# Gargantext lib
from gargantext.util.db import session, aliased
from gargantext.util.lists import WeightedMatrix, UnweightedList, Translations
from gargantext.util.http import JsonHttpResponse
from gargantext.models import Node, Ngram, NodeNgram, NodeNgramNgram, NodeHyperdata
from graph.cooccurrences import countCooccurrences
from graph.distances import clusterByDistances
from graph.bridgeness import filterByBridgeness
from graph.mail_notification import notify_owner
from graph.growth import compute_growth
from gargantext.util.scheduling import scheduled
from gargantext.constants import graph_constraints
from celery import shared_task
from datetime import datetime
@shared_task
def compute_graph( corpus_id=None , cooc_id=None
, field1='ngrams' , field2='ngrams'
, start=None , end=None
, mapList_id=None , groupList_id=None
, distance=None , bridgeness=None
, n_min=1, n_max=None , limit=1000
, isMonopartite=True , threshold = 3
, save_on_db= True , reset=True
) :
'''
All steps to compute a graph:
1) count Cooccurrences (function countCooccurrences)
main parameters: threshold, isMonopartite
2) filter and cluster By Distances (function clusterByDistances)
main parameter: distance
TODO option clustering='louvain'
or 'percolation' or 'random walk' or ...
3) filter By Bridgeness (function filterByBridgeness)
main parameter: bridgeness
4) format the graph (formatGraph)
main parameter: format_
'''
print("GRAPH # ... Computing cooccurrences.")
(cooc_id, cooc_matrix) = countCooccurrences( corpus_id=corpus_id, cooc_id=cooc_id
, field1=field1, field2=field2
, start=start , end =end
, mapList_id=mapList_id , groupList_id=groupList_id
, isMonopartite=True , threshold = threshold
, distance=distance , bridgeness=bridgeness
, save_on_db = True , reset = reset
)
print("GRAPH #%d ... Cooccurrences computed." % (cooc_id))
print("GRAPH #%d ... Clustering with %s distance." % (cooc_id,distance))
G, partition, ids, weight = clusterByDistances ( cooc_matrix
, field1="ngrams", field2="ngrams"
, distance=distance
)
print("GRAPH #%d ... Filtering by bridgeness %d." % (cooc_id, bridgeness))
data = filterByBridgeness(G,partition,ids,weight,bridgeness,"node_link",field1,field2)
if start is not None and end is not None:
growth= dict()
for (ng_id, score) in compute_growth(corpus_id, groupList_id, mapList_id, start, end):
growth[ng_id] = float(score) + 100 # for the normalization, should not be negativ
for node in data['nodes']:
node['attributes']['growth'] = growth[node['id']]
print("GRAPH #%d ... Saving Graph in hyperdata as json." % cooc_id)
node = session.query(Node).filter(Node.id == cooc_id).first()
if node.hyperdata.get(distance, None) is None:
print("GRAPH #%d ... Distance %s has not been computed already." % (cooc_id, distance))
node.hyperdata[distance] = dict()
node.hyperdata[distance][bridgeness] = data
node.hyperdata[distance]["nodes"] = len(G.nodes())
node.hyperdata[distance]["edges"] = len(G.edges())
node.save_hyperdata()
session.commit()
print("GRAPH #%d ... Notify by email owner of the graph." % cooc_id)
corpus = session.query(Node).filter(Node.id==corpus_id).first()
#notify_owner(corpus, cooc_id, distance, bridgeness)
print("GRAPH #%d ... Returning data as json." % cooc_id)
return data
def get_graph( request=None , corpus=None
, field1='ngrams' , field2='ngrams'
, mapList_id = None , groupList_id = None
, cooc_id=None , type='node_link'
, start=None , end=None
, distance='conditional', bridgeness=5
, threshold=1 , isMonopartite=True
, saveOnly=True
) :
'''
Get_graph : main steps:
0) Check the parameters
get_graph :: GraphParameters -> Either (Dic Nodes Links) (Dic State Length)
where type Length = Int
get_graph first checks the parameters and return either graph data or a dict with
state "type" with an integer to indicate the size of the parameter
(maybe we could add a String in that step to factor and give here the error message)
1) compute_graph (see function above)
2) return graph
'''
overwrite_node_contents = False
# Case of graph has been computed already
if cooc_id is not None:
print("GRAPH#%d ... Loading data already computed." % int(cooc_id))
node = session.query(Node).filter(Node.id == cooc_id).first()
# Structure of the Node.hyperdata[distance][bridbeness]
# All parameters (but distance and bridgeness)
# are in Node.hyperdata["parameters"]
# Check distance of the graph
if node.hyperdata.get(distance, None) is not None:
