def test_add_bad_result(self):
r = TextBasedResults('e2')
with self.assertRaises(AssertionError):
r.add_result(Result(2, 1, 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'), float('inf'), 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'), 0, 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(0, 0, 1, None))
def dataset_stat_latex_print(dataset_name):
'''
Print the avg precision, recall and F1 score in latex format
to console.
'''
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
#package results
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
result_list = []
for e in extractor_slugs:
result_tuple = (
get_extractor_cls(e).NAME,
txt_results.precision_statistics(e)[0],
txt_results.recall_statistics(e)[0],
txt_results.f1score_statistics(e)[0],
)
result_list.append(result_tuple)
result_list.sort(key=lambda i: i[3])
result_list.reverse()
for r in result_list:
print '\\texttt{%s} & %.4f & %.4f & %.4f \\\\ \\hline' % r
def test_add_bad_result(self):
r = TextBasedResults('e2')
with self.assertRaises(AssertionError):
r.add_result(Result(2,1,1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'),float('inf'),1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'),0,1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(0,0,1,None))
def dataset_stat_latex_print(dataset_name):
"""
Print the avg precision, recall and F1 score in latex format
to console.
"""
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
# package results
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
result_list = []
for e in extractor_slugs:
result_tuple = (
get_extractor_cls(e).NAME,
txt_results.precision_statistics(e)[0],
txt_results.recall_statistics(e)[0],
txt_results.f1score_statistics(e)[0],
)
result_list.append(result_tuple)
result_list.sort(key=lambda i: i[3])
result_list.reverse()
for r in result_list:
print "\\texttt{%s} & %.4f & %.4f & %.4f \\\\ \\hline" % r
def local_evaluate(dataset_type, dataset_name, update_ext_slug=None):
results = TextBasedResults()
if update_ext_slug:
results.load(dataset_name)
ex_cls = get_extractor_cls(update_ext_slug)
single_evaluation(ex_cls, results, dataset_type, dataset_name)
else:
for extractor_cls in extractor_list:
single_evaluation(extractor_cls, results, dataset_type,
dataset_name)
results.dataset_len = len(LocalDatasetLoader(dataset_name))
results.save(dataset_name)
results.print_results()
def setUp(self):
self.results = TextBasedResults('e1')
# Result(precision, recall, f1_score, id)
self.results.add_result(Result(0, 0, float('inf'), None))
self.results.add_result(Result(float('inf'), 0, float('nan'), None))
self.results.add_result(Result(float('inf'), 0, float('nan'), None))
self.results.add_result(Result(0, float('inf'), float('nan'), None))
self.results.add_result(Result(0, float('inf'), float('nan'), None))
self.results.add_result(
Result(float('inf'), float('inf'), float('nan'), None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.dataset_len = 12
def local_evaluate(dataset_type, dataset_name, update_ext_slug = None):
results = TextBasedResults()
if update_ext_slug:
results.load(dataset_name)
ex_cls = get_extractor_cls(update_ext_slug)
single_evaluation(ex_cls, results, dataset_type, dataset_name)
else:
for extractor_cls in extractor_list:
single_evaluation(extractor_cls, results, dataset_type, dataset_name)
results.dataset_len = len(LocalDatasetLoader(dataset_name))
results.save(dataset_name)
results.print_results()
def setUp(self):
self.results = TextBasedResults('e1')
# Result(precision, recall, f1_score, id)
self.results.add_result(Result(0,0,float('inf'),None))
self.results.add_result(Result(float('inf'),0,float('nan'),None))
