def __fleiss_kappa_linear__(dataset, **kwargs):
'''
Calculates Fleiss' :math:`\kappa` (or multi-:math:`\kappa`), originally proposed in
[DaviesFleiss1982]_. For 2 coders, this is equivalent to Cohen's :math:`\kappa`
[Cohen1960]_.
'''
metric_kwargs = dict(kwargs)
metric_kwargs['return_parts'] = True
# Arguments
return_parts = kwargs['return_parts']
# Check that there are more than 2 coders
if len([
True
for coder_segs in dataset.values() if len(coder_segs.keys()) < 2
]) > 0:
raise Exception('Less than 2 coders specified.')
# Check that there are an identical number of items
if len(set([len(coder_segs.values())
for coder_segs in dataset.values()])) != 1:
raise Exception('Unequal number of items contained.')
# Initialize totals
all_numerators, all_denominators, _, coders_boundaries = \
__actual_agreement_linear__(dataset, **metric_kwargs)
# Calculate Aa
A_a = Decimal(sum(all_numerators)) / sum(all_denominators)
# Calculate Ae
coders = list(coders_boundaries.keys())
P_segs = list()
for m in range(0, len(coders) - 1):
for n in range(m + 1, len(coders)):
boundaries_m = sum(info[0]
for info in coders_boundaries[coders[m]])
total_boundaries_m = sum(info[1]
for info in coders_boundaries[coders[m]])
boundaries_n = sum(info[0]
for info in coders_boundaries[coders[n]])
total_boundaries_n = sum(info[1]
for info in coders_boundaries[coders[n]])
P_segs.append((Decimal(boundaries_m) / total_boundaries_m) *
(Decimal(boundaries_n) / total_boundaries_n))
P_seg = Decimal(sum(P_segs)) / len(P_segs)
A_e = P_seg
# Calculate pi
kappa = (A_a - A_e) / (Decimal('1') - A_e)
# Return
if return_parts:
return A_a, A_e
else:
return kappa
def __fleiss_kappa_linear__(dataset, **kwargs):
'''
Calculates Fleiss' :math:`\kappa` (or multi-:math:`\kappa`), originally proposed in
[DaviesFleiss1982]_. For 2 coders, this is equivalent to Cohen's :math:`\kappa`
[Cohen1960]_.
'''
metric_kwargs = dict(kwargs)
metric_kwargs['return_parts'] = True
# Arguments
return_parts = kwargs['return_parts']
# Check that there are more than 2 coders
if len([True for coder_segs in dataset.values()
if len(coder_segs.keys()) < 2]) > 0:
raise Exception('Less than 2 coders specified.')
# Check that there are an identical number of items
if len(set([len(coder_segs.values()) for coder_segs in dataset.values()])) != 1:
raise Exception('Unequal number of items contained.')
# Initialize totals
all_numerators, all_denominators, _, coders_boundaries = \
__actual_agreement_linear__(dataset, **metric_kwargs)
# Calculate Aa
A_a = Decimal(sum(all_numerators)) / sum(all_denominators)
# Calculate Ae
coders = list(coders_boundaries.keys())
P_segs = list()
for m in range(0, len(coders) - 1):
for n in range(m + 1, len(coders)):
boundaries_m = sum(info[0] for info in
coders_boundaries[coders[m]])
total_boundaries_m = sum(info[1] for info in
coders_boundaries[coders[m]])
boundaries_n = sum(info[0] for info in
coders_boundaries[coders[n]])
total_boundaries_n = sum(info[1] for info in
coders_boundaries[coders[n]])
P_segs.append((Decimal(boundaries_m) / total_boundaries_m) *
(Decimal(boundaries_n) / total_boundaries_n))
P_seg = Decimal(sum(P_segs)) / len(P_segs)
A_e = P_seg
# Calculate pi
kappa = (A_a - A_e) / (Decimal('1') - A_e)
# Return
if return_parts:
return A_a, A_e
else:
return kappa
def __fleiss_pi_linear__(dataset, **kwargs):
'''
Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in
[Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`
[SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi`
[Scott1955]_.
'''
metric_kwargs = dict(kwargs)
metric_kwargs['return_parts'] = True
# Arguments
return_parts = kwargs['return_parts']
# Check that there are an equal number of items for each coder
if len(set([len(coder_segs.values())
for coder_segs in dataset.values()])) != 1:
raise Exception('Unequal number of items contained.')
# Initialize totals
all_numerators, all_denominators, _, coders_boundaries = \
__actual_agreement_linear__(dataset, **metric_kwargs)
# Calculate Aa
A_a = Decimal(sum(all_numerators)) / sum(all_denominators)
# Calculate Ae
p_e_segs = list()
for boundaries_info in coders_boundaries.values():
for item in boundaries_info:
boundaries, total_boundaries = item
p_e_seg = Decimal(boundaries) / total_boundaries
p_e_segs.append(p_e_seg)
# Calculate P_e_seg
P_e_seg = Decimal(sum(p_e_segs)) / len(p_e_segs)
A_e = (P_e_seg**2)
# Calculate pi
pi = (A_a - A_e) / (Decimal('1') - A_e)
# Return
if return_parts:
return A_a, A_e
else:
return pi
def __fleiss_pi_linear__(dataset, **kwargs):
'''
Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in
[Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`
[SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi`
[Scott1955]_.
'''
metric_kwargs = dict(kwargs)
metric_kwargs['return_parts'] = True
# Arguments
return_parts = kwargs['return_parts']
# Check that there are an equal number of items for each coder
if len(set([len(coder_segs.values()) for coder_segs in dataset.values()])) != 1:
raise Exception('Unequal number of items contained.')
# Initialize totals
all_numerators, all_denominators, _, coders_boundaries = \
__actual_agreement_linear__(dataset, **metric_kwargs)
# Calculate Aa
A_a = Decimal(sum(all_numerators)) / sum(all_denominators)
# Calculate Ae
p_e_segs = list()
for boundaries_info in coders_boundaries.values():
for item in boundaries_info:
boundaries, total_boundaries = item
p_e_seg = Decimal(boundaries) / total_boundaries
p_e_segs.append(p_e_seg)
# Calculate P_e_seg
P_e_seg = Decimal(sum(p_e_segs)) / len(p_e_segs)
A_e = (P_e_seg ** 2)
# Calculate pi
pi = (A_a - A_e) / (Decimal('1') - A_e)
# Return
if return_parts:
return A_a, A_e
else:
return pi
