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from gargantext.models import Node, NodeNgram, NodeNgramNgram, \
NodeHyperdata
from gargantext.util.db import session, aliased
from graph.louvain import best_partition
from copy import copy
from collections import defaultdict
from math import log,sqrt
#from operator import itemgetter
import math
import numpy as np
import pandas as pd
import networkx as nx
def clusterByDistances( cooc_matrix
, field1=None, field2=None
, distance=None):
'''
clusterByDistance :: Coocs[nga, ngb => ccweight] -> (Graph, Partition, {ids}, {weight})
'''
# implicit global session
authorized = ['conditional', 'distributional', 'cosine']
if distance not in authorized:
raise ValueError("Distance must be in %s" % str(authorized))
matrix = defaultdict(lambda : defaultdict(float))
ids = defaultdict(lambda : defaultdict(int))
labels = dict()
weight = dict()
for cooc in cooc_matrix.items:
ngram1_id = cooc[0]
ngram2_id = cooc[1]
ccweight = cooc_matrix.items[cooc]
matrix[ngram1_id][ngram2_id] = ccweight
matrix[ngram2_id][ngram1_id] = ccweight
ids[ngram1_id] = (field1, ngram1_id)
ids[ngram2_id] = (field2, ngram2_id)
weight[ngram1_id] = weight.get(ngram1_id, 0) + ccweight
weight[ngram2_id] = weight.get(ngram2_id, 0) + ccweight
x = pd.DataFrame(matrix).fillna(0)
if distance == 'conditional':
x = x / x.sum(axis=1)
#y = y / y.sum(axis=0)
xs = x.sum(axis=1) - x
ys = x.sum(axis=0) - x
# top inclus ou exclus
n = ( xs + ys) / (2 * (x.shape[0] - 1))
# top generic or specific
m = ( xs - ys) / (2 * (x.shape[0] - 1))
n = n.sort_index(inplace=False)
m = m.sort_index(inplace=False)
nodes_included = 500 #int(round(size/20,0))
#nodes_excluded = int(round(size/10,0))
nodes_specific = 500 #int(round(size/10,0))
#nodes_generic = int(round(size/10,0))
# TODO use the included score for the node size
n_index = pd.Index.intersection(x.index, n.index[:nodes_included])
# Generic:
#m_index = pd.Index.intersection(x.index, m.index[:nodes_generic])
# Specific:
m_index = pd.Index.intersection(x.index, m.index[-nodes_specific:])
#m_index = pd.Index.intersection(x.index, n.index[:nodes_included])
x_index = pd.Index.union(n_index, m_index)
xx = x[list(x_index)].T[list(x_index)]
# Removing unconnected nodes
xxx = xx.values
threshold = min(xxx.max(axis=1))
matrix_filtered = np.where(xxx >= threshold, xxx, 0)
#matrix_filtered = matrix_filtered.resize((90,90))
G = nx.from_numpy_matrix(np.matrix(matrix_filtered))
G = nx.relabel_nodes(G, dict(enumerate([ ids[id_][1] for id_ in list(xx.columns)])))
elif distance == 'cosine':
scd = defaultdict(lambda : defaultdict(int))
for i in matrix.keys():
for j in matrix.keys():
numerator = sum(
[
matrix[i][k] * matrix[j][k]
for k in matrix.keys()
if i != j and k != i and k != j
]
)
denominator = sqrt(
sum([
matrix[i][k]
for k in matrix.keys()
if k != i and k != j #and matrix[i][k] > 0
])
*
sum([
matrix[i][k]
for k in matrix.keys()
if k != i and k != j #and matrix[i][k] > 0
])
)
try:
scd[i][j] = numerator / denominator
except Exception as error:
scd[i][j] = 0
minmax = min([ max([ scd[i][j] for i in scd.keys()]) for j in scd.keys()])
G = nx.DiGraph()
G.add_edges_from(
[
(i, j, {'weight': scd[i][j]})
for i in scd.keys() for j in scd.keys()
if i != j and scd[i][j] > minmax and scd[i][j] > scd[j][i]
]
)
elif distance == 'distributional':
mi = defaultdict(lambda : defaultdict(int))
total_cooc = x.sum().sum()
for i in matrix.keys():
si = sum([matrix[i][j] for j in matrix[i].keys() if i != j])
for j in matrix[i].keys():
sj = sum([matrix[j][k] for k in matrix[j].keys() if j != k])
if i!=j :
mi[i][j] = log( matrix[i][j] / ((si * sj) / total_cooc) )
r = defaultdict(lambda : defaultdict(int))
for i in matrix.keys():
for j in matrix.keys():
sumMin = sum(
[
min(mi[i][k], mi[j][k])
for k in matrix.keys()
if i != j and k != i and k != j and mi[i][k] > 0
]
)
sumMi = sum(
[
mi[i][k]
for k in matrix.keys()
if k != i and k != j and mi[i][k] > 0
]
)
try:
r[i][j] = sumMin / sumMi
except Exception as error:
r[i][j] = 0
# Need to filter the weak links, automatic threshold here
minmax = min([ max([ r[i][j] for i in r.keys()]) for j in r.keys()])
G = nx.DiGraph()
G.add_edges_from(
[
(i, j, {'weight': r[i][j]})
for i in r.keys() for j in r.keys()
if i != j and r[i][j] > minmax and r[i][j] > r[j][i]
]
)
# degree_max = max([(n, d) for n,d in G.degree().items()], key=itemgetter(1))[1]
# nodes_to_remove = [n for (n,d) in G.degree().items() if d <= round(degree_max/2)]
# G.remove_nodes_from(nodes_to_remove)
# Removing too connected nodes (find automatic way to do it)
#edges_to_remove = [ e for e in G.edges_iter() if
# nodes_to_remove = [n for n in degree if degree[n] <= 1]
# G.remove_nodes_from(nodes_to_remove)
def getWeight(item):
return item[1]
#
# node_degree = sorted(G.degree().items(), key=getWeight, reverse=True)
# #print(node_degree)
# nodes_too_connected = [n[0] for n in node_degree[0:(round(len(node_degree)/5))]]
#
# for n in nodes_too_connected:
# n_edges = list()
# for v in nx.neighbors(G,n):
# #print((n, v), G[n][v]['weight'], ":", (v,n), G[v][n]['weight'])
# n_edges.append(((n, v), G[n][v]['weight']))
#
# n_edges_sorted = sorted(n_edges, key=getWeight, reverse=True)
# #G.remove_edges_from([ e[0] for e in n_edges_sorted[round(len(n_edges_sorted)/2):]])
# #G.remove_edges_from([ e[0] for e in n_edges_sorted[(round(len(nx.neighbors(G,n))/3)):]])
# G.remove_edges_from([ e[0] for e in n_edges_sorted[10:]])
G.remove_nodes_from(nx.isolates(G))
partition = best_partition(G.to_undirected())
return(G,partition,ids,weight)