self.results.add_result(Result(float('inf'),0,float('nan'),None))
self.results.add_result(Result(0,float('inf'),float('nan'),None))
self.results.add_result(Result(0,float('inf'),float('nan'),None))
self.results.add_result(Result(float('inf'),float('inf'),float('nan'),None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.dataset_len = 12
def dataset_contents_print_latex(dataset_name):
'''Print the error case analysis in latex'''
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
# package data
elist = extractor_list_filter(txt_results.text_eval_results.keys())
for e in elist:
print '\\texttt{%s} & %d & %d & %d & %d & %d & %d \\\\ \\hline' % \
(
e.NAME,
txt_results.result_contents(e.SLUG).rel_empty,
txt_results.result_contents(e.SLUG).rel_ret_empty,
txt_results.result_contents(e.SLUG).ret_empty,
txt_results.result_contents(e.SLUG).missmatch,
txt_results.result_contents(e.SLUG).fail,
txt_results.result_contents(e.SLUG).succ,
)
def dataset_contents_print_latex(dataset_name):
"""Print the error case analysis in latex"""
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
# package data
elist = extractor_list_filter(txt_results.text_eval_results.keys())
for e in elist:
print "\\texttt{%s} & %d & %d & %d & %d & %d & %d \\\\ \\hline" % (
e.NAME,
txt_results.result_contents(e.SLUG).rel_empty,
txt_results.result_contents(e.SLUG).rel_ret_empty,
txt_results.result_contents(e.SLUG).ret_empty,
txt_results.result_contents(e.SLUG).missmatch,
txt_results.result_contents(e.SLUG).fail,
txt_results.result_contents(e.SLUG).succ,
)
def extractor_stat_plot(dataset_name, img_name):
"""Plot the distributions of per-document precision, recall & F1 score """
# np.seterr(all='raise')
fig = plt.figure()
# get results and repackage the data
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
elist = extractor_list_filter(txt_results.text_eval_results.keys())
for ex_index, extractor_cls in enumerate(elist):
# repackage results
extractor_results = txt_results.filtered_results(extractor_cls.SLUG)
results_list_prec = [r.precision for r in extractor_results]
results_list_rec = [r.recall for r in extractor_results]
results_list_f1 = [r.f1_score for r in extractor_results]
width = 0.05 # the width of the bars
ind = np.arange(0, 1, width)
n = len(ind)
print extractor_cls.NAME
eq_count_prec = equidistant_count(0, 1, width, results_list_prec)
print len(results_list_prec)
print sum(eq_count_prec)
eq_count_rec = equidistant_count(0, 1, width, results_list_rec)
print len(results_list_rec)
print sum(eq_count_rec)
eq_count_f1 = equidistant_count(0, 1, width, results_list_f1)
print len(results_list_f1)
print sum(eq_count_f1)
# plotting
ax = fig.add_subplot(6, 3, ex_index + 1, projection="3d")
ax.bar3d(
ind,
np.array([0] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_prec,
color="b",
linewidth=0.3,
alpha=0.4,
)
ax.bar3d(
ind,
np.array([1] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_rec,
color="c",
linewidth=0.3,
alpha=0.5,
)
ax.bar3d(
ind,
np.array([2] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_f1,
color="m",
linewidth=0.3,
alpha=0.8,
)
ax.set_title(extractor_cls.NAME, size="small")
# ax.set_xlabel('\nlimits',size = 'x-small', linespacing=2)
ax.set_zlabel("\nnum. of instances", size="x-small", linespacing=1)
ax.yaxis.set_ticks([])
resize_axis_tick_labels(ax.xaxis)
resize_axis_tick_labels(ax.zaxis)
ax.grid(True, alpha=0.7)
# with 3d plotting we need to use proxy artist because legends
# are not supported
blue = plt.Rectangle((0, 0), 1, 1, fc="b") # proxys
cyan = plt.Rectangle((0, 0), 1, 1, fc="c")
mag = plt.Rectangle((0, 0), 1, 1, fc="m")
