>\n<!-- p1&#45;&#45;p6 -->\n<g id=\"edge5\" class=\"edge\">\n<title>p1&#45;&#45;p6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1254.57,-618.99C1335.52,-606.45 1437.09,-590.71 1524.08,-577.23\"/>\n</g>\n<!-- p3 -->\n<g id=\"node3\" class=\"node\">\n<title>p3</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"794.5,-481 214.5,-481 214.5,-445 794.5,-445 794.5,-481\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-458.32\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p2&#45;&#45;p3 -->\n<g id=\"edge2\" class=\"edge\">\n<title>p2&#45;&#45;p3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-528.51C504.5,-514.62 504.5,-495.23 504.5,-481.34\"/>\n</g>\n<!-- p4 -->\n<g id=\"node4\" class=\"node\">\n<title>p4</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"539.5,-409 469.5,-409 469.5,-373 539.5,-373 539.5,-409\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-386.32\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p3&#45;&#45;p4 -->\n<g id=\"edge3\" class=\"edge\">\n<title>p3&#45;&#45;p4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-444.7C504.5,-433.85 504.5,-419.92 504.5,-409.1\"/>\n</g>\n<!-- p5 -->\n<g id=\"node5\" class=\"node\">\n<title>p5</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1009,-337 0,-337 0,-294.5 1009,-294.5 1009,-337\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-319.7\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-302.45\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p4&#45;&#45;p5 -->\n<g id=\"edge4\" class=\"edge\">\n<title>p4&#45;&#45;p5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-372.64C504.5,-362.06 504.5,-348.49 504.5,-337.37\"/>\n</g>\n<!-- p7 -->\n<g id=\"node7\" class=\"node\">\n<title>p7</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1925.88,-481 1501.12,-481 1501.12,-445 1925.88,-445 1925.88,-481\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-458.32\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;date_id&quot;)) + (1)].alias(&quot;date_id&quot;)]</text>\n</g>\n<!-- p6&#45;&#45;p7 -->\n<g id=\"edge6\" class=\"edge\">\n<title>p6&#45;&#45;p7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-516.78C1713.5,-504.83 1713.5,-491.4 1713.5,-481.09\"/>\n</g>\n<!-- p8 -->\n<g id=\"node8\" class=\"node\">\n<title>p8</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1755.25,-409 1671.75,-409 1671.75,-373 1755.25,-373 1755.25,-409\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-386.32\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n</g>\n<!-- p7&#45;&#45;p8 -->\n<g id=\"edge7\" class=\"edge\">\n<title>p7&#45;&#45;p8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-444.7C1713.5,-433.85 1713.5,-419.92 1713.5,-409.1\"/>\n</g>\n<!-- p9 -->\n<g id=\"node9\" class=\"node\">\n<title>p9</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1748.5,-333.75 1678.5,-333.75 1678.5,-297.75 1748.5,-297.75 1748.5,-333.75\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-311.07\" font-family=\"Times,serif\" font-size=\"14.00\">π 11/11</text>\n</g>\n<!-- p8&#45;&#45;p9 -->\n<g id=\"edge8\" class=\"edge\">\n<title>p8&#45;&#45;p9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-372.64C1713.5,-361.05 1713.5,-345.86 1713.5,-334.24\"/>\n</g>\n<!-- p10 -->\n<g id=\"node10\" class=\"node\">\n<title>p10</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1854.25,-258.5 1572.75,-258.5 1572.75,-222.5 1854.25,-222.5 1854.25,-258.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-235.82\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p9&#45;&#45;p10 -->\n<g id=\"edge9\" class=\"edge\">\n<title>p9&#45;&#45;p10</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-297.39C1713.5,-285.8 1713.5,-270.61 1713.5,-258.99\"/>\n</g>\n<!-- p11 -->\n<g id=\"node11\" class=\"node\">\n<title>p11</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2003.5,-186.5 1423.5,-186.5 1423.5,-150.5 2003.5,-150.5 2003.5,-186.