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Commit bce397dd authored by Jörg Martin's avatar Jörg Martin
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Coverage plots now showing error of mean

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Experiments/plot_coverage.py
+ 93
− 33
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......@@ -178,7 +178,7 @@ def compute_coverages(data, eiv, number_of_draws,
####
def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
against_theoretical = False, label = ''):
against_theoretical = False, label = '', mean_error=True):
"""
Plot numerical coverages against q (used coverage value), if
against_theoretical is False, or the theoretical coverage, if
......@@ -187,8 +187,10 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
:param noneiv_coverages: The output of `compute_coverages` with `eiv=False`
:param color: String, denoting the color.
:param against_theoretical: Boolean, see above.
:param label: String. Will be combined with "eiv_"/"noneiv_" and included
as label in the plot.
:param label: String. Will be included as label in the plot.
:param mean_error: Boolean. If True the standard deviation is divided
by the square root of the number of elements, to display the error
of the mean (and not the dispersion).
"""
eiv_numerical_coverage, eiv_theoretical_coverage = eiv_coverages
noneiv_numerical_coverage, noneiv_theoretical_coverage = noneiv_coverages
......@@ -198,6 +200,8 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
# take mean/std over seed dimension
mean_eiv_numerical_coverage = np.mean(eiv_numerical_coverage, axis=-1)
std_eiv_numerical_coverage = np.std(eiv_numerical_coverage, axis=-1)
if mean_error:
std_eiv_numerical_coverage /= np.sqrt(eiv_numerical_coverage.shape[1])
if against_theoretical:
# show theoretical coverage on x-axis
x_values = np.mean(eiv_theoretical_coverage, axis=-1)
......@@ -206,7 +210,7 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
x_values = np.array(q_range)
# plot mean
plt.plot(x_values, mean_eiv_numerical_coverage,
color=color, linestyle='solid', label=f'eiv_{label}')
color=color, linestyle='solid', label=label)
# plot std
plt.fill_between(x_values,
mean_eiv_numerical_coverage - std_eiv_numerical_coverage,
......@@ -216,6 +220,9 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
# take mean/std over seed dimension
mean_noneiv_numerical_coverage = np.mean(noneiv_numerical_coverage, axis=-1)
std_noneiv_numerical_coverage = np.std(noneiv_numerical_coverage, axis=-1)
if mean_error:
std_noneiv_numerical_coverage /= \
np.sqrt(noneiv_numerical_coverage.shape[1])
if against_theoretical:
# show theoretical coverage on x-axis
x_values = np.mean(noneiv_theoretical_coverage, axis=-1)
......@@ -224,7 +231,7 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
x_values = np.array(q_range)
# plot mean
plt.plot(x_values, mean_noneiv_numerical_coverage,
color=color, linestyle='dashed', label=f'noneiv_{label}')
color=color, linestyle='dashed')
# plot std
plt.fill_between(x_values,
mean_noneiv_numerical_coverage - std_noneiv_numerical_coverage,
......@@ -232,7 +239,8 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
color=color, alpha=0.3)
def coverage_residual_plot(eiv_coverages, noneiv_coverages, color,
absolute_values=True, against_theoretical=False, label=''):
absolute_values=True, against_theoretical=False, label='',
mean_error=True):
"""
Plot deviation of numerical coverages to q (used coverage value), if
against_theoretical is False, or to the theoretical coverage, if
......@@ -244,8 +252,10 @@ def coverage_residual_plot(eiv_coverages, noneiv_coverages, color,
:param absolute_values:Boolean. If True (default) the absolute value of the
differences will be plotted.
:param against_theoretical: Boolean, see above.
:param label: String. Will be combined with "eiv_"/"noneiv_" and included
as label in the plot.
:param label: String. Will be included as label in the plot.
:param mean_error: Boolean. If True the standard deviation is divided
by the square root of the number of elements, to display the error
of the mean (and not the dispersion).
"""
if not against_theoretical:
eiv_coverages = eiv_coverages[0]
......@@ -266,29 +276,59 @@ def coverage_residual_plot(eiv_coverages, noneiv_coverages, color,
noneiv_cov_res = np.abs(noneiv_cov_res)
mean_eiv_cov_res = np.mean(eiv_cov_res, axis=-1)
std_eiv_cov_res = np.std(eiv_cov_res, axis=-1)
if mean_error:
std_eiv_cov_res /= np.sqrt(eiv_cov_res.shape[1])
mean_noneiv_cov_res = np.mean(noneiv_cov_res, axis=-1)
std_noneiv_cov_res = np.std(noneiv_cov_res, axis=-1)
if mean_error:
std_noneiv_cov_res /= np.sqrt(noneiv_cov_res.shape[1])
plt.plot(q_range, mean_eiv_cov_res,
color=color, linestyle='solid', label=f'eiv_{label}')
color=color, linestyle='solid', label=label)
plt.fill_between(q_range,
mean_eiv_cov_res - std_eiv_cov_res,
mean_eiv_cov_res + std_eiv_cov_res,
color=color, alpha=0.5)
plt.plot(q_range, mean_noneiv_cov_res,
color=color, linestyle='dashed', label=f'noneiv_{label}')
color=color, linestyle='dashed')
plt.fill_between(q_range,
mean_noneiv_cov_res - std_noneiv_cov_res,
mean_noneiv_cov_res + std_noneiv_cov_res,
color=color, alpha=0.3)
def coverage_difference_plot(eiv_coverages, noneiv_coverages, color, label='',
mean_error=True):
"""
Plot difference of numerical coverages from EiV to the one of non-EiV.
