Beispiel #1
0
    def set_structure(self, edge_dict, value_dict=None):
        """
        Set the structure of a BayesNet object. This
        function is mostly used to instantiate a BN
        skeleton after structure learning algorithms.

        See "structure_learn" folder & algorithms

        Arguments
        ---------
        *edge_dict* : a dictionary,
            where key = rv,
            value = list of rv's children
            NOTE: THIS MUST BE DIRECTED ALREADY!

        *value_dict* : a dictionary,
            where key = rv,
            value = list of rv's possible values

        Returns
        -------
        None

        Effects
        -------
        - sets self.V in topsort order from edge_dict
        - sets self.E
        - creates self.F structure and sets the parents

        Notes
        -----

        """

        self.V = topsort(edge_dict)
        self.E = edge_dict
        self.F = dict([(rv,{}) for rv in self.nodes()])
        for rv in self.nodes():
            self.F[rv] = {
                'parents':[p for p in self.nodes() if rv in self.children(p)],
                'cpt': [],
                'values': []
            }
            if value_dict is not None:
                self.F[rv]['values'] = value_dict[rv]
Beispiel #2
0
    def set_structure(self, edge_dict, value_dict=None):
        """
        Set the structure of a BayesNet object. This
        function is mostly used to instantiate a BN
        skeleton after structure learning algorithms.

        See "structure_learn" folder & algorithms

        Arguments
        ---------
        *edge_dict* : a dictionary,
            where key = rv,
            value = list of rv's children
            NOTE: THIS MUST BE DIRECTED ALREADY!

        *value_dict* : a dictionary,
            where key = rv,
            value = list of rv's possible values

        Returns
        -------
        None

        Effects
        -------
        - sets self.V in topsort order from edge_dict
        - sets self.E
        - creates self.F structure and sets the parents

        Notes
        -----

        """

        self.V = topsort(edge_dict)
        self.E = edge_dict
        self.F = dict([(rv, {}) for rv in self.nodes()])
        for rv in self.nodes():
            self.F[rv] = {
                'parents': [p for p in self.nodes() if rv in self.children(p)],
                'cpt': [],
                'values': []
            }
            if value_dict is not None:
                self.F[rv]['values'] = value_dict[rv]
Beispiel #3
0
def read_bif(path):
    """
    This function reads a .bif file into a
    BayesNet object. It's probably not the 
    fastest or prettiest but it gets the job
    done.

    Arguments
    ---------
    *path* : a string
        The path

    Returns
    -------
    *bn* : a BayesNet object

    Effects
    -------
    None

    Notes
    -----
    *V* : a list of strings
    *E* : a dict, where key = vertex, val = list of its children
    *F* : a dict, where key = rv, val = another dict with
                keys = 'parents', 'values', cpt'

    """
    _parents = {
    }  # key = vertex, value = list of vertices in the scope (includind itself)
    _cpt = {}  # key = vertex, value = list (then numpy array)
    _vals = {}  # key=vertex, val=list of its possible values

    with open(path, 'r') as f:
        while True:
            line = f.readline()
            if 'variable' in line:
                new_vertex = line.split()[1]

                _parents[new_vertex] = []
                _cpt[new_vertex] = []
                #_vals[new_vertex] = []

                new_line = f.readline()
                new_vals = new_line.replace(',',
                                            ' ').split()[6:-1]  # list of vals
                _vals[new_vertex] = new_vals
                num_outcomes = len(new_vals)
            elif 'probability' in line:
                line = line.replace(',', ' ')
                child_rv = line.split()[2]
                parent_rvs = line.split()[4:-2]

                if len(parent_rvs) == 0:  # prior
                    new_line = f.readline().replace(';',
                                                    ' ').replace(',',
                                                                 ' ').split()
                    prob_values = new_line[1:]
                    _cpt[child_rv].append(map(float, prob_values))
                    #_cpt[child_rv] = map(float,prob_values)
                else:  # not a prior
                    _parents[child_rv].extend(list(parent_rvs))
                    while True:
                        new_line = f.readline()
                        if '}' in new_line:
                            break
                        new_line = new_line.replace(',', ' ').replace(
                            ';', ' ').replace('(', ' ').replace(')',
                                                                ' ').split()
                        prob_values = new_line[-(len(_vals[new_vertex])):]
                        prob_values = map(float, prob_values)
                        _cpt[child_rv].append(prob_values)
            if line == '':
                break

    # CREATE FACTORS
    _F = {}
    _E = {}
    for rv in _vals.keys():
        _E[rv] = [c for c in _vals.keys() if rv in _parents[c]]
        f = {
            'parents': _parents[rv],
            'values': _vals[rv],
            'cpt': [item for sublist in _cpt[rv] for item in sublist]
        }
        _F[rv] = f

    bn = BayesNet()
    bn.F = _F
    bn.E = _E
    bn.V = list(topsort(_E))

    return bn
Beispiel #4
0
def read_json(path):
    """
    Read a BayesNet object from the json format. This
    format has the ".bn" extension and is completely
    unique to neuroBN.

