plot.py

''' Plotting losses etc.  '''


import numpy as np
import pandas as pd
import matplotlib as mpl
from eval import Evaluation
mpl.use('Agg')
import matplotlib.pyplot as plt

logs = {
    'seq2seq_sample_imagenet_log.csv': 'Student-forcing',
    'seq2seq_teacher_imagenet_log.csv': 'Teacher-forcing'
}

RESULT_DIR = 'tasks/R2R/results/'
PLOT_DIR = 'tasks/R2R/plots/'


def plot_training_curves():
  ''' Plot the validation loss, navigation error and success rate during training. '''

  font = {
      'size': 12
  }
  mpl.rc('font', **font)

  dfs = {}
  for log in logs:
    dfs[log] = pd.read_csv('tasks/R2R/plots/'+log)

  plots = [
      ('Loss', 'loss', ['val_seen loss', 'val_unseen loss', 'train loss']),
      ('Navigation Error', 'm', [
       'val_seen nav_error', 'val_unseen nav_error']),
      ('Success', '%', ['val_seen success', 'val_unseen success'])
  ]

  colors = {
      'Student-forcing Val Seen': 'C0',
      'Student-forcing Val Unseen': 'C2',
      'Student-forcing Train': 'C4',
      'Teacher-forcing Val Seen': 'C1',
      'Teacher-forcing Val Unseen': 'C3',
      'Teacher-forcing Train': 'C5'
  }

  fig, axes = plt.subplots(ncols=3, squeeze=True, figsize=(13, 3.25))
  handles = []
  labels = []
  for i, (title, ylabel, x_vars) in enumerate(plots):
    for log in logs:
      df = dfs[log]
      x = df['iteration']
      for col_name in x_vars:
        y = df[col_name]
        label = ' Train'
        if 'unseen' in col_name:
          label = ' Val Unseen'
        elif 'seen' in col_name:
          label = ' Val Seen'
        if i == 0:
          label = logs[log]+label
          labels.append(labels)
          handles.append(axes[i].plot(x, y, colors[label], label=label))
        else:
          axes[i].plot(x, y, colors[logs[log]+label], label=logs[log]+label)
    axes[i].set_title(title)
    axes[i].set_xlabel('Iteration')
    axes[i].set_ylabel(ylabel)

  plt.tight_layout()
  fig.subplots_adjust(bottom=0.4)
  handles, labels = axes[0].get_legend_handles_labels()
  axes[1].legend(handles=handles, labels=labels, loc='upper center',
                 bbox_to_anchor=(0.5, -0.35), fancybox=False, shadow=False, ncol=3)
  plt.setp(axes[1].get_legend().get_texts(), fontsize='12')
  plt.savefig('%s/training.png' % (PLOT_DIR))


def plot_final_scores():
  ''' Plot the scores '''
  font = {
      'size': 12
  }
  mpl.rc('font', **font)
  fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 4)
                         )  # create figure & 1 axis
  outfiles = [
      RESULT_DIR + 'seq2seq_sample_imagenet_%s_iter_20000.json',
      RESULT_DIR + 'seq2seq_teacher_imagenet_%s_iter_5000.json',
      RESULT_DIR + '%s_stop_agent.json',
      RESULT_DIR + '%s_random_agent.json'
  ]
  for split in ['val_seen']:
    ev = Evaluation([split])
    for i, outfile in enumerate(outfiles):
      score_summary, scores = ev.score(outfile % split)
      if i == 1:
        method = 'Teacher-forcing'
        ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method,
                normed=True, histtype='step', linewidth=2.5, color='C1')
      elif i == 0:
        method = 'Student-forcing'
        ax.hist(scores['nav_errors'], bins=range(0, 30, 3),
                label=method, alpha=0.7, normed=True, color='C0')
      elif i == 2:
        method = 'Start locations'
        ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method,
                normed=True, histtype='step', linewidth=2.5, color='C3')
      elif i == 3:
        method = 'Random agent'
        ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method,
                normed=True, histtype='step', linewidth=2.5, color='C2')
  ax.set_title('Val Seen Navigation Error')
  ax.set_xlabel('Error (m)')
  ax.set_ylabel('Frequency')
  ax.set_ylim([0, 0.14])
  ax.set_xlim([0, 30])
  plt.axvline(x=3, color='black', linestyle='--')
  legend = ax.legend(loc='upper right')
  plt.tight_layout()
  plt.savefig('%s/val_seen_error.png' % (PLOT_DIR))
  plt.close(fig)


if __name__ == '__main__':
  plot_training_curves()
  plot_final_scores()