Commit d673a1ec authored by c24b's avatar c24b

METRIC SPEC

parent 60679e31
"""
Computes a specificity metric from the ngram cooccurrence matrix.
+ SAVE => WeightedList => NodeNgram
"""
from gargantext.models import Node, Ngram, NodeNgram, NodeNgramNgram
from gargantext.util.db import session, aliased, func, bulk_insert
from gargantext.util.lists import WeightedList
from collections import defaultdict
from pandas import DataFrame
import pandas as pd
def compute_specificity(corpus, cooc_id=None, cooc_matrix=None, overwrite_id = None):
'''
Compute the specificity, simple calculus.
Parameters:
- cooc_id: mandatory id of a cooccurrences node to use as base
- overwrite_id: optional preexisting specificity node to overwrite
'''
matrix = defaultdict(lambda : defaultdict(float))
if cooc_id == None and cooc_matrix == None:
raise TypeError("compute_specificity: needs a cooc_id or cooc_matrix param")
elif cooc_id:
cooccurrences = (session.query(NodeNgramNgram)
.filter(NodeNgramNgram.node_id==cooc_id)
)
# no filtering: cooc already filtered on mainlist_id at creation
for cooccurrence in cooccurrences:
matrix[cooccurrence.ngram1_id][cooccurrence.ngram2_id] = cooccurrence.weight
matrix[cooccurrence.ngram2_id][cooccurrence.ngram1_id] = cooccurrence.weight
elif cooc_matrix:
# copy WeightedMatrix into local matrix structure
for (ngram1_id, ngram2_id) in cooc_matrix.items:
w = cooc_matrix.items[(ngram1_id, ngram2_id)]
matrix[ngram1_id][ngram2_id] = w
nb_ngrams = len(matrix)
print("SPECIFICITY: computing on %i ngrams" % nb_ngrams)
x = DataFrame(matrix).fillna(0)
# proba (x/y) ( <= on divise chaque ligne par son total)
x = x / x.sum(axis=1)
# vectorisation
# d:Matrix => v: Vector (len = nb_ngrams)
# v = d.sum(axis=1) (- lui-même)
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 (asc is spec, desc is generic)
v = ( xs - ys) / ( 2 * (x.shape[0] - 1))
## d ##
#######
# Grenelle biodiversité kilomètres site élus île
# Grenelle 0 0 4 0 0 0
# biodiversité 0 0 0 0 4 0
# kilomètres 4 0 0 0 4 0
# site 0 0 0 0 4 6
# élus 0 4 4 4 0 0
# île 0 0 0 6 0 0
## d.sum(axis=1) ##
###################
# Grenelle 4
# biodiversité 4
# kilomètres 8
# site 10
# élus 12
# île 6
# résultat temporaire
# -------------------
# pour l'instant on va utiliser les sommes en ligne comme ranking de spécificité
# (**même** ordre qu'avec la formule d'avant le refactoring mais calcul + simple)
# TODO analyser la cohérence math ET sem de cet indicateur
#v.sort_values(inplace=True)
# [ ('biodiversité' , 0.333 ),
# ('Grenelle' , 0.5 ),
# ('île' , 0.599 ),
# ('kilomètres' , 1.333 ),
# ('site' , 1.333 ),
# ('élus' , 1.899 ) ]
# ----------------
# specificity node
if overwrite_id:
# overwrite pre-existing id
the_id = overwrite_id
session.query(NodeNgram).filter(NodeNgram.node_id==the_id).delete()
session.commit()
else:
specnode = corpus.add_child(
typename = "SPECIFICITY",
name = "Specif (in:%s)" % corpus.id
)
session.add(specnode)
session.commit()
the_id = specnode.id
# print(v)
pd.options.display.float_format = '${:,.2f}'.format
if not v.empty:
data = WeightedList(
zip( v.index.tolist()
, v.values.tolist()[0]
)
)
data.save(the_id)
else:
print("WARNING: had no terms in COOCS => empty SPECIFICITY node")
return(the_id)
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