{"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":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"import keras\nimport keras.backend as K\nfrom keras.layers import LSTM,Dropout,Dense,TimeDistributed,Conv1D,MaxPooling1D,Flatten\nfrom keras.models import Sequential\nimport tensorflow as tf\nimport gc\nfrom numba import jit\nfrom IPython.display import display, clear_output\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nimport sys\nsns.set_style(\"whitegrid\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2023-04-11T14:00:27.807900Z","iopub.execute_input":"2023-04-11T14:00:27.809265Z","iopub.status.idle":"2023-04-11T14:00:27.815108Z","shell.execute_reply.started":"2023-04-11T14:00:27.809217Z","shell.execute_reply":"2023-04-11T14:00:27.813352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time \ntrain_set = pq.read_pandas('../input/vsb-power-line-fault-detection/train.parquet').to_pandas()","metadata":{"execution":{"iopub.status.busy":"2023-04-11T14:00:32.109844Z","iopub.execute_input":"2023-04-11T14:00:32.110224Z"},"trusted":true},"execution_count":null,"outputs":[]}]}