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-163.82\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p10&#45;&#45;p11 -->\n<g id=\"edge10\" class=\"edge\">\n<title>p10&#45;&#45;p11</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-222.2C1713.5,-211.35 1713.5,-197.42 1713.5,-186.6\"/>\n</g>\n<!-- p12 -->\n<g id=\"node12\" class=\"node\">\n<title>p12</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1748.5,-114.5 1678.5,-114.5 1678.5,-78.5 1748.5,-78.5 1748.5,-114.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-91.83\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p11&#45;&#45;p12 -->\n<g id=\"edge11\" class=\"edge\">\n<title>p11&#45;&#45;p12</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-150.2C1713.5,-139.35 1713.5,-125.42 1713.5,-114.6\"/>\n</g>\n<!-- p13 -->\n<g id=\"node13\" class=\"node\">\n<title>p13</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2218,-42.5 1209,-42.5 1209,0 2218,0 2218,-42.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-25.2\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-7.95\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p12&#45;&#45;p13 -->\n<g id=\"edge12\" class=\"edge\">\n<title>p12&#45;&#45;p13</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-78.14C1713.5,-67.56 1713.5,-53.99 1713.5,-42.87\"/>\n</g>\n</g>\n</svg>\n"},"metadata":{}}],"execution_count":25},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"len_train   = train.select(pl.col(\"date_id\")).collect().shape[0]\nvalid_records = int(len_train * CONFIG.valid_ratio)\nlen_ofl_mdl = len_train - valid_records\nlast_tr_dt  = train.select(pl.col(\"date_id\")).collect().row(len_ofl_mdl)[0]\n\nprint(f\"\\n len_train = {len_train}\")\nprint(f\"\\n len_ofl_mdl = {len_ofl_mdl}\")\nprint(f\"\\n---> Last offline train date = {last_tr_dt}\\n\")\n\ntraining_data = train.filter(pl.col(\"date_id\").le(last_tr_dt))\nvalidation_data   = train.filter(pl.col(\"date_id\").gt(last_tr_dt))","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:30.029304Z","iopub.execute_input":"2024-11-19T11:54:30.029752Z","iopub.status.idle":"2024-11-19T11:54:37.213597Z","shell.execute_reply.started":"2024-11-19T11:54:30.029711Z","shell.execute_reply":"2024-11-19T11:54:37.209573Z"},"trusted":true},"outputs":[{"name":"stdout","text":"\n len_train = 22104280\n\n len_ofl_mdl = 20999066\n\n---> Last offline train date = 1669\n\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"validation_data","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:54:40.12064Z","iopub.execute_input":"2024-11-19T11:54:40.121315Z","iopub.status.idle":"2024-11-19T11:54:40.159429Z","shell.execute_reply.started":"2024-11-19T11:54:40.121255Z","shell.execute_reply":"2024-11-19T11:54:40.158009Z"},"trusted":true},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"<LazyFrame at 0x7D103E2FD7B0>","text/html":"<h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimized version</p><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n -->\n<!-- Title: polars_query Pages: 1 -->\n<svg width=\"2771pt\" height=\"753pt\"\n viewBox=\"0.00 0.00 2771.25 752.50\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 748.5)\">\n<title>polars_query</title>\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-748.5 2767.25,-748.5 2767.25,4 -4,4\"/>\n<!-- p1 -->\n<g id=\"node1\" class=\"node\">\n<title>p1</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1249.25,-744.5 967.75,-744.5 967.75,-708.5 1249.25,-708.5 1249.25,-744.5\"/>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-721.83\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1669)]</text>\n</g>\n<!-- p2 -->\n<g id=\"node2\" class=\"node\">\n<title>p2</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1254.12,-672.5 962.88,-672.5 962.88,-612.75 1254.12,-612.75 1254.12,-672.5\"/>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-655.2\" font-family=\"Times,serif\" font-size=\"14.00\">JOIN LEFT</text>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-637.95\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)];</text>\n<text text-anchor=\"middle\" x=\"1108.5\" y=\"-620.7\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)]</text>\n</g>\n<!