def time_align_visualize(alignments, time, y, namespace='time_align'):
plt.figure()
heat = np.flip(alignments + alignments.T + np.eye(alignments.shape[0]),
axis=0)
sns.heatmap(heat, cmap="YlGnBu")
plt.savefig(namespace + '_heatmap.svg')
G = nx.from_numpy_matrix(alignments)
G = nx.maximum_spanning_tree(G)
pos = {}
for i in range(len(G.nodes)):
pos[i] = np.array([time[i], y[i]])
mst_edges = set(nx.maximum_spanning_tree(G).edges())
weights = [
G[u][v]['weight'] if (not (u, v) in mst_edges) else 8
for u, v in G.edges()
]
plt.figure()
nx.draw(G, pos, edges=G.edges(), width=10)
plt.ylim([-1, 1])
plt.savefig(namespace + '.svg')
def getFullGraphs(greenw,
edges,
wnodes,
unitWeights=False,
nocolor=False): #oWordMap
G = nx.Graph()
for w in wnodes:
G.add_node(w)
for e in edges: #add all edges, use sum of both weights
weight = edges[e]
if weight == 0: continue
nodes = e.split(esep)
eid2 = nodes[1] + esep + nodes[0]
if eid2 in edges:
weight += edges[eid2]
edges[eid2] = 0
edges[e] = 0
#G.add_edge(oWordMap.get(nodes[0],nodes[0]), oWordMap.get(nodes[1],nodes[1]), weight=weight)
G.add_edge(nodes[0], nodes[1], weight=1 if unitWeights else weight)
#keep large graph components
#Gcomp= sorted(, key=len,reverse=True)
Gcomp = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)
if not len(Gcomp): return None
Gc = Gcomp[0]
Gc = nx.maximum_spanning_tree(Gc)
if len(Gcomp) > 1:
for g in Gcomp[1:4]:
if len(g) > 0.1 * len(Gcomp[0]) and len(g) > 10:
Gc = nx.compose(Gc, nx.maximum_spanning_tree(
g)) #keep graph if at least 10% of largest component
return getStyledGraph(Gc, greenw, wnodes, nocolor=nocolor,
tree=True) #oWordMap
def capture_results(A, X, name):
store = dict(
# X=X,
)
### Un-Supervised Metric (knee-finding heuristic) ###
plt.figure()
knee, A_thres = all_thres(X, pct_thres=None, plot=True) # find knee
plt.savefig(tmp / f'{name}_knee.png')
ex.add_artifact(tmp / f'{name}_knee.png')
graph_compare(A, G, A_thres, name, 'knee')
f_knee = metrics.fbeta_score(A.flatten(), A_thres.flatten(), 1.)
print(f'{name} - Knee F_1 = {f_knee:.3f}') # get knee-based fscore
unsup = dict(
knee=knee,
# X_knee = A_thres,
fscore_knee=f_knee)
store['unsupervised'] = unsup
### Supervised Optimum (Best F1-Score) ###
p_, r_, t_ = precision_recall_curve(A.flatten(), X.flatten())
aps_ = average_precision_score(A.flatten(), X.flatten())
f_ = 2 * p_[:-1] * r_[:-1] / (p_[:-1] + r_[:-1])
ts_ = t_[np.nanargmax(f_)] # best threshold for f1-score
print(f'{name} - Opt. F_1 = {np.nanmax(f_):.3f}'
) # get knee-based fscore
B_thres = np.where(X >= ts_, 1., 0.)
graph_compare(A, G, B_thres, name, 'f-score')
sup = dict(
# X_opt=B_thres,
precision=p_,
recall=r_,
thres=t_,
fscores=np.where(np.isnan(f_), 0, f_),
thres_opt=ts_,
fscore_opt=np.nanmax(f_),
aps=aps_,
)
store['supervised'] = sup
T = nx.maximum_spanning_tree(G)
pathfind = nx.to_numpy_array(nx.maximum_spanning_tree(nx.Graph(X)))
C_thres = np.where(pathfind > 0, 1, 0)
f_pf = metrics.fbeta_score(
nx.to_numpy_array(T).flatten(), C_thres.flatten(), 1.)
