id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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250,000 | trailofbits/protofuzz | protofuzz/pbimport.py | _load_module | def _load_module(path):
'Helper to load a Python file at path and return as a module'
module_name = os.path.splitext(os.path.basename(path))[0]
module = None
if sys.version_info.minor < 5:
loader = importlib.machinery.SourceFileLoader(module_name, path)
module = loader.load_mod... | python | def _load_module(path):
'Helper to load a Python file at path and return as a module'
module_name = os.path.splitext(os.path.basename(path))[0]
module = None
if sys.version_info.minor < 5:
loader = importlib.machinery.SourceFileLoader(module_name, path)
module = loader.load_mod... | [
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250,001 | trailofbits/protofuzz | protofuzz/pbimport.py | _compile_proto | def _compile_proto(full_path, dest):
'Helper to compile protobuf files'
proto_path = os.path.dirname(full_path)
protoc_args = [find_protoc(),
'--python_out={}'.format(dest),
'--proto_path={}'.format(proto_path),
full_path]
proc = subprocess... | python | def _compile_proto(full_path, dest):
'Helper to compile protobuf files'
proto_path = os.path.dirname(full_path)
protoc_args = [find_protoc(),
'--python_out={}'.format(dest),
'--proto_path={}'.format(proto_path),
full_path]
proc = subprocess... | [
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250,002 | trailofbits/protofuzz | protofuzz/pbimport.py | from_file | def from_file(proto_file):
'''
Take a filename |protoc_file|, compile it via the Protobuf
compiler, and import the module.
Return the module if successfully compiled, otherwise raise either
a ProtocNotFound or BadProtobuf exception.
'''
if not proto_file.endswith('.proto'):
... | python | def from_file(proto_file):
'''
Take a filename |protoc_file|, compile it via the Protobuf
compiler, and import the module.
Return the module if successfully compiled, otherwise raise either
a ProtocNotFound or BadProtobuf exception.
'''
if not proto_file.endswith('.proto'):
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250,003 | trailofbits/protofuzz | protofuzz/pbimport.py | types_from_module | def types_from_module(pb_module):
'''
Return protobuf class types from an imported generated module.
'''
types = pb_module.DESCRIPTOR.message_types_by_name
return [getattr(pb_module, name) for name in types] | python | def types_from_module(pb_module):
'''
Return protobuf class types from an imported generated module.
'''
types = pb_module.DESCRIPTOR.message_types_by_name
return [getattr(pb_module, name) for name in types] | [
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250,004 | trailofbits/protofuzz | protofuzz/gen.py | Permuter._resolve_child | def _resolve_child(self, path):
'Return a member generator by a dot-delimited path'
obj = self
for component in path.split('.'):
ptr = obj
if not isinstance(ptr, Permuter):
raise self.MessageNotFound("Bad element path [wrong type]")
# pylint:... | python | def _resolve_child(self, path):
'Return a member generator by a dot-delimited path'
obj = self
for component in path.split('.'):
ptr = obj
if not isinstance(ptr, Permuter):
raise self.MessageNotFound("Bad element path [wrong type]")
# pylint:... | [
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250,005 | trailofbits/protofuzz | protofuzz/gen.py | Permuter.make_dependent | def make_dependent(self, source, target, action):
'''
Create a dependency between path 'source' and path 'target' via the
callable 'action'.
>>> permuter._generators
[IterValueGenerator(one), IterValueGenerator(two)]
>>> permuter.make_dependent('one', 'two', lambda x: x ... | python | def make_dependent(self, source, target, action):
'''
Create a dependency between path 'source' and path 'target' via the
callable 'action'.
>>> permuter._generators
[IterValueGenerator(one), IterValueGenerator(two)]
>>> permuter.make_dependent('one', 'two', lambda x: x ... | [
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250,006 | trailofbits/protofuzz | protofuzz/gen.py | Permuter.get | def get(self):
'Retrieve the most recent value generated'
# If you attempt to use a generator comprehension below, it will
# consume the StopIteration exception and just return an empty tuple,
# instead of stopping iteration normally
return tuple([(x.name(), x.get()) for x in sel... | python | def get(self):
'Retrieve the most recent value generated'
# If you attempt to use a generator comprehension below, it will
# consume the StopIteration exception and just return an empty tuple,
# instead of stopping iteration normally
return tuple([(x.name(), x.get()) for x in sel... | [
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250,007 | trailofbits/protofuzz | protofuzz/values.py | _fuzzdb_integers | def _fuzzdb_integers(limit=0):
'Helper to grab some integers from fuzzdb'
path = os.path.join(BASE_PATH, 'integer-overflow/integer-overflows.txt')
stream = _open_fuzzdb_file(path)
for line in _limit_helper(stream, limit):
yield int(line.decode('utf-8'), 0) | python | def _fuzzdb_integers(limit=0):
'Helper to grab some integers from fuzzdb'
path = os.path.join(BASE_PATH, 'integer-overflow/integer-overflows.txt')
stream = _open_fuzzdb_file(path)
for line in _limit_helper(stream, limit):
yield int(line.decode('utf-8'), 0) | [
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250,008 | trailofbits/protofuzz | protofuzz/values.py | _fuzzdb_get_strings | def _fuzzdb_get_strings(max_len=0):
'Helper to get all the strings from fuzzdb'
ignored = ['integer-overflow']
for subdir in pkg_resources.resource_listdir('protofuzz', BASE_PATH):
if subdir in ignored:
continue
path = '{}/{}'.format(BASE_PATH, subdir)
listing = pkg_re... | python | def _fuzzdb_get_strings(max_len=0):
'Helper to get all the strings from fuzzdb'
ignored = ['integer-overflow']
for subdir in pkg_resources.resource_listdir('protofuzz', BASE_PATH):
if subdir in ignored:
continue
path = '{}/{}'.format(BASE_PATH, subdir)
listing = pkg_re... | [
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250,009 | trailofbits/protofuzz | protofuzz/values.py | get_integers | def get_integers(bitwidth, unsigned, limit=0):
'''
Get integers from fuzzdb database
bitwidth - The bitwidth that has to contain the integer
unsigned - Whether the type is unsigned
limit - Limit to |limit| results
'''
if unsigned:
start, stop = 0, ((1 << bitwidth) - 1)
els... | python | def get_integers(bitwidth, unsigned, limit=0):
'''
Get integers from fuzzdb database
bitwidth - The bitwidth that has to contain the integer
unsigned - Whether the type is unsigned
limit - Limit to |limit| results
'''
if unsigned:
start, stop = 0, ((1 << bitwidth) - 1)
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250,010 | trailofbits/protofuzz | protofuzz/values.py | get_floats | def get_floats(bitwidth, limit=0):
'''
Return a number of interesting floating point values
'''
assert bitwidth in (32, 64, 80)
values = [0.0, -1.0, 1.0, -1231231231231.0123, 123123123123123.123]
for val in _limit_helper(values, limit):
yield val | python | def get_floats(bitwidth, limit=0):
'''
Return a number of interesting floating point values
'''
assert bitwidth in (32, 64, 80)
values = [0.0, -1.0, 1.0, -1231231231231.0123, 123123123123123.123]
for val in _limit_helper(values, limit):
yield val | [
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250,011 | trailofbits/protofuzz | protofuzz/protofuzz.py | _int_generator | def _int_generator(descriptor, bitwidth, unsigned):
'Helper to create a basic integer value generator'
vals = list(values.get_integers(bitwidth, unsigned))
return gen.IterValueGenerator(descriptor.name, vals) | python | def _int_generator(descriptor, bitwidth, unsigned):
'Helper to create a basic integer value generator'
vals = list(values.get_integers(bitwidth, unsigned))
return gen.IterValueGenerator(descriptor.name, vals) | [
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250,012 | trailofbits/protofuzz | protofuzz/protofuzz.py | _string_generator | def _string_generator(descriptor, max_length=0, limit=0):
