def check_constraint():
text = constraint_text.lower()
text = text.split(",")
# CONSTRAINT BUILDER
c_builder = Buffer.StringIO()
if constraint_targets is not None:
for dictionary in constraint_targets:
graph = dictionary[St.graph]
data_list = dictionary[St.data]
properties = data_list[0][St.properties]
prop = properties[0] if Ut.is_nt_format(properties[0]) else "<{}>".format(properties[0])
# WRITING THE CONSTRAINT ON THE GRAPH
graph_q = """
{{
GRAPH <{0}>
{{
?lookup {1} ?constraint .
}}
}}
""".format(graph, prop)
c_builder.write(graph_q) if len(c_builder.getvalue()) == 0 else \
c_builder.write("UNION {}".format(graph_q))
# WRITING THE FILTER
if len(c_builder.getvalue()) > 0:
for i in range(0, len(text)):
if i == 0 :
c_builder.write("""
FILTER (LCASE(STR(?constraint)) = "{}" """.format(text[i].strip()))
else:
c_builder.write("""
|| LCASE(STR(?constraint)) = "{}" """.format(text[i].strip()))
c_builder.write(")")
# # THE RESULT OF THE QUERY ABOUT THE LINKED RESOURCES
query = Qry.cluster_rsc_strengths_query(resources, linkset)
query = query.replace("# CONSTRAINTS IF ANY", c_builder.getvalue())
# print query
response = Qry.sparql_xml_to_matrix(query)
if response[St.result] is None:
return False
return True
def cluster_d_test(linkset, network_size=3, network_size_max=3, targets=None, constraint_targets=None,
constraint_text="", directory=None, greater_equal=True, print_it=False, limit=None,
only_good=False, activated=False):
# FOR CONSTRAINTS TO WORK, IT SHOULD NOT BE NONE
network = []
print "\nLINK NETWORK INVESTIGATION"
if activated is False:
print "\tTHE FUNCTION I NOT ACTIVATED"
return ""
elif network_size > network_size_max and greater_equal is False:
print "\t[network_size] SHOULD BE SMALLER THAN [network_size_max]"
return ""
date = datetime.date.isoformat(datetime.date.today()).replace('-', '')
linkset_name = Ut.get_uri_local_name(linkset)
linkset = linkset.strip()
if network_size_max - network_size == 0:
greater_equal = False
check = False
# RUN THE CLUSTER
clusters_0 = Cls.links_clustering(linkset, limit)
if greater_equal is True:
temp_size = 0
for cluster, cluster_val in clusters_0.items():
new_size = len(list(cluster_val["nodes"]))
if new_size > temp_size:
temp_size = new_size
network_size_max = temp_size
print "THE BIGGEST NETWORK'S: {}".format(network_size_max)
def check_constraint():
text = constraint_text.lower()
text = text.split(",")
# CONSTRAINT BUILDER
c_builder = Buffer.StringIO()
if constraint_targets is not None:
for dictionary in constraint_targets:
graph = dictionary[St.graph]
data_list = dictionary[St.data]
properties = data_list[0][St.properties]
prop = properties[0] if Ut.is_nt_format(properties[0]) else "<{}>".format(properties[0])
# WRITING THE CONSTRAINT ON THE GRAPH
graph_q = """
{{
GRAPH <{0}>
{{
?lookup {1} ?constraint .
