{"cells":[{"metadata":{},"cell_type":"markdown","source":"# **Handling Cyclic Data **","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"**What is Cyclic data??**\n\nData which has a unique set of values which repeat in a cycle are known as cyclic data. Time related features  are mainly cyclic in nature. For example, months of a year, day of a week, hours of time, minutes of time etc..These features have a set of values and all the observations will have a value from this set only. In many ML problems we encounter such features. But most of us ignore it. I suggest to handle them separately as handling such features properly have proved to help in improvement of the accuracy. Let us see how to handle them.  ","execution_count":null},{"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 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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/cat-in-the-dat-ii/train.csv')\ntrain.columns","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Here month and day are cyclic. Lets handle them\n","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"df = train[['month','day']].copy()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Unique values of month:',df.month.unique())\nprint('Unique values of day:',df.day.unique())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Lets first handle na. We will replace them with mode.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"for column in df.columns:\n    df[column].fillna(df[column].mode()[0], inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Unique values of month:',df.month.unique())\nprint('Unique values of day:',df.day.unique())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"So we see no nan is present now.\n\nAlso, we can understand that month is from 1-12\nAnd day is weekday from 1-7\n\nThese are cyclic data.\n\nLet us handle them now.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\n\ndf['day_sin'] = np.sin(df.day*(2.*np.pi/7))\ndf['day_cos'] = np.cos(df.day*(2.*np.pi/7))\ndf['month_sin'] = np.sin((df.month-1)*(2.*np.pi/12))\ndf['month_cos'] = np.cos((df.month-1)*(2.*np.pi/12))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df.head(10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**The logic**\n\n We map each cyclical variable onto a circle such that the lowest value for that variable appears right next to the largest value. We compute the x- and y- component of that point using sin and cos trigonometric functions.\n \n For handling month we consider them from 0-11 and refer the below figure. ","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![i](https://i.ibb.co/hF8yWhT/cyclic.png)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Go on and implement this in your codes.\n\n[Reference](http://blog.davidkaleko.com/feature-engineering-cyclical-features.html)","execution_count":null}],"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":4,"nbformat_minor":4}