graph_compare(A, T, C_thres, name, 'pathfinder')
pf = dict(
# X_pf = C_thres,
fscore_pf=f_pf, )
store['pathfinder'] = pf
# pprint.pprint(store)
return store
def test_prim_maximum_spanning_tree_edges_specify_weight(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color="red", distance=7)
G.add_edge(1, 3, weight=30, color="blue", distance=1)
G.add_edge(2, 3, weight=1, color="green", distance=1)
G.add_node(13, color="purple")
G.graph["foo"] = "bar"
T = nx.maximum_spanning_tree(G, algorithm="prim")
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
T = nx.maximum_spanning_tree(G, weight="distance", algorithm="prim")
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
def test_prim_maximum_spanning_tree_edges_specify_weight(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color='red', distance=7)
G.add_edge(1, 3, weight=30, color='blue', distance=1)
G.add_edge(2, 3, weight=1, color='green', distance=1)
G.add_node(13, color='purple')
G.graph['foo'] = 'bar'
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
T = nx.maximum_spanning_tree(G, weight='distance', algorithm='prim')
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
def test_prim_maximum_spanning_tree_edges_specify_weight(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color='red', distance=7)
G.add_edge(1, 3, weight=30, color='blue', distance=1)
G.add_edge(2, 3, weight=1, color='green', distance=1)
G.add_node(13, color='purple')
G.graph['foo'] = 'bar'
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
T = nx.maximum_spanning_tree(G, weight='distance', algorithm='prim')
assert_equal(sorted(T.edges()), [(1, 2), (1, 3)])
assert_equal(sorted(T.nodes()), [1, 2, 3, 13])
def mstree_plot(A_thres, title=None, ax=None):
"""plot maximum spanning tree with layered edges for viz"""
if ax is None:
ax = plt.gca()
G = nx.from_pandas_adjacency(A_thres, create_using=nx.Graph)
G = nx.convert_node_labels_to_integers(G, label_attribute='item')
D = nx.maximum_spanning_tree(G)
nontree_edges = nx.from_numpy_array(
nx.to_numpy_array(G) - nx.to_numpy_array(D) > 0, create_using=nx.Graph)
pos = nx.layout.kamada_kawai_layout(D)
if title is not None:
ax.set_title(title)
draw_G(D,
pos,
fp=nontree_edges,
withlabels=True,
font_size=8.,
font_family='serif',
legend=False,
ax=ax,
node_size=20.)
# print(f'C_β = {nx.average_clustering(G):.2f}')
ax.axis('off')
ax.set_clip_on(False)
def test_kruskal_maximum_spanning_tree_disconnected(self):
G = nx.Graph()
G.add_path([1, 2])
G.add_path([10, 20])
T = nx.maximum_spanning_tree(G, algorithm="kruskal")
assert_equal(sorted(map(sorted, T.edges())), [[1, 2], [10, 20]])
assert_equal(sorted(T.nodes()), [1, 2, 10, 20])
def test_prim_maximum_spanning_tree_disconnected(self):
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(10, 20)
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(sorted(map(sorted, T.edges())), [[1, 2], [10, 20]])
assert_equal(sorted(T.nodes()), [1, 2, 10, 20])
def test_prim_maximum_spanning_tree_disconnected(self):
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(10, 20)
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(sorted(map(sorted, T.edges())), [[1, 2], [10, 20]])
assert_equal(sorted(T.nodes()), [1, 2, 10, 20])
def naive_dij(c):
all_students = list(range(1, c.students + 1))
non_home = list(range(1, c.home)) + list(range(c.home + 1, c.v + 1))
# c.scout(random.choice(non_home), all_students)
G = c.G
# for _ in range(100):
# u, v = random.choice(list(c.G.edges()))
# c.remote(u, v)
MST = nx.maximum_spanning_tree(G)
# 找到所有MST的
# query_student = all_students[:len(all_students)//2]
query_student = all_students
query_total = [c.scout(vertex, query_student) for vertex in non_home]
query_result = []
# print(non_home)
# print(c.home)
for q in query_total:
witness = 0
for s in query_student:
if q[s]:
witness += 1
query_result.append(witness)
# print(query_result)
# construct the expected bots
number_of_bots = c.bots
max_num_index_list = []
for _ in range(len(query_result)):
i = query_result.index(max(query_result))
if i < c.home - 1:
max_num_index_list.append(i + 1)
# print("#:", i+1," value: ",query_result[i])
else:
max_num_index_list.append(i + 2)
query_result[i] = 0
# Begin Digstra
robots_remain = c.bots
# pred, dist = nx.dijkstra_predecessor_and_distance(G, c.home, cutoff=None, weight='weight')
# print("Home: ",c.home)
# print(max_num_index_list)
for bot_num in max_num_index_list:
if robots_remain > 0:
path = nx.dijkstra_path(G, bot_num, c.home)
# print(path)
# print("bots remain",robots_remain)
judge = c.remote(bot_num, path[1])
if judge > 0:
for i in range(2, len(path)):
c.remote(path[i - 1], path[i])
robots_remain -= 1
def backbone_inf(X, resolution=0.5):
import networkx as nx
conn, G = nearest_neighbor(X, k=15)
groups, n_clust = leiden(conn, resolution=resolution)
mean_cluster = [[] for x in range(n_clust)]
for i, cat in enumerate(np.unique(groups)):
idx = np.where(groups == cat)[0]
mean_cluster[int(cat)] = np.mean(X[idx, :], axis=0)
mst = np.zeros((n_clust, n_clust))
for i in range(n_clust):
for j in range(n_clust):
mst[i, j] = np.linalg.norm(np.array(mean_cluster[i]) -
np.array(mean_cluster[j]),
ord=2)
G = nx.from_numpy_matrix(-mst)
T = nx.maximum_spanning_tree(G, weight='weight', algorithm='kruskal')
T = nx.to_numpy_matrix(T)