}}
}}
""".format(graph, prop)
c_builder.write(graph_q) if len(c_builder.getvalue()) == 0 else \
c_builder.write("UNION {}".format(graph_q))
# WRITING THE FILTER
if len(c_builder.getvalue()) > 0:
for i in range(0, len(text)):
if i == 0 :
c_builder.write("""
FILTER (LCASE(STR(?constraint)) = "{}" """.format(text[i].strip()))
else:
c_builder.write("""
|| LCASE(STR(?constraint)) = "{}" """.format(text[i].strip()))
c_builder.write(")")
# # THE RESULT OF THE QUERY ABOUT THE LINKED RESOURCES
query = Qry.cluster_rsc_strengths_query(resources, linkset)
query = query.replace("# CONSTRAINTS IF ANY", c_builder.getvalue())
# print query
response = Qry.sparql_xml_to_matrix(query)
if response[St.result] is None:
return False
return True
for index in range(network_size, network_size_max + 1):
count_1 = 0
count_2 = 0
curr_network_size = index
print "\nCLUSTERS OF SIZE {}".format(index)
sheet_builder = Buffer.StringIO()
analysis_builder = Buffer.StringIO()
sheet_builder.write("Count ID STRUCTURE E-STRUCTURE-SIZE A. NETWORK QUALITY"
" M. NETWORK QUALITY REFERENCE\n")
for cluster, cluster_val in clusters_0.items():
# network = []
resources = ""
uri_size = 0
count_1 += 1
children = list(cluster_val["nodes"])
strengths = cluster_val["strengths"]
cluster_size = len(children)
# if "<http://www.grid.ac/institutes/grid.10493.3f>" not in children:
# continue
check = cluster_size >= curr_network_size if greater_equal else cluster_size == curr_network_size
# NETWORK OF A PARTICULAR SIZE
if check:
# file_name = i_cluster[0]
# 2: FETCHING THE CORRESPONDENTS
smallest_hash = float('inf')
child_list = ""
for child in children:
# CREATE THE HASHED ID AS THE CLUSTER NAME
hashed = hash(child)
if hashed <= smallest_hash:
smallest_hash = hashed
# GENERAL INFO 1: RESOURCES INVOLVED
child_list += "\t{}\n".format(child)
# LIST OF RESOURCES IN THE CLUTER
use = "<{}>".format(child) if Ut.is_nt_format(child) is not True else child
resources += "\n\t\t\t\t{}".format(use)
if len(child) > uri_size:
uri_size = len(child)
# MAKE SURE THE FILE NAME OF THE CLUSTER IS ALWAYS THE SAME
file_name = "{}".format(str(smallest_hash).replace("-", "N")) if str(
smallest_hash).startswith("-") \
else "P{}".format(smallest_hash)
if constraint_targets is not None and check_constraint() is False:
continue
count_2 += 1
# # THE RESULT OF THE QUERY ABOUT THE LINKED RESOURCES
query = Qry.cluster_rsc_strengths_query(resources, linkset)
response = Qry.sparql_xml_to_matrix(query)
# GENERAL INFO 2:
info = "SIZE {} \nCLUSTER {} \nNAME {}\n".format(cluster_size, count_1, file_name)
info2 = "CLUSTER [{}] NAME [{}] SIZE [{}]".format(count_1, file_name, cluster_size)
analysis_builder.write("{}\n".format(info))
analysis_builder.write("RESOURCES INVOLVED\n")
analysis_builder.write(child_list)
analysis_builder.write("\nCORRESPONDENT FOUND ")
analysis_builder.write(
Qry.display_matrix(response, spacing=uri_size, output=True, line_feed='.', is_activated=True))
# INFO TYPE 3: PROPERTY-VALUES OF THE RESOURCES INVOLVED
analysis_builder.write("\n\nDISAMBIGUATION HELPER ")
if targets is None:
analysis_builder.write(Cls.disambiguate_network(linkset, children))
else:
report = Cls.disambiguate_network_2(children, targets)
if report is not None:
analysis_builder.write(report)
# GENERATING THE NETWORK AS A TUPLE WHERE A TUPLE REPRESENT TWO RESOURCES IN A RELATIONSHIP :-)
network = []
link_count = 0
for link in cluster_val["links"]:
link_count += 1
name_1 = "{}-{}".format(Ut.hash_it(link[0]), Ut.get_uri_local_name(link[0]))
name_2 = "{}-{}".format(Ut.hash_it(link[1]), Ut.get_uri_local_name(link[1]))
network += [(name_1, name_2)]
# GET THE AUTOMATED FLAG
if print_it:
print ""
print analysis_builder.getvalue()
# SETTING THE DIRECTORY
if directory:
if network:
automated_decision = metric(network)["AUTOMATED_DECISION"]
if only_good is True and automated_decision.startswith("GOOD") is not True:
count_2 -= 1
continue
print "{:>5} {}".format(count_2, info2)
eval_sheet(targets, count_2, "{}_{}".format(cluster_size, file_name),
sheet_builder, linkset, children, automated_decision)
else:
print network
# linkset_name = Ut.get_uri_local_name(linkset)
# date = datetime.date.isoformat(datetime.date.today()).replace('-', '')
temp_directory = "{}{}".format(directory, "\{}_Analysis_{}\{}\{}_{}\\".format(
curr_network_size, date, linkset_name, cluster_size, file_name))
if not os.path.exists(temp_directory):
os.makedirs(temp_directory)
""""""""""""" PLOTTING """""""""""""