    Arguments
    ---------
    *path* : a string
        The file path

    Returns
    -------
    None

    Effects
    -------
    - Instantiates and sets a new BayesNet object

    Notes
    -----
    
    This function reads in a libpgm-style format into a bn object

    File Format:
        {
            "V": ["Letter", "Grade", "Intelligence", "SAT", "Difficulty"],
            "E": [["Intelligence", "Grade"],
                ["Difficulty", "Grade"],
                ["Intelligence", "SAT"],
                ["Grade", "Letter"]],
            "Vdata": {
                "Letter": {
                    "ord": 4,
                    "numoutcomes": 2,
                    "vals": ["weak", "strong"],
                    "parents": ["Grade"],
                    "children": None,
                    "cprob": [[.1, .9],[.4, .6],[.99, .01]]
                },
                ...
        }


    """
    def byteify(input):
        if isinstance(input, dict):
            return {
                byteify(key): byteify(value)
                for key, value in input.iteritems()
            }
        elif isinstance(input, list):
            return [byteify(element) for element in input]
        elif isinstance(input, unicode):
            return input.encode('utf-8')
        else:
            return input

    bn = BayesNet()

    f = open(path, 'r')
    ftxt = f.read()

    success = False
    try:
        data = byteify(json.loads(ftxt))
        bn.V = data['V']
        bn.E = data['E']
        bn.F = data['F']
        success = True
    except ValueError:
        print "Could not read file - check format"
    bn.V = topsort(bn.E)

    return bn
Beispiel #5
0
def read_bif(path):
    """
    This function reads a .bif file into a
    BayesNet object. It's probably not the 
    fastest or prettiest but it gets the job
    done.

    Arguments
    ---------
    *path* : a string
        The path

    Returns
    -------
    *bn* : a BayesNet object

    Effects
    -------
    None

    Notes
    -----
    *V* : a list of strings
    *E* : a dict, where key = vertex, val = list of its children
    *F* : a dict, where key = rv, val = another dict with
                keys = 'parents', 'values', cpt'

    """
    _parents = {}  # key = vertex, value = list of vertices in the scope (includind itself)
    _cpt = {}  # key = vertex, value = list (then numpy array)
    _vals = {}  # key=vertex, val=list of its possible values

    with open(path, "r") as f:
        while True:
            line = f.readline()
            if "variable" in line:
                new_vertex = line.split()[1]

                _parents[new_vertex] = []
                _cpt[new_vertex] = []
                # _vals[new_vertex] = []

                new_line = f.readline()
                new_vals = new_line.replace(",", " ").split()[6:-1]  # list of vals
                _vals[new_vertex] = new_vals
                num_outcomes = len(new_vals)
            elif "probability" in line:
                line = line.replace(",", " ")
                child_rv = line.split()[2]
                parent_rvs = line.split()[4:-2]

                if len(parent_rvs) == 0:  # prior
                    new_line = f.readline().replace(";", " ").replace(",", " ").split()
                    prob_values = new_line[1:]
                    _cpt[child_rv].append(map(float, prob_values))
                    # _cpt[child_rv] = map(float,prob_values)
                else:  # not a prior
                    _parents[child_rv].extend(list(parent_rvs))
                    while True:
                        new_line = f.readline()
                        if "}" in new_line:
                            break
                        new_line = (
                            new_line.replace(",", " ").replace(";", " ").replace("(", " ").replace(")", " ").split()
                        )
                        prob_values = new_line[-(len(_vals[new_vertex])) :]
                        prob_values = map(float, prob_values)
                        _cpt[child_rv].append(prob_values)
            if line == "":
                break

    # CREATE FACTORS
    _F = {}
    _E = {}
    for rv in _vals.keys():
        _E[rv] = [c for c in _vals.keys() if rv in _parents[c]]
        f = {"parents": _parents[rv], "values": _vals[rv], "cpt": [item for sublist in _cpt[rv] for item in sublist]}
        _F[rv] = f

    bn = BayesNet()
    bn.F = _F
    bn.E = _E
    bn.V = list(topsort(_E))

    return bn
Beispiel #6
0
def read_json(path):
    """
    Read a BayesNet object from the json format. This
    format has the ".bn" extension and is completely
    unique to neuroBN.

    Arguments
    ---------
    *path* : a string
        The file path

    Returns
    -------
    None

    Effects
    -------
    - Instantiates and sets a new BayesNet object

    Notes
    -----
    
    This function reads in a libpgm-style format into a bn object

    File Format:
        {
            "V": ["Letter", "Grade", "Intelligence", "SAT", "Difficulty"],
            "E": [["Intelligence", "Grade"],
                ["Difficulty", "Grade"],
                ["Intelligence", "SAT"],
                ["Grade", "Letter"]],
            "Vdata": {
                "Letter": {
                    "ord": 4,
                    "numoutcomes": 2,
                    "vals": ["weak", "strong"],
                    "parents": ["Grade"],
                    "children": None,
                    "cprob": [[.1, .9],[.4, .6],[.99, .01]]
                },
                ...
        }


    """

    def byteify(input):
        if isinstance(input, dict):
            return {byteify(key): byteify(value) for key, value in input.iteritems()}
        elif isinstance(input, list):
            return [byteify(element) for element in input]
        elif isinstance(input, unicode):
            return input.encode("utf-8")
        else:
            return input

    bn = BayesNet()

    f = open(path, "r")
    ftxt = f.read()

    success = False
    try:
        data = byteify(json.loads(ftxt))
        bn.V = data["V"]
        bn.E = data["E"]
        bn.F = data["F"]
        success = True
    except ValueError:
        print "Could not read file - check format"
    bn.V = topsort(bn.E)

    return bn