# conn is the adj of the MST.
return groups, mean_cluster, T
def DMST(G, dummy_wt=0.1):
G_copy = G.copy()
edge_map = dict()
for e in G_copy.edges(data="weight"):
edge_map[(e[0], e[1])] = e[2]
G_copy.add_node("-1")
for node in G_copy.nodes():
if str(node) == "-1":
root = node
for node in G_copy.nodes():
if str(node) != "-1":
G_copy.add_edge(root, node, weight=dummy_wt)
T = nx.maximum_spanning_tree(G_copy.to_undirected()).to_directed()
rem = list()
attr = dict()
for e in T.edges(data="weight"):
p1 = (e[0], e[1])
p2 = (e[1], e[0])
if p1 not in edge_map:
rem.append(e)
elif (p2 not in edge_map) or (edge_map[p1] > edge_map[p2]):
attr[p1] = {"weight": edge_map[p1]}
else:
rem.append(e)
nx.set_edge_attributes(T, attr)
for r in rem:
T.remove_edge(r[0], r[1])
T.remove_node(root)
return T
def _generate_graphs(self):
'''
Creates the complete graph from the proximity matrix and finds its
maximum spanning tree.
'''
self._complete_graph = nx.from_pandas_adjacency(self._proximity)
self._maxst = nx.maximum_spanning_tree(self._complete_graph)
def tree_decomp(self):
clusters = self.clusters
graph = nx.empty_graph(len(clusters))
for atom, nei_cls in enumerate(self.atom_cls):
if len(nei_cls) <= 1: continue
bonds = [c for c in nei_cls if len(clusters[c]) == 2]
rings = [c for c in nei_cls
if len(clusters[c]) > 4] #need to change to 2
if len(nei_cls) > 2 and len(bonds) >= 2:
clusters.append([atom])
c2 = len(clusters) - 1
graph.add_node(c2)
for c1 in nei_cls:
graph.add_edge(c1, c2, weight=100)
elif len(rings) > 2: #Bee Hives, len(nei_cls) > 2
clusters.append([atom]) #temporary value, need to change
c2 = len(clusters) - 1
graph.add_node(c2)
for c1 in nei_cls:
graph.add_edge(c1, c2, weight=100)
else:
for i, c1 in enumerate(nei_cls):
for c2 in nei_cls[i + 1:]:
inter = set(clusters[c1]) & set(clusters[c2])
graph.add_edge(c1, c2, weight=len(inter))
n, m = len(graph.nodes), len(graph.edges)
assert n - m <= 1 #must be connected
return graph if n - m == 1 else nx.maximum_spanning_tree(graph)
def tree_decomp(self):
clusters = self.clusters
graph = nx.empty_graph(len(clusters))
for atom, nei_cls in enumerate(self.atom_cls):
if len(nei_cls) <= 1: continue
inter = set(self.clusters[nei_cls[0]])
for cid in nei_cls:
inter = inter & set(self.clusters[cid])
assert len(inter) >= 1
if len(nei_cls) > 2 and len(
inter) == 1: # two rings + one bond has problem!