# FIRE THE DRAWING: Supported formats: eps, pdf, pgf, png, ps, raw, rgba, svg, svgz.
analysis_builder.write(
draw_graph(graph=network,
file_path="{}{}.{}".format(temp_directory, "cluster_{}".format(file_name), "pdf"),
show_image=False)
)
""""""""""""" WRITING TO DISC """""""""""""
# WRITE TO DISC
Ut.write_2_disc(file_directory=temp_directory, file_name="cluster_{}".format(file_name, ),
data=analysis_builder.getvalue(), extension="txt")
analysis_builder = Buffer.StringIO()
if directory:
# if len(sheet_builder.getvalue()) > 150 and count_2 == 2:
if len(sheet_builder.getvalue()) > 150 and len(clusters_0) == count_1:
tmp_directory = "{}{}".format(directory, "\{}_Analysis_{}\{}\\".format(
curr_network_size, date, linkset_name))
""""""""""""" WRITING CLUSTER SHEET TO DISC """""""""""""
print "\n\tWRITING CLUSTER SHEET AT\n\t{}".format(tmp_directory)
Ut.write_2_disc(file_directory=tmp_directory, file_name="{}_ClusterSheet".format(cluster_size),
data=sheet_builder.getvalue(), extension="txt")
# if count_2 == 2:
# break
if greater_equal is True:
# no need to continue as we already did all network greater of equal to "network-size" input
break
print "\t>>> FOUND: {} CLUSTERS OF SIZE {}".format(count_2, curr_network_size)
if directory is None:
return "{}\t{}".format(curr_network_size, count_2)
def cluster_d_test_stats(linkset, network_size=3, targets=None,
directory=None, greater_equal=True, print_it=False, limit=None, activated=False):
network = []
print "LINK NETWORK INVESTIGATION"
if activated is False:
print "\tTHE FUNCTION I NOT ACTIVATED"
return ""
date = datetime.date.isoformat(datetime.date.today()).replace('-', '')
linkset_name = Ut.get_uri_local_name(linkset)
count_1 = 0
count_2 = 0
sheet_builder = Buffer.StringIO()
analysis_builder = Buffer.StringIO()
sheet_builder.write("Count ID STRUCTURE E-STRUCTURE-SIZE A. NETWORK QUALITY"
" M. NETWORK QUALITY REFERENCE\n")
linkset = linkset.strip()
check = False
# RUN THE CLUSTER
clusters_0 = Cls.links_clustering(linkset, limit)
for cluster, cluster_val in clusters_0.items():
# network = []
resources = ""
uri_size = 0
count_1 += 1
children = list(cluster_val["nodes"])
strengths = cluster_val["strengths"]
cluster_size = len(children)
# if "<http://www.grid.ac/institutes/grid.10493.3f>" not in children:
# continue
check = cluster_size >= network_size if greater_equal else cluster_size == network_size
# NETWORK OF A PARTICULAR SIZE
if check:
count_2 += 1
# file_name = i_cluster[0]
# 2: FETCHING THE CORRESPONDENTS
smallest_hash = float('inf')
child_list = ""
for child in children:
hashed = hash(child)
if hashed <= smallest_hash:
smallest_hash = hashed
# GENERAL INFO 1: RESOURCES INVOLVED
child_list += "\t{}\n".format(child)
use = "<{}>".format(child) if Ut.is_nt_format(child) is not True else child
resources += "\n\t\t\t\t{}".format(use)
if len(child) > uri_size:
uri_size = len(child)
if directory:
# MAKE SURE THE FILE NAME OF THE CLUSTER IS ALWAYS THE SAME
file_name = "{}".format(str(smallest_hash).replace("-", "N")) if str(
smallest_hash).startswith("-") \
else "P{}".format(smallest_hash)