On the x axis q is plotted.
:param eiv_coverages: The output of `compute_coverages` with `eiv=True`
:param noneiv_coverages: The output of `compute_coverages` with `eiv=False`
:param color: String, denoting the color.
:param label: String. Will be included as label in the plot.
:param mean_error: Boolean. If True the standard deviation is divided
by the square root of the number of elements, to display the error
of the mean (and not the dispersion).
"""
cov_diff = eiv_coverages[0] - noneiv_coverages[0]
mean_cov_diff = np.mean(cov_diff, axis=-1)
std_cov_diff = np.std(cov_diff, axis=-1)
if mean_error:
std_cov_diff /= np.sqrt(cov_diff.shape[1])
plt.plot(q_range, mean_cov_diff,
color=color, linestyle='solid', label=label)
plt.fill_between(q_range, mean_cov_diff - std_cov_diff,
mean_cov_diff + std_cov_diff,
color=color, alpha=0.5)
# create figures, together with title and axis labels
plt.figure(1)
plt.clf()
plt.title('Coverage for datasets with ground truth (vs. q)')
plt.title('Coverage for datasets with ground truth')
plt.xlabel('q')
plt.ylabel('coverage')
plt.figure(2)
......@@ -298,20 +338,29 @@ plt.xlabel('th. coverage')
plt.ylabel('coverage')
plt.figure(3)
plt.clf()
plt.title('q Deviation of coverage of noisy labels')
plt.title('Coverage of noisy labels (vs. q)')
plt.xlabel('q')
plt.ylabel('coverage')
plt.figure(4)
plt.clf()
plt.title('q Deviation of coverage of noisy labels')
plt.xlabel('q')
plt.ylabel('coverage')
plt.figure(5)
plt.clf()
plt.title('q Deviation of coverage for datasets with ground truth')
plt.xlabel('q')
plt.ylabel('deviation cov from q')
plt.figure(5)
plt.figure(6)
plt.clf()
plt.title('Theory deviation of noisy coverage')
plt.xlabel('q')
plt.ylabel('deviation cov from theor. cov')
plt.figure(7)
plt.clf()
plt.title('Difference coverages')
plt.xlabel('q')
plt.ylabel('EiV cov - nonEiV-cov')
# datasets to plot and their coloring
datasets = ['linear', 'quadratic','yacht','wine','power',
'protein','concrete','california','energy','kin8nm','msd','naval']
......@@ -335,21 +384,27 @@ for use_ground_truth in [False, True]:
# create plots
if use_ground_truth:
plt.figure(1)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color=color,
against_theoretical=False, label=data)
plt.figure(4)
coverage_residual_plot(eiv_coverages, noneiv_coverages, color=color,
against_theoretical=False, label=data)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=False, label=data)
plt.figure(5)
coverage_residual_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=False, label=data)
else:
plt.figure(2)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color=color,
against_theoretical=True, label=data)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=True, label=data)
plt.figure(3)
coverage_residual_plot(eiv_coverages, noneiv_coverages, color=color,
against_theoretical=False, label=data)
plt.figure(5)
coverage_residual_plot(eiv_coverages, noneiv_coverages, color=color,
against_theoretical=True, label=data)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=False, label=data)
plt.figure(4)
coverage_residual_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=False, label=data)
plt.figure(6)
coverage_residual_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=True, label=data)
plt.figure(7)
coverage_difference_plot(eiv_coverages, noneiv_coverages,
color=color, label=data)
# add diagonals, where meaningful
plt.figure(1)
......@@ -364,21 +419,26 @@ plt.plot(x_diag, x_diag, color='k', linestyle='dotted' )
# add legends
for fig_nr in range(1,6):
for fig_nr in range(1,8):
plt.figure(fig_nr)
plt.legend()
plt.figure(1)
plt.savefig('results/figures/summary_coverage_ground_truth.pdf')
plt.figure(2)
plt.savefig('results/figures/summary_coverage_noisy.pdf')
plt.savefig('results/figures/summary_coverage_noisy_vs_th.pdf')
plt.figure(3)
plt.savefig('results/figures/summary_q_deviation_coverage_noisy.pdf')
plt.savefig('results/figures/summary_coverage_noisy_vs_q.pdf')
plt.figure(4)
plt.savefig('results/figures/summary_q_deviation_coverage_ground_truth.pdf')
plt.savefig('results/figures/summary_q_deviation_coverage_noisy.pdf')
plt.figure(5)
plt.savefig('results/figures/summary_theory_deviation_coverage_ground_truth.pdf')
plt.savefig('results/figures/summary_q_deviation_coverage_ground_truth.pdf')
plt.figure(6)
plt.savefig('results/figures/summary_th_deviation_coverage_ground_truth.pdf')
plt.figure(7)
plt.savefig('results/figures/summary_difference_coverage.pdf')
# show plot
plt.show()
#####
plt.show()
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