clusters.append([atom])
c2 = len(clusters) - 1
graph.add_node(c2)
for c1 in nei_cls:
graph.add_edge(c1, c2, weight=100)
else:
for i, c1 in enumerate(nei_cls):
for c2 in nei_cls[i + 1:]:
union = set(clusters[c1]) | set(clusters[c2])
graph.add_edge(c1, c2, weight=len(union))
n, m = len(graph.nodes), len(graph.edges)
assert n - m <= 1 #must be connected
return graph if n - m == 1 else nx.maximum_spanning_tree(graph)
def test_kruskal_maximum_spanning_tree_disconnected(self):
G = nx.Graph()
G.add_path([1, 2])
G.add_path([10, 20])
T = nx.maximum_spanning_tree(G, algorithm='kruskal')
assert_equal(sorted(map(sorted, T.edges())), [[1, 2], [10, 20]])
assert_equal(sorted(T.nodes()), [1, 2, 10, 20])
def resolve_diamond(self):
"""Resolve relationships that have a diamond graphs.
This method is a pipline to solve diamond graphs by depending on MST to
resolve the present cycles and maintains information by copying data from
the cut ties.
"""
self.relationships['weight'] = self.merge_cost()
G = nx.from_pandas_edgelist(self.relationships,
source='parent_entity',
target='child_entity',
edge_attr=['weight'])
if len(list(nx.cycle_basis(G))) > 0:
self.resolve_reference()
G = nx.from_pandas_edgelist(self.relationships,
source='parent_entity',
target='child_entity',
edge_attr=['weight'])
X = nx.maximum_spanning_tree(G)
edges = [x for x in G.edges() if x not in X.edges()]
for edge in edges:
if edge[0] != edge[1]:
self.merge(edge, remove=True)
def maxtree(G, conn):
if sufficient(G):
J = nx.maximum_spanning_tree(G)
st.write("#### Maximum tree")
viz.draw(J, conn, cmap=cmap)
st.pyplot()
def gerani_paper_arrg_to_aht(
graph: nx.MultiDiGraph,
max_number_of_nodes: int = 100,
weight: str = "moi",
alpha_coefficient: float = 0.5,
) -> nx.Graph:
logger.info("Generate Aspect Hierarchical Tree based on ARRG")
aspects_weighted_page_rank = calculate_weighted_page_rank(graph, "weight")
graph = calculate_moi_by_gerani(
graph=graph,
weighted_page_rank=aspects_weighted_page_rank,
alpha_coefficient=alpha_coefficient,
)
graph_flatten = merge_multiedges(graph,
node_attrib_name=weight,
default_node_weight=0)
sorted_nodes = sorted(
list(aspects_weighted_page_rank.items()),
key=lambda node_degree_pair: node_degree_pair[1],
reverse=True,
)
csv_name = "/tmp/gerani_page_ranks.csv"
pd.DataFrame(sorted_nodes, columns=["aspect", weight]).to_csv(csv_name)
mlflow.log_artifact(csv_name)
top_nodes = list(pluck(0, sorted_nodes[:max_number_of_nodes]))
sub_graph = graph_flatten.subgraph(top_nodes)
maximum_spanning_tree = nx.maximum_spanning_tree(sub_graph, weight=weight)
nx.set_node_attributes(maximum_spanning_tree,
dict(sub_graph.nodes.items()))
return maximum_spanning_tree
def determine_junctions(g, junction_threshold=100):
""""
Determine the backbones of the graph with ambiguous nodes being removed
"""
gmst = nx.maximum_spanning_tree(g, weight="n")
junctions = determine_junctions_of_trees(gmst, junction_threshold)
return junctions
def show_network(
ax,
net,
topic_category_map,
label,
loc,
size_lookup,
color_lookup,
norm=2000,
norm_2=1.2,
layout=nx.kamada_kawai_layout,
ec="white",
alpha=0.6,
):
"""
Plots a network visualisation of a topic netwirk
"""
new_net = net.copy()
new_net_2 = nx.maximum_spanning_tree(new_net)
# Calculate the layout
pos = layout(new_net_2, center=(0.5, 0.5))
# Node size
node_s = list(
[size_lookup[x]**norm_2 for x in dict(new_net_2.degree).keys()])
# Node colour
node_c = []
for x in new_net_2.nodes:
if x not in topic_category_map.keys():
node_c.append("white")
else:
if topic_category_map[x] not in color_lookup.keys():
node_c.append("white")
else:
c = color_lookup[topic_category_map[x]]
node_c.append(c)
# Draw the network. There is quite a lot of complexity here
nx.draw_networkx_nodes(
new_net_2,
pos,
node_size=node_s,
node_color=node_c,
cmap="tab20c",
alpha=alpha,
