{ "info": { "author": "Rouzbeh Afrasiabi", "author_email": "rouzbeh.afrasiabi@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python :: 3.6" ], "description": "# Traintorch (alpha)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4b74c08973343128d17532b4b84e154)](https://www.codacy.com/manual/rouzbeh-afrasiabi/traintorch?utm_source=github.com&utm_medium=referral&utm_content=rouzbeh-afrasiabi/traintorch&utm_campaign=Badge_Grade)\n\n\n

\nPackage for live visualization of model validation metrics during training of a machine learning model in jupyter notebooks. The package utilizes a sliding window mechanism to reduce memory usage.\n

\n\n## Requirements:\n\n```\npandas==0.25.1\nmatplotlib==3.1.1\nipython==7.8.0\nnumpy==1.17.2\npycm==2.2\n```\n ## Installation:\n \n ### Latest release:\n ```\n pip install traintorch\n ```\n \n### Latest Version\n\n ```\n pip install git+https://github.com/rouzbeh-afrasiabi/traintorch.git\n ```\n\n## Example \n\n### Simple Usage\n```python\nfrom traintorch import *\n\n#custom metrics\nfirst=metric('Loss',w_size=10,average=False)\nsecond=metric('Accuracy',w_size=10,average=False)\n\n\n#create an instance of traintorch\ntracker=traintorch(n_custom_plots=2,main_grid_hspace=.1, figsize=(15,10),show_table=True)\n#combine all metrics together\ntracker.append([first,second])\n\n\nrange_max=1000\nfor i in range(0,range_max,1):\n \n first.update(train_loss=1/(i+1),test_loss=1/(i**2+1))\n second.update(y=i/(i*2+1))\n tracker.plot()\n```\n

\n \n \n

\n\n\n\n### Using pycm metrics and doing comparison\n\n\n```python\nfrom traintorch import *\n\n\n#custom metric\nfirst=metric('Loss',w_size=10,average=False)\n\n#pycm metrics\noverall_selected=['ACC Macro']\ncm_metrics_a=pycmMetrics(overall_selected,name='train',w_size=10)\ncm_metrics_b=pycmMetrics(overall_selected,name='test',w_size=10)\n\n#compare two metrics of the same kind\ncompare_a=collate(cm_metrics_a,cm_metrics_b,'ACC Macro')\n\n#create an instance of traintorch\ntracker=traintorch(n_custom_plots=1,main_grid_hspace=.1,figsize=(15,15),show_table=True)\n\n#combine all metrics together\ntracker.append([first,cm_metrics_a,cm_metrics_b,compare_a])\n\n\nrange_max=1000\nfor i in range(0,range_max,1):\n \n actual_a=np.random.choice([0, 1], size=(20,), p=[1./3, 2./3])\n predicted_a=np.random.choice([0, 1], size=(20,),p=[1-(i/range_max), i/range_max])\n actual_b=np.random.choice([0, 1], size=(20,), p=[1./3, 2./3])\n predicted_b=np.random.choice([0, 1], size=(20,),p=[1-(i/range_max), i/range_max])\n cm_metrics_a.update(actual_a,predicted_a)\n cm_metrics_b.update(actual_b,predicted_b)\n first.update(train=1/(i+1),test=1/(i**2+1))\n compare_a.update()\n tracker.plot()\n\n```\n

\n \n \n

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