Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/isomorphism/matchhelpers.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/isomorphism/matchhelpers.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,387 @@ +"""Functions which help end users define customize node_match and +edge_match functions to use during isomorphism checks. +""" +from itertools import permutations +import types + +__all__ = [ + "categorical_node_match", + "categorical_edge_match", + "categorical_multiedge_match", + "numerical_node_match", + "numerical_edge_match", + "numerical_multiedge_match", + "generic_node_match", + "generic_edge_match", + "generic_multiedge_match", +] + + +def copyfunc(f, name=None): + """Returns a deepcopy of a function.""" + return types.FunctionType( + f.__code__, f.__globals__, name or f.__name__, f.__defaults__, f.__closure__ + ) + + +def allclose(x, y, rtol=1.0000000000000001e-05, atol=1e-08): + """Returns True if x and y are sufficiently close, elementwise. + + Parameters + ---------- + rtol : float + The relative error tolerance. + atol : float + The absolute error tolerance. + + """ + # assume finite weights, see numpy.allclose() for reference + for xi, yi in zip(x, y): + if not (abs(xi - yi) <= atol + rtol * abs(yi)): + return False + return True + + +def close(x, y, rtol=1.0000000000000001e-05, atol=1e-08): + """Returns True if x and y are sufficiently close. + + Parameters + ---------- + rtol : float + The relative error tolerance. + atol : float + The absolute error tolerance. + + """ + # assume finite weights, see numpy.allclose() for reference + return abs(x - y) <= atol + rtol * abs(y) + + +categorical_doc = """ +Returns a comparison function for a categorical node attribute. + +The value(s) of the attr(s) must be hashable and comparable via the == +operator since they are placed into a set([]) object. If the sets from +G1 and G2 are the same, then the constructed function returns True. + +Parameters +---------- +attr : string | list + The categorical node attribute to compare, or a list of categorical + node attributes to compare. +default : value | list + The default value for the categorical node attribute, or a list of + default values for the categorical node attributes. + +Returns +------- +match : function + The customized, categorical `node_match` function. + +Examples +-------- +>>> import networkx.algorithms.isomorphism as iso +>>> nm = iso.categorical_node_match("size", 1) +>>> nm = iso.categorical_node_match(["color", "size"], ["red", 2]) + +""" + + +def categorical_node_match(attr, default): + if isinstance(attr, str): + + def match(data1, data2): + return data1.get(attr, default) == data2.get(attr, default) + + else: + attrs = list(zip(attr, default)) # Python 3 + + def match(data1, data2): + return all(data1.get(attr, d) == data2.get(attr, d) for attr, d in attrs) + + return match + + +try: + categorical_edge_match = copyfunc(categorical_node_match, "categorical_edge_match") +except NotImplementedError: + # IronPython lacks support for types.FunctionType. + # https://github.com/networkx/networkx/issues/949 + # https://github.com/networkx/networkx/issues/1127 + def categorical_edge_match(*args, **kwargs): + return categorical_node_match(*args, **kwargs) + + +def categorical_multiedge_match(attr, default): + if isinstance(attr, str): + + def match(datasets1, datasets2): + values1 = {data.get(attr, default) for data in datasets1.values()} + values2 = {data.get(attr, default) for data in datasets2.values()} + return values1 == values2 + + else: + attrs = list(zip(attr, default)) # Python 3 + + def match(datasets1, datasets2): + values1 = set() + for data1 in datasets1.values(): + x = tuple(data1.get(attr, d) for attr, d in attrs) + values1.add(x) + values2 = set() + for data2 in datasets2.values(): + x = tuple(data2.get(attr, d) for attr, d in attrs) + values2.add(x) + return values1 == values2 + + return match + + +# Docstrings for categorical functions. +categorical_node_match.__doc__ = categorical_doc +categorical_edge_match.__doc__ = categorical_doc.replace("node", "edge") +tmpdoc = categorical_doc.replace("node", "edge") +tmpdoc = tmpdoc.replace("categorical_edge_match", "categorical_multiedge_match") +categorical_multiedge_match.__doc__ = tmpdoc + + +numerical_doc = """ +Returns a comparison function for a numerical node attribute. + +The value(s) of the attr(s) must be numerical and sortable. If the +sorted list of values from G1 and G2 are the same within some +tolerance, then the constructed function returns True. + +Parameters +---------- +attr : string | list + The numerical node attribute to compare, or a list of numerical + node attributes to compare. +default : value | list + The default value for the numerical node attribute, or a list of + default values for the numerical node attributes. +rtol : float + The relative error tolerance. +atol : float + The absolute error tolerance. + +Returns +------- +match : function + The customized, numerical `node_match` function. + +Examples +-------- +>>> import networkx.algorithms.isomorphism as iso +>>> nm = iso.numerical_node_match("weight", 1.0) +>>> nm = iso.numerical_node_match(["weight", "linewidth"], [0.25, 0.5]) + +""" + + +def numerical_node_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08): + if isinstance(attr, str): + + def match(data1, data2): + return close( + data1.get(attr, default), data2.get(attr, default), rtol=rtol, atol=atol + ) + + else: + attrs = list(zip(attr, default)) # Python 3 + + def match(data1, data2): + values1 = [data1.get(attr, d) for attr, d in attrs] + values2 = [data2.get(attr, d) for attr, d in attrs] + return allclose(values1, values2, rtol=rtol, atol=atol) + + return match + + +try: + numerical_edge_match = copyfunc(numerical_node_match, "numerical_edge_match") +except NotImplementedError: + # IronPython lacks support for types.FunctionType. + # https://github.com/networkx/networkx/issues/949 + # https://github.com/networkx/networkx/issues/1127 + def numerical_edge_match(*args, **kwargs): + return numerical_node_match(*args, **kwargs) + + +def numerical_multiedge_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08): + if isinstance(attr, str): + + def match(datasets1, datasets2): + values1 = sorted([data.get(attr, default) for data in datasets1.values()]) + values2 = sorted([data.get(attr, default) for data in datasets2.values()]) + return allclose(values1, values2, rtol=rtol, atol=atol) + + else: + attrs = list(zip(attr, default)) # Python 3 + + def match(datasets1, datasets2): + values1 = [] + for data1 in datasets1.values(): + x = tuple(data1.get(attr, d) for attr, d in attrs) + values1.append(x) + values2 = [] + for data2 in datasets2.values(): + x = tuple(data2.get(attr, d) for attr, d in attrs) + values2.append(x) + values1.sort() + values2.sort() + for xi, yi in zip(values1, values2): + if not allclose(xi, yi, rtol=rtol, atol=atol): + return False + else: + return True + + return match + + +# Docstrings for numerical functions. +numerical_node_match.__doc__ = numerical_doc +numerical_edge_match.__doc__ = numerical_doc.replace("node", "edge") +tmpdoc = numerical_doc.replace("node", "edge") +tmpdoc = tmpdoc.replace("numerical_edge_match", "numerical_multiedge_match") +numerical_multiedge_match.__doc__ = tmpdoc + + +generic_doc = """ +Returns a comparison function for a generic attribute. + +The value(s) of the attr(s) are compared using the specified +operators. If all the attributes are equal, then the constructed +function returns True. + +Parameters +---------- +attr : string | list + The node attribute to compare, or a list of node attributes + to compare. +default : value | list + The default value for the node attribute, or a list of + default values for the node attributes. +op : callable | list + The operator to use when comparing attribute values, or a list + of operators to use when comparing values for each attribute. + +Returns +------- +match : function + The customized, generic `node_match` function. + +Examples +-------- +>>> from operator import eq +>>> from networkx.algorithms.isomorphism.matchhelpers import close +>>> from networkx.algorithms.isomorphism import generic_node_match +>>> nm = generic_node_match("weight", 1.0, close) +>>> nm = generic_node_match("color", "red", eq) +>>> nm = generic_node_match(["weight", "color"], [1.0, "red"], [close, eq]) + +""" + + +def generic_node_match(attr, default, op): + if isinstance(attr, str): + + def match(data1, data2): + return op(data1.get(attr, default), data2.get(attr, default)) + + else: + attrs = list(zip(attr, default, op)) # Python 3 + + def match(data1, data2): + for attr, d, operator in attrs: + if not operator(data1.get(attr, d), data2.get(attr, d)): + return False + else: + return True + + return match + + +try: + generic_edge_match = copyfunc(generic_node_match, "generic_edge_match") +except NotImplementedError: + # IronPython lacks support for types.FunctionType. + # https://github.com/networkx/networkx/issues/949 + # https://github.com/networkx/networkx/issues/1127 + def generic_edge_match(*args, **kwargs): + return generic_node_match(*args, **kwargs) + + +def generic_multiedge_match(attr, default, op): + """Returns a comparison function for a generic attribute. + + The value(s) of the attr(s) are compared using the specified + operators. If all the attributes are equal, then the constructed + function returns True. Potentially, the constructed edge_match + function can be slow since it must verify that no isomorphism + exists between the multiedges before it returns False. + + Parameters + ---------- + attr : string | list + The edge attribute to compare, or a list of node attributes + to compare. + default : value | list + The default value for the edge attribute, or a list of + default values for the dgeattributes. + op : callable | list + The operator to use when comparing attribute values, or a list + of operators to use when comparing values for each attribute. + + Returns + ------- + match : function + The customized, generic `edge_match` function. + + Examples + -------- + >>> from operator import eq + >>> from networkx.algorithms.isomorphism.matchhelpers import close + >>> from networkx.algorithms.isomorphism import generic_node_match + >>> nm = generic_node_match("weight", 1.0, close) + >>> nm = generic_node_match("color", "red", eq) + >>> nm = generic_node_match(["weight", "color"], [1.0, "red"], [close, eq]) + ... + + """ + + # This is slow, but generic. + # We must test every possible isomorphism between the edges. + if isinstance(attr, str): + attr = [attr] + default = [default] + op = [op] + attrs = list(zip(attr, default)) # Python 3 + + def match(datasets1, datasets2): + values1 = [] + for data1 in datasets1.values(): + x = tuple(data1.get(attr, d) for attr, d in attrs) + values1.append(x) + values2 = [] + for data2 in datasets2.values(): + x = tuple(data2.get(attr, d) for attr, d in attrs) + values2.append(x) + for vals2 in permutations(values2): + for xi, yi in zip(values1, vals2): + if not all(map(lambda x, y, z: z(x, y), xi, yi, op)): + # This is not an isomorphism, go to next permutation. + break + else: + # Then we found an isomorphism. + return True + else: + # Then there are no isomorphisms between the multiedges. + return False + + return match + + +# Docstrings for numerical functions. +generic_node_match.__doc__ = generic_doc +generic_edge_match.__doc__ = generic_doc.replace("node", "edge")