fig.legend((blue, cyan, mag), ("precision", "recall", "f1 score"), fancybox=True, prop=dict(size="x-small"))
w, h = fig.get_size_inches()
fig.set_size_inches(w * 1.5, h * 2.5)
fig.subplots_adjust(wspace=0.025, hspace=0.15)
# save plot
out_path = os.path.join(settings.PATH_LOCAL_DATA, "plot-output", img_name)
fig.savefig(out_path, bbox_inches="tight")
def test_add_good_result(self):
r = TextBasedResults('e3')
try:
r.add_result(Result(0.2, 0.2, 0.2, None))
except AssertionError:
self.fail()
class TestTextBasedResults(unittest2.TestCase):
def setUp(self):
self.results = TextBasedResults('e1')
# Result(precision, recall, f1_score, id)
self.results.add_result(Result(0, 0, float('inf'), None))
self.results.add_result(Result(float('inf'), 0, float('nan'), None))
self.results.add_result(Result(float('inf'), 0, float('nan'), None))
self.results.add_result(Result(0, float('inf'), float('nan'), None))
self.results.add_result(Result(0, float('inf'), float('nan'), None))
self.results.add_result(
Result(float('inf'), float('inf'), float('nan'), None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.add_result(Result(0.2, 0.2, 0.2, None))
self.results.dataset_len = 12
def tearDown(self):
self.results.text_eval_results['e1'] = []
def test_results_contents(self):
contents = self.results.result_contents('e1')
self.assertEqual(contents.fail, 2)
self.assertEqual(contents.succ, 4)
self.assertEqual(contents.rel_empty, 2)
self.assertEqual(contents.ret_empty, 2)
self.assertEqual(contents.rel_ret_empty, 1)
self.assertEqual(contents.missmatch, 1)
def test_result_filter(self):
fr = self.results.filtered_results('e1')
self.assertEqual(len(fr), 4)
def test_precision_statistics(self):
avg, std = self.results.precision_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_recall_statistics(self):
avg, std = self.results.recall_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_f1score_statistics(self):
avg, std = self.results.f1score_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_add_bad_result(self):
r = TextBasedResults('e2')
with self.assertRaises(AssertionError):
r.add_result(Result(2, 1, 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'), float('inf'), 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'), 0, 1, None))
with self.assertRaises(AssertionError):
r.add_result(Result(0, 0, 1, None))
def test_add_good_result(self):
r = TextBasedResults('e3')
try:
r.add_result(Result(0.2, 0.2, 0.2, None))
except AssertionError:
self.fail()
def dataset_contents_plot(dataset_name, img_name):
"""Plot the error case analysis."""
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
# package data
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
package = [
("|rel| = 0", "#9DFADE", [txt_results.result_contents(ex).rel_empty for ex in extractor_slugs]),
(
"|rel intersect ret| = 0",
"#3C70A3",
[txt_results.result_contents(ex).rel_ret_empty for ex in extractor_slugs],
),
("|ret| = 0", "#5CCBED", [txt_results.result_contents(ex).ret_empty for ex in extractor_slugs]),
("mismatch", "#A76CF5", [txt_results.result_contents(ex).missmatch for ex in extractor_slugs]),
("failed", "#C43156", [txt_results.result_contents(ex).fail for ex in extractor_slugs]),
("successful", "#31C460", [txt_results.result_contents(ex).succ for ex in extractor_slugs]),
]
num_of_extractors = len(extractor_slugs)
ind = np.arange(num_of_extractors) # the x locations for the groups
width = 0.6
fig = plt.gcf()
fig.legend(
[plt.Rectangle((0, 0), 1, 1, fc=p[1]) for p in package],
[p[0] for p in package],
fancybox=True,
prop=dict(size="x-small"),
)
# with successful instances
ax1 = plt.subplot(121)
bottom_y = np.zeros(num_of_extractors)
