diff --git a/criteria_cache.py b/criteria_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..b04c925052d0b07819b6625710669a8049194629
--- /dev/null
+++ b/criteria_cache.py
@@ -0,0 +1,68 @@
+# coding = utf-8
+
+import argparse, os, re, json, glob
+import pandas as pd
+import networkx as nx
+import numpy as np
+
+
+from strpython.eval.automatic_annotation import AnnotationAutomatic,save_cache,add_cache
+from strpython.models.str import STR
+from tqdm import tqdm,TqdmSynchronisationWarning
+from joblib import Parallel, delayed
+from multiprocessing import cpu_count
+
+
+import warnings
+warnings.simplefilter("ignore", TqdmSynchronisationWarning)
+tqdm.pandas()
+
+annotater = AnnotationAutomatic()
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("csvinputdir")
+parser.add_argument("graph_dir")
+parser.add_argument("output_file")
+
+args = parser.parse_args()
+
+if not os.path.exists(args.csvinputdir) or not os.path.exists(args.graph_dir):
+ raise FileNotFoundError("Error in Input")
+
+all_cp=[]
+fns = glob.glob("{0}/*".format(args.csvinputdir))
+for fn in fns:
+ df = pd.read_csv(fn)
+ cps=df["G1 G2".split()].apply(lambda x:"_".join(np.sort(x.values).astype(str)) ,axis=1).values
+ all_cp.extend(cps.tolist())
+all_cp=set(all_cp)
+df = pd.DataFrame([cp.split("_") for cp in all_cp],columns="G1 G2".split())
+str_graph_path = args.graph_dir
+
+
+strs = {}
+for file in tqdm(glob.glob(os.path.join(str_graph_path, "*.gexf")),desc="Load Graphs"):
+ id_ = int(re.findall("\d+", file)[-1])
+ strs[id_] = STR.from_networkx_graph(nx.read_gexf(file))
+
+#print(strs)
+def foo(x):
+ try:
+ return annotater.all(strs[int(x.G1)], strs[int(x.G2)],int(x.G1), int(x.G2))
+ except KeyError as e:
+ add_cache(int(x.G1), int(x.G2),[0, 0, 0, 0])
+ return [0, 0, 0, 0]
+
+
+df["res"] = df.progress_apply(lambda x: foo(x), axis=1) #Parallel(n_jobs=4)(delayed(foo)(x) for x in tqdm(df.itertuples(),total=df.size,desc="Extracting Crit"))#
+df.res=df.res.apply(lambda x :list(map(int,x)) if x else [])
+df["c1"] = df.res.apply(lambda x: x[0] if len(x)>0 else 0)
+df["c2"] = df.res.apply(lambda x: x[1] if len(x)>0 else 0)
+df["c3"] = df.res.apply(lambda x: x[2] if len(x)>0 else 0)
+df["c4"] = df.res.apply(lambda x: x[3] if len(x)>0 else 0)
+
+del df["res"]
+save_cache()
+df.to_csv(args.output_file)
+
diff --git a/strpython/eval/automatic_annotation.py b/strpython/eval/automatic_annotation.py
index b9f5fc1dd90443debafed84a47de60a51375c0c0..89f918289851bd430d3cef6b976a6416805d9104 100644
--- a/strpython/eval/automatic_annotation.py
+++ b/strpython/eval/automatic_annotation.py
@@ -56,9 +56,7 @@ class AnnotationAutomatic(object):
def all(self,str1,str2,id1=None,id2=None):
cache_data=get_from_cache(id1,id2)
if not cache_data:
- crit_ = [self.criterion1(str1, str2), self.criterion2(str1, str2),
- self.criterion3(str1, str2, id1, id2),
- self.criterion4(str1, str2, id1, id2)]
+ crit_ = [self.criterion1(str1, str2), self.criterion2(str1, str2),self.criterion3(str1, str2, id1, id2),self.criterion4(str1, str2, id1, id2)]
add_cache(id1,id2,crit_)
return crit_
return cache_data
diff --git a/strpython/helpers/collision.py b/strpython/helpers/collision.py
index 5c1b1345b4afab70c043d240634d4285b14372b7..7f4647b166d8986ad3bd512bb855bb990c66bf3e 100644
--- a/strpython/helpers/collision.py
+++ b/strpython/helpers/collision.py
@@ -80,9 +80,9 @@ def getGEO(id_se):
return None
data=data[0]
- if "path" in data:
+ if "path" in data.other:
return explode(gpd.read_file(os.path.join(config.osm_boundaries_directory, data.other["path"]))).convex_hull
- elif "coord" in data:
+ elif "coord" in data.other:
return gpd.GeoDataFrame(gpd.GeoSeries([Point(data.coord.lon, data.coord.lat).buffer(1.0)])).rename(
columns={0: 'geometry'})
return None
diff --git a/strpython/models/str.py b/strpython/models/str.py
index 2d673ba3fb27a977de11d77eed0463a0962e91d8..72aa9caf1ce9cc3dc1519e99026eb2b9493a75d9 100644
--- a/strpython/models/str.py
+++ b/strpython/models/str.py
@@ -96,7 +96,7 @@ class STR(object):
except KeyError: # If no label found, grab one from the geo-database
data = gazetteer.get_by_id(nod)
if data:
- sp_en[nod] = data[0].name
+ sp_en[nod] = data[0].label
str_ = STR(tagged_, sp_en,toponym_first=False)
str_.set_graph(g)
@@ -310,6 +310,7 @@ class STR(object):
data = gazetteer.get_by_id(id_se)
if len(data) > 0:
STR.__cache_entity_data[id_se] = data[0]
+ return data[0]
def transform_spatial_entities(self, transform_map: dict):
"""
@@ -492,17 +493,17 @@ class STR(object):
data_se1, data_se2 = self.get_data(se1), self.get_data(se2)
- if "P47" in data_se2 and se1 in get_p47_adjacency_data(data_se2):
+ if "P47" in data_se2.other and se1 in get_p47_adjacency_data(data_se2):
return True
# print("P47")
- elif "P47" in data_se1 and se2 in get_p47_adjacency_data(data_se1):
+ elif "P47" in data_se1.other and se2 in get_p47_adjacency_data(data_se1):
return True
# print("P47")
if collisionTwoSEBoundaries(se1, se2):
return True
- if "coord" in data_se1 and "coord" in data_se2:
+ if data_se1 and data_se2 and "coord" in data_se1.other and "coord" in data_se2.other:
if Point(data_se1.coord.lon, data_se1.coord.lat).distance(
Point(data_se2.coord.lon, data_se2.coord.lat)) < 1 and len(
set(data_se1.class_) & stop_class) < 1 and len(set(data_se2.class_) & stop_class) < 1:
@@ -642,7 +643,7 @@ class STR(object):
data = gazetteer.get_by_id(se)[0]
try:
points.append(Point(data.coord.lon, data.coord.lat))
- label.append(data.name)
+ label.append(data.label)
# class_.append(most_common(data["class"]))
except KeyError:
pass