diff --git a/data/disambiguation_data/padiweb_disambiguation/data_lang.json b/data/disambiguation_data/padiweb_disambiguation/data_lang.json
new file mode 100644
index 0000000000000000000000000000000000000000..62981767258f0c7baa517d1ccd2dffbeb2bf208a
--- /dev/null
+++ b/data/disambiguation_data/padiweb_disambiguation/data_lang.json
@@ -0,0 +1 @@
+{"289": "en", "504": "en", "262": "en", "276": "en", "510": "tl", "29": "en", "15": "en", "114": "en", "100": "en", "128": "en", "470": "en", "316": "en", "302": "en", "464": "en", "458": "en", "459": "en", "303": "en", "465": "en", "471": "en", "317": "en", "129": "en", "101": "en", "115": "en", "14": "en", "28": "en", "277": "en", "511": "en", "505": "en", "263": "en", "288": "en", "513": "en", "275": "en", "261": "en", "507": "en", "249": "en", "16": "en", "103": "en", "117": "en", "498": "en", "467": "en", "301": "en", "315": "en", "473": "en", "329": "en", "328": "en", "314": "en", "472": "en", "466": "en", "300": "en", "499": "en", "116": "en", "102": "en", "17": "en", "248": "en", "260": "en", "506": "en", "512": "en", "274": "en", "258": "en", "270": "en", "516": "en", "502": "en", "264": "en", "13": "en", "106": "en", "112": "en", "489": "en", "338": "ro", "304": "en", "462": "en", "476": "en", "310": "en", "477": "en", "311": "en", "305": "ro", "463": "en", "339": "en", "488": "en", "113": "en", "107": "en", "12": "en", "503": "en", "265": "en", "271": "en", "517": "en", "259": "en", "298": "en", "529": "en", "267": "en", "501": "en", "515": "en", "273": "en", "10": "en", "38": "en", "139": "en", "111": "en", "105": "en", "449": "en", "313": "en", "475": "en", "461": "en", "307": "en", "460": "en", "306": "en", "312": "en", "474": "en", "448": "en", "104": "en", "110": "en", "138": "en", "39": "en", "11": "en", "514": "en", "272": "en", "266": "en", "500": "en", "528": "en", "299": "en", "201": "en", "215": "en", "229": "en", "76": "en", "188": "en", "62": "en", "89": "en", "177": "en", "163": "en", "375": "en", "413": "en", "407": "en", "361": "en", "349": "en", "348": "en", "406": "en", "360": "en", "374": "en", "412": "en", "162": "en", "88": "en", "176": "en", "63": "en", "77": "en", "189": "en", "228": "en", "214": "en", "200": "en", "216": "en", "202": "en", "49": "en", "61": "en", "75": "en", "160": "en", "174": "en", "148": "en", "389": "en", "362": "en", "404": "en", "410": "en", "376": "en", "438": "en", "439": "en", "411": "en", "377": "en", "363": "en", "405": "en", "388": "en", "149": "en", "175": "en", "161": "en", "74": "en", "60": "en", "48": "en", "203": "en", "217": "en", "213": "en", "207": "en", "64": "tl", "70": "en", "58": "en", "159": "en", "165": "en", "171": "en", "398": "en", "429": "en", "401": "en", "367": "en", "373": "en", "415": "en", "372": "en", "414": "en", "400": "en", "366": "en", "428": "en", "399": "en", "170": "en", "164": "en", "158": "en", "59": "en", "71": "en", "65": "en", "206": "en", "212": "en", "238": "en", "204": "en", "210": "en", "73": "en", "67": "en", "199": "en", "9": "en", "172": "es", "98": "en", "166": "en", "358": "en", "416": "en", "370": "en", "364": "en", "402": "en", "365": "en", "403": "en", "417": "en", "371": "en", "359": "en", "99": "en", "167": "en", "173": "es", "8": "en", "66": "en", "198": "en", "72": "en", "211": "en", "205": "en", "239": "en", "220": "en", 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\ No newline at end of file
diff --git a/data/graph_exp_fev_18/result_eval_backup/database_graph_viewer.db b/data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewer.db
