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Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser diff --git a/BNReasoner.py b/BNReasoner.py index 34975ac6..d6ccfb68 100644 --- a/BNReasoner.py +++ b/BNReasoner.py @@ -1,18 +1,356 @@ -from typing import Union -from BayesNet import BayesNet - - -class BNReasoner: - def __init__(self, net: Union[str, BayesNet]): - """ - :param net: either file path of the bayesian network in BIFXML format or BayesNet object - """ - if type(net) == str: - # constructs a BN object - self.bn = BayesNet() - # Loads the BN from an BIFXML file - self.bn.load_from_bifxml(net) - else: - self.bn = net - - # TODO: This is where your methods should go +from typing import Union, Dict, List +from xmlrpc.client import Boolean +from BayesNet import BayesNet +from copy import deepcopy + +import numpy as np +import pandas as pd +from typing import Dict, List, Optional, Tuple, Union + +from itertools import product, combinations + +class BNReasoner: + def __init__(self, net: Union[str, BayesNet]): + if type(net) == str: + # constructs a BN object + self.bn = BayesNet() + # Loads the BN from an BIFXML file + self.bn.load_from_bifxml(net) + else: + self.bn = net + + # TODO: This is where your methods should go + def pruning(self,query: List[str], values: Dict[str, bool]) -> BayesNet: + + bayes_net = deepcopy(self.bn) + finished = True # Continue checking if the pruning can continue + all_cpts = bayes_net.get_all_cpts() # A dictionary of all cps in the network indexed by the variable they belong to + while finished: # Stop pruning when the network cannot be pruned further + finished = False + for var in bayes_net.get_all_variables(): + if bayes_net.get_children(var) == [] and var not in query and var not in values: # Check if node is part of query + bayes_net.del_var(var) # Delete node if not part of query + finished = True + for a, b in values.items(): # Get node name and value + for variable in bayes_net.get_all_variables(): # Get all variables + cpt = all_cpts[variable] + if a in cpt.columns: # If variable from input matches a variable from a cpt column: + new_cpt = cpt.drop(cpt[cpt[a] != b].index) # If the same node exist with the opposite value, delete it + bayes_net.update_cpt(variable, new_cpt) # Update the cpt table without the opposite value + else: + continue + for child in bayes_net.get_children(a): # Check if the removed node has children, if yes delete edge + bayes_net.del_edge((a, child)) # Delete edge if input variable has child + finished = True + print(bayes_net.get_all_cpts()," dit is p") + return bayes_net + + def d_separation(self, x: List[str], y: List[str], z: List[str]) -> bool: + bayes_net = deepcopy(self.bn) + reach_single = [] + iterated = [] + + for var in bayes_net.get_all_variables(): + if bayes_net.get_children(var) == [] and var not in x and var not in y and var not in z: + bayes_net.del_var(var) + for var in z: + for child in bayes_net.get_children(var): + bayes_net.del_edge([var, child]) + for var in x: + iterated.append(var) # Append variables to check + reach_single.extend(bayes_net.get_children(var)) # Append children + while list(set(reach_single) - set(iterated)) !=[]: # While loop makes sure children of children are checked + for a in list(set(reach_single) - set(iterated)): # Make sure all children are checked + reach_single.extend(bayes_net.get_children(a)) # Append children of children if there are any + iterated.append(a) # Append variable to prevent loop from checking again + for var_2 in y: + if var_2 in reach_single: # If y in x, not d-seperated + print(x, 'and', y, 'are not d-separated by', z) + return False + print(x, 'and', y, 'are d-separated by', z) # Else d-seperated + return True + + def random_order(self, network: BayesNet = None) -> List[str]: + if network is None: + return list(np.random.permutation(self.bn.get_all_variables())) + else: + return list(np.random.permutation(network.get_all_variables())) + + def min_degree(self, network: BayesNet = None) -> List[str]: + if network is None: + bayes_net = deepcopy(self.bn) + else: + bayes_net = deepcopy(network) + + return [x[0] for x in sorted(bayes_net.get_interaction_graph().degree(), key = lambda x: x[1])] # Sort list and only return the variable name + + def min_fill(self, network: BayesNet = None) -> List[str]: + if network is None: + bayes_net = deepcopy(self.bn) + else: + bayes_net = deepcopy(network) + + i_graph = bayes_net.get_interaction_graph() + order_edges = [] + for node in i_graph: + i = 0 + neighbors = i_graph.neighbors(node) + for a in neighbors: + neigh_a = i_graph.neighbors(a) + for b in neighbors: + if a == b: # Do nothing + continue + if b not in neigh_a: # Count if b is not in neigh_a + i += 1 + order_edges.append((node, i)) + return [x[0] for x in sorted(order_edges, key=lambda x: x[1])] # Sort the list and return only the variable name + + def init_factor(self, variables: List[str], value=0) -> pd.DataFrame: + truth_table = product([True, False], repeat=len(variables)) + factor = pd.DataFrame(truth_table, columns=variables) + factor['p'] = value + return factor + + def sum_out_factors(self, factor: Union[str, pd.DataFrame], subset: Union[str, list]) -> pd.DataFrame: + if isinstance(factor, str): + factor = self.bn.get_cpt(factor) + if isinstance(subset, str): + subset = [subset] + + new_factor = factor.drop(subset + ['p'], axis=1).drop_duplicates() + new_factor['p'] = 0 + subset_factor = self.init_factor(subset) + + for i, y in new_factor.iterrows(): + for _, z in subset_factor.iterrows(): + new_factor.loc[i, 'p'] = new_factor.loc[i, 'p'] + self.bn.get_compatible_instantiations_table( + y[:-1].append(z[:-1]), factor)['p'].sum() + # sum() instead of float() here, since the compatible table can be empty at times, this works around it + + return new_factor.reset_index(drop=True) + + def maximise_out(self, factor: Union[str, pd.DataFrame], subset: Union[str, list]) -> pd.DataFrame: + if isinstance(factor, str): + factor = self.bn.get_cpt(factor) + if isinstance(subset, str): + subset = [subset] + + # Copy the factor, drop the variable(s) to be maximized, the extensions and 'p' and drop all duplicates to get + # each possible instantiation. + ext = [c for c in factor.columns if c[:3] == 'ext'] + instantiations = deepcopy(factor).drop(subset + ext + ['p'], axis=1).drop_duplicates() + res_factor = pd.DataFrame(columns=factor.columns) + + if len(instantiations.columns) == 0: + try: + res_factor = res_factor.append(factor.iloc[factor['p'].idxmax()]) + except IndexError: + print('w') + else: + for _, instantiation in instantiations.iterrows(): + cpt = self.bn.get_compatible_instantiations_table(instantiation, factor) + res_factor = res_factor.append(factor.iloc[cpt['p'].idxmax()]) + + # For each maximized-out variable(s), rename them to ext(variable) + for v in subset: + x = res_factor.pop(v) + res_factor[f'ext({v})'] = x + + return res_factor.reset_index(drop=True) + + + def factor_multiplication(self, factors: List[Union[str, pd.DataFrame]]) -> pd.DataFrame: + # If there are strings in the input-list of factors, replace them with the corresponding cpt + for x, y in enumerate(factors): + if isinstance(y, str): + factors[x] = self.bn.get_cpt(y) + + new_factor = factors[0].drop('p', axis=1) + for i, factor in enumerate(factors[1:]): + try: + new_factor = new_factor.merge(factor.drop('p', axis=1), how='outer') + except pd.errors.MergeError: + new_factor = new_factor.join(factor.drop('p', axis=1), how='outer') + new_factor['p'] = 1 + + for i, z in new_factor.iterrows(): + for _, f in enumerate(factors): + new_factor.loc[i, 'p'] = new_factor.loc[i, 'p'] * self.bn.get_compatible_instantiations_table( + z[:-1], f)['p'].sum() + # sum() instead of float() here, since the compatible table can be empty at times, this works around it + + # Reordering new_factor, putting the extensions to the back + cols = new_factor.columns + ext = [c for c in cols if c[:3] == 'ext'] + if len(ext) > 0: + rest = list(np.setdiff1d(cols, ext)) + new_factor = new_factor[rest + ext] + + return new_factor.reset_index(drop=True) + + + def MPE(self, evidence: pd.Series, order_func: str = None) -> pd.DataFrame: + N = deepcopy(self.bn) + # Prune Edges + for var in evidence.keys(): + for child in N.get_children(var): + N.del_edge((var, child)) + + new = N.get_compatible_instantiations_table(evidence, N.get_cpt(child)).reset_index(drop=True) + new = new.drop([var], axis=1) + N.update_cpt(child, new) + u = N.get_compatible_instantiations_table(evidence, N.get_cpt(var)).reset_index(drop=True) + N.update_cpt(var, u) + + if order_func is None or order_func == "random": + order = self.random_order(N) + elif order_func == "min_degree": + order = self.min_degree(N) + elif order_func == "min_fill": + order = self.min_fill(N) + else: + raise Exception("Wrong order argument") + + S = N.get_all_cpts() + for var_pi in order: + # Pop all functions from S, which mention var_pi... + func_k = [S.pop(key) for key, cpt in deepcopy(S).items() if var_pi in cpt] + + new_factor = self.factor_multiplication(func_k) if len(func_k) > 1 else func_k[0] + new_factor = self.maximise_out(new_factor, var_pi) + + # And replace them with the new factor + S[var_pi] = new_factor + + res_factor = self.factor_multiplication(list(S.values())) if len(S) > 1 else S.popitem()[1] + return res_factor + + def MAP(self, M: List[str], evidence: pd.Series, order_func: str = None) -> pd.DataFrame: + if len(np.intersect1d(list(evidence.keys()), M)) > 0: + raise Exception("Evidence cannot intersect with M") + + N = deepcopy(self.bn) + N = self.pruning(M, evidence) + for e in evidence.items(): + N.update_cpt(e[0], self.bn.get_compatible_instantiations_table(evidence, + N.get_cpt(e[0])).reset_index(drop=True)) + + if order_func is None or order_func == "random": + order = self.random_order(N) + elif order_func == "min_degree": + order = self.min_degree(N) + elif order_func == "min_fill": + order = self.min_fill(N) + else: + raise Exception("Wrong order argument") + + S = N.get_all_cpts() + for var_pi in order: + # Pop all functions from S, which mention var_pi... + func_k = [S.pop(key) for key, cpt in deepcopy(S).items() if var_pi in cpt] + + new_factor = self.factor_multiplication(func_k) if len(func_k) > 1 else func_k[0] + if var_pi in M: + new_factor = self.maximise_out(new_factor, var_pi) + else: + new_factor = self.sum_out_factors(new_factor, var_pi) + # And replace them with the new factor + # print(new_factor) + S[var_pi] = new_factor + + + res_factor = self.factor_multiplication(list(S.values())) if len(S) > 1 else S.popitem()[1] + return res_factor + + def compute_marginal(self, query: List[str], evidence: pd.Series = None, order: List[str] = None) -> pd.DataFrame: + if order is None: + order = self.random_order() + + S = self.bn.get_all_cpts() + + if evidence is not None: # If there's evidence, reduce all CPTs using the evidence + for var in self.bn.get_all_variables(): + var_cpt = self.bn.get_cpt(var) + if any(evidence.keys().intersection(var_cpt.columns)): # If the evidence occurs in the cpt + new_cpt = self.bn.get_compatible_instantiations_table(evidence, var_cpt) + S[var] = new_cpt + + pi = [nv for nv in order if nv not in query] + for var_pi in pi: + # Pop all functions from S, which mention var_pi... + func_k = [S.pop(key) for key, cpt in deepcopy(S).items() if var_pi in cpt] + + new_factor = self.factor_multiplication(func_k) + new_factor = self.sum_out_factors(new_factor, var_pi) + # And replace them with the new factor + S[var_pi] = new_factor + + res_factor = self.factor_multiplication(list(S.values())) if len(S) > 1 else S.popitem()[1] + + if evidence is not None: # Normalizing over pr_evidence + cpt_e = self.compute_marginal(list(evidence.keys()), order=order) + pr_evidence = float(self.bn.get_compatible_instantiations_table(evidence, cpt_e)['p']) + res_factor['p'] = res_factor['p'] / pr_evidence + + return res_factor + + def elimination(self, data: pd.DataFrame, var: List[str]) -> pd.DataFrame: + print(data, "initial dataframe") + remaining = data.drop(columns=var) # Drop column thats need to be summed out + rem_list = list(remaining.columns.values)[:-1] # Get remaining dataframe + if len(rem_list) == 0: # If empty return empty frame + print("This was the only variable, thus no data could be returned") + return pd.DataFrame() + eliminated = remaining.groupby( # Else sum of matching values of variables + rem_list).aggregate({'p': 'sum'}) + eliminated.reset_index(inplace=True) + return eliminated + + + def loop_over_children(self,bn, y, parent): + # print("parent: ", parent) + children = BayesNet.get_children(bn, parent) + # print("children: ", children) + if len(children) == 0: + return True + else: + for child in children: + if child == y: + return False + else: + if not self.loop_over_children(bn, y, child): + return False + return True + + def check_independence(self, bn, X, Y, Z): + Not_all_parents_of_X = False + for x in X: + for parent in BayesNet.get_all_variables(bn): + if x in BayesNet.get_children(bn, parent): + if parent not in Z: + Not_all_parents_of_X = True + break + if Not_all_parents_of_X: + break + + if Not_all_parents_of_X: + for y in Y: + for parent in BayesNet.get_all_variables(bn): + if y in BayesNet.get_children(bn, parent): + if parent not in Z: + return False + for y in Y: + # print(y) + for x in X: + # print(self.loop_over_children(bn, x, y)) + if not self.loop_over_children(bn, x, y): + # print(x, "is not a descendent of ", y) + return False + return True + + for x in X: + for y in Y: + if not self.loop_over_children(bn, y, x): + return False + + return True diff --git a/BayesNet.py b/BayesNet.py index 845d7e32..0f4023d8 100644 --- a/BayesNet.py +++ b/BayesNet.py @@ -1,228 +1,228 @@ -from typing import List, Tuple, Dict -import networkx as nx -import matplotlib.pyplot as plt -from pgmpy.readwrite import XMLBIFReader -import math -import itertools -import pandas as pd -from copy import deepcopy - - -class BayesNet: - - def __init__(self) -> None: - # initialize graph structure - self.structure = nx.DiGraph() - - # LOADING FUNCTIONS ------------------------------------------------------------------------------------------------ - def create_bn(self, variables: List[str], edges: List[Tuple[str, str]], cpts: Dict[str, pd.DataFrame]) -> None: - """ - Creates the BN according to the python objects passed in. - - :param variables: List of names of the variables. - :param edges: List of the directed edges. - :param cpts: Dictionary of conditional probability tables. - """ - # add nodes - [self.add_var(v, cpt=cpts[v]) for v in variables] - - # add edges - [self.add_edge(e) for e in edges] - - # check for cycles - if not nx.is_directed_acyclic_graph(self.structure): - raise Exception('The provided graph is not acyclic.') - - def load_from_bifxml(self, file_path: str) -> None: - """ - Load a BayesNet from a file in BIFXML file format. See description of BIFXML here: - http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/ - - :param file_path: Path to the BIFXML file. - """ - # Read and parse the bifxml file - with open(file_path) as f: - bn_file = f.read() - bif_reader = XMLBIFReader(string=bn_file) - - # load cpts - cpts = {} - # iterating through vars - for key, values in bif_reader.get_values().items(): - values = values.transpose().flatten() - n_vars = int(math.log2(len(values))) - worlds = [list(i) for i in itertools.product([False, True], repeat=n_vars)] - # create empty array - cpt = [] - # iterating through worlds within a variable - for i in range(len(values)): - # add the probability to each possible world - worlds[i].append(values[i]) - cpt.append(worlds[i]) - - # determine column names - columns = bif_reader.get_parents()[key] - columns.reverse() - columns.append(key) - columns.append('p') - cpts[key] = pd.DataFrame(cpt, columns=columns) - - # load vars - variables = bif_reader.get_variables() - - # load edges - edges = bif_reader.get_edges() - - self.create_bn(variables, edges, cpts) - - # METHODS THAT MIGHT ME USEFUL ------------------------------------------------------------------------------------- - - def get_children(self, variable: str) -> List[str]: - """ - Returns the children of the variable in the graph. - :param variable: Variable to get the children from - :return: List of children - """ - return [c for c in self.structure.successors(variable)] - - def get_cpt(self, variable: str) -> pd.DataFrame: - """ - Returns the conditional probability table of a variable in the BN. - :param variable: Variable of which the CPT should be returned. - :return: Conditional probability table of 'variable' as a pandas DataFrame. - """ - try: - return self.structure.nodes[variable]['cpt'] - except KeyError: - raise Exception('Variable not in the BN') - - def get_all_variables(self) -> List[str]: - """ - Returns a list of all variables in the structure. - :return: list of all variables. - """ - return [n for n in self.structure.nodes] - - def get_all_cpts(self) -> Dict[str, pd.DataFrame]: - """ - Returns a dictionary of all cps in the network indexed by the variable they belong to. - :return: Dictionary of all CPTs - """ - cpts = {} - for var in self.get_all_variables(): - cpts[var] = self.get_cpt(var) - - return cpts - - def get_interaction_graph(self): - """ - Returns a networkx.Graph as interaction graph of the current BN. - :return: The interaction graph based on the factors of the current BN. - """ - # Create the graph and add all variables - int_graph = nx.Graph() - [int_graph.add_node(var) for var in self.get_all_variables()] - - # connect all variables with an edge which are mentioned in a CPT together - for var in self.get_all_variables(): - involved_vars = list(self.get_cpt(var).columns)[:-1] - for i in range(len(involved_vars)-1): - for j in range(i+1, len(involved_vars)): - if not int_graph.has_edge(involved_vars[i], involved_vars[j]): - int_graph.add_edge(involved_vars[i], involved_vars[j]) - return int_graph - - @staticmethod - def get_compatible_instantiations_table(instantiation: pd.Series, cpt: pd.DataFrame): - """ - Get all the entries of a CPT which are compatible with the instantiation. - - :param instantiation: a series of assignments as tuples. E.g.: pd.Series({"A": True, "B": False}) - :param cpt: cpt to be filtered - :return: table with compatible instantiations and their probability value - """ - var_names = instantiation.index.values - var_names = [v for v in var_names if v in cpt.columns] # get rid of excess variables names - compat_indices = cpt[var_names] == instantiation[var_names].values - compat_indices = [all(x[1]) for x in compat_indices.iterrows()] - compat_instances = cpt.loc[compat_indices] - return compat_instances - - def update_cpt(self, variable: str, cpt: pd.DataFrame) -> None: - """ - Replace the conditional probability table of a variable. - :param variable: Variable to be modified - :param cpt: new CPT - """ - self.structure.nodes[variable]["cpt"] = cpt - - @staticmethod - def reduce_factor(instantiation: pd.Series, cpt: pd.DataFrame) -> pd.DataFrame: - """ - Creates and returns a new factor in which all probabilities which are incompatible with the instantiation - passed to the method to 0. - - :param instantiation: a series of assignments as tuples. E.g.: pd.Series({"A": True, "B": False}) - :param cpt: cpt to be reduced - :return: cpt with their original probability value and zero probability for incompatible instantiations - """ - var_names = instantiation.index.values - var_names = [v for v in var_names if v in cpt.columns] # get rid of excess variables names - if len(var_names) > 0: # only reduce the factor if the evidence appears in it - new_cpt = deepcopy(cpt) - incompat_indices = cpt[var_names] != instantiation[var_names].values - incompat_indices = [any(x[1]) for x in incompat_indices.iterrows()] - new_cpt.loc[incompat_indices, 'p'] = 0.0 - return new_cpt - else: - return cpt - - def draw_structure(self) -> None: - """ - Visualize structure of the BN. - """ - nx.draw(self.structure, with_labels=True, node_size=3000) - plt.show() - - # BASIC HOUSEKEEPING METHODS --------------------------------------------------------------------------------------- - - def add_var(self, variable: str, cpt: pd.DataFrame) -> None: - """ - Add a variable to the BN. - :param variable: variable to be added. - :param cpt: conditional probability table of the variable. - """ - if variable in self.structure.nodes: - raise Exception('Variable already exists.') - else: - self.structure.add_node(variable, cpt=cpt) - - def add_edge(self, edge: Tuple[str, str]) -> None: - """ - Add a directed edge to the BN. - :param edge: Tuple of the directed edge to be added (e.g. ('A', 'B')). - :raises Exception: If added edge introduces a cycle in the structure. - """ - if edge in self.structure.edges: - raise Exception('Edge already exists.') - else: - self.structure.add_edge(edge[0], edge[1]) - - # check for cycles - if