edgecolors="darkgrey",
ax=ax,
)
edge_w = [e[2]["weight"] / norm for e in new_net_2.edges(data=True)]
nx.draw_networkx_edges(new_net_2,
pos,
width=edge_w,
edge_color=ec,
ax=ax,
alpha=alpha)
def post_processing_tree(adj):
import networkx as nx
# deg = np.sum(adj, axis = 1)
# norm_adj = 1 / np.sqrt(deg)[:,None] * adj * 1 / np.sqrt(deg)[None,:]
G = nx.from_numpy_matrix(adj, create_using=nx.Graph)
T = nx.maximum_spanning_tree(G, weight='weight', algorithm='kruskal')
T = nx.to_numpy_matrix(T)
T = np.where(T != 0, 1, 0)
return T
def test_prim_maximum_spanning_tree_attributes(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color='red', distance=7)
G.add_edge(2, 3, weight=1, color='green', distance=2)
G.add_edge(1, 3, weight=10, color='blue', distance=1)
G.add_node(13, color='purple')
G.graph['foo'] = 'bar'
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(T.graph, G.graph)
assert_equal(T.node[13], G.node[13])
assert_equal(T.edge[1][2], G.edge[1][2])
def test_prim_maximum_spanning_tree_attributes(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color='red', distance=7)
G.add_edge(2, 3, weight=1, color='green', distance=2)
G.add_edge(1, 3, weight=10, color='blue', distance=1)
G.add_node(13, color='purple')
G.graph['foo'] = 'bar'
T = nx.maximum_spanning_tree(G, algorithm='prim')
assert_equal(T.graph, G.graph)
assert_equal(T.node[13], G.node[13])
assert_equal(T.edge[1][2], G.edge[1][2])
def test_prim_maximum_spanning_tree_attributes(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1, color="red", distance=7)
G.add_edge(2, 3, weight=1, color="green", distance=2)
G.add_edge(1, 3, weight=10, color="blue", distance=1)
G.add_node(13, color="purple")
G.graph["foo"] = "bar"
T = nx.maximum_spanning_tree(G, algorithm="prim")
assert_equal(T.graph, G.graph)
assert_equal(T.node[13], G.node[13])
assert_equal(T.edge[1][2], G.edge[1][2])
def sample_random_view_tree(views, view0, landmarks):
G = build_view_graph(views, landmarks)
for s, t, data in G.edges(data=True):
data['weight'] = random.random()
T = nx.maximum_spanning_tree(G)
minimum_tree = list(nx.dfs_edges(T, source=view0))
return minimum_tree
def get_keywords(G, top, D=None):
G_tmp = nx.maximum_spanning_tree(G)
k = min(G.number_of_nodes(), top)
B = nx.betweenness_centrality(G_tmp, weight='weight')
D = nx.degree(G, weight='weight') if not D else D
P = nx.pagerank(G_tmp)
val_tmp = {i: B[i] * D[i] * (P[i] + 0.0001) for i in G.nodes()}
return ' '.join([
i[0]
for i in sorted(val_tmp.items(), key=lambda x: x[1], reverse=True)[:k]
])
def compare_result(gama):
import glob
import numpy as np
import pandas as pd
files = glob.glob('./financial_institution2/*')
mdate = []
mG1 = []
mG2 = []
mG3 = []
mG4 = []
for k in range(len(files)):
mdate.append(files[k][-15:-4])
G = network_from_sinduja_data(k)
G1 = planar_maximally_filter(G)
G2 = simplification_minimum_lost_connectivity(G, gama)
minT = nx.minimum_spanning_tree(G, weight='weight')
maxT = nx.maximum_spanning_tree(G, weight='weight')
w1 = [
weight1 for u1, v1, weight1 in G1.edges(data='weight', default=1)
]
w2 = [
weight2 for u2, v2, weight2 in G2.edges(data='weight', default=1)
]
w3 = [
weight3 for u3, v3, weight3 in minT.edges(data='weight', default=1)
]
w4 = [
weight4 for u4, v4, weight4 in maxT.edges(data='weight', default=1)
]
print len(w1)
print len(w2)
mG1.append(np.mean(w1))
mG2.append(np.mean(w2))
mG3.append(np.mean(w3))
mG4.append(np.mean(w4))
df = pd.DataFrame(
{
'date': mdate,
'PMFG': mG1,
'minimum_lost': mG2,
'min_tree': mG3,
'max_tree': mG4
},
columns=['date', 'PMFG', 'minimum_lost', 'min_tree', 'max_tree'])
df['date'] = pd.to_datetime(df.date)
df = df.sort_values(by='date')
#print df.groupby(df.date.dt.year)['PMFG', 'minimum_lost'].transform('mean')
#df = df.groupby([df.date.dt.strftime('%Y')])['PMFG','minimum_lost','min_tree','max_tree'].mean()