-- p1&#45;&#45;p2 -->\n<g id=\"edge1\" class=\"edge\">\n<title>p1&#45;&#45;p2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1108.5,-708.14C1108.5,-697.9 1108.5,-684.69 1108.5,-672.89\"/>\n</g>\n<!-- p3 -->\n<g id=\"node3\" class=\"node\">\n<title>p3</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"645.25,-564.88 363.75,-564.88 363.75,-528.88 645.25,-528.88 645.25,-564.88\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-542.2\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p2&#45;&#45;p3 -->\n<g id=\"edge2\" class=\"edge\">\n<title>p2&#45;&#45;p3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M962.54,-622.59C873.39,-610.51 757.17,-593.96 654.5,-576.75 633.97,-573.31 611.92,-569.26 591.23,-565.31\"/>\n</g>\n<!-- p7 -->\n<g id=\"node7\" class=\"node\">\n<title>p7</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2763.25,-576.75 663.75,-576.75 663.75,-517 2763.25,-517 2763.25,-576.75\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-559.45\" font-family=\"Times,serif\" font-size=\"14.00\">AGG [col(&quot;responder_0_lag_1&quot;).last(), col(&quot;responder_1_lag_1&quot;).last(), col(&quot;responder_2_lag_1&quot;).last(), col(&quot;responder_3_lag_1&quot;).last(), col(&quot;responder_4_lag_1&quot;).last(), col(&quot;responder_5_lag_1&quot;).last(), col(&quot;responder_6_lag_1&quot;).last(), col(&quot;responder_7_lag_1&quot;).last(), col(&quot;responder_8_lag_1&quot;).last()]</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-542.2\" font-family=\"Times,serif\" font-size=\"14.00\">BY</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-524.95\" font-family=\"Times,serif\" font-size=\"14.00\">[col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)]</text>\n</g>\n<!-- p2&#45;&#45;p7 -->\n<g id=\"edge6\" class=\"edge\">\n<title>p2&#45;&#45;p7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1254.57,-618.99C1335.52,-606.45 1437.09,-590.71 1524.08,-577.23\"/>\n</g>\n<!-- p4 -->\n<g id=\"node4\" class=\"node\">\n<title>p4</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"794.5,-481 214.5,-481 214.5,-445 794.5,-445 794.5,-481\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-458.32\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p3&#45;&#45;p4 -->\n<g id=\"edge3\" class=\"edge\">\n<title>p3&#45;&#45;p4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-528.51C504.5,-514.62 504.5,-495.23 504.5,-481.34\"/>\n</g>\n<!-- p5 -->\n<g id=\"node5\" class=\"node\">\n<title>p5</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"539.5,-409 469.5,-409 469.5,-373 539.5,-373 539.5,-409\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-386.32\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p4&#45;&#45;p5 -->\n<g id=\"edge4\" class=\"edge\">\n<title>p4&#45;&#45;p5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-444.7C504.5,-433.85 504.5,-419.92 504.5,-409.1\"/>\n</g>\n<!-- p6 -->\n<g id=\"node6\" class=\"node\">\n<title>p6</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1009,-337 0,-337 0,-294.5 1009,-294.5 1009,-337\"/>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-319.7\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"504.5\" y=\"-302.45\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p5&#45;&#45;p6 -->\n<g id=\"edge5\" class=\"edge\">\n<title>p5&#45;&#45;p6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M504.5,-372.64C504.5,-362.06 504.5,-348.49 504.5,-337.37\"/>\n</g>\n<!-- p8 -->\n<g id=\"node8\" class=\"node\">\n<title>p8</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1925.88,-481 1501.12,-481 1501.12,-445 1925.88,-445 1925.88,-481\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-458.32\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;date_id&quot;)) + (1)].alias(&quot;date_id&quot;)]</text>\n</g>\n<!