'Helper to create a string generator'
vals = list(values.get_strings(max_length, limit))
return gen.IterValueGenerator(descriptor.name, vals) | python | def _string_generator(descriptor, max_length=0, limit=0):
'Helper to create a string generator'
vals = list(values.get_strings(max_length, limit))
return gen.IterValueGenerator(descriptor.name, vals) | [
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250,013 | trailofbits/protofuzz | protofuzz/protofuzz.py | _float_generator | def _float_generator(descriptor, bitwidth):
'Helper to create floating point values'
return gen.IterValueGenerator(descriptor.name, values.get_floats(bitwidth)) | python | def _float_generator(descriptor, bitwidth):
'Helper to create floating point values'
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250,014 | trailofbits/protofuzz | protofuzz/protofuzz.py | _enum_generator | def _enum_generator(descriptor):
'Helper to create protobuf enums'
vals = descriptor.enum_type.values_by_number.keys()
return gen.IterValueGenerator(descriptor.name, vals) | python | def _enum_generator(descriptor):
'Helper to create protobuf enums'
vals = descriptor.enum_type.values_by_number.keys()
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250,015 | trailofbits/protofuzz | protofuzz/protofuzz.py | _prototype_to_generator | def _prototype_to_generator(descriptor, cls):
'Helper to map a descriptor to a protofuzz generator'
_fd = D.FieldDescriptor
generator = None
ints32 = [_fd.TYPE_INT32, _fd.TYPE_UINT32, _fd.TYPE_FIXED32,
_fd.TYPE_SFIXED32, _fd.TYPE_SINT32]
ints64 = [_fd.TYPE_INT64, _fd.TYPE_UINT64, _fd.... | python | def _prototype_to_generator(descriptor, cls):
'Helper to map a descriptor to a protofuzz generator'
_fd = D.FieldDescriptor
generator = None
ints32 = [_fd.TYPE_INT32, _fd.TYPE_UINT32, _fd.TYPE_FIXED32,
_fd.TYPE_SFIXED32, _fd.TYPE_SINT32]
ints64 = [_fd.TYPE_INT64, _fd.TYPE_UINT64, _fd.... | [
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250,016 | trailofbits/protofuzz | protofuzz/protofuzz.py | descriptor_to_generator | def descriptor_to_generator(cls_descriptor, cls, limit=0):
'Convert a protobuf descriptor to a protofuzz generator for same type'
generators = []
for descriptor in cls_descriptor.fields_by_name.values():
generator = _prototype_to_generator(descriptor, cls)
if limit != 0:
genera... | python | def descriptor_to_generator(cls_descriptor, cls, limit=0):
'Convert a protobuf descriptor to a protofuzz generator for same type'
generators = []
for descriptor in cls_descriptor.fields_by_name.values():
generator = _prototype_to_generator(descriptor, cls)
if limit != 0:
genera... | [
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250,017 | trailofbits/protofuzz | protofuzz/protofuzz.py | _assign_to_field | def _assign_to_field(obj, name, val):
'Helper to assign an arbitrary value to a protobuf field'
target = getattr(obj, name)
if isinstance(target, containers.RepeatedScalarFieldContainer):
target.append(val)
elif isinstance(target, containers.RepeatedCompositeFieldContainer):
target = ta... | python | def _assign_to_field(obj, name, val):
'Helper to assign an arbitrary value to a protobuf field'
target = getattr(obj, name)
if isinstance(target, containers.RepeatedScalarFieldContainer):
target.append(val)
elif isinstance(target, containers.RepeatedCompositeFieldContainer):
target = ta... | [
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250,018 | trailofbits/protofuzz | protofuzz/protofuzz.py | _fields_to_object | def _fields_to_object(descriptor, fields):
'Helper to convert a descriptor and a set of fields to a Protobuf instance'
# pylint: disable=protected-access
obj = descriptor._concrete_class()
for name, value in fields:
if isinstance(value, tuple):
subtype = descriptor.fields_by_name[na... | python | def _fields_to_object(descriptor, fields):
'Helper to convert a descriptor and a set of fields to a Protobuf instance'
# pylint: disable=protected-access
obj = descriptor._concrete_class()
for name, value in fields:
if isinstance(value, tuple):
subtype = descriptor.fields_by_name[na... | [
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250,019 | trailofbits/protofuzz | protofuzz/protofuzz.py | _module_to_generators | def _module_to_generators(pb_module):
'''
Convert a protobuf module to a dict of generators.
This is typically used with modules that contain multiple type definitions.
'''
if not pb_module:
return None
message_types = pb_module.DESCRIPTOR.message_types_by_name
return {k: ProtobufGe... | python | def _module_to_generators(pb_module):
'''
Convert a protobuf module to a dict of generators.
This is typically used with modules that contain multiple type definitions.
'''
if not pb_module:
return None
message_types = pb_module.DESCRIPTOR.message_types_by_name
return {k: ProtobufGe... | [
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250,020 | trailofbits/protofuzz | protofuzz/protofuzz.py | ProtobufGenerator.add_dependency | def add_dependency(self, source, target, action):
'''
Create a dependency between fields source and target via callable
action.
>>> permuter = protofuzz.from_description_string("""
... message Address {
... required uint32 one = 1;
... required uint... | python | def add_dependency(self, source, target, action):
'''
Create a dependency between fields source and target via callable
action.
>>> permuter = protofuzz.from_description_string("""
... message Address {
... required uint32 one = 1;
... required uint... | [
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... required uint32 two = 2;
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250,021 | sepandhaghighi/pycm | pycm/pycm_compare.py | Compare.print_report | def print_report(self):
"""
Print Compare report.
:return: None
"""
report = compare_report_print(
self.sorted, self.scores, self.best_name)
print(report) | python | def print_report(self):
report = compare_report_print(
self.sorted, self.scores, self.best_name)
print(report) | [
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250,022 | sepandhaghighi/pycm | pycm/pycm_class_func.py | F_calc | def F_calc(TP, FP, FN, beta):
"""
Calculate F-score.
:param TP: true positive
:type TP : int
:param FP: false positive
:type FP : int
:param FN: false negative
:type FN : int
:param beta : beta coefficient
:type beta : float
:return: F score as float
"""
try:
... | python | def F_calc(TP, FP, FN, beta):
try:
result = ((1 + (beta)**2) * TP) / \
((1 + (beta)**2) * TP + FP + (beta**2) * FN)
return result
except ZeroDivisionError:
return "None" | [
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250,023 | sepandhaghighi/pycm | pycm/pycm_class_func.py | G_calc | def G_calc(item1, item2):
"""
Calculate G-measure & G-mean.
:param item1: PPV or TPR or TNR
:type item1 : float
:param item2: PPV or TPR or TNR
:type item2 : float
:return: G-measure or G-mean as float
"""
try:
result = math.sqrt(item1 * item2)
return result
exce... | python | def G_calc(item1, item2):
try:
result = math.sqrt(item1 * item2)
return result
except Exception:
return "None" | [
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:param item2: PPV or TPR or TNR
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250,024 | sepandhaghighi/pycm | pycm/pycm_class_func.py | RACC_calc | def RACC_calc(TOP, P, POP):
"""
Calculate random accuracy.
:param TOP: test outcome positive
:type TOP : int
:param P: condition positive
:type P : int
:param POP: population
:type POP:int
:return: RACC as float
"""
try:
result = (TOP * P) / ((POP) ** 2)
ret... | python | def RACC_calc(TOP, P, POP):
try:
result = (TOP * P) / ((POP) ** 2)
return result
except Exception:
return "None" | [
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250,025 | sepandhaghighi/pycm | pycm/pycm_class_func.py | CEN_misclassification_calc | def CEN_misclassification_calc(
table,
TOP,
P,
i,
j,
subject_class,
modified=False):
"""
Calculate misclassification probability of classifying.
