{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 in \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 \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[{"output_type":"stream","text":"['test', 'sample_submission.csv', 'train.csv']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","collapsed":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":false},"cell_type":"markdown","source":"**when** the earthquake will take place  \npredict the time remaining (**time_to_failure**) from real-time seismic data (**acoustic_data**)  \nevaluated using the **mean absolute error**  \nthe time between the **last** row of the segment (**seg_id**) and the next laboratory earthquake"},{"metadata":{"trusted":true},"cell_type":"code","source":"# first 3 segments\ntrain = pd.read_csv('../input/train.csv', nrows=450000)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.options.display.precision\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.options.display.precision = 18\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head(150000) #.info()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nsns.set()\n","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# 1st segment\nax = sns.lineplot(x=np.arange(150000), y=train['acoustic_data'][:150000])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# 2nd segment\nax = sns.lineplot(x=np.arange(150000), y=train['acoustic_data'][150000:300000])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# 3rd segment\nax = sns.lineplot(x=np.arange(150000), y=train['acoustic_data'][300000:450000])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.dtypes #.value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.nunique() #.sort_values()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.acoustic_data.value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.options.display.max_rows\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.options.display.max_rows = 250\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.acoustic_data.value_counts().sort_index(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.lineplot(train.acoustic_data.value_counts().index, train.acoustic_data.value_counts())\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.distplot(train.acoustic_data, bins=20, kde=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.duplicated().sum()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.isna().sum() #.sort_values(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.eq(0).sum() #.sort_values(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.lt(0).sum() #.sort_values(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.min() #.sort_values(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.max() #.sort_values(ascending=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del train\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# all targets\nrows = 150000\ntarget = pd.read_csv('../input/train.csv', usecols=[1]).iloc[rows-1::rows, 0]\n","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.lineplot(x=np.arange(target.size), y=target)\n","execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"target.diff().gt(0).sum()\n","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"16"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"target.mean()\n","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"5.682698488340905"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"target.min()\n","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"0.0063976571678"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"target.max()\n","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"16.103195567"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.distplot(target, bins=20, kde=False)\n","execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.boxplot(target)\n","execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]}],"metadata":{"kernelspec":{"display_name":"Python 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