similarity index 100%
rename from data/graph_exp_fev_18/result_eval_backup/database_graph_viewer.db
rename to data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewer.db
diff --git a/data/graph_exp_fev_18/result_eval_backup/database_graph_viewerV1.db b/data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewerV1.db
similarity index 100%
rename from data/graph_exp_fev_18/result_eval_backup/database_graph_viewerV1.db
rename to data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewerV1.db
diff --git a/data/graph_exp_fev_18/result_eval_backup/database_graph_viewerv2.db b/data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewerv2.db
similarity index 100%
rename from data/graph_exp_fev_18/result_eval_backup/database_graph_viewerv2.db
rename to data/graph_data/graph_exp_fev_18/result_eval_backup/database_graph_viewerv2.db
diff --git a/data/graph_exp_july_19/selected.json b/data/graph_data/graph_exp_july_19/selected.json
similarity index 100%
rename from data/graph_exp_july_19/selected.json
rename to data/graph_data/graph_exp_july_19/selected.json
diff --git a/data/graph_exp_may_25/selected.json b/data/graph_data/graph_exp_may_25/selected.json
similarity index 100%
rename from data/graph_exp_may_25/selected.json
rename to data/graph_data/graph_exp_may_25/selected.json
diff --git a/eval_disambiguation.py b/eval_disambiguation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a719c3357cf9e9b5aaa4be030dbdaf8b54ca39e
--- /dev/null
+++ b/eval_disambiguation.py
@@ -0,0 +1,69 @@
+# coding = utf-8
+
+import argparse
+import sys
+import numpy as np
+from numpy import inf
+import glob,re,sys,os,json
+import pandas as pd
+from strpython.eval.disambiguation import *
+import logging
+for _ in ("boto", "elasticsearch", "urllib3"):
+ logging.getLogger(_).setLevel(logging.CRITICAL)
+
+
+parser= argparse.ArgumentParser()
+
+parser.add_argument("corpus_name",default="padiweb",help="Corpus you want to evaluate",choices=["padiweb","agromada"])
+parser.add_argument("measure",default="accuracy",help="Performance measure you want to compute",choices=["accuracy","accuracy_k","mean_distance_error"])
+parser.add_argument("-k",type=float,default=1,help="K value for the accuracy@k computation")
+
+args= parser.parse_args()
+
+if args.corpus_name == "padiweb":
+ corpus_dir="data/disambiguation_data/padiweb_disambiguation"
+ data_lang = json.load(open("data/disambiguation_data/padiweb_disambiguation/data_lang.json"))
+
+else:
+ corpus_dir = "data/disambiguation_data/mada_disambiguisation"
+ data_lang = json.load(open("/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json"))
+
+data_lang = {int(k): v for k, v in data_lang.items()}
+
+corpus_files=glob.glob("{0}/*.csv".format(corpus_dir))
+
+acc_MC,acc_GEO,acc_wiki=[],[],[]
+i=0
+
+for fn in corpus_files:
+ i+=1
+ id_=int(re.findall(r"\d+",fn)[-1])
+ #sys.stdout.write("\r{0}/{1}".format(i,len(fns)))
+ try:
+ df=pd.read_csv(fn)
+ lang=data_lang[id_]
+ acc_MC.append(efficiencyMostCommon(df,lang,args.measure,args.k))
+ acc_GEO.append(efficiencyGeodict(df,lang,args.measure,args.k))
+ acc_wiki.append(efficiencyWiki(df,lang,args.measure,args.k))
+ except Exception as e:
+ print(e)
+ acc_GEO=np.array(acc_GEO)
+ acc_GEO[acc_GEO == inf] = 0
+ acc_GEO=acc_GEO.tolist()
+ sys.stdout.write("\r{0}/{1} -- {5}Wiki : {2} |Â {5}MC : {3} | {5}GEO : {4}".format(
+ i,
+ len(corpus_files),
+ np.mean(np.nan_to_num(acc_wiki)),
+ np.mean(np.nan_to_num(acc_MC)),