:param table: input matrix
:type table : dict
:param TOP: test outcome positive
:type TOP : in... | python | def CEN_misclassification_calc(
table,
TOP,
P,
i,
j,
subject_class,
modified=False):
try:
result = TOP + P
if modified:
result -= table[subject_class][subject_class]
result = table[i][j] / result
return result
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250,026 | sepandhaghighi/pycm | pycm/pycm_output.py | html_init | def html_init(name):
"""
Return HTML report file first lines.
:param name: name of file
:type name : str
:return: html_init as str
"""
result = ""
result += "<html>\n"
result += "<head>\n"
result += "<title>" + str(name) + "</title>\n"
result += "</head>\n"
result += "<b... | python | def html_init(name):
result = ""
result += "<html>\n"
result += "<head>\n"
result += "<title>" + str(name) + "</title>\n"
result += "</head>\n"
result += "<body>\n"
result += '<h1 style="border-bottom:1px solid ' \
'black;text-align:center;">PyCM Report</h1>'
return result | [
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250,027 | sepandhaghighi/pycm | pycm/pycm_output.py | html_dataset_type | def html_dataset_type(is_binary, is_imbalanced):
"""
Return HTML report file dataset type.
:param is_binary: is_binary flag (binary : True , multi-class : False)
:type is_binary: bool
:param is_imbalanced: is_imbalanced flag (imbalance : True , balance : False)
:type is_imbalanced: bool
:re... | python | def html_dataset_type(is_binary, is_imbalanced):
result = "<h2>Dataset Type : </h2>\n"
balance_type = "Balanced"
class_type = "Binary Classification"
if is_imbalanced:
balance_type = "Imbalanced"
if not is_binary:
class_type = "Multi-Class Classification"
result += "<ul>\n\n<li>{... | [
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250,028 | sepandhaghighi/pycm | pycm/pycm_output.py | color_check | def color_check(color):
"""
Check input color format.
:param color: input color
:type color : tuple
:return: color as list
"""
if isinstance(color, (tuple, list)):
if all(map(lambda x: isinstance(x, int), color)):
if all(map(lambda x: x < 256, color)):
re... | python | def color_check(color):
if isinstance(color, (tuple, list)):
if all(map(lambda x: isinstance(x, int), color)):
if all(map(lambda x: x < 256, color)):
return list(color)
if isinstance(color, str):
color_lower = color.lower()
if color_lower in TABLE_COLOR.keys()... | [
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250,029 | sepandhaghighi/pycm | pycm/pycm_output.py | html_table_color | def html_table_color(row, item, color=(0, 0, 0)):
"""
Return background color of each cell of table.
:param row: row dictionary
:type row : dict
:param item: cell number
:type item : int
:param color : input color
:type color : tuple
:return: background color as list [R,G,B]
"""... | python | def html_table_color(row, item, color=(0, 0, 0)):
result = [0, 0, 0]
color_list = color_check(color)
max_color = max(color_list)
back_color_index = 255 - int((item / (sum(list(row.values())) + 1)) * 255)
for i in range(3):
result[i] = back_color_index - (max_color - color_list[i])
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:param item: cell number
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:return: background color as list [R,G,B] | [
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250,030 | sepandhaghighi/pycm | pycm/pycm_output.py | html_table | def html_table(classes, table, rgb_color, normalize=False):
"""
Return HTML report file confusion matrix.
:param classes: matrix classes
:type classes: list
:param table: matrix
:type table : dict
:param rgb_color : input color
:type rgb_color : tuple
:param normalize : save normali... | python | def html_table(classes, table, rgb_color, normalize=False):
result = ""
result += "<h2>Confusion Matrix "
if normalize:
result += "(Normalized)"
result += ": </h2>\n"
result += '<table>\n'
result += '<tr align="center">' + "\n"
result += '<td>Actual</td>\n'
result += '<td>Predic... | [
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250,031 | sepandhaghighi/pycm | pycm/pycm_output.py | html_overall_stat | def html_overall_stat(
overall_stat,
digit=5,
overall_param=None,
recommended_list=()):
"""
Return HTML report file overall stat.
:param overall_stat: overall stat
:type overall_stat : dict
:param digit: scale (the number of digits to the right of the decimal point i... | python | def html_overall_stat(
overall_stat,
digit=5,
overall_param=None,
recommended_list=()):
result = ""
result += "<h2>Overall Statistics : </h2>\n"
result += '<table style="border:1px solid black;border-collapse: collapse;">\n'
overall_stat_keys = sorted(overall_stat.keys())... | [
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:param overall_param : Overall parameters list for print, Example : ["Kappa","Scott PI]
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250,032 | sepandhaghighi/pycm | pycm/pycm_output.py | html_class_stat | def html_class_stat(
classes,
class_stat,
digit=5,
class_param=None,
recommended_list=()):
"""
Return HTML report file class_stat.
:param classes: matrix classes
:type classes: list
:param class_stat: class stat
:type class_stat:dict
:param digit: sca... | python | def html_class_stat(
classes,
class_stat,
digit=5,
class_param=None,
recommended_list=()):
result = ""
result += "<h2>Class Statistics : </h2>\n"
result += '<table style="border:1px solid black;border-collapse: collapse;">\n'
result += '<tr align="center">\n<td>Cl... | [
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250,033 | sepandhaghighi/pycm | pycm/pycm_output.py | table_print | def table_print(classes, table):
"""
Return printable confusion matrix.
:param classes: classes list
:type classes:list
:param table: table
:type table:dict
:return: printable table as str
"""
classes_len = len(classes)
table_list = []
for key in classes:
table_list.... | python | def table_print(classes, table):
classes_len = len(classes)
table_list = []
for key in classes:
table_list.extend(list(table[key].values()))
table_list.extend(classes)
table_max_length = max(map(len, map(str, table_list)))
shift = "%-" + str(7 + table_max_length) + "s"
result = shift... | [
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250,034 | sepandhaghighi/pycm | pycm/pycm_output.py | csv_matrix_print | def csv_matrix_print(classes, table):
"""
Return matrix as csv data.
:param classes: classes list
:type classes:list
:param table: table
:type table:dict
:return:
"""
result = ""
classes.sort()
for i in classes:
for j in classes:
result += str(table[i][j]... | python | def csv_matrix_print(classes, table):
result = ""
classes.sort()
for i in classes:
for j in classes:
result += str(table[i][j]) + ","
result = result[:-1] + "\n"
return result[:-1] | [
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250,035 | sepandhaghighi/pycm | pycm/pycm_output.py | csv_print | def csv_print(classes, class_stat, digit=5, class_param=None):
"""
Return csv file data.
:param classes: classes list
:type classes:list
:param class_stat: statistic result for each class
:type class_stat:dict
:param digit: scale (the number of digits to the right of the decimal point in a ... | python | def csv_print(classes, class_stat, digit=5, class_param=None):
result = "Class"
classes.sort()
for item in classes:
result += ',"' + str(item) + '"'
result += "\n"
class_stat_keys = sorted(class_stat.keys())
if isinstance(class_param, list):
if set(class_param) <= set(class_stat_... | [
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:param classes: classes list
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:param class_stat: statistic result for each class
:type class_stat:dict
:param digit: scale (the number of digits to the right of the decimal point in a number.)
:type digit : int
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250,036 | sepandhaghighi/pycm | pycm/pycm_output.py | stat_print | def stat_print(
classes,
class_stat,
overall_stat,
digit=5,
overall_param=None,
class_param=None):
"""
Return printable statistics table.
