print('min_epoch_count: {}'.format(min_epoch_count))
if len(accuracies[tasks]) > max_epoch_count:
max_epoch_count = len(accuracies[tasks])
print('max_epoch_count: {}'.format(max_epoch_count))
except:
print('{} not found'.format(tasks))
return accuracies, min_epoch_count, max_epoch_count
if __name__ == '__main__':
accuracies, min_epoch_count, max_epoch_count = read_pkls()
pprint(accuracies)
plot_accuracies(
epoch_limit,
min_epoch_count,
max_epoch_count,
accuracies.items(),
task_ids,
'Ablation study',
'components_removed_training_accuracy.png',
y_offsets={
'main': 0.0015,
'no_story_again': -0.0015,
'no_same_rnn': -0.001
},
ylim=(0.85, 1),
moving_avg=4,
)
min_epoch_count = 100000
epoch_limit = 200
hpconfigs = [
'hpconfig_story_len_10',
'hpconfig_story_len_20',
'hpconfig_story_len_30',
'hpconfig_story_len_40',
'hpconfig_story_len_50',
'hpconfig_story_len_60',
'hpconfig', # all
]
if __name__ == '__main__':
accuracies, min_epoch_count, max_epoch_count = read_pkls(
hpconfigs, 'hpconfig_story_len_(\d+)')
pprint(accuracies)
labels = {k: 'story_len = {}'.format(k) for k in accuracies.keys()}
labels['main'] = 'main'
plot_accuracies(
epoch_limit,
min_epoch_count,
max_epoch_count,
accuracies.items(),
task_ids,
'Story length',
'story_len_training_accuracy.png',
labels=labels,
y_offsets={},
)
min_epoch_count = 100000
epoch_limit = 200
hpconfigs = [
'hpconfig_1_reasoning_steps',
'hpconfig_3_reasoning_steps',
'hpconfig_4_reasoning_steps',
'hpconfig_5_reasoning_steps',
'hpconfig',
]
if __name__ == '__main__':
accuracies, min_epoch_count, max_epoch_count = read_pkls(
hpconfigs, 'hpconfig_(\d+)_reasoning_steps')
pprint(accuracies)
labels = {
k: 'steps = {}'.format(k)
for k in accuracies.keys() if k != 'main'
}
labels['main'] = 'steps = 2'
plot_accuracies(epoch_limit,
min_epoch_count,
max_epoch_count,
accuracies.items(),
task_ids,
'Reasoning Steps',
'reasoning_steps_training_accuracy.png',
labels=labels,
y_offsets={},
ylim=(0.6, 1))
'hpconfig_50percent_dataset', 'hpconfig_75percent_dataset', 'hpconfig'
]
if __name__ == '__main__':
accuracies, min_epoch_count, max_epoch_count = read_pkls(
hpconfigs, 'hpconfig_(\d+)percent_dataset')
pprint(accuracies)
labels = {
k: 'size = {:0.2f}'.format(float(k) / 100)
for k in accuracies.keys() if k != 'main'
}
labels['main'] = 'main'
plot_accuracies(epoch_limit,
min_epoch_count,
max_epoch_count,
accuracies.items(),
task_ids,
'Dataset Size',
'dataset_training_accuracy.png',
labels=labels,
y_offsets={
'10': 0.0,
'20': 0.0,
'30': -0.005,
'40': 0.005,
'50': 0.005,
'75': -0.007,
'main': 0.007
},
ylim=(0.6, 1))
import sys
sys.path.append('..')
import matplotlib.pyplot as plt
import importlib
from anikattu.utilz import initialize_task
plt.style.use('ggplot')
import pickle
from plot_combined_accuracy import task_names, task_ids, plot_accuracies
from heatmap_task_accuracies import read_pkls
root_dirs = {}
accuracies = {}
max_epoch_count = 0
min_epoch_count = 100000
epoch_limit = 200
if __name__ == '__main__':
accuracies, min_epoch_count, max_epoch_count = read_pkls()
plot_accuracies(epoch_limit,
min_epoch_count, max_epoch_count,
accuracies.items(), task_ids,
'Individual Training Accuracy',
'individual_training_accuracy.png')