+ np.mean(np.nan_to_num(acc_GEO)),
+ args.measure
+ )
+ )
+
+
+# In[63]:
+
+
+print("\naccGEO",np.mean(np.nan_to_num(acc_GEO)))
+print("acc_MC",np.mean(np.nan_to_num(acc_MC)))
+print("accWiki",np.mean(np.nan_to_num(acc_wiki)))
diff --git a/generate_data_csv.py b/generate_data_csv.py
index 28127de14def73f900ccfbff5f42d1c33900a199..dfcc9ee74b8a7f22d38b7c9dd453b50d48095705 100644
--- a/generate_data_csv.py
+++ b/generate_data_csv.py
@@ -5,7 +5,6 @@ import argparse,glob, string,time,re
from progressbar import ProgressBar, Timer, Bar, ETA, Counter
-from strpython.helpers.boundary import get_all_shapes
from strpython.models.str import STR
from strpython.nlp.disambiguator.geodict_gaurav import *
from strpython.pipeline import *
@@ -62,10 +61,13 @@ print("Parameters entered : ",args)
if os.path.exists(args.csv_input_dir):
files_glob= glob.glob(args.csv_input_dir+"/*.csv")
+if not files_glob:
+ files_glob = glob.glob(args.csv_input_dir + "/*.txt")
else:
exit()
-
+if not os.path.exists(args.graphs_output_dir):
+ os.makedirs(args.graphs_output_dir)
start = time.time()
associated_es={}
@@ -92,8 +94,7 @@ for k,v in associated_es.items():
for k2 in v:
all_es.add(k2)
-#logging.info("Get All Shapes from Database for all ES")
-#all_shapes=get_all_shapes(list(all_es))
+
i=0
def foo_(x):
@@ -116,7 +117,7 @@ with ProgressBar(max_value=len(files_glob),
# print("BUG",df)
df["label"]=df.GID.apply(foo_)
df = df.rename(columns={"GID": "id"})
- str_=STR.from_pandas(df,[],all_shapes).build()
+ str_=STR.from_pandas(df,[]).build()
nx.write_gexf(str_, args.graphs_output_dir + "/{0}.gexf".format(id_))
i+=1
pg.update(i)
diff --git a/notebooks/Eval.ipynb b/notebooks/Eval.ipynb
index f5096dac998463eb6ad959beb2fda3fcc0d4645e..d0a24d70435bb8ce1948dacef73ae12fa18395d9 100644
--- a/notebooks/Eval.ipynb
+++ b/notebooks/Eval.ipynb
@@ -22,13 +22,10 @@
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
- ],
- "text/vnd.plotly.v1+html": [
- "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
},
{
"name": "stdout",
@@ -78204,560 +78201,12 @@
"outputs": [
{
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- "application/vnd.plotly.v1+json": {
- "data": [
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]
},
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
@@ -82556,9 +79683,9 @@
},
"varInspector": {
"cols": {
- "lenName": 16,
- "lenType": 16,
- "lenVar": 40
+ "lenName": 16.0,
+ "lenType": 16.0,
+ "lenVar": 40.0
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diff --git a/notebooks/EvalDesambiguisationPADIWEB.ipynb b/notebooks/EvalDesambiguisationPADIWEB.ipynb
index 83ab07b833c92b0800b57eb2ce03f2ec1c482def..189ca41d2ee687c5ceed5c474a554606e6fcfdb3 100644
--- a/notebooks/EvalDesambiguisationPADIWEB.ipynb
+++ b/notebooks/EvalDesambiguisationPADIWEB.ipynb
@@ -5,8 +5,8 @@
"execution_count": 1,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T12:57:56.566077Z",
- "start_time": "2018-06-19T12:57:56.076820Z"
+ "end_time": "2018-08-27T15:11:06.231565Z",
+ "start_time": "2018-08-27T15:11:05.795641Z"
}
},
"outputs": [],
@@ -20,8 +20,8 @@
"execution_count": 2,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T12:57:56.766774Z",
- "start_time": "2018-06-19T12:57:56.761060Z"
+ "end_time": "2018-08-27T15:11:06.238529Z",
+ "start_time": "2018-08-27T15:11:06.233600Z"
}
},
"outputs": [
@@ -42,15 +42,15 @@
"execution_count": 3,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T12:58:25.165818Z",
- "start_time": "2018-06-19T12:58:25.056576Z"
+ "end_time": "2018-08-27T15:11:06.330207Z",