:param classes: classes list
:type classes:list
:param class_stat: statistic result for each class
:type clas... | python | def stat_print(
classes,
class_stat,
overall_stat,
digit=5,
overall_param=None,
class_param=None):
shift = max(map(len, PARAMS_DESCRIPTION.values())) + 5
classes_len = len(classes)
overall_stat_keys = sorted(overall_stat.keys())
result = ""
if isinstan... | [
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:param overall_stat : overall statistic result
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250,037 | sepandhaghighi/pycm | pycm/pycm_output.py | compare_report_print | def compare_report_print(sorted_list, scores, best_name):
"""
Return compare report.
:param sorted_list: sorted list of cm's
:type sorted_list: list
:param scores: scores of cm's
:type scores: dict
:param best_name: best cm name
:type best_name: str
:return: printable result as str
... | python | def compare_report_print(sorted_list, scores, best_name):
title_items = ["Rank", "Name", "Class-Score", "Overall-Score"]
class_scores_len = map(lambda x: len(
str(x["class"])), list(scores.values()))
shifts = ["%-" +
str(len(sorted_list) +
4) +
"s", "%-"... | [
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:param sorted_list: sorted list of cm's
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:param scores: scores of cm's
:type scores: dict
:param best_name: best cm name
:type best_name: str
:return: printable result as str | [
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250,038 | sepandhaghighi/pycm | pycm/pycm_output.py | online_help | def online_help(param=None):
"""
Open online document in web browser.
:param param: input parameter
:type param : int or str
:return: None
"""
try:
PARAMS_LINK_KEYS = sorted(PARAMS_LINK.keys())
if param in PARAMS_LINK_KEYS:
webbrowser.open_new_tab(DOCUMENT_ADR + ... | python | def online_help(param=None):
try:
PARAMS_LINK_KEYS = sorted(PARAMS_LINK.keys())
if param in PARAMS_LINK_KEYS:
webbrowser.open_new_tab(DOCUMENT_ADR + PARAMS_LINK[param])
elif param in range(1, len(PARAMS_LINK_KEYS) + 1):
webbrowser.open_new_tab(
DOCUMEN... | [
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250,039 | sepandhaghighi/pycm | pycm/pycm_util.py | rounder | def rounder(input_number, digit=5):
"""
Round input number and convert to str.
:param input_number: input number
:type input_number : anything
:param digit: scale (the number of digits to the right of the decimal point in a number.)
:type digit : int
:return: round number as str
"""
... | python | def rounder(input_number, digit=5):
if isinstance(input_number, tuple):
tuple_list = list(input_number)
tuple_str = []
for i in tuple_list:
if isfloat(i):
tuple_str.append(str(numpy.around(i, digit)))
else:
tuple_str.append(str(i))
... | [
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250,040 | sepandhaghighi/pycm | pycm/pycm_util.py | class_filter | def class_filter(classes, class_name):
"""
Filter classes by comparing two lists.
:param classes: matrix classes
:type classes: list
:param class_name: sub set of classes
:type class_name : list
:return: filtered classes as list
"""
result_classes = classes
if isinstance(class_n... | python | def class_filter(classes, class_name):
result_classes = classes
if isinstance(class_name, list):
if set(class_name) <= set(classes):
result_classes = class_name
return result_classes | [
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250,041 | sepandhaghighi/pycm | pycm/pycm_util.py | vector_check | def vector_check(vector):
"""
Check input vector items type.
:param vector: input vector
:type vector : list
:return: bool
"""
for i in vector:
if isinstance(i, int) is False:
return False
if i < 0:
return False
return True | python | def vector_check(vector):
for i in vector:
if isinstance(i, int) is False:
return False
if i < 0:
return False
return True | [
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250,042 | sepandhaghighi/pycm | pycm/pycm_util.py | matrix_check | def matrix_check(table):
"""
Check input matrix format.
:param table: input matrix
:type table : dict
:return: bool
"""
try:
if len(table.keys()) == 0:
return False
for i in table.keys():
if table.keys() != table[i].keys() or vector_check(
... | python | def matrix_check(table):
try:
if len(table.keys()) == 0:
return False
for i in table.keys():
if table.keys() != table[i].keys() or vector_check(
list(table[i].values())) is False:
return False
return True
except Exception:
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250,043 | sepandhaghighi/pycm | pycm/pycm_util.py | vector_filter | def vector_filter(actual_vector, predict_vector):
"""
Convert different type of items in vectors to str.
:param actual_vector: actual values
:type actual_vector : list
:param predict_vector: predict value
:type predict_vector : list
:return: new actual and predict vector
"""
temp = ... | python | def vector_filter(actual_vector, predict_vector):
temp = []
temp.extend(actual_vector)
temp.extend(predict_vector)
types = set(map(type, temp))
if len(types) > 1:
return [list(map(str, actual_vector)), list(map(str, predict_vector))]
return [actual_vector, predict_vector] | [
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250,044 | sepandhaghighi/pycm | pycm/pycm_util.py | class_check | def class_check(vector):
"""
Check different items in matrix classes.
:param vector: input vector
:type vector : list
:return: bool
"""
for i in vector:
if not isinstance(i, type(vector[0])):
return False
return True | python | def class_check(vector):
for i in vector:
if not isinstance(i, type(vector[0])):
return False
return True | [
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250,045 | sepandhaghighi/pycm | pycm/pycm_util.py | one_vs_all_func | def one_vs_all_func(classes, table, TP, TN, FP, FN, class_name):
"""
One-Vs-All mode handler.
:param classes: classes
:type classes : list
:param table: input matrix
:type table : dict
:param TP: true positive dict for all classes
:type TP : dict
:param TN: true negative dict for al... | python | def one_vs_all_func(classes, table, TP, TN, FP, FN, class_name):
try:
report_classes = [str(class_name), "~"]
report_table = {str(class_name): {str(class_name): TP[class_name],
"~": FN[class_name]},
"~": {str(class_name): FP[class_nam... | [
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:param classes: classes
:type classes : list
:param table: input matrix
:type table : dict
:param TP: true positive dict for all classes
:type TP : dict
:param TN: true negative dict for all classes
:type TN : dict
:param FP: false positive dict for all clas... | [
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250,046 | sepandhaghighi/pycm | pycm/pycm_util.py | normalized_table_calc | def normalized_table_calc(classes, table):
"""
Return normalized confusion matrix.
:param classes: classes list
:type classes:list
:param table: table
:type table:dict
:return: normalized table as dict
"""
map_dict = {k: 0 for k in classes}
new_table = {k: map_dict.copy() for k ... | python | def normalized_table_calc(classes, table):
map_dict = {k: 0 for k in classes}
new_table = {k: map_dict.copy() for k in classes}
for key in classes:
div = sum(table[key].values())
if div == 0:
div = 1
for item in classes:
new_table[key][item] = numpy.around(tab... | [
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250,047 | sepandhaghighi/pycm | pycm/pycm_util.py | transpose_func | def transpose_func(classes, table):
"""
Transpose table.
:param classes: classes
:type classes : list
:param table: input matrix
:type table : dict
:return: transposed table as dict
"""
transposed_table = table
for i, item1 in enumerate(classes):
for j, item2 in enumerat... | python | def transpose_func(classes, table):
transposed_table = table
for i, item1 in enumerate(classes):
for j, item2 in enumerate(classes):
if i > j:
temp = transposed_table[item1][item2]
transposed_table[item1][item2] = transposed_table[item2][item1]
... | [
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:param classes: classes
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250,048 | sepandhaghighi/pycm | pycm/pycm_util.py | matrix_params_from_table | def matrix_params_from_table(table, transpose=False):
"""
Calculate TP,TN,FP,FN from confusion matrix.