not nx.is_directed_acyclic_graph(self.structure): - self.structure.remove_edge(edge[0], edge[1]) - raise ValueError('Edge would make graph cyclic.') - - def del_var(self, variable: str) -> None: - """ - Delete a variable from the BN. - :param variable: Variable to be deleted. - """ - self.structure.remove_node(variable) - - def del_edge(self, edge: Tuple[str, str]) -> None: - """ - Delete an edge form the structure of the BN. - :param edge: Edge to be deleted (e.g. ('A', 'B')). - """ - self.structure.remove_edge(edge[0], edge[1]) +from typing import List, Tuple, Dict +import networkx as nx +import matplotlib.pyplot as plt +from pgmpy.readwrite import XMLBIFReader +import math +import itertools +import pandas as pd +from copy import deepcopy + + +class BayesNet: + + def __init__(self) -> None: + # initialize graph structure + self.structure = nx.DiGraph() + + # LOADING FUNCTIONS ------------------------------------------------------------------------------------------------ + def create_bn(self, variables: List[str], edges: List[Tuple[str, str]], cpts: Dict[str, pd.DataFrame]) -> None: + """ + Creates the BN according to the python objects passed in. + + :param variables: List of names of the variables. + :param edges: List of the directed edges. + :param cpts: Dictionary of conditional probability tables. + """ + # add nodes + [self.add_var(v, cpt=cpts[v]) for v in variables] + + # add edges + [self.add_edge(e) for e in edges] + + # check for cycles + if not nx.is_directed_acyclic_graph(self.structure): + raise Exception('The provided graph is not acyclic.') + + def load_from_bifxml(self, file_path: str) -> None: + """ + Load a BayesNet from a file in BIFXML file format. See description of BIFXML here: + http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/ + + :param file_path: Path to the BIFXML file. + """ + # Read and parse the bifxml file + with open(file_path) as f: + bn_file = f.read() + bif_reader = XMLBIFReader(string=bn_file) + + # load cpts + cpts = {} + # iterating through vars + for key, values in bif_reader.get_values().items(): + values = values.transpose().flatten() + n_vars = int(math.log2(len(values))) + worlds = [list(i) for i in itertools.product([False, True], repeat=n_vars)] + # create empty array + cpt = [] + # iterating through worlds within a variable + for i in range(len(values)): + # add the probability to each possible world + worlds[i].append(values[i]) + cpt.append(worlds[i]) + + # determine column names + columns = bif_reader.get_parents()[key] + columns.reverse() + columns.append(key) + columns.append('p') + cpts[key] = pd.DataFrame(cpt, columns=columns) + + # load vars + variables = bif_reader.get_variables() + + # load edges + edges = bif_reader.get_edges() + + self.create_bn(variables, edges, cpts) + + # METHODS THAT MIGHT ME USEFUL ------------------------------------------------------------------------------------- + + def get_children(self, variable: str) -> List[str]: + """ + Returns the children of the variable in the graph. + :param variable: Variable to get the children from + :return: List of children + """ + return [c for c in self.structure.successors(variable)] + + def get_cpt(self, variable: str) -> pd.DataFrame: + """ + Returns the conditional probability table of a variable in the BN. + :param variable: Variable of which the CPT should be returned. + :return: Conditional probability table of 'variable' as a pandas DataFrame. + """ + try: + return self.structure.nodes[variable]['cpt'] + except KeyError: + raise Exception('Variable not in the BN') + + def get_all_variables(self) -> List[str]: + """ + Returns a list of all variables in the structure. + :return: list of all variables. + """ + return [n for n in self.structure.nodes] + + def get_all_cpts(self) -> Dict[str, pd.DataFrame]: + """ + Returns a dictionary of all cps in the network indexed by the variable they belong to. + :return: Dictionary of all CPTs + """ + cpts = {} + for var in self.get_all_variables(): + cpts[var] = self.get_cpt(var) + + return cpts + + def get_interaction_graph(self): + """ + Returns a networkx.Graph as interaction graph of the current BN. + :return: The interaction graph based on the factors of the current BN. + """ + # Create the graph and add all variables + int_graph = nx.Graph() + [int_graph.add_node(var) for var in self.get_all_variables()] + + # connect all variables with an edge which are mentioned in a CPT together + for var in self.get_all_variables(): + involved_vars = list(self.get_cpt(var).columns)[:-1] + for i in range(len(involved_vars)-1): + for j in range(i+1, len(involved_vars)): + if not int_graph.has_edge(involved_vars[i], involved_vars[j]): + int_graph.add_edge(involved_vars[i], involved_vars[j]) + return int_graph + + @staticmethod + def get_compatible_instantiations_table(instantiation: pd.Series, cpt: pd.DataFrame): + """ + Get all the entries of a CPT which are compatible with the instantiation. + + :param instantiation: a series of assignments as tuples. E.g.: pd.Series({"A": True, "B": False}) + :param cpt: cpt to be filtered + :return: table with compatible instantiations and their probability value + """ + var_names = instantiation.index.values + var_names = [v for v in var_names