for pdata in package:
ax1.bar(ind, pdata[2], width, bottom=bottom_y, color=pdata[1], ecolor="g", linewidth=0.2, alpha=0.95)
bottom_y += pdata[2]
ax2 = plt.subplot(122)
bottom_y = np.zeros(num_of_extractors)
del package[-1]
for pdata in package:
ax2.bar(ind, pdata[2], width, bottom=bottom_y, color=pdata[1], ecolor="g", linewidth=0.2, alpha=0.95)
bottom_y += pdata[2]
# xticks labels
extractor_names = [get_extractor_cls(e).NAME for e in extractor_slugs]
ax1.set_xticks(ind + width / 2.0)
ax1.set_xticklabels(extractor_names, size="xx-small", rotation="vertical")
ax2.set_xticks(ind + width / 2.0)
ax2.set_xticklabels(extractor_names, size="xx-small", rotation="vertical")
# grid settings
fig.suptitle("Boundary cases")
ax1.grid(True, alpha=0.5)
ax2.grid(True, alpha=0.5)
# adjustment
w, h = fig.get_size_inches()
fig.set_size_inches(w * 1.5, h * 1.5)
fig.subplots_adjust(bottom=0.2)
# output
out_path = os.path.join(settings.PATH_LOCAL_DATA, "plot-output", img_name)
fig.savefig(out_path, bbox_inches="tight")
def extractor_stat_plot(dataset_name, img_name):
'''Plot the distributions of per-document precision, recall & F1 score '''
#np.seterr(all='raise')
fig = plt.figure()
# get results and repackage the data
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
elist = extractor_list_filter(txt_results.text_eval_results.keys())
for ex_index, extractor_cls in enumerate(elist):
# repackage results
extractor_results = txt_results.filtered_results(extractor_cls.SLUG)
results_list_prec = [r.precision for r in extractor_results]
results_list_rec = [r.recall for r in extractor_results]
results_list_f1 = [r.f1_score for r in extractor_results]
width = 0.05 # the width of the bars
ind = np.arange(0, 1, width)
n = len(ind)
print extractor_cls.NAME
eq_count_prec = equidistant_count(0, 1, width, results_list_prec)
print len(results_list_prec)
print sum(eq_count_prec)
eq_count_rec = equidistant_count(0, 1, width, results_list_rec)
print len(results_list_rec)
print sum(eq_count_rec)
eq_count_f1 = equidistant_count(0, 1, width, results_list_f1)
print len(results_list_f1)
print sum(eq_count_f1)
# plotting
ax = fig.add_subplot(6, 3, ex_index + 1, projection='3d')
ax.bar3d(ind,
np.array([0] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_prec,
color='b',
linewidth=0.3,
alpha=0.4)
ax.bar3d(ind,
np.array([1] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_rec,
color='c',
linewidth=0.3,
alpha=0.5)
ax.bar3d(ind,
np.array([2] * n),
np.array([0] * n),
dx=width,
dy=width * 2,
dz=eq_count_f1,
color='m',
linewidth=0.3,
alpha=0.8)
ax.set_title(extractor_cls.NAME, size='small')
#ax.set_xlabel('\nlimits',size = 'x-small', linespacing=2)
ax.set_zlabel('\nnum. of instances', size='x-small', linespacing=1)
ax.yaxis.set_ticks([])
resize_axis_tick_labels(ax.xaxis)
resize_axis_tick_labels(ax.zaxis)
ax.grid(True, alpha=0.7)
# with 3d plotting we need to use proxy artist because legends
# are not supported
blue = plt.Rectangle((0, 0), 1, 1, fc='b') # proxys
cyan = plt.Rectangle((0, 0), 1, 1, fc='c')
mag = plt.Rectangle((0, 0), 1, 1, fc='m')
fig.legend((blue, cyan, mag), ('precision', 'recall', 'f1 score'),
fancybox=True,
prop=dict(size='x-small'))
w, h = fig.get_size_inches()
fig.set_size_inches(w * 1.5, h * 2.5)
fig.subplots_adjust(wspace=0.025, hspace=0.15)
# save plot
out_path = os.path.join(settings.PATH_LOCAL_DATA, 'plot-output', img_name)
fig.savefig(out_path, bbox_inches='tight')
def test_add_good_result(self):
r = TextBasedResults('e3')
try:
r.add_result(Result(0.2,0.2,0.2,None))
except AssertionError:
self.fail()
def dataset_contents_plot(dataset_name, img_name):
'''Plot the error case analysis.'''