:param table: input matrix
:type table : dict
:param transpose : transpose flag
:type transpose : bool
:return: [classes_list,table,TP,TN,FP,FN]
"""
classes = sorted(table.keys())
... | python | def matrix_params_from_table(table, transpose=False):
classes = sorted(table.keys())
map_dict = {k: 0 for k in classes}
TP_dict = map_dict.copy()
TN_dict = map_dict.copy()
FP_dict = map_dict.copy()
FN_dict = map_dict.copy()
for i in classes:
TP_dict[i] = table[i][i]
sum_row =... | [
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250,049 | sepandhaghighi/pycm | pycm/pycm_util.py | matrix_params_calc | def matrix_params_calc(actual_vector, predict_vector, sample_weight):
"""
Calculate TP,TN,FP,FN for each class.
:param actual_vector: actual values
:type actual_vector : list
:param predict_vector: predict value
:type predict_vector : list
:param sample_weight : sample weights list
:typ... | python | def matrix_params_calc(actual_vector, predict_vector, sample_weight):
if isinstance(actual_vector, numpy.ndarray):
actual_vector = actual_vector.tolist()
if isinstance(predict_vector, numpy.ndarray):
predict_vector = predict_vector.tolist()
classes = set(actual_vector).union(set(predict_vect... | [
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:type sample_weight : list
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250,050 | sepandhaghighi/pycm | pycm/pycm_util.py | imbalance_check | def imbalance_check(P):
"""
Check if the dataset is imbalanced.
:param P: condition positive
:type P : dict
:return: is_imbalanced as bool
"""
p_list = list(P.values())
max_value = max(p_list)
min_value = min(p_list)
if min_value > 0:
balance_ratio = max_value / min_valu... | python | def imbalance_check(P):
p_list = list(P.values())
max_value = max(p_list)
min_value = min(p_list)
if min_value > 0:
balance_ratio = max_value / min_value
else:
balance_ratio = max_value
is_imbalanced = False
if balance_ratio > BALANCE_RATIO_THRESHOLD:
is_imbalanced = ... | [
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250,051 | sepandhaghighi/pycm | pycm/pycm_util.py | binary_check | def binary_check(classes):
"""
Check if the problem is a binary classification.
:param classes: all classes name
:type classes : list
:return: is_binary as bool
"""
num_classes = len(classes)
is_binary = False
if num_classes == 2:
is_binary = True
return is_binary | python | def binary_check(classes):
num_classes = len(classes)
is_binary = False
if num_classes == 2:
is_binary = True
return is_binary | [
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:type classes : list
:return: is_binary as bool | [
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250,052 | sepandhaghighi/pycm | pycm/pycm_util.py | statistic_recommend | def statistic_recommend(classes, P):
"""
Return recommend parameters which are more suitable due to the input dataset characteristics.
:param classes: all classes name
:type classes : list
:param P: condition positive
:type P : dict
:return: recommendation_list as list
"""
if imbal... | python | def statistic_recommend(classes, P):
if imbalance_check(P):
return IMBALANCED_RECOMMEND
if binary_check(classes):
return BINARY_RECOMMEND
return MULTICLASS_RECOMMEND | [
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:type classes : list
:param P: condition positive
:type P : dict
:return: recommendation_list as list | [
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250,053 | sepandhaghighi/pycm | Otherfiles/version_check.py | print_result | def print_result(failed=False):
"""
Print final result.
:param failed: failed flag
:type failed: bool
:return: None
"""
message = "Version tag tests "
if not failed:
print("\n" + message + "passed!")
else:
print("\n" + message + "failed!")
print("Passed : " + str... | python | def print_result(failed=False):
message = "Version tag tests "
if not failed:
print("\n" + message + "passed!")
else:
print("\n" + message + "failed!")
print("Passed : " + str(TEST_NUMBER - Failed) + "/" + str(TEST_NUMBER)) | [
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:param failed: failed flag
:type failed: bool
:return: None | [
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250,054 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | AUNP_calc | def AUNP_calc(classes, P, POP, AUC_dict):
"""
Calculate AUNP.
:param classes: classes
:type classes : list
:param P: condition positive
:type P : dict
:param POP: population
:type POP : dict
:param AUC_dict: AUC (Area under the ROC curve) for each class
:type AUC_dict : dict
... | python | def AUNP_calc(classes, P, POP, AUC_dict):
try:
result = 0
for i in classes:
result += (P[i] / POP[i]) * AUC_dict[i]
return result
except Exception:
return "None" | [
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:param POP: population
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:param AUC_dict: AUC (Area under the ROC curve) for each class
:type AUC_dict : dict
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250,055 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | overall_MCC_calc | def overall_MCC_calc(classes, table, TOP, P):
"""
Calculate Overall_MCC.
:param classes: classes
:type classes : list
:param table: input matrix
:type table : dict
:param TOP: test outcome positive
:type TOP : dict
:param P: condition positive
:type P : dict
:return: Overal... | python | def overall_MCC_calc(classes, table, TOP, P):
try:
cov_x_y = 0
cov_x_x = 0
cov_y_y = 0
matrix_sum = sum(list(TOP.values()))
for i in classes:
cov_x_x += TOP[i] * (matrix_sum - TOP[i])
cov_y_y += P[i] * (matrix_sum - P[i])
cov_x_y += (table[... | [
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:param TOP: test outcome positive
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:param P: condition positive
:type P : dict
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250,056 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | convex_combination | def convex_combination(classes, TP, TOP, P, class_name, modified=False):
"""
Calculate Overall_CEN coefficient.
:param classes: classes
:type classes : list
:param TP: true Positive Dict For All Classes
:type TP : dict
:param TOP: test outcome positive
:type TOP : dict
:param P: con... | python | def convex_combination(classes, TP, TOP, P, class_name, modified=False):
try:
class_number = len(classes)
alpha = 1
if class_number == 2:
alpha = 0
matrix_sum = sum(list(TOP.values()))
TP_sum = sum(list(TP.values()))
up = TOP[class_name] + P[class_name]
... | [
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250,057 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | ncr | def ncr(n, r):
"""
Calculate n choose r.
:param n: n
:type n : int
:param r: r
:type r :int
:return: n choose r as int
"""
r = min(r, n - r)
numer = reduce(op.mul, range(n, n - r, -1), 1)
denom = reduce(op.mul, range(1, r + 1), 1)
return numer // denom | python | def ncr(n, r):
r = min(r, n - r)
numer = reduce(op.mul, range(n, n - r, -1), 1)
denom = reduce(op.mul, range(1, r + 1), 1)
return numer // denom | [
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250,058 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | p_value_calc | def p_value_calc(TP, POP, NIR):
"""
Calculate p_value.
:param TP: true positive
:type TP : dict
:param POP: population
:type POP : int
:param NIR: no information rate
:type NIR : float
:return: p_value as float
"""
try:
n = POP
x = sum(list(TP.values()))
... | python | def p_value_calc(TP, POP, NIR):
try:
n = POP
x = sum(list(TP.values()))
p = NIR
result = 0
for j in range(x):
result += ncr(n, j) * (p ** j) * ((1 - p) ** (n - j))
return 1 - result
except Exception:
return "None" | [
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250,059 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | hamming_calc | def hamming_calc(TP, POP):
"""
Calculate hamming loss.
:param TP: true positive
:type TP : dict
:param POP: population
:type POP : int
:return: hamming loss as float
"""
try:
length = POP
return (1 / length) * (length - sum(TP.values()))
except Exception:
... | python | def hamming_calc(TP, POP):
try:
length = POP
return (1 / length) * (length - sum(TP.values()))
except Exception:
return "None" | [
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250,060 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | zero_one_loss_calc | def zero_one_loss_calc(TP, POP):
"""
Calculate zero-one loss.
:param TP: true Positive
:type TP : dict
:param POP: population
:type POP : int
:return: zero_one loss as integer
"""
try:
length = POP
return (length - sum(TP.values()))
except Exception:
retu... | python | def zero_one_loss_calc(TP, POP):
try:
length = POP
return (length - sum(TP.values()))
except Exception:
return "None" | [
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250,061 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | entropy_calc | def entropy_calc(item, POP):
"""
Calculate reference and response likelihood.