-- p7&#45;&#45;p8 -->\n<g id=\"edge7\" class=\"edge\">\n<title>p7&#45;&#45;p8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-516.78C1713.5,-504.83 1713.5,-491.4 1713.5,-481.09\"/>\n</g>\n<!-- p9 -->\n<g id=\"node9\" class=\"node\">\n<title>p9</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1755.25,-409 1671.75,-409 1671.75,-373 1755.25,-373 1755.25,-409\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-386.32\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n</g>\n<!-- p8&#45;&#45;p9 -->\n<g id=\"edge8\" class=\"edge\">\n<title>p8&#45;&#45;p9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-444.7C1713.5,-433.85 1713.5,-419.92 1713.5,-409.1\"/>\n</g>\n<!-- p10 -->\n<g id=\"node10\" class=\"node\">\n<title>p10</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1748.5,-333.75 1678.5,-333.75 1678.5,-297.75 1748.5,-297.75 1748.5,-333.75\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-311.07\" font-family=\"Times,serif\" font-size=\"14.00\">π 11/11</text>\n</g>\n<!-- p9&#45;&#45;p10 -->\n<g id=\"edge9\" class=\"edge\">\n<title>p9&#45;&#45;p10</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-372.64C1713.5,-361.05 1713.5,-345.86 1713.5,-334.24\"/>\n</g>\n<!-- p11 -->\n<g id=\"node11\" class=\"node\">\n<title>p11</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1854.25,-258.5 1572.75,-258.5 1572.75,-222.5 1854.25,-222.5 1854.25,-258.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-235.82\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p10&#45;&#45;p11 -->\n<g id=\"edge10\" class=\"edge\">\n<title>p10&#45;&#45;p11</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-297.39C1713.5,-285.8 1713.5,-270.61 1713.5,-258.99\"/>\n</g>\n<!-- p12 -->\n<g id=\"node12\" class=\"node\">\n<title>p12</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2003.5,-186.5 1423.5,-186.5 1423.5,-150.5 2003.5,-150.5 2003.5,-186.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-163.82\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p11&#45;&#45;p12 -->\n<g id=\"edge11\" class=\"edge\">\n<title>p11&#45;&#45;p12</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-222.2C1713.5,-211.35 1713.5,-197.42 1713.5,-186.6\"/>\n</g>\n<!-- p13 -->\n<g id=\"node13\" class=\"node\">\n<title>p13</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1748.5,-114.5 1678.5,-114.5 1678.5,-78.5 1748.5,-78.5 1748.5,-114.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-91.83\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p12&#45;&#45;p13 -->\n<g id=\"edge12\" class=\"edge\">\n<title>p12&#45;&#45;p13</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-150.2C1713.5,-139.35 1713.5,-125.42 1713.5,-114.6\"/>\n</g>\n<!-- p14 -->\n<g id=\"node14\" class=\"node\">\n<title>p14</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2218,-42.5 1209,-42.5 1209,0 2218,0 2218,-42.5\"/>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-25.2\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"1713.5\" y=\"-7.95\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p13&#45;&#45;p14 -->\n<g id=\"edge13\" class=\"edge\">\n<title>p13&#45;&#45;p14</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1713.5,-78.14C1713.5,-67.56 1713.5,-53.99 1713.5,-42.87\"/>\n</g>\n</g>\n</svg>\n"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"training_data.collect().\\\nwrite_parquet(\n    f\"training.parquet\", partition_by = \"date_id\",\n)","metadata":{"execution":{"iopub.status.busy":"2024-11-19T11:55:18.792798Z","iopub.execute_input":"2024-11-19T11:55:18.793274Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"validation_data.collect().