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
# package data
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
package = [
('|rel| = 0', '#9DFADE',
[txt_results.result_contents(ex).rel_empty
for ex in extractor_slugs]),
('|rel intersect ret| = 0', '#3C70A3', [
txt_results.result_contents(ex).rel_ret_empty
for ex in extractor_slugs
]),
('|ret| = 0', '#5CCBED',
[txt_results.result_contents(ex).ret_empty
for ex in extractor_slugs]),
('mismatch', '#A76CF5',
[txt_results.result_contents(ex).missmatch
for ex in extractor_slugs]),
('failed', '#C43156',
[txt_results.result_contents(ex).fail for ex in extractor_slugs]),
('successful', '#31C460',
[txt_results.result_contents(ex).succ for ex in extractor_slugs]),
]
num_of_extractors = len(extractor_slugs)
ind = np.arange(num_of_extractors) # the x locations for the groups
width = 0.6
fig = plt.gcf()
fig.legend(
[plt.Rectangle((0, 0), 1, 1, fc=p[1]) for p in package],
[p[0] for p in package],
fancybox=True,
prop=dict(size='x-small'),
)
# with successful instances
ax1 = plt.subplot(121)
bottom_y = np.zeros(num_of_extractors)
for pdata in package:
ax1.bar(ind,
pdata[2],
width,
bottom=bottom_y,
color=pdata[1],
ecolor='g',
linewidth=0.2,
alpha=0.95)
bottom_y += pdata[2]
ax2 = plt.subplot(122)
bottom_y = np.zeros(num_of_extractors)
del package[-1]
for pdata in package:
ax2.bar(ind,
pdata[2],
width,
bottom=bottom_y,
color=pdata[1],
ecolor='g',
linewidth=0.2,
alpha=0.95)
bottom_y += pdata[2]
# xticks labels
extractor_names = [get_extractor_cls(e).NAME for e in extractor_slugs]
ax1.set_xticks(ind + width / 2.)
ax1.set_xticklabels(extractor_names, size='xx-small', rotation='vertical')
ax2.set_xticks(ind + width / 2.)
ax2.set_xticklabels(extractor_names, size='xx-small', rotation='vertical')
# grid settings
fig.suptitle('Boundary cases')
ax1.grid(True, alpha=0.5)
ax2.grid(True, alpha=0.5)
# adjustment
w, h = fig.get_size_inches()
fig.set_size_inches(w * 1.5, h * 1.5)
fig.subplots_adjust(bottom=0.2)
# output
out_path = os.path.join(settings.PATH_LOCAL_DATA, 'plot-output', img_name)
fig.savefig(out_path, bbox_inches='tight')
def dataset_stat_plot(dataset_name, img_name):
'''
Plot the avg precision, recall and F1 score bar chart for the given dataset
name.
'''
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
#package results
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
packaged_data = (
('Precision', [(txt_results.precision_statistics(e), e)
for e in extractor_slugs]),
('Recall', [(txt_results.recall_statistics(e), e)
for e in extractor_slugs]),
('F1 score', [(txt_results.f1score_statistics(e), e)
for e in extractor_slugs]),
)
bar_color = ('b', 'c', 'm')
for i, pdata in enumerate(packaged_data):
# package plotting values
num_of_extractors = len(extractor_slugs)
ind = np.arange(num_of_extractors) # the x locations for the groups
width = 0.6 # the width of the bars
result_list = pdata[1]
result_list.sort(key=lambda i: i[0][0])
result_list.reverse()
avg = [x[0][0] for x in result_list]
stddev = [x[0][1] for x in result_list]
# plot
plt.subplot(3, 1, i + 1)
plt.grid(True, alpha=0.5)
rects_avg = plt.bar(ind,
avg,
width,
color=bar_color[i],
ecolor='g',
yerr=stddev,
linewidth=0.5,
alpha=0.8)
# lables and titles
extractor_names = [get_extractor_cls(r[1]).NAME for r in result_list]
plt.title(pdata[0])
plt.xticks(ind + width / 2.,
extractor_names,
size='xx-small',
rotation='vertical')
plt.legend(
(rects_avg[0], ),
('avg', ),
fancybox=True,
prop=dict(size='x-small'),
loc=4 # lower right
)
for rect in rects_avg:
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width() / 2.25,
rect.get_height() + 0.01,
'%1.2f' % height,
ha='center',
va='bottom',
size='x-small')
#subplots adjusting
plt.subplots_adjust(wspace=0.5, hspace=0.9)
#adjust figure height
fig = plt.gcf()
w, h = fig.get_size_inches()
fig.set_size_inches(w, h * 1.6)
# output
out_path = os.path.join(settings.PATH_LOCAL_DATA, 'plot-output', img_name)
plt.savefig(out_path)
def dataset_stat_plot(dataset_name, img_name):
"""
Plot the avg precision, recall and F1 score bar chart for the given dataset
name.
