Package for live visualization of metrics during training of a machine learning model
Project description
Traintorch (alpha)
Requirements:
pandas==0.25.1
matplotlib==3.1.1
ipython==7.8.0
numpy==1.17.2
pycm==2.2
Installation:
Latest release:
pip install traintorch
Latest Version
pip install git+https://github.com/rouzbeh-afrasiabi/traintorch.git
Example
Simple Usage
from traintorch import * #custom metrics first=metric('Loss',w_size=10,average=False) second=metric('Accuracy',w_size=10,average=False) #create an instance of traintorch tracker=traintorch(n_custom_plots=2,main_grid_hspace=.1, figsize=(15,10),show_table=True) #combine all metrics together tracker.append([first,second]) range_max=1000 for i in range(0,range_max,1): first.update(train_loss=1/(i+1),test_loss=1/(i**2+1)) second.update(y=i/(i*2+1)) tracker.plot()
Using pycm metrics and doing comparison
from traintorch import * #custom metric first=metric('Loss',w_size=10,average=False) #pycm metrics overall_selected=['ACC Macro'] cm_metrics_a=pycmMetrics(overall_selected,name='train',w_size=10) cm_metrics_b=pycmMetrics(overall_selected,name='test',w_size=10) #compare two metrics of the same kind compare_a=collate(cm_metrics_a,cm_metrics_b,'ACC Macro') #create an instance of traintorch tracker=traintorch(n_custom_plots=1,main_grid_hspace=.1,figsize=(15,15),show_table=True) #combine all metrics together tracker.append([first,cm_metrics_a,cm_metrics_b,compare_a]) range_max=1000 for i in range(0,range_max,1): actual_a=np.random.choice([0, 1], size=(20,), p=[1./3, 2./3]) predicted_a=np.random.choice([0, 1], size=(20,),p=[1-(i/range_max), i/range_max]) actual_b=np.random.choice([0, 1], size=(20,), p=[1./3, 2./3]) predicted_b=np.random.choice([0, 1], size=(20,),p=[1-(i/range_max), i/range_max]) cm_metrics_a.update(actual_a,predicted_a) cm_metrics_b.update(actual_b,predicted_b) first.update(train=1/(i+1),test=1/(i**2+1)) compare_a.update() tracker.plot()
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