plot_training_history.py

#!/usr/bin/env python
u"""
plot_training_history.py
by Yara Mohajerani (Last Update 10/2018)

Plot training history generated by unet_train.py

Update History
    10/2018 Written
"""
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#from scipy.interpolate import spline

#-- train model and make predictions
def plot_history(parameters):
    glacier = parameters['GLACIER_NAME']
    n_batch = int(parameters['BATCHES'])
    n_epochs = int(parameters['EPOCHS'])
    n_layers = int(parameters['LAYERS_DOWN'])
    n_init = int(parameters['N_INIT'])
    suffix = parameters['SUFFIX']
    drop = float(parameters['DROPOUT'])
    imb_str = '_%.2fweight'%(float(parameters['imb_str']))
    #-- set up configurations based on parameters
    if parameters['AUGMENT'] in ['Y','y']:
        aug_config = np.int(parameters['AUG_CONFIG'])
        aug_str = '_augment-x%i'%aug_config
    else:
        aug_config = 0
        aug_str = ''
    
    if parameters['CROP'] in ['Y','y']:
        crop_str = '_cropped'
    else:
        crop_str = ''
    
    if parameters['NORMALIZE'] in ['y','Y']:
        normalize = True
        norm_str = '_normalized'
    else:
        normalize = False
        norm_str = ''
    
    if parameters['LINEAR'] in ['Y','Y']:
        linear = True
        lin_str = '_linear'
    else:
        linear = False
        lin_str = ''

    drop_str = ''
    if drop>0:
        drop_str = '_w%.1fdrop'%drop

    #-- plotting
    if parameters['PLOT'] in ['y','Y']:
        PLOT = True
    else:
        PLOT = False

    if parameters['LABEL_WIDTH'] == '3':
        lbl_width = ''
    else:
        lbl_width = '_%ipx'%int(parameters['LABEL_WIDTH'])

    if (normalize) and (drop!=0):
        sys.exit('Both batch normalization and dropout are selecte. Choose one.')

    #-- directory setup
    #- current directory
    current_dir = os.path.dirname(os.path.realpath(__file__))
    main_dir = os.path.join(current_dir,'..','FrontLearning_data')
    glacier_ddir = os.path.join(main_dir,'%s.dir'%glacier)
    data_dir = os.path.join(glacier_ddir, 'data')
    trn_dir = os.path.join(data_dir,'train')
    tst_dir = os.path.join(data_dir,'test')


    #-- input file
    infile = os.path.join(glacier_ddir,\
        'training_history_%ibatches_%iepochs_%ilayers_%iinit%s%s%s%s%s%s%s%s.txt'\
        %(n_batch,n_epochs,n_layers,n_init,lin_str,imb_str,drop_str,norm_str,\
        aug_str,suffix,crop_str,lbl_width))

    history = pd.read_table(infile,delim_whitespace=True)
    epochs = history['Epoch'] + 1 #- start from 1 instead of 0
    for item,name in zip(['acc','loss'],['Accuracy','Loss']):
        fig = plt.figure(1,figsize=(8,6))
        plt.plot(epochs,history[item],'b-')
        plt.plot(epochs,history['val_%s'%item],'r-')
        #-- also plot smoothd lines
        epoch_smooth = np.linspace(np.min(history['Epoch']),np.max(history['Epoch']),100)
        #smooth = spline(epochs,history[item],epoch_smooth)
        #smooth_val = spline(epochs,history['val_%s'%item],epoch_smooth)
        #plt.plot(epoch_smooth,smooth,'b:')
        #plt.plot(epoch_smooth,smooth_val,'r:')
        plt.title('Model %s'%name)
        plt.ylabel(name)
        plt.xlabel('Epochs')
        plt.legend(['Training', 'Validation'], loc='upper left')
        plt.savefig(os.path.join(glacier_ddir,\
            'training_history_%s_%ibatches_%iepochs_%ilayers_%iinit%s%s%s%s%s%s%s%s.pdf'\
            %(item,n_batch,n_epochs,n_layers,n_init,lin_str,imb_str,drop_str,norm_str,\
            aug_str,suffix,crop_str,lbl_width)),format='pdf')
        plt.close(fig)

#-- main function to get parameters and pass them along to fitting function
def main():
    if (len(sys.argv) == 1):
        sys.exit('You need to input at least one parameter file to set run configurations.')
    else:
        #-- Input Parameter Files (sys.argv[0] is the python code)
        input_files = sys.argv[1:]
        #-- for each input parameter file
        for file in input_files:
            #-- keep track of progress
            print(os.path.basename(file))
            #-- variable with parameter definitions
            parameters = {}
            #-- Opening parameter file and assigning file ID number (fid)
            fid = open(file, 'r')
            #-- for each line in the file will extract the parameter (name and value)
            for fileline in fid:
                #-- Splitting the input line between parameter name and value
                part = fileline.split()
                #-- filling the parameter definition variable
                parameters[part[0]] = part[1]
            #-- close the parameter file
            fid.close()

            #-- pass parameters to training function
            plot_history(parameters)

if __name__ == '__main__':
    main()