#df.index.name = 'date'
#df.reset_index(level=0, inplace=True)
return df
def ML_spanning_tree(data):
n = data.shape[1]
triu = list(zip(*np.triu_indices(n, k=1)))
scores = score_edges(data)
G = nx.empty_graph(n)
for i in range(n * (n - 1) // 2):
G.add_edge( triu[i][0], triu[i][1], weight=scores[i])
T = nx.maximum_spanning_tree(G, algorithm="kruskal")
return Graph(n, dol=nx.to_dict_of_lists(T)), scores
def makeG(df_size,df_attr,df_state,price,path):
# df_size relates to size of node for example it could be power price
# df_attr relates to the power output
import numpy as np
#G=GfromAEMO(df_size) #23/07
G=GfromAEMO(df_size)
G=nx.maximum_spanning_tree(G)
G=attributes_one(G,df_size.replace([np.inf, -np.inf], np.nan),df_attr,df_state,price)
G=attributes(G)
G=attributes_color(G)
graphout=graph_build_lga(G,path,False)
return G
def fit(
self, X: pd.DataFrame, y: pd.Series, *, weights: pd.Series = None
) -> "TreeBayesianNetworkClassifier":
if len(X) <= 0:
raise ValueError(f"len(X) must be positive, but is {len(X)}")
if len(y) != len(X):
raise ValueError(f"len(y) must equal len(X), but is {len(y)}")
if weights is None:
weights = pd.Series(np.ones(len(X)))
if len(weights) != len(X):
raise ValueError(f"len(weights) must equal len(X), but is {len(weights)}")
self.classes_ = unique_labels(y)
self.features_ = np.array(X.columns)
data = __class__._compose_data(X, y)
G = nx.Graph()
N = len(data)
# add nodes
for col in data.columns:
sr = data[col]
probs = weights.groupby(sr).sum() / weights.sum()
labels = unique_labels(sr)
G.add_node(col, probs=probs, labels=labels)
# add edges
for i_f1 in range(len(data.columns) - 1):
for i_f2 in range(i_f1 + 1, len(data.columns)):
cols = sorted([data.columns[i_f1], data.columns[i_f2]])
contingency = weighted_contingency_matrix(*data[cols], weights)
mutual_info = weighted_mutual_info_score(contingency)
# compute joint probability distribution
nd = reduce(mul, contingency.shape) # arity of the ``*cols`` domain
pseudocount = contingency.sum() / nd # for Laplace smoothing
probs = (contingency + pseudocount) / (
N + (pseudocount * nd)
) # uses Laplace smoothing
df = pd.DataFrame(probs)
df.index = G.nodes[cols[0]]["labels"]
df.columns = G.nodes[cols[-1]]["labels"]
sr = df.stack()
sr.index.names = cols
G.add_edge(*cols, joint_probs=sr, mutual_info=mutual_info)
# extract maximum spanning tree by mutual information
T = nx.maximum_spanning_tree(G, weight="mutual_info")
arborescence = root_tree(T, root=__class__._Y_COL_PREFIX)
self.network_ = arborescence
return self
def test_weight_attribute(self):
G = nx.Graph()
G.add_edge(0, 1, weight=1, distance=7)
G.add_edge(0, 2, weight=30, distance=1)
G.add_edge(1, 2, weight=1, distance=1)
G.add_node(3)
T = nx.minimum_spanning_tree(G, algorithm=self.algo, weight='distance')
assert_nodes_equal(sorted(T), list(range(4)))
assert_edges_equal(sorted(T.edges()), [(0, 2), (1, 2)])
T = nx.maximum_spanning_tree(G, algorithm=self.algo, weight='distance')
assert_nodes_equal(sorted(T), list(range(4)))
assert_edges_equal(sorted(T.edges()), [(0, 1), (0, 2)])
def test_weight_attribute(self):
G = nx.Graph()
G.add_edge(0, 1, weight=1, distance=7)
G.add_edge(0, 2, weight=30, distance=1)
G.add_edge(1, 2, weight=1, distance=1)
G.add_node(3)
T = nx.minimum_spanning_tree(G, algorithm=self.algo, weight='distance')
assert_nodes_equal(sorted(T), list(range(4)))
assert_edges_equal(sorted(T.edges()), [(0, 2), (1, 2)])
T = nx.maximum_spanning_tree(G, algorithm=self.algo, weight='distance')
assert_nodes_equal(sorted(T), list(range(4)))
assert_edges_equal(sorted(T.edges()), [(0, 1), (0, 2)])
def build_chow_liu_tree(X, n):
"""
Build a Chow-Liu tree from the data, X. n is the number of features. The weight on each edge is
the negative of the mutual information between those features. The tree is returned as a networkx
object.
