def plot_misclasses_mean_std_for_exps_with_tube(df):
test_misclasses = extract_from_results(
df, lambda r: r.monitor_channels['test_misclass'])
test_misclasses, exps_by_epoch = get_padded_chan_vals(test_misclasses,
pad_by=np.nan)
valid_misclasses = extract_from_results(
df, lambda r: r.monitor_channels['valid_misclass'])
valid_misclasses, exps_by_epoch = get_padded_chan_vals(valid_misclasses,
pad_by=np.nan)
train_misclasses = extract_from_results(
df, lambda r: r.monitor_channels['train_misclass'])
train_misclasses, exps_by_epoch = get_padded_chan_vals(train_misclasses,
pad_by=np.nan)
plot_with_tube(range(train_misclasses.shape[1]),
np.nanmean(train_misclasses, axis=0),
np.nanstd(train_misclasses, axis=0))
plot_with_tube(range(valid_misclasses.shape[1]),
np.nanmean(valid_misclasses, axis=0),
np.nanstd(valid_misclasses, axis=0),
color=seaborn.color_palette()[1])
plot_with_tube(range(test_misclasses.shape[1]),
np.nanmean(test_misclasses, axis=0),
np.nanstd(test_misclasses, axis=0),
color=seaborn.color_palette()[2])
plt.plot(exps_by_epoch / float(exps_by_epoch[0]),
color='black',
linestyle='dashed')
def plot_mean_std_misclasses_over_time(misclasses):
padded_misclasses, _ = get_padded_chan_vals(misclasses['train'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[0])
padded_misclasses, _ = get_padded_chan_vals(misclasses['valid'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[1])
padded_misclasses, n_exps_by_epoch = get_padded_chan_vals(misclasses['test'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[2])
plt.plot(n_exps_by_epoch / float(n_exps_by_epoch[0]), color='black', lw=1)
plt.ylim(0,1)
def plot_mean_std_misclasses_over_time(misclasses):
padded_misclasses, _ = get_padded_chan_vals(misclasses['train'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[0])
padded_misclasses, _ = get_padded_chan_vals(misclasses['valid'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[1])
padded_misclasses, n_exps_by_epoch = get_padded_chan_vals(
misclasses['test'])
plot_mean_and_std(padded_misclasses, color=seaborn.color_palette()[2])
plt.plot(n_exps_by_epoch / float(n_exps_by_epoch[0]), color='black', lw=1)
plt.ylim(0, 1)
def plot_misclasses_mean_std_for_exps_with_tube(df):
test_misclasses = extract_from_results(df, lambda r: r.monitor_channels['test_misclass'])
test_misclasses, exps_by_epoch = get_padded_chan_vals(test_misclasses, pad_by=np.nan)
valid_misclasses = extract_from_results(df, lambda r: r.monitor_channels['valid_misclass'])
valid_misclasses, exps_by_epoch = get_padded_chan_vals(valid_misclasses, pad_by=np.nan)
train_misclasses = extract_from_results(df, lambda r: r.monitor_channels['train_misclass'])
train_misclasses, exps_by_epoch = get_padded_chan_vals(train_misclasses, pad_by=np.nan)
plot_with_tube(range(train_misclasses.shape[1]),np.nanmean(train_misclasses, axis=0),
np.nanstd(train_misclasses, axis=0))
plot_with_tube(range(valid_misclasses.shape[1]),np.nanmean(valid_misclasses, axis=0),
np.nanstd(valid_misclasses, axis=0), color=seaborn.color_palette()[1])
plot_with_tube(range(test_misclasses.shape[1]),np.nanmean(test_misclasses, axis=0),
np.nanstd(test_misclasses, axis=0), color=seaborn.color_palette()[2])
plt.plot(exps_by_epoch / float(exps_by_epoch[0]), color='black', linestyle='dashed')
