{"cells":[{"metadata":{},"cell_type":"markdown","source":"# The unique distribution of scalar_coupling_constant\n\nWhen I first observed the distribution of the target variable. I noticed it wasn't a simple normal distribution. It appears that there are different \"clusters\" of values where the target value resides. Can we do something to itentify which \"cluster\" an observation appears in?\n\nLooking at this closer, I have a hypothesis that we may see better results if we modeled certain \"coupling types\" independently."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport matplotlib.pylab as plt\nplt.style.use('ggplot')\ncolor_pal = [x['color'] for x in plt.rcParams['axes.prop_cycle']]","execution_count":11,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('../input/train.csv')\ntest_df = pd.read_csv('../input/test.csv')\nstructures = pd.read_csv('../input/structures.csv')\nss = pd.read_csv('../input/sample_submission.csv')","execution_count":5,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# First, lets explore the distribution of the target\nI feel that most of the time a histogram is the best way to get an understanding of a single feature."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Distribution of the target\ntrain_df['scalar_coupling_constant'].plot(kind='hist', figsize=(20, 5), bins=1000, title='Distribution of the scalar coupling constant (target)')\nplt.show()","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 1 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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Plot Target by Observation Kind"},{"metadata":{"trusted":true},"cell_type":"code","source":"color_index = 0\naxes_index = 0\nfig, axes = plt.subplots(8, 1, figsize=(20, 30), sharex=True)\nfor mtype, d in train_df.groupby('type'):\n    d['scalar_coupling_constant'].plot(kind='hist',\n                  bins=1000,\n                  title='Distribution of Distance Feature for {}'.format(mtype),\n                  color=color_pal[color_index],\n                  ax=axes[axes_index])\n    if color_index == 6:\n        color_index = 0\n    else:\n        color_index += 1\n    axes_index += 1\nplt.show()","execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x2160 with 8 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Distributions in the same plot.\nIt's pretty clear that the target is closely related to the type of coupling. All of the ~80+ `scalar_coupling_constant` values are type: 1JHC."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.groupby('type')['scalar_coupling_constant'].plot(kind='hist',\n                                                          bins=1000,\n                                                          figsize=(20, 5),\n                                                          alpha=0.8,\n                                                         title='scalar_coupling_constant by coupling type')\nplt.legend()\nplt.show()","execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 1 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Maybe we should we model each \"type\" independently?"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}