# # THE RESULT OF THE QUERY ABOUT THE LINKED RESOURCES
query = Qry.cluster_rsc_strengths_query(resources, linkset)
response = Qry.sparql_xml_to_matrix(query)
# GENERAL INFO 2:
info = "SIZE {} \nCLUSTER {} \nNAME {}\n".format(cluster_size, count_1, file_name)
info2 = "CLUSTER [{}] NAME [{}] SIZE [{}]".format(count_1, file_name, cluster_size)
analysis_builder.write("{}\n".format(info))
print "{:>5} {}".format(count_2, info2)
analysis_builder.write("RESOURCES INVOLVED\n")
analysis_builder.write(child_list)
analysis_builder.write("\nCORRESPONDENT FOUND ")
analysis_builder.write(
Qry.display_matrix(response, spacing=uri_size, output=True, line_feed='.', is_activated=True))
# INFO TYPE 3: PROPERTY-VALUES OF THE RESOURCES INVOLVED
analysis_builder.write("\n\nDISAMBIGUATION HELPER ")
if targets is None:
analysis_builder.write(Cls.disambiguate_network(linkset, children))
else:
analysis_builder.write(Cls.disambiguate_network_2(children, targets))
# GENERATING THE NETWORK AS A TUPLE WHERE A TUPLE REPRESENT TWO RESOURCES IN A RELATIONSHIP :-)
network = []
link_count = 0
for link in cluster_val["links"]:
link_count += 1
name_1 = "{}".format(Ut.get_uri_local_name(link[0]))
name_2 = "{}".format(Ut.get_uri_local_name(link[1]))
network += [(name_1, name_2)]
if print_it:
print ""
print analysis_builder.getvalue()
# SETTING THE DIRECTORY
if directory:
# linkset_name = Ut.get_uri_local_name(linkset)
# date = datetime.date.isoformat(datetime.date.today()).replace('-', '')
temp_directory = "{}{}".format(directory, "\{}_Analysis_{}\{}\{}_{}\\".format(
network_size, date, linkset_name, cluster_size, file_name))
if not os.path.exists(temp_directory):
os.makedirs(temp_directory)
""""""""""""" PLOTTING """""""""""""
# FIRE THE DRAWING: Supported formats: eps, pdf, pgf, png, ps, raw, rgba, svg, svgz.
analysis_builder.write(
draw_graph(graph=network,
file_path="{}{}.{}".format(temp_directory, "cluster_{}".format(file_name), "pdf"),
show_image=False)
)
""""""""""""" WRITING TO DISC """""""""""""
# WRITE TO DISC
Ut.write_2_disc(file_directory=temp_directory, file_name="cluster_{}".format(file_name, ),
data=analysis_builder.getvalue(), extension="txt")
analysis_builder = Buffer.StringIO()
if network:
automated_decision = metric(network)["AUTOMATED_DECISION"]
eval_sheet(targets, count_2, "{}_{}".format(cluster_size, file_name),
sheet_builder, linkset, children, automated_decision)
else:
print network
if directory:
# if len(sheet_builder.getvalue()) > 150 and count_2 == 2:
if len(sheet_builder.getvalue()) > 150 and len(clusters_0) == count_1:
tmp_directory = "{}{}".format(directory, "\{}_Analysis_{}\{}\\".format(
network_size, date, linkset_name))
""""""""""""" WRITING CLUSTER SHEET TO DISC """""""""""""
print "\nWRITING CLUSTER SHEET AT\n\t{}".format(tmp_directory)
Ut.write_2_disc(file_directory=tmp_directory, file_name="{}_ClusterSheet".format(cluster_size),
data=sheet_builder.getvalue(), extension="txt")
# if count_2 == 2:
# break
print ">>> FOUND: {}".format(count_2)
if directory is None:
return "{}\t{}".format(network_size, count_2)