\\\nwrite_parquet(\n    \"validation.parquet\", partition_by = \"date_id\",\n)","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport polars as pl\nimport numpy as np\nimport gc\nfrom matplotlib import pyplot as plt\nimport matplotlib.cm as cm\nfrom sklearn.model_selection import StratifiedGroupKFold","metadata":{"execution":{"iopub.status.busy":"2024-11-20T02:56:30.074912Z","iopub.execute_input":"2024-11-20T02:56:30.075424Z","iopub.status.idle":"2024-11-20T02:56:30.082135Z","shell.execute_reply.started":"2024-11-20T02:56:30.075382Z","shell.execute_reply":"2024-11-20T02:56:30.080766Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"class CONFIG:\n    target_col = \"responder_6\"\n    lag_cols_original = [\"date_id\", \"symbol_id\"] + [f\"responder_{idx}\" for idx in range(9)]\n    lag_cols_rename = { f\"responder_{idx}\" : f\"responder_{idx}_lag_1\" for idx in range(9)}\n    valid_ratio = 0.05\n    start_dt = 1100","metadata":{"execution":{"iopub.status.busy":"2024-11-20T02:56:41.365506Z","iopub.execute_input":"2024-11-20T02:56:41.366091Z","iopub.status.idle":"2024-11-20T02:56:41.373247Z","shell.execute_reply.started":"2024-11-20T02:56:41.366035Z","shell.execute_reply":"2024-11-20T02:56:41.372043Z"},"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"# Use last 2 parquets\ntrain = pl.scan_parquet(\n    f\"/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet\"\n).select(\n    pl.int_range(pl.len(), dtype=pl.UInt32).alias(\"id\"),\n    pl.all(),\n).with_columns(#添加或修改列\n    (pl.col(CONFIG.target_col)*2).cast(pl.Int32).alias(\"label\"),\n).filter(\n    pl.col(\"date_id\").gt(CONFIG.start_dt)#选择1100之后的数据进行训练\n)","metadata":{"execution":{"iopub.status.busy":"2024-11-20T03:03:46.178552Z","iopub.execute_input":"2024-11-20T03:03:46.179619Z","iopub.status.idle":"2024-11-20T03:03:46.215482Z","shell.execute_reply.started":"2024-11-20T03:03:46.179573Z","shell.execute_reply":"2024-11-20T03:03:46.214232Z"},"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"lags = train.select(pl.col(CONFIG.lag_cols_original))\nlags = lags.rename(CONFIG.lag_cols_rename)\nlags = lags.with_columns(\n    date_id = pl.col('date_id') + 1,  # lagged by 1 day\n    )\nlags = lags.group_by([\"date_id\", \"symbol_id\"], maintain_order=True).last()  # pick up last record of previous date\nlags","metadata":{"execution":{"iopub.status.busy":"2024-11-20T03:16:40.96833Z","iopub.execute_input":"2024-11-20T03:16:40.968891Z","iopub.status.idle":"2024-11-20T03:16:41.361157Z","shell.execute_reply.started":"2024-11-20T03:16:40.968853Z","shell.execute_reply":"2024-11-20T03:16:41.360094Z"},"trusted":true},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"<LazyFrame at 0x79208BAAD6F0>","text/html":"<h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimized version</p><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n -->\n<!-- Title: polars_query Pages: 1 -->\n<svg width=\"2108pt\" height=\"578pt\"\n viewBox=\"0.00 0.00 2107.50 578.25\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 574.25)\">\n<title>polars_query</title>\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-574.25 2103.5,-574.25 2103.5,4 -4,4\"/>\n<!-- p1 -->\n<g id=\"node1\" class=\"node\">\n<title>p1</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"2099.5,-570.25 0,-570.25 0,-510.5 2099.5,-510.5 2099.5,-570.25\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-552.95\" font-family=\"Times,serif\" font-size=\"14.00\">AGG [col(&quot;responder_0_lag_1&quot;).last(), col(&quot;responder_1_lag_1&quot;).last(), col(&quot;responder_2_lag_1&quot;).last(), col(&quot;responder_3_lag_1&quot;).last(), col(&quot;responder_4_lag_1&quot;).last(), col(&quot;responder_5_lag_1&quot;).last(), col(&quot;responder_6_lag_1&quot;).last(), col(&quot;responder_7_lag_1&quot;).last(), col(&quot;responder_8_lag_1&quot;).last()]</text>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-535.7\" font-family=\"Times,serif\" font-size=\"14.00\">BY</text>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-518.45\" font-family=\"Times,serif\" font-size=\"14.00\">[col(&quot;date_id&quot;), col(&quot;symbol_id&quot;)]</text>\n</g>\n<!-- p2 -->\n<g id=\"node2\" class=\"node\">\n<title>p2</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1262.12,-474.5 837.38,-474.5 837.38,-438.5 1262.12,-438.5 1262.12,-474.