if v in cpt.columns] # get rid of excess variables names + compat_indices = cpt[var_names] == instantiation[var_names].values + compat_indices = [all(x[1]) for x in compat_indices.iterrows()] + compat_instances = cpt.loc[compat_indices] + return compat_instances + + def update_cpt(self, variable: str, cpt: pd.DataFrame) -> None: + """ + Replace the conditional probability table of a variable. + :param variable: Variable to be modified + :param cpt: new CPT + """ + self.structure.nodes[variable]["cpt"] = cpt + + @staticmethod + def reduce_factor(instantiation: pd.Series, cpt: pd.DataFrame) -> pd.DataFrame: + """ + Creates and returns a new factor in which all probabilities which are incompatible with the instantiation + passed to the method to 0. + + :param instantiation: a series of assignments as tuples. E.g.: pd.Series({"A": True, "B": False}) + :param cpt: cpt to be reduced + :return: cpt with their original probability value and zero probability for incompatible instantiations + """ + var_names = instantiation.index.values + var_names = [v for v in var_names if v in cpt.columns] # get rid of excess variables names + if len(var_names) > 0: # only reduce the factor if the evidence appears in it + new_cpt = deepcopy(cpt) + incompat_indices = cpt[var_names] != instantiation[var_names].values + incompat_indices = [any(x[1]) for x in incompat_indices.iterrows()] + new_cpt.loc[incompat_indices, 'p'] = 0.0 + return new_cpt + else: + return cpt + + def draw_structure(self) -> None: + """ + Visualize structure of the BN. + """ + nx.draw(self.structure, with_labels=True, node_size=3000) + plt.show() + + # BASIC HOUSEKEEPING METHODS --------------------------------------------------------------------------------------- + + def add_var(self, variable: str, cpt: pd.DataFrame) -> None: + """ + Add a variable to the BN. + :param variable: variable to be added. + :param cpt: conditional probability table of the variable. + """ + if variable in self.structure.nodes: + raise Exception('Variable already exists.') + else: + self.structure.add_node(variable, cpt=cpt) + + def add_edge(self, edge: Tuple[str, str]) -> None: + """ + Add a directed edge to the BN. + :param edge: Tuple of the directed edge to be added (e.g. ('A', 'B')). + :raises Exception: If added edge introduces a cycle in the structure. + """ + if edge in self.structure.edges: + raise Exception('Edge already exists.') + else: + self.structure.add_edge(edge[0], edge[1]) + + # check for cycles + if not nx.is_directed_acyclic_graph(self.structure): + self.structure.remove_edge(edge[0], edge[1]) + raise ValueError('Edge would make graph cyclic.') + + def del_var(self, variable: str) -> None: + """ + Delete a variable from the BN. + :param variable: Variable to be deleted. + """ + self.structure.remove_node(variable) + + def del_edge(self, edge: Tuple[str, str]) -> None: + """ + Delete an edge form the structure of the BN. + :param edge: Edge to be deleted (e.g. ('A', 'B')). + """ + self.structure.remove_edge(edge[0], edge[1]) diff --git a/MAP.jpg b/MAP.jpg new file mode 100644 index 00000000..9232eb49 Binary files /dev/null and b/MAP.jpg differ diff --git a/MPE.jpg b/MPE.jpg new file mode 100644 index 00000000..c5044db1 Binary files /dev/null and b/MPE.jpg differ diff --git a/Part2.py b/Part2.py new file mode 100644 index 00000000..2388bdee --- /dev/null +++ b/Part2.py @@ -0,0 +1,64 @@ +from BNReasoner import BNReasoner +from BayesNet import BayesNet +import pandas as pd +import time +import os +import warnings +warnings.simplefilter(action='ignore', category=FutureWarning) + +def main(folder_name, output_folder): + node_counts = range(10, 51, 2) + random_list = [] + md_list = [] + mf_list = [] + random_list2 = [] + md_list2 = [] + mf_list2 = [] + + for node_count in node_counts: + net = folder_name +"/network_"+str(node_count)+".XMLBIF" + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + print(node_count) + methods = ['random', 'min_degree', 'min_fill'] + variable_list = bn.get_all_variables() + + for method in methods: + start = time.time() + outcome_mpe = bnr.MPE(pd.Series({variable_list[-1]: True, variable_list[-2]: True}),method) + end = time.time() + total_time = end - start + if(method == 'random'): + random_list.append(total_time) + elif(method == 'min_degree'): + md_list.append(total_time) + elif(method == 'min_fill'): + mf_list.append(total_time) + + start = time.time() + outcome_map = bnr.MAP([variable_list[0], variable_list[1]], pd.Series({variable_list[-1]: True,variable_list[-2]: True}),method) + end = time.time() + total_time = end - start + + if(method == 'random'): + random_list2.append(total_time) + elif(method == 'min_degree'): + md_list2.append(total_time) + elif(method == 'min_fill'): + mf_list2.append(total_time) + + + df1 = pd.DataFrame({'Node_Count': node_counts, 'Random': random_list, 'MinDegree': md_list, 'MinFill': mf_list}) + df2 = pd.DataFrame({'Node_Count': node_counts, 'Random': random_list2, 'MinDegree': md_list2, 'MinFill': mf_list2}) + + + if not os.path.exists(output_folder): + os.makedirs(output_folder) + df1.to_excel(output_folder+'/MPE.xlsx', index=False) + df2.to_excel(output_folder+'/MAP.xlsx', index=False) + +if __name__ == "__main__": + folder_name = "testing/Part_2" + output_folder = "Output" + main(folder_name, output_folder) \ No newline at end of file diff --git a/README.md b/README.md index 840e4302..0e159732 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,27 @@ -# Useful Pointers for Assignment 2 of KR21 +######### How to run the code ############ + +# PART 1 + +1. Run "python test.py" +2. For all methods the condition is True. If you want to run for specific helper method then make the other varibles false. + + +# Part 2 + +1. generate_bn.py is used to create random baysian networks. +2. folder testing->Part_2 consists of random baysian network part 2 was tested on. +3. to run the script to get the runtime of MAP and MPE for 3 different heuristics, run "python Part2.py" which will dump 2 excel in output folder. + +# Part 3 + +1. run "python use_case.py" + + + + + +# Useful Pointers for Assignment 2 of KR21 ## BIFXML file format The BIFXML file format is meant to provide an easy means of exchanging Bayesian networks. It works with standard XML tags. The detailed description of the format can be found at @@ -88,3 +110,4 @@ Beware that the returned value is always a tuple of (row_number, row_content). ## Networkx methods It is likely, that you will not have to use this package at all. One possible methods that could be useful is `networkx.neighbors(G, var)` which provides a list of all neighbors of the variable 'var' in the graph 'G'. + diff --git a/generate_bn.py b/generate_bn.py new file mode 100644 index 00000000..a811ab19 --- /dev/null +++ b/generate_bn.py @@ -0,0 +1,29 @@ +# import networkx as nx +import numpy as np +from pgmpy.models import BayesianNetwork + + +# Set random seed for reproducibility +np.random.seed(123) + +# Set the range of node numbers to generate networks for +start = 10 +end = 50 +skip = 3 +node_counts = range(start, end+1, skip) + +# Set the number of networks to generate for each node number +n_networks = 1 + +# Set the probability of an edge between any two nodes +edge_prob1 = 0.03 + +# Iterate over the range of node numbers +for n_node in node_counts: + + # Iterate over the number of networks + for i in range(n_networks): + + G = BayesianNetwork.get_random(n_nodes=n_node, edge_prob=edge_prob1, n_states=2) + + G.save(f'testing/Part_2/network_{n_node}.XMLBIF', filetype='XMLBIF') diff --git a/requirements.txt b/requirements.txt index f60dda17..7fe177fb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -networkx~=2.6.3 -matplotlib~=3.4.3 -pgmpy~=0.1.16 +networkx~=2.6.3 +matplotlib~=3.4.3 +pgmpy~=0.1.16 pandas~=1.3.4 \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 00000000..653695b5 --- /dev/null +++ b/test.py @@ -0,0 +1,158 @@ +from BNReasoner import BNReasoner +from BayesNet import BayesNet +import pandas as pd +import networkx as nx +import matplotlib.pyplot as plt +import numpy as np +import warnings +warnings.filterwarnings("ignore") + +if __name__ == "__main__": + + Pruning = True + check_d_separation = True + Independence = True + Marginalization = True + MaxingOut = True + FactorMultiplication = True + Ordering_min_degree = True + Ordering_min_fill = True + Elimination = True + Marginal_distribution = True + checkMAP = True + checkMEP = True + + ### Test pruning + if Pruning: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + print("#----------------------------------------Pruning---------------------------------------#") + outcome = bnr.pruning(["Wet Grass?"],{'Rain?': False,'Winter?':True}) + print(outcome) + + ### D-seperation + if check_d_separation: + net = 'testing/dog_problem.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + #outcome_d_not = bnr.d_separation(["family-out"], ["hear-bark"], ["light-on"]) # Not seperated + print("#-----------------------------------------D-seperation---------------------------------#") + outcome_d = bnr.d_separation(["family-out"], ["bowel-problem"], ["light-on"]) # Seperated + + print(outcome_d) + + ## Test independence + if Independence: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + Y = {"Winter?"} + X = {"Slippery Road?"} + Z = {} + if bnr.check_independence(bnr.bn, X,Y,Z): + print("#--------------------------------Independence--------------------------------------#") + print(X, "is independent from ", Y, "given ", Z) + else: + print("#--------------------------------Independence--------------------------------------#") + print(X, "is not independent from ", Y, "given ", Z) + + ### Test marginalization + if Marginalization: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + X = "Wet Grass?" + Y = BayesNet.get_cpt(bnr.bn,X) + outcome_marg = bnr.sum_out_factors(Y,X) + print("#-------------------------------Marginalization--------------------------------------#") + print(outcome_marg) + + ### Test maxing out + if MaxingOut: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + X = "Wet Grass?" + Y = BayesNet.get_cpt(bnr.bn,X) + outcome_max = bnr.maximise_out(Y,X) + print("#-------------------------------Maxing out-------------------------------------------#") + print(outcome_max) + + ### Test factor multiplication + if FactorMultiplication: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_factor = bnr.factor_multiplication(['Rain?', 'Wet