+ "start_time": "2018-08-27T15:11:06.240613Z"
}
},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"\n",
- "from config.configuration import config\n",
+ "from strpython.config.configuration import config\n",
"\n",
"es = Elasticsearch(config.es_server)\n",
"def get_data_by_geoname_id(id):\n",
@@ -67,12 +67,18 @@
"execution_count": 4,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T12:58:25.614490Z",
- "start_time": "2018-06-19T12:58:25.607038Z"
+ "end_time": "2018-08-27T15:11:06.346204Z",
+ "start_time": "2018-08-27T15:11:06.332072Z"
}
},
"outputs": [],
"source": [
+ "test=pd.read_csv(\"ens2.csv\")\n",
+ "def foo(x):\n",
+ " try:\n",
+ " test[test[\"sp_en\"] == x[\"id\"]].geonames_id.values[0]\n",
+ " except:\n",
+ " \"nan\"\n",
"def parse_file(fn):\n",
" id_=int(re.findall(r\"\\d+\",fn)[-1])\n",
" lang=langdetect.detect(open(\"data/EPI_ELENA/raw_text/{0}.txt\".format(id_)).read())\n",
@@ -81,18 +87,41 @@
" df=df[(df[\"type\"]==\"location\") & (df[\"annotation\"]==\"correct\")]\n",
" except:\n",
" return\n",
+ " df[\"geoname\"]=df[\"info\"].apply(lambda x:foo(x))\n",
" df[\"GID\"]=df[\"info\"].apply(lambda x:get_data_by_geoname_id(x[\"id\"])[\"id\"])\n",
" df[\"content\"]=df[\"content\"].apply(lambda x:re.sub(r\"\\s+\",\" \",x.strip()))\n",
- " return df,lang\n"
+ " return df,lang\n",
+ "\n",
+ "def parse_file2(fn):\n",
+ " id_=int(re.findall(r\"\\d+\",fn)[-1])\n",
+ " lang=langdetect.detect(open(\"data/EPI_ELENA/raw_text/{0}.txt\".format(id_)).read())\n",
+ " df=pd.read_json(fn,orient=\"index\")\n",
+ " try:\n",
+ " df=df[(df[\"type\"]==\"location\") & (df[\"annotation\"]==\"correct\")]\n",
+ " except:\n",
+ " return\n",
+ " return df"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T13:00:51.545645Z",
- "start_time": "2018-06-19T13:00:51.538149Z"
+ "end_time": "2018-08-27T15:08:33.366321Z",
+ "start_time": "2018-08-27T15:08:33.358349Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-08-27T15:11:06.356525Z",
+ "start_time": "2018-08-27T15:11:06.348143Z"
}
},
"outputs": [],
@@ -107,8 +136,8 @@
"execution_count": 6,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T12:58:56.147169Z",
- "start_time": "2018-06-19T12:58:56.132754Z"
+ "end_time": "2018-08-27T15:11:06.370866Z",
+ "start_time": "2018-08-27T15:11:06.358409Z"
}
},
"outputs": [],
@@ -118,19 +147,19 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 7,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:46:38.252413Z",
- "start_time": "2018-06-03T18:46:35.836908Z"
+ "end_time": "2018-08-27T15:11:09.749290Z",
+ "start_time": "2018-08-27T15:11:06.373193Z"
}
},
"outputs": [],
"source": [
- "%autoreload\n",
- "from nlp.disambiguator.geodict_gaurav import GauravGeodict\n",
- "from nlp.disambiguator.most_common import MostCommonDisambiguator\n",
- "from nlp.disambiguator.wikipedia_cooc import WikipediaDisambiguator\n",
+ "\n",
+ "from strpython.nlp.disambiguator.geodict_gaurav import GauravGeodict\n",
+ "from strpython.nlp.disambiguator.most_common import MostCommonDisambiguator\n",
+ "from strpython.nlp.disambiguator.wikipedia_cooc import WikipediaDisambiguator\n",
"disMost_common=MostCommonDisambiguator()\n",
"disGaurav=GauravGeodict()\n",
"disWiki=WikipediaDisambiguator()"
@@ -141,8 +170,8 @@
"execution_count": 8,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:40:57.064904Z",
- "start_time": "2018-06-03T18:40:57.043921Z"
+ "end_time": "2018-08-27T15:11:09.759142Z",
+ "start_time": "2018-08-27T15:11:09.751214Z"
}
},
"outputs": [],
