{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Import and load dfs\n\nReferences: [Tutorial to the G-Research Crypto Competition](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)","metadata":{"papermill":{"duration":0.019059,"end_time":"2021-11-11T10:14:46.963536","exception":false,"start_time":"2021-11-11T10:14:46.944477","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import os\nimport random\nimport pandas as pd\nimport numpy as np\nfrom lightgbm import LGBMRegressor\nimport gresearch_crypto\nimport datetime\nfrom sklearn.preprocessing import RobustScaler\nimport warnings\nimport gc\nimport time\nwarnings.filterwarnings('ignore')\n\nTRAIN_CSV = '/kaggle/input/g-research-crypto-forecasting/train.csv'\nASSET_DETAILS_CSV = '/kaggle/input/g-research-crypto-forecasting/asset_details.csv'\n\nSEED = 2021\n\nREMOVE_LB_TEST_OVERLAPPING_DATA = True","metadata":{"papermill":{"duration":2.96235,"end_time":"2021-11-11T10:14:49.941016","exception":false,"start_time":"2021-11-11T10:14:46.978666","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-11-16T04:41:29.54346Z","iopub.execute_input":"2021-11-16T04:41:29.543824Z","iopub.status.idle":"2021-11-16T04:41:30.538582Z","shell.execute_reply.started":"2021-11-16T04:41:29.543721Z","shell.execute_reply":"2021-11-16T04:41:30.537623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print( 'step4',datetime.datetime.now().strftime('%Y年%m月%d日 %H:%M:%S'))\nall_df_test = []\n\nenv = gresearch_crypto.make_env()\niter_test = env.iter_test()\n\nfor i, (df_test, df_pred) in enumerate(iter_test):\n        \n    df_pred.Target = 0\n    #if len(df_test)!=14:\n    #    rrore\n    time.sleep(0.1)\n    # Display the first prediction dataframe\n    # Send submissions\n    env.predict(df_pred)\n    ","metadata":{"papermill":{"duration":1.592887,"end_time":"2021-11-11T10:37:24.72357","exception":false,"start_time":"2021-11-11T10:37:23.130683","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-11-16T04:41:30.53991Z","iopub.execute_input":"2021-11-16T04:41:30.540622Z","iopub.status.idle":"2021-11-16T04:41:30.562992Z","shell.execute_reply.started":"2021-11-16T04:41:30.540585Z","shell.execute_reply":"2021-11-16T04:41:30.562266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(df_test)","metadata":{"execution":{"iopub.status.busy":"2021-11-16T04:41:30.564529Z","iopub.execute_input":"2021-11-16T04:41:30.564747Z","iopub.status.idle":"2021-11-16T04:41:30.570853Z","shell.execute_reply.started":"2021-11-16T04:41:30.564719Z","shell.execute_reply":"2021-11-16T04:41:30.570299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"😺😅㊙🔰🗑⬛🟥🟨🟩","metadata":{}}]}