graph = node.hyperdata[distance]
# Check bridgeness of the graph
if graph.get(str(bridgeness), None) is not None:
return graph[str(bridgeness)]
# new graph: we give it an empty node with new id and status
elif saveOnly:
# NB: we do creation already here (instead of same in countCooccurrences)
# to guarantee a unique ref id to the saveOnly graph (async generation)
new_node = corpus.add_child(
typename = "COOCCURRENCES",
name = "GRAPH (in corpus %s)" % corpus.id
)
session.add(new_node)
session.commit()
cooc_id = new_node.id
cooc_name = new_node.name
cooc_date = new_node.date
# and the empty content will need redoing by countCooccurrences
overwrite_node_contents = True
print("GRAPH #%d ... Created new empty data node for saveOnly" % int(cooc_id))
# Case of graph has not been computed already
# First, check the parameters
# Case of mapList not big enough
# ==============================
# if we do not have any mapList_id already
if mapList_id is None:
mapList_id = session.query(Node.id).filter(Node.typename == "MAPLIST").first()[0]
mapList_size = session.query(NodeNgram).filter(NodeNgram.node_id == mapList_id).count()
if mapList_size < graph_constraints['mapList']:
# Do not compute the graph if mapList is not big enough
return {'state': "mapListError", "length" : mapList_size}
# Instantiate query for case of corpus not big enough
# ===================================================
corpus_size_query = (session.query(Node)
.filter(Node.typename=="DOCUMENT")
.filter(Node.parent_id == corpus.id)
)
# Filter corpus by date if any start date
# ---------------------------------------
if start is not None:
#date_start = datetime.datetime.strptime ("2001-2-3 10:11:12", "%Y-%m-%d %H:%M:%S")
date_start = datetime.strptime (str(start), "%Y-%m-%d")
date_start_utc = date_start.strftime("%Y-%m-%d %H:%M:%S")
Start=aliased(NodeHyperdata)
corpus_size_query = (corpus_size_query.join( Start
, Start.node_id == Node.id
)
.filter( Start.key == 'publication_date')
.filter( Start.value_utc >= date_start_utc)
)
# Filter corpus by date if any end date
# -------------------------------------
if end is not None:
date_end = datetime.strptime (str(end), "%Y-%m-%d")
date_end_utc = date_end.strftime("%Y-%m-%d %H:%M:%S")
End=aliased(NodeHyperdata)
corpus_size_query = (corpus_size_query.join( End
, End.node_id == Node.id
)
.filter( End.key == 'publication_date')
.filter( End.value_utc <= date_end_utc )
)
# Finally test if the size of the corpora is big enough
# --------------------------------
corpus_size = corpus_size_query.count()
if saveOnly is not None and saveOnly == "True":
scheduled(compute_graph)( corpus_id=corpus.id, cooc_id=cooc_id
#, field1="ngrams", field2="ngrams"
, start=start , end =end
, mapList_id=mapList_id , groupList_id=groupList_id
, isMonopartite=True , threshold = threshold
, distance=distance , bridgeness=bridgeness
, save_on_db = True , reset=overwrite_node_contents
#, limit=size
)
return { "state" : "saveOnly"
, "target_id" : cooc_id
, "target_name": cooc_name
, "target_date": cooc_date
}
elif corpus_size > graph_constraints['corpusMax']:
# Then compute cooc asynchronously with celery
scheduled(compute_graph)( corpus_id=corpus.id, cooc_id=cooc_id
#, field1="ngrams", field2="ngrams"
, start=start , end =end
, mapList_id=mapList_id , groupList_id=groupList_id
, isMonopartite=True , threshold = threshold
, distance=distance , bridgeness=bridgeness
, save_on_db = True , reset=overwrite_node_contents
#, limit=size
)
# Dict to inform user that corpus maximum is reached
# then graph is computed asynchronously
return {"state" : "corpusMax", "length" : corpus_size}
elif corpus_size <= graph_constraints['corpusMin']:
# Do not compute the graph if corpus is not big enough
return {"state" : "corpusMin", "length" : corpus_size}
else:
# If graph_constraints are ok then compute the graph in live
data = compute_graph( corpus_id=corpus.id, cooc_id=cooc_id
#, field1="ngrams", field2="ngrams"
, start=start , end =end
, mapList_id=mapList_id , groupList_id=groupList_id
, isMonopartite=True , threshold = threshold
, distance=distance , bridgeness=bridgeness
, save_on_db = True , reset=overwrite_node_contents
#, limit=size
)
# case when 0 coocs are observed (usually b/c not enough ngrams in maplist)
if len(data) == 0:
print("GRAPH # ... GET_GRAPH: 0 coocs in matrix")
data = {'nodes':[], 'links':[]} # empty data
return data