@@ -160,8 +189,8 @@
"execution_count": 9,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:40:58.360243Z",
- "start_time": "2018-06-03T18:40:58.203320Z"
+ "end_time": "2018-08-27T15:11:09.831909Z",
+ "start_time": "2018-08-27T15:11:09.760876Z"
}
},
"outputs": [],
@@ -171,49 +200,49 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 10,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:46:54.196478Z",
- "start_time": "2018-06-03T18:46:53.863582Z"
+ "end_time": "2018-08-27T15:11:10.512110Z",
+ "start_time": "2018-08-27T15:11:09.833822Z"
}
},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rivers State GD4106855 12.73152386775468\n",
- "Kano GD4103071 21.675014816832682\n",
- "Kano GD4103071 21.675014816832682\n",
- "Lagos GD4468122 124.6205202335819\n",
- "Lagos GD4468122 124.6205202335819\n",
- "Port Harcourt GD791183 15.777445058883712\n"
- ]
- },
{
"data": {
"text/plain": [
- "0.6666666666666666"
+ "( after annotation content index \\\n",
+ " 17 NaN correct Latvia 165 \n",
+ " 3 1.0 correct Latvia 13 \n",
+ " 7 NaN correct Latvia 35 \n",
+ " \n",
+ " info length type \\\n",
+ " 17 {'coordinates': [57, 25], 'countryCode': 'LV',... 1 location \n",
+ " 3 {'coordinates': [57, 25], 'countryCode': 'LV',... 1 location \n",
+ " 7 {'coordinates': [57, 25], 'countryCode': 'LV',... 1 location \n",
+ " \n",
+ " use_for_all geoname GID \n",
+ " 17 NaN None GD5551940 \n",
+ " 3 1.0 None GD5551940 \n",
+ " 7 NaN None GD5551940 , 'en')"
]
},
- "execution_count": 19,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df,lang=parse_file(fns[0])\n",
- "accuracyMostCommon(df,lang)\n"
+ "parse_file(fns[0])\n"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:45:45.708459Z",
- "start_time": "2018-06-03T18:45:45.679984Z"
+ "end_time": "2018-08-27T15:11:10.542154Z",
+ "start_time": "2018-08-27T15:11:10.514743Z"
}
},
"outputs": [],
@@ -226,7 +255,7 @@
" return (df2.GID == df2.disambiguation).sum()/len(df2)\n",
"def accuracyWiki(df,lang):\n",
" df2=df[-df[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"content\",\"GID\"]]\n",
- " res_dis=disWiki.disambiguate(df2[\"content\"].unique(),lang)\n",
+ " res_dis=disWiki.disambiguate_wiki(df2[\"content\"].unique(),lang)\n",
" df2[\"disambiguation\"]=df2.content.apply(lambda x:res_dis[x] if x in res_dis else \"0\")\n",
" return (df2.GID == df2.disambiguation).sum()/len(df2)\n",
"#df\n",
@@ -235,11 +264,11 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 12,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:53:53.880676Z",
- "start_time": "2018-06-03T18:48:05.294472Z"
+ "end_time": "2018-08-27T15:13:54.566181Z",
+ "start_time": "2018-08-27T15:11:10.544793Z"
}
},
"outputs": [
@@ -247,10 +276,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
+ "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in long_scalars\n",
+ " # This is added back by InteractiveShellApp.init_path()\n",
"/usr/local/lib/python3.6/site-packages/pandas/core/ops.py:816: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" result = getattr(x, name)(y)\n",
- "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in long_scalars\n",
- " # This is added back by InteractiveShellApp.init_path()\n"
+ "GET http://localhost:9200/gazetteer/place/_search [status:400 request:0.006s]\n",
+ "GET http://localhost:9200/gazetteer/place/_search [status:400 request:0.004s]\n",
+ "GET http://localhost:9200/gazetteer/place/_search [status:400 request:0.003s]\n"
]
}
],