:param item : TOP or P
:type item : dict
:param POP: population
:type POP : dict
:return: reference or response likelihood as float
"""
try:
result = 0
for i in item.keys():
lik... | python | def entropy_calc(item, POP):
try:
result = 0
for i in item.keys():
likelihood = item[i] / POP[i]
if likelihood != 0:
result += likelihood * math.log(likelihood, 2)
return -result
except Exception:
return "None" | [
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250,062 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | cross_entropy_calc | def cross_entropy_calc(TOP, P, POP):
"""
Calculate cross entropy.
:param TOP: test outcome positive
:type TOP : dict
:param P: condition positive
:type P : dict
:param POP: population
:type POP : dict
:return: cross entropy as float
"""
try:
result = 0
for i ... | python | def cross_entropy_calc(TOP, P, POP):
try:
result = 0
for i in TOP.keys():
reference_likelihood = P[i] / POP[i]
response_likelihood = TOP[i] / POP[i]
if response_likelihood != 0 and reference_likelihood != 0:
result += reference_likelihood * \
... | [
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250,063 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | joint_entropy_calc | def joint_entropy_calc(classes, table, POP):
"""
Calculate joint entropy.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param POP: population
:type POP : dict
:return: joint entropy as float
"""
try:
... | python | def joint_entropy_calc(classes, table, POP):
try:
result = 0
for i in classes:
for index, j in enumerate(classes):
p_prime = table[i][j] / POP[i]
if p_prime != 0:
result += p_prime * math.log(p_prime, 2)
return -result
excep... | [
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250,064 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | conditional_entropy_calc | def conditional_entropy_calc(classes, table, P, POP):
"""
Calculate conditional entropy.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param P: condition positive
:type P : dict
:param POP: population
:type... | python | def conditional_entropy_calc(classes, table, P, POP):
try:
result = 0
for i in classes:
temp = 0
for index, j in enumerate(classes):
p_prime = 0
if P[i] != 0:
p_prime = table[i][j] / P[i]
if p_prime != 0:
... | [
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250,065 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | lambda_B_calc | def lambda_B_calc(classes, table, TOP, POP):
"""
Calculate Goodman and Kruskal's lambda B.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param TOP: test outcome positive
:type TOP : dict
:param POP: population
... | python | def lambda_B_calc(classes, table, TOP, POP):
try:
result = 0
length = POP
maxresponse = max(list(TOP.values()))
for i in classes:
result += max(list(table[i].values()))
result = (result - maxresponse) / (length - maxresponse)
return result
except Excep... | [
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:type classes : list
:param table: confusion matrix table
:type table : dict
:param TOP: test outcome positive
:type TOP : dict
:param POP: population
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250,066 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | lambda_A_calc | def lambda_A_calc(classes, table, P, POP):
"""
Calculate Goodman and Kruskal's lambda A.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param P: condition positive
:type P : dict
:param POP: population
:type... | python | def lambda_A_calc(classes, table, P, POP):
try:
result = 0
maxreference = max(list(P.values()))
length = POP
for i in classes:
col = []
for col_item in table.values():
col.append(col_item[i])
result += max(col)
result = (res... | [
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250,067 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | chi_square_calc | def chi_square_calc(classes, table, TOP, P, POP):
"""
Calculate chi-squared.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param TOP: test outcome positive
:type TOP : dict
:param P: condition positive
:typ... | python | def chi_square_calc(classes, table, TOP, P, POP):
try:
result = 0
for i in classes:
for index, j in enumerate(classes):
expected = (TOP[j] * P[i]) / (POP[i])
result += ((table[i][j] - expected)**2) / expected
return result
except Exception:
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250,068 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | kappa_se_calc | def kappa_se_calc(PA, PE, POP):
"""
Calculate kappa standard error.
:param PA: observed agreement among raters (overall accuracy)
:type PA : float
:param PE: hypothetical probability of chance agreement (random accuracy)
:type PE : float
:param POP: population
:type POP:int
:return... | python | def kappa_se_calc(PA, PE, POP):
try:
result = math.sqrt((PA * (1 - PA)) / (POP * ((1 - PE)**2)))
return result
except Exception:
return "None" | [
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250,069 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | micro_calc | def micro_calc(TP, item):
"""
Calculate PPV_Micro and TPR_Micro.
:param TP: true positive
:type TP:dict
:param item: FN or FP
:type item : dict
:return: PPV_Micro or TPR_Micro as float
"""
try:
TP_sum = sum(TP.values())
item_sum = sum(item.values())
return TP... | python | def micro_calc(TP, item):
try:
TP_sum = sum(TP.values())
item_sum = sum(item.values())
return TP_sum / (TP_sum + item_sum)
except Exception:
return "None" | [
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250,070 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | macro_calc | def macro_calc(item):
"""
Calculate PPV_Macro and TPR_Macro.
:param item: PPV or TPR
:type item:dict
:return: PPV_Macro or TPR_Macro as float
"""
try:
item_sum = sum(item.values())
item_len = len(item.values())
return item_sum / item_len
except Exception:
... | python | def macro_calc(item):
try:
item_sum = sum(item.values())
item_len = len(item.values())
return item_sum / item_len
except Exception:
return "None" | [
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250,071 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | PC_PI_calc | def PC_PI_calc(P, TOP, POP):
"""
Calculate percent chance agreement for Scott's Pi.
:param P: condition positive
:type P : dict
:param TOP: test outcome positive
:type TOP : dict
:param POP: population
:type POP:dict
:return: percent chance agreement as float
"""
try:
... | python | def PC_PI_calc(P, TOP, POP):
try:
result = 0
for i in P.keys():
result += ((P[i] + TOP[i]) / (2 * POP[i]))**2
return result
except Exception:
return "None" | [
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250,072 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | PC_AC1_calc | def PC_AC1_calc(P, TOP, POP):
"""
Calculate percent chance agreement for Gwet's AC1.
:param P: condition positive
:type P : dict
:param TOP: test outcome positive
:type TOP : dict
:param POP: population
:type POP:dict
:return: percent chance agreement as float
"""
try:
... | python | def PC_AC1_calc(P, TOP, POP):
try:
result = 0
classes = list(P.keys())
for i in classes:
pi = ((P[i] + TOP[i]) / (2 * POP[i]))
result += pi * (1 - pi)
result = result / (len(classes) - 1)
return result
except Exception:
return "None" | [
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250,073 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | overall_jaccard_index_calc | def overall_jaccard_index_calc(jaccard_list):
"""
Calculate overall jaccard index.
:param jaccard_list : list of jaccard index for each class
:type jaccard_list : list
:return: (jaccard_sum , jaccard_mean) as tuple
"""
try:
jaccard_sum = sum(jaccard_list)
jaccard_mean = jacc... | python | def overall_jaccard_index_calc(jaccard_list):
try:
jaccard_sum = sum(jaccard_list)
jaccard_mean = jaccard_sum / len(jaccard_list)
return (jaccard_sum, jaccard_mean)
except Exception:
return "None" | [
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:type jaccard_list : list
:return: (jaccard_sum , jaccard_mean) as tuple | [
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250,074 | sepandhaghighi/pycm | pycm/pycm_overall_func.py | overall_accuracy_calc | def overall_accuracy_calc(TP, POP):
"""
Calculate overall accuracy.
:param TP: true positive
:type TP : dict
:param POP: population
:type POP:int
:return: overall_accuracy as float
"""
try:
overall_accuracy = sum(TP.values()) / POP
return overall_accuracy
except ... | python | def overall_accuracy_calc(TP, POP):
try:
overall_accuracy = sum(TP.values()) / POP
return overall_accuracy
except Exception:
return "None" | [
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250,075 | sepandhaghighi/pycm | pycm/pycm_interpret.py | AUC_analysis | def AUC_analysis(AUC):
"""
Analysis AUC with interpretation table.