"""
# get results
txt_results = TextBasedResults()
txt_results.load(dataset_name)
txt_results.print_results()
# package results
elist = extractor_list_filter(txt_results.text_eval_results.keys())
extractor_slugs = tuple([e.SLUG for e in elist])
packaged_data = (
("Precision", [(txt_results.precision_statistics(e), e) for e in extractor_slugs]),
("Recall", [(txt_results.recall_statistics(e), e) for e in extractor_slugs]),
("F1 score", [(txt_results.f1score_statistics(e), e) for e in extractor_slugs]),
)
bar_color = ("b", "c", "m")
for i, pdata in enumerate(packaged_data):
# package plotting values
num_of_extractors = len(extractor_slugs)
ind = np.arange(num_of_extractors) # the x locations for the groups
width = 0.6 # the width of the bars
result_list = pdata[1]
result_list.sort(key=lambda i: i[0][0])
result_list.reverse()
avg = [x[0][0] for x in result_list]
stddev = [x[0][1] for x in result_list]
# plot
plt.subplot(3, 1, i + 1)
plt.grid(True, alpha=0.5)
rects_avg = plt.bar(ind, avg, width, color=bar_color[i], ecolor="g", yerr=stddev, linewidth=0.5, alpha=0.8)
# lables and titles
extractor_names = [get_extractor_cls(r[1]).NAME for r in result_list]
plt.title(pdata[0])
plt.xticks(ind + width / 2.0, extractor_names, size="xx-small", rotation="vertical")
plt.legend((rects_avg[0],), ("avg",), fancybox=True, prop=dict(size="x-small"), loc=4) # lower right
for rect in rects_avg:
height = rect.get_height()
plt.text(
rect.get_x() + rect.get_width() / 2.25,
rect.get_height() + 0.01,
"%1.2f" % height,
ha="center",
va="bottom",
size="x-small",
)
# subplots adjusting
plt.subplots_adjust(wspace=0.5, hspace=0.9)
# adjust figure height
fig = plt.gcf()
w, h = fig.get_size_inches()
fig.set_size_inches(w, h * 1.6)
# output
out_path = os.path.join(settings.PATH_LOCAL_DATA, "plot-output", img_name)
plt.savefig(out_path)
class TestTextBasedResults(unittest2.TestCase):
def setUp(self):
self.results = TextBasedResults('e1')
# Result(precision, recall, f1_score, id)
self.results.add_result(Result(0,0,float('inf'),None))
self.results.add_result(Result(float('inf'),0,float('nan'),None))
self.results.add_result(Result(float('inf'),0,float('nan'),None))
self.results.add_result(Result(0,float('inf'),float('nan'),None))
self.results.add_result(Result(0,float('inf'),float('nan'),None))
self.results.add_result(Result(float('inf'),float('inf'),float('nan'),None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.add_result(Result(0.2,0.2,0.2,None))
self.results.dataset_len = 12
def tearDown(self):
self.results.text_eval_results['e1'] = []
def test_results_contents(self):
contents = self.results.result_contents('e1')
self.assertEqual(contents.fail, 2)
self.assertEqual(contents.succ, 4)
self.assertEqual(contents.rel_empty, 2)
self.assertEqual(contents.ret_empty, 2)
self.assertEqual(contents.rel_ret_empty, 1)
self.assertEqual(contents.missmatch, 1)
def test_result_filter(self):
fr = self.results.filtered_results('e1')
self.assertEqual(len(fr), 4)
def test_precision_statistics(self):
avg, std = self.results.precision_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_recall_statistics(self):
avg, std = self.results.recall_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_f1score_statistics(self):
avg, std = self.results.f1score_statistics('e1')
self.assertEqual(avg, 0.2)
self.assertEqual(std, 0.)
def test_add_bad_result(self):
r = TextBasedResults('e2')
with self.assertRaises(AssertionError):
r.add_result(Result(2,1,1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'),float('inf'),1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(float('inf'),0,1,None))
with self.assertRaises(AssertionError):
r.add_result(Result(0,0,1,None))
def test_add_good_result(self):
r = TextBasedResults('e3')
try:
r.add_result(Result(0.2,0.2,0.2,None))
except AssertionError:
self.fail()