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-451.82\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;date_id&quot;)) + (1)].alias(&quot;date_id&quot;)]</text>\n</g>\n<!-- p1&#45;&#45;p2 -->\n<g id=\"edge1\" class=\"edge\">\n<title>p1&#45;&#45;p2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-510.28C1049.75,-498.33 1049.75,-484.9 1049.75,-474.59\"/>\n</g>\n<!-- p3 -->\n<g id=\"node3\" class=\"node\">\n<title>p3</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1091.5,-402.5 1008,-402.5 1008,-366.5 1091.5,-366.5 1091.5,-402.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-379.82\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n</g>\n<!-- p2&#45;&#45;p3 -->\n<g id=\"edge2\" class=\"edge\">\n<title>p2&#45;&#45;p3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-438.2C1049.75,-427.35 1049.75,-413.42 1049.75,-402.6\"/>\n</g>\n<!-- p4 -->\n<g id=\"node4\" class=\"node\">\n<title>p4</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1084.75,-330.5 1014.75,-330.5 1014.75,-294.5 1084.75,-294.5 1084.75,-330.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-307.82\" font-family=\"Times,serif\" font-size=\"14.00\">π 11/11</text>\n</g>\n<!-- p3&#45;&#45;p4 -->\n<g id=\"edge3\" class=\"edge\">\n<title>p3&#45;&#45;p4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-366.2C1049.75,-355.35 1049.75,-341.42 1049.75,-330.6\"/>\n</g>\n<!-- p5 -->\n<g id=\"node5\" class=\"node\">\n<title>p5</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1190.5,-258.5 909,-258.5 909,-222.5 1190.5,-222.5 1190.5,-258.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-235.82\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;date_id&quot;)) &gt; (1100)]</text>\n</g>\n<!-- p4&#45;&#45;p5 -->\n<g id=\"edge4\" class=\"edge\">\n<title>p4&#45;&#45;p5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-294.2C1049.75,-283.35 1049.75,-269.42 1049.75,-258.6\"/>\n</g>\n<!-- p6 -->\n<g id=\"node6\" class=\"node\">\n<title>p6</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1339.75,-186.5 759.75,-186.5 759.75,-150.5 1339.75,-150.5 1339.75,-186.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-163.82\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;responder_6&quot;)) * (2.0)].strict_cast(Int32).alias(&quot;label&quot;)]</text>\n</g>\n<!-- p5&#45;&#45;p6 -->\n<g id=\"edge5\" class=\"edge\">\n<title>p5&#45;&#45;p6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-222.2C1049.75,-211.35 1049.75,-197.42 1049.75,-186.6\"/>\n</g>\n<!-- p7 -->\n<g id=\"node7\" class=\"node\">\n<title>p7</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1084.75,-114.5 1014.75,-114.5 1014.75,-78.5 1084.75,-78.5 1084.75,-114.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-91.83\" font-family=\"Times,serif\" font-size=\"14.00\">π 94/94</text>\n</g>\n<!-- p6&#45;&#45;p7 -->\n<g id=\"edge6\" class=\"edge\">\n<title>p6&#45;&#45;p7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-150.2C1049.75,-139.35 1049.75,-125.42 1049.75,-114.6\"/>\n</g>\n<!-- p8 -->\n<g id=\"node8\" class=\"node\">\n<title>p8</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1554.25,-42.5 545.25,-42.5 545.25,0 1554.25,0 1554.25,-42.5\"/>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-25.2\" font-family=\"Times,serif\" font-size=\"14.00\">Parquet SCAN [/kaggle/input/jane&#45;street&#45;real&#45;time&#45;market&#45;data&#45;forecasting/train.parquet/partition_id=0/part&#45;0.parquet, ... 9 other sources]</text>\n<text text-anchor=\"middle\" x=\"1049.75\" y=\"-7.95\" font-family=\"Times,serif\" font-size=\"14.00\">π */93;</text>\n</g>\n<!-- p7&#45;&#45;p8 -->\n<g id=\"edge7\" class=\"edge\">\n<title>p7&#45;&#45;p8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.75,-78.14C1049.75,-67.56 1049.75,-53.99 1049.75,-42.87\"/>\n</g>\n</g>\n</svg>\n"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}