Grass?']) + print("#-------------------------------Factor multiplication-------------------------------#") + print(outcome_factor) + + ### Test ordering min + if Ordering_min_degree: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_min_degree = bnr.min_degree() + print("#-------------------------------Ordering min degree----------------------------------#") + print(outcome_min_degree) + + + ### Test ordering min fill + if Ordering_min_fill: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_min_fill = bnr.min_fill() + print("#--------------------------------Ordering min fill----------------------------------#") + print(outcome_min_fill) + + ### Elimination + if Elimination: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_elim = bnr.elimination(bn.get_cpt('Wet Grass?'), ['Rain?']) + print("#-------------------------------Elimination---------------------------------------#") + print(outcome_elim) + + ### Marginal distribution + if Marginal_distribution: + net = 'testing/lecture_example.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_md = bnr.compute_marginal(['Wet Grass?', 'Slippery Road?'], order=bnr.min_degree()) + print("#-------------------------------Marginal distribution-----------------------------#") + print(outcome_md) + + ### MAP + if checkMAP: + net = 'testing/lecture_example2.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_map = bnr.MAP(['I', 'J'], pd.Series({'O': True})) + print("#-------------------------------------MAP-----------------------------------------#") + print(outcome_map) + + + ## MPE + if checkMEP: + net = 'testing/lecture_example2.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(net) + outcome_mpe = bnr.MPE(pd.Series({'J': True, 'O': False})) + print("#-------------------------------------MPE-----------------------------------------#") + print(outcome_mpe) + diff --git a/testing/Part_2/network_10.XMLBIF b/testing/Part_2/network_10.XMLBIF new file mode 100644 index 00000000..4682195f --- /dev/null +++ b/testing/Part_2/network_10.XMLBIF @@ -0,0 +1,108 @@ + + + + UNTITLED + + 0 + 0 + 1 + weight = None + + + 1 + 0 + 1 + weight = None + + + 2 + 0 + 1 + weight = None + + + 3 + 0 + 1 + weight = None + + + 4 + 0 + 1 + weight = None + + + 5 + 0 + 1 + weight = None + + + 6 + 0 + 1 + weight = None + + + 7 + 0 + 1 + weight = None + + + 8 + 0 + 1 + weight = None + + + 9 + 0 + 1 + weight = None + + + 0 + 0.4349455206174165 0.5650544793825836
+ + + 1 + 0.1710485664667887 0.8289514335332113
+ + + 2 + 0.380084503251582 0.6199154967484181
+ + + 3 + 0.5259154670781337 0.47408453292186625
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+ + + diff --git a/use_case.py b/use_case.py new file mode 100644 index 00000000..9c51f932 --- /dev/null +++ b/use_case.py @@ -0,0 +1,65 @@ +from BNReasoner import BNReasoner +from BayesNet import BayesNet +import pandas as pd +import warnings +warnings.filterwarnings("ignore") + +if __name__ == "__main__": + print("begin") + + net = 'testing/use_case.BIFXML' + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + + + Marginal_distribution = True + checkMAP = False + checkMEP = False + + + ### Marginal distribution, a-priori for stroke or bleed + if Marginal_distribution: + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + #outcome_md = bnr.compute_marginal(['Stroke'], order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Stroke history': True, "High blood pressure": True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Anticoagulant': True, "High blood pressure": True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Diabetes': True, "70+": True, "Liver/kidney disease":True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Diabetes': True, "70+": True, "Anticoagulant":True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Smoking': True, "70+": True, "Hyperlipidemia":True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Smoking': True, "70+": True, "Diabetes":True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Smoking': True, "Blood thick": True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Stroke history': True, "Hyperlipidemia": True}), order=bnr.min_degree()) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Liver/kidney disease': True, "Stroke history": True}), order=bnr.min_degree()) + outcome_md = bnr.compute_marginal(['Internal Bleeding'], order=bnr.min_degree()) + print(outcome_md) + ''' + + ### Marginal distribution, apostpriori for stroke or bleed + if Marginal_distribution: + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + #outcome_md = bnr.compute_marginal(['Stroke'], pd.Series({'Stroke history': True, "High blood pressure": True}), order=bnr.min_degree()) + outcome_md = bnr.compute_marginal(['Internal Bleeding'], pd.Series({'Anticoagulant':True, "Diabetes":True}), order=bnr.min_degree()) + print(outcome_md) +''' + + ### MAP + if checkMAP: + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_map = bnr.MAP(["70+", "Smoker"], pd.Series({'Stroke history': True, "High blood pressure": True, "Hyperlipidemie":True})) + print(outcome_map) + + + ### MPE + if checkMEP: + bn = BayesNet() + bn.load_from_bifxml(net) + bnr = BNReasoner(bn) + outcome_mpe = bnr.MPE(pd.Series({'Stroke': True, "High blood pressure":True})) + print(outcome_mpe.to_string())