:param AUC: area under the ROC curve
:type AUC : float
:return: interpretation result as str
"""
try:
if AUC == "None":
return "None"
if AUC < 0.6:
return "Poor"
if AUC >= 0.6 ... | python | def AUC_analysis(AUC):
try:
if AUC == "None":
return "None"
if AUC < 0.6:
return "Poor"
if AUC >= 0.6 and AUC < 0.7:
return "Fair"
if AUC >= 0.7 and AUC < 0.8:
return "Good"
if AUC >= 0.8 and AUC < 0.9:
return "Very ... | [
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:param AUC: area under the ROC curve
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:return: interpretation result as str | [
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250,076 | sepandhaghighi/pycm | pycm/pycm_interpret.py | kappa_analysis_cicchetti | def kappa_analysis_cicchetti(kappa):
"""
Analysis kappa number with Cicchetti benchmark.
:param kappa: kappa number
:type kappa : float
:return: strength of agreement as str
"""
try:
if kappa < 0.4:
return "Poor"
if kappa >= 0.4 and kappa < 0.59:
retu... | python | def kappa_analysis_cicchetti(kappa):
try:
if kappa < 0.4:
return "Poor"
if kappa >= 0.4 and kappa < 0.59:
return "Fair"
if kappa >= 0.59 and kappa < 0.74:
return "Good"
if kappa >= 0.74 and kappa <= 1:
return "Excellent"
return ... | [
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250,077 | sepandhaghighi/pycm | pycm/pycm_interpret.py | kappa_analysis_koch | def kappa_analysis_koch(kappa):
"""
Analysis kappa number with Landis-Koch benchmark.
:param kappa: kappa number
:type kappa : float
:return: strength of agreement as str
"""
try:
if kappa < 0:
return "Poor"
if kappa >= 0 and kappa < 0.2:
return "Slig... | python | def kappa_analysis_koch(kappa):
try:
if kappa < 0:
return "Poor"
if kappa >= 0 and kappa < 0.2:
return "Slight"
if kappa >= 0.20 and kappa < 0.4:
return "Fair"
if kappa >= 0.40 and kappa < 0.6:
return "Moderate"
if kappa >= 0.60... | [
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250,078 | sepandhaghighi/pycm | pycm/pycm_interpret.py | kappa_analysis_altman | def kappa_analysis_altman(kappa):
"""
Analysis kappa number with Altman benchmark.
:param kappa: kappa number
:type kappa : float
:return: strength of agreement as str
"""
try:
if kappa < 0.2:
return "Poor"
if kappa >= 0.20 and kappa < 0.4:
return "Fa... | python | def kappa_analysis_altman(kappa):
try:
if kappa < 0.2:
return "Poor"
if kappa >= 0.20 and kappa < 0.4:
return "Fair"
if kappa >= 0.40 and kappa < 0.6:
return "Moderate"
if kappa >= 0.60 and kappa < 0.8:
return "Good"
if kappa >=... | [
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250,079 | sepandhaghighi/pycm | setup.py | get_requires | def get_requires():
"""Read requirements.txt."""
requirements = open("requirements.txt", "r").read()
return list(filter(lambda x: x != "", requirements.split())) | python | def get_requires():
requirements = open("requirements.txt", "r").read()
return list(filter(lambda x: x != "", requirements.split())) | [
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250,080 | sepandhaghighi/pycm | setup.py | read_description | def read_description():
"""Read README.md and CHANGELOG.md."""
try:
with open("README.md") as r:
description = "\n"
description += r.read()
with open("CHANGELOG.md") as c:
description += "\n"
description += c.read()
return description
e... | python | def read_description():
try:
with open("README.md") as r:
description = "\n"
description += r.read()
with open("CHANGELOG.md") as c:
description += "\n"
description += c.read()
return description
except Exception:
return '''
PyCM... | [
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250,081 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.print_matrix | def print_matrix(self, one_vs_all=False, class_name=None):
"""
Print confusion matrix.
:param one_vs_all : One-Vs-All mode flag
:type one_vs_all : bool
:param class_name : target class name for One-Vs-All mode
:type class_name : any valid type
:return: None
... | python | def print_matrix(self, one_vs_all=False, class_name=None):
classes = self.classes
table = self.table
if one_vs_all:
[classes, table] = one_vs_all_func(
classes, table, self.TP, self.TN, self.FP, self.FN, class_name)
print(table_print(classes, table)) | [
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:param one_vs_all : One-Vs-All mode flag
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:param class_name : target class name for One-Vs-All mode
:type class_name : any valid type
:return: None | [
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250,082 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.stat | def stat(self, overall_param=None, class_param=None, class_name=None):
"""
Print statistical measures table.
:param overall_param : overall parameters list for print, Example : ["Kappa","Scott PI]
:type overall_param : list
:param class_param : class parameters list for print, E... | python | def stat(self, overall_param=None, class_param=None, class_name=None):
classes = class_filter(self.classes, class_name)
print(
stat_print(
classes,
self.class_stat,
self.overall_stat,
self.digit, overall_param, class_param)) | [
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:type overall_param : list
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:type class_param : list
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250,083 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.save_html | def save_html(
self,
name,
address=True,
overall_param=None,
class_param=None,
class_name=None, color=(0, 0, 0), normalize=False):
"""
Save ConfusionMatrix in HTML file.
:param name: filename
:type name : str
... | python | def save_html(
self,
name,
address=True,
overall_param=None,
class_param=None,
class_name=None, color=(0, 0, 0), normalize=False):
try:
message = None
table = self.table
if normalize:
tabl... | [
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:param address: flag for address return
:type address : bool
:param overall_param : overall parameters list for save, Example : ["Kappa","Scott PI]
:type overall_param : list
:param class_p... | [
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250,084 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.save_csv | def save_csv(
self,
name,
address=True,
class_param=None,
class_name=None,
matrix_save=True,
normalize=False):
"""
Save ConfusionMatrix in CSV file.
:param name: filename
:type name : str
:param ... | python | def save_csv(
self,
name,
address=True,
class_param=None,
class_name=None,
matrix_save=True,
normalize=False):
try:
message = None
classes = class_filter(self.classes, class_name)
csv_file = o... | [
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:param class_param : class parameters list for save, Example : ["TPR","TNR","AUC"]
:type class_param : list
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250,085 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.save_obj | def save_obj(self, name, address=True):
"""
Save ConfusionMatrix in .obj file.
:param name: filename
:type name : str
:param address: flag for address return
:type address : bool
:return: saving Status as dict {"Status":bool , "Message":str}
"""
t... | python | def save_obj(self, name, address=True):
try:
message = None
obj_file = open(name + ".obj", "w")
actual_vector_temp = self.actual_vector
predict_vector_temp = self.predict_vector
matrix_temp = {k: self.table[k].copy() for k in self.classes}
... | [
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250,086 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.F_beta | def F_beta(self, beta):
"""
Calculate FBeta score.
:param beta: beta parameter
:type beta : float
:return: FBeta score for classes as dict
"""
try:
F_dict = {}
for i in self.TP.keys():
F_dict[i] = F_calc(
... | python | def F_beta(self, beta):
try:
F_dict = {}
for i in self.TP.keys():
F_dict[i] = F_calc(
TP=self.TP[i],
FP=self.FP[i],
FN=self.FN[i],
beta=beta)
return F_dict
except Exception... | [
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:type beta : float
:return: FBeta score for classes as dict | [
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250,087 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.IBA_alpha | def IBA_alpha(self, alpha):
"""
Calculate IBA_alpha score.