@@ -270,21 +302,31 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 13,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-05-17T01:37:02.209200Z",
- "start_time": "2018-05-17T01:37:02.200462Z"
+ "end_time": "2018-08-27T15:13:54.577715Z",
+ "start_time": "2018-08-27T15:13:54.568059Z"
}
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
+ " out=out, **kwargs)\n",
+ "/usr/local/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n"
+ ]
+ },
{
"data": {
"text/plain": [
- "0.5139891137064413"
+ "nan"
]
},
- "execution_count": 63,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -296,21 +338,31 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 14,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-05-17T01:37:02.250591Z",
- "start_time": "2018-05-17T01:37:02.246260Z"
+ "end_time": "2018-08-27T15:13:54.584996Z",
+ "start_time": "2018-08-27T15:13:54.579637Z"
}
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
+ " out=out, **kwargs)\n",
+ "/usr/local/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n"
+ ]
+ },
{
"data": {
"text/plain": [
- "0.5267050989770068"
+ "nan"
]
},
- "execution_count": 64,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -321,21 +373,21 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 15,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-03T18:55:22.909028Z",
- "start_time": "2018-06-03T18:55:22.904693Z"
+ "end_time": "2018-08-27T15:13:54.591617Z",
+ "start_time": "2018-08-27T15:13:54.587000Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
- "0.5630869832932465"
+ "0.5782357139650866"
]
},
- "execution_count": 22,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -347,7 +399,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-06-19T13:01:36.778853Z",
@@ -359,28 +411,23 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 16,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-06-19T13:10:53.120884Z",
- "start_time": "2018-06-19T13:09:52.611805Z"
+ "end_time": "2018-08-27T15:13:54.802963Z",
+ "start_time": "2018-08-27T15:13:54.593650Z"
}
},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.6/site-packages/pandas/core/ops.py:816: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
- " result = getattr(x, name)(y)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "151959 7898\n",
- "19.24018738921246\n"
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'helpers'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-16-d620a808fc3e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mhelpers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgazeteer_helpers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcount_of_se\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msum_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'helpers'"
]
}
],
diff --git a/run_test_disambiguisation.sh b/run_test_disambiguisation.sh
new file mode 100755
index 0000000000000000000000000000000000000000..59cba8bdcb6d3e5875a7794f2e4c32cad21e7523
--- /dev/null
+++ b/run_test_disambiguisation.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+python3 eval_disambiguation.py padiweb accuracy > accuracy_res_padi.txt
+python3 eval_disambiguation.py agromada accuracy > accuracy_res_mada.txt
+
+python3 eval_disambiguation.py padiweb mean_distance_error > mean_distance_res_padi.txt
+python3 eval_disambiguation.py agromada mean_distance_error > mean_distance_res_mada.txt
+
+python3 eval_disambiguation.py padiweb accuracy_k -k=1 >>accuracyk1y_res_padi.txt
+python3 eval_disambiguation.py padiweb accuracy_k -k=0.5 > accuracyk0-5_res_padi.txt
+