"""
G = nx.Graph()
for v in range(n):
G.add_node(v)
for u in range(v):
G.add_edge(u, v, weight=calculate_mutual_information(X, u, v))
T = nx.maximum_spanning_tree(G)
return T
def main():
data = get_dataset()
G = nx.Graph()
nodes = data.columns
for i in range(len(nodes)):
for j in range(i + 1, len(nodes)):
G.add_edge(nodes[i], nodes[j], weight=mi(nodes[i], nodes[j], data))
T = nx.maximum_spanning_tree(G)
terminal_nodes = [x for x in T.nodes() if T.degree(x) == 1]
root = random.choice(terminal_nodes)
print(root)
T = nx.bfs_tree(T, root)
def main():
# 1st row: Number of agents;Number of meetings;Number of variables
# Open file
inputFilename = 'constraint_graphs/dcop_constraint_graph'
# inputFilename = 'constraint_graphs/dcop_simple'
# inputFilename = 'constraint_graphs/DCOP_Problem_10'
input = open(inputFilename, 'r')
# Read first line
[nrAgents, nrMeetings, nrVars] = u.readLine(input)
# Read agents/variables/constraints
agents = u.readMeetings(input, nrVars)
# Read preferences per agent
agents = u.readPreferences(input, agents, nrAgents)
# Create graph
G = nx.Graph()
# Add agents/nodes
G = ptree.addNodes(G, agents)
# Add edges and keep track of back-edges
[G, back_edges_candidates] = ptree.addEdges(G, agents, nrMeetings)
# Convert to spanning tree
T = nx.maximum_spanning_tree(G)
# T = nx.dfs_successors(G, 0)
# Create back edges
T = ptree.addBackEdges(T, back_edges_candidates)
# Print nodes
print('----------------ALL NODES----------------')
# u.printNodes(T)
layout = graphviz_layout(T, prog="dot")
edges = T.edges()
colors = [T[u][v]['color'] for u, v in edges]
#print(list(nx.bfs_edges(T,3)))
compute_utils(T, ptree.getLeafNodes(T))
nx.draw(T, layout, edge_color=colors, with_labels=True)
plt.show()
# Print nodes
print('----------------ALL NODES----------------')
u.printNodes(T)
def test_kruskal_maximum_spanning_tree_isolate(self):
G = nx.Graph()
G.add_nodes_from([1, 2])
T = nx.maximum_spanning_tree(G, algorithm="kruskal")
assert_equal(sorted(T.nodes()), [1, 2])
assert_equal(sorted(T.edges()), [])
def test_prim_maximum_spanning_tree(self):
T = nx.maximum_spanning_tree(self.G, algorithm='prim')
assert_equal(T.edges(data=True), self.maximum_spanning_edgelist)
def test_multigraph_keys_tree_max(self):
G = nx.MultiGraph()
G.add_edge(0, 1, key="a", weight=2)
G.add_edge(0, 1, key="b", weight=1)
T = nx.maximum_spanning_tree(G)
assert_equal([(0, 1, 2)], list(T.edges(data="weight")))
def test_maximum_spanning_tree(self):
T = nx.maximum_spanning_tree(self.G, algorithm="kruskal")
assert_equal(sorted(T.edges(data=True)), self.maximum_spanning_edgelist)
def test_maximum_tree(self):
T = nx.maximum_spanning_tree(self.G, algorithm=self.algo)
actual = sorted(T.edges(data=True))
assert_edges_equal(actual, self.maximum_spanning_edgelist)