:param alpha: alpha parameter
:type alpha: float
:return: IBA_alpha score for classes as dict
"""
try:
IBA_dict = {}
for i in self.classes:
IBA_dict[i] = IBA_calc(s... | python | def IBA_alpha(self, alpha):
try:
IBA_dict = {}
for i in self.classes:
IBA_dict[i] = IBA_calc(self.TPR[i], self.TNR[i], alpha=alpha)
return IBA_dict
except Exception:
return {} | [
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250,088 | sepandhaghighi/pycm | pycm/pycm_obj.py | ConfusionMatrix.relabel | def relabel(self, mapping):
"""
Rename ConfusionMatrix classes.
:param mapping: mapping dictionary
:type mapping : dict
:return: None
"""
if not isinstance(mapping, dict):
raise pycmMatrixError(MAPPING_FORMAT_ERROR)
if self.classes != list(map... | python | def relabel(self, mapping):
if not isinstance(mapping, dict):
raise pycmMatrixError(MAPPING_FORMAT_ERROR)
if self.classes != list(mapping.keys()):
raise pycmMatrixError(MAPPING_CLASS_NAME_ERROR)
for row in self.classes:
temp_dict = {}
temp_dict_nor... | [
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:param mapping: mapping dictionary
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:return: None | [
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250,089 | manns/pyspread | pyspread/src/gui/_toolbars.py | ToolbarBase.add_tools | def add_tools(self):
"""Adds tools from self.toolbardata to self"""
for data in self.toolbardata:
# tool type is in data[0]
if data[0] == "T":
# Simple tool
_, msg_type, label, tool_tip = data
icon = icons[label]
... | python | def add_tools(self):
for data in self.toolbardata:
# tool type is in data[0]
if data[0] == "T":
# Simple tool
_, msg_type, label, tool_tip = data
icon = icons[label]
self.label2id[label] = tool_id = wx.NewId()
... | [
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250,090 | manns/pyspread | pyspread/src/gui/_toolbars.py | ToolbarBase.OnTool | def OnTool(self, event):
"""Toolbar event handler"""
msgtype = self.ids_msgs[event.GetId()]
post_command_event(self, msgtype) | python | def OnTool(self, event):
msgtype = self.ids_msgs[event.GetId()]
post_command_event(self, msgtype) | [
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250,091 | manns/pyspread | pyspread/src/gui/_toolbars.py | MainToolbar.OnToggleTool | def OnToggleTool(self, event):
"""Tool event handler"""
config["check_spelling"] = str(event.IsChecked())
toggle_id = self.parent.menubar.FindMenuItem(_("View"),
_("Check spelling"))
if toggle_id != -1:
# Check may fail if... | python | def OnToggleTool(self, event):
config["check_spelling"] = str(event.IsChecked())
toggle_id = self.parent.menubar.FindMenuItem(_("View"),
_("Check spelling"))
if toggle_id != -1:
# Check may fail if translation is incomplete
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250,092 | manns/pyspread | pyspread/src/gui/_toolbars.py | WidgetToolbar._get_button_label | def _get_button_label(self):
"""Gets Button label from user and returns string"""
dlg = wx.TextEntryDialog(self, _('Button label:'))
if dlg.ShowModal() == wx.ID_OK:
label = dlg.GetValue()
else:
label = ""
dlg.Destroy()
return label | python | def _get_button_label(self):
dlg = wx.TextEntryDialog(self, _('Button label:'))
if dlg.ShowModal() == wx.ID_OK:
label = dlg.GetValue()
else:
label = ""
dlg.Destroy()
return label | [
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250,093 | manns/pyspread | pyspread/src/gui/_toolbars.py | WidgetToolbar.OnButtonCell | def OnButtonCell(self, event):
"""Event handler for cell button toggle button"""
if self.button_cell_button_id == event.GetId():
if event.IsChecked():
label = self._get_button_label()
post_command_event(self, self.ButtonCellMsg, text=label)
else:
... | python | def OnButtonCell(self, event):
if self.button_cell_button_id == event.GetId():
if event.IsChecked():
label = self._get_button_label()
post_command_event(self, self.ButtonCellMsg, text=label)
else:
post_command_event(self, self.ButtonCellMsg... | [
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250,094 | manns/pyspread | pyspread/src/gui/_toolbars.py | WidgetToolbar.OnVideoCell | def OnVideoCell(self, event):
"""Event handler for video cell toggle button"""
if self.video_cell_button_id == event.GetId():
if event.IsChecked():
wildcard = _("Media files") + " (*.*)|*.*"
videofile, __ = self.get_filepath_findex_from_user(
... | python | def OnVideoCell(self, event):
if self.video_cell_button_id == event.GetId():
if event.IsChecked():
wildcard = _("Media files") + " (*.*)|*.*"
videofile, __ = self.get_filepath_findex_from_user(
wildcard, "Choose video or audio file", wx.OPEN)
... | [
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250,095 | manns/pyspread | pyspread/src/gui/_toolbars.py | FindToolbar.make_menu | def make_menu(self):
"""Creates the search menu"""
menu = wx.Menu()
item = menu.Append(-1, "Recent Searches")
item.Enable(False)
for __id, txt in enumerate(self.search_history):
menu.Append(__id, txt)
return menu | python | def make_menu(self):
menu = wx.Menu()
item = menu.Append(-1, "Recent Searches")
item.Enable(False)
for __id, txt in enumerate(self.search_history):
menu.Append(__id, txt)
return menu | [
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250,096 | manns/pyspread | pyspread/src/gui/_toolbars.py | FindToolbar.OnMenu | def OnMenu(self, event):
"""Search history has been selected"""
__id = event.GetId()
try:
menuitem = event.GetEventObject().FindItemById(__id)
selected_text = menuitem.GetItemLabel()
self.search.SetValue(selected_text)
except AttributeError:
... | python | def OnMenu(self, event):
__id = event.GetId()
try:
menuitem = event.GetEventObject().FindItemById(__id)
selected_text = menuitem.GetItemLabel()
self.search.SetValue(selected_text)
except AttributeError:
# Not called by menu
event.Skip() | [
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250,097 | manns/pyspread | pyspread/src/gui/_toolbars.py | FindToolbar.OnSearch | def OnSearch(self, event):
"""Event handler for starting the search"""
search_string = self.search.GetValue()
if search_string not in self.search_history:
self.search_history.append(search_string)
if len(self.search_history) > 10:
self.search_history.pop(0)
... | python | def OnSearch(self, event):
search_string = self.search.GetValue()
if search_string not in self.search_history:
self.search_history.append(search_string)
if len(self.search_history) > 10:
self.search_history.pop(0)
self.menu = self.make_menu()
self.search... | [
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250,098 | manns/pyspread | pyspread/src/gui/_toolbars.py | FindToolbar.OnSearchDirectionButton | def OnSearchDirectionButton(self, event):
"""Event handler for search direction toggle button"""
if "DOWN" in self.search_options:
flag_index = self.search_options.index("DOWN")
self.search_options[flag_index] = "UP"
elif "UP" in self.search_options:
flag_ind... | python | def OnSearchDirectionButton(self, event):
if "DOWN" in self.search_options:
flag_index = self.search_options.index("DOWN")
self.search_options[flag_index] = "UP"
elif "UP" in self.search_options:
flag_index = self.search_options.index("UP")
self.search_opt... | [
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250,099 | manns/pyspread | pyspread/src/gui/_toolbars.py | FindToolbar.OnSearchFlag | def OnSearchFlag(self, event):
"""Event handler for search flag toggle buttons"""
for label in self.search_options_buttons:
button_id = self.label2id[label]
if button_id == event.GetId():
if event.IsChecked():
self.search_options.append(label)... | python | def OnSearchFlag(self, event):
for label in self.search_options_buttons:
button_id = self.label2id[label]
if button_id == event.GetId():
if event.IsChecked():
self.search_options.append(label)
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flag_index = self... | [
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