+python3 eval_disambiguation.py agromada accuracy_k -k=1 >> accuracyk1_res_mada.txt
+python3 eval_disambiguation.py agromada accuracy_k -k=0.5 > accuracyk0-5_res_mada.txt
\ No newline at end of file
diff --git a/strpython/eval/disambiguation.py b/strpython/eval/disambiguation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1536fe7ea12a3f50c4f27e65a3f2a3b6ad2d72e5
--- /dev/null
+++ b/strpython/eval/disambiguation.py
@@ -0,0 +1,88 @@
+# coding = utf-8
+
+from shapely.geometry import Point
+from ..helpers.geodict_helpers import *
+from ..nlp.disambiguator.geodict_gaurav import GauravGeodict
+from ..nlp.disambiguator.most_common import MostCommonDisambiguator
+from ..nlp.disambiguator.wikipedia_cooc import WikipediaDisambiguator
+
+import langdetect
+import pandas as pd
+import re
+import glob, re, sys
+
+disMost_common = MostCommonDisambiguator()
+disGaurav = GauravGeodict()
+disWiki = WikipediaDisambiguator()
+
+
+def get_coord(id):
+ try:
+ c = get_data(id).coord
+ return Point(c["lon"], c["lat"])
+ except Exception as e:
+ return None
+
+
+def dist(id1, id2):
+ p1, p2 = get_coord(id1), get_coord(id2)
+ if not p1 or not p2:
+ return -1
+ else:
+ return p1.distance(p2)
+
+
+def efficiencyMostCommon(df, lang, score="accuracy",k=1):
+ df2 = df[-df["GID"].isin(["O", "NR", "o"])][["text", "GID"]]
+ df2["disambiguation"] = df2.text.apply(lambda x: disMost_common.disambiguate_(x, lang)[0])
+ if score == "mean_distance_error":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return df2["distance"][df2["distance"] >= 0].mean()
+ if score == "accuracy_k":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return ((df2["distance"] < k) & (df2["distance"] >= 0)).sum() / ((df2["distance"] >= 0).sum())
+ return (df2.GID == df2.disambiguation).sum() / len(df2)
+
+
+def efficiencyGeodict(df, lang, score="accuracy",k=1):
+ df2 = df[-df["GID"].isin(["O", "NR", "o"])][["text", "GID"]]
+ res_dis = disGaurav.eval(df2["text"].unique(), lang)
+ df2["disambiguation"] = df2.text.apply(lambda x: res_dis[x] if x in res_dis else None)
+ if score == "mean_distance_error":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return df2["distance"][df2["distance"] >= 0].mean()
+ if score == "accuracy_k":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return ((df2["distance"] < k) & (df2["distance"] >= 0)).sum() / ((df2["distance"] >= 0).sum())
+
+ return (df2.GID == df2.disambiguation).sum() / len(df2)
+
+
+def efficiencyWiki(df, lang, score="accuracy",k=1):
+ df2 = df[-df["GID"].isin(["O", "NR", "o"])][["text", "GID"]]
+ res_dis = disWiki.disambiguate_wiki(df2["text"].unique(), lang)
+ df2["disambiguation"] = df2.text.apply(lambda x: res_dis[x] if x in res_dis else None)
+ if score == "mean_distance_error":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return df2["distance"][df2["distance"] >= 0].mean()
+ elif score == "accuracy_k":
+ df2["distance"] = df2.apply(lambda row: dist(row.GID, row.disambiguation) if "GID" in row else -1, axis=1)
+ return ((df2["distance"] < k) & (df2["distance"] >= 0)).sum() / ((df2["distance"] >= 0).sum())
+ else:
+ return (df2.GID == df2.disambiguation).sum() / len(df2)
+
+
+def parse_file_EPI(fn, path_rawtext):
+ id_ = int(re.findall(r"\d+", fn)[-1])
+ lang = langdetect.detect(open("{1}/{0}.txt".format(id_, path_rawtext.rstrip("/"))).read())
+ df = pd.read_json(fn, orient="index")
+ try:
+ df = df[(df["type"] == "location") & (df["annotation"] == "correct")]
+ except:
+ return
+ df["text"] = df["content"].apply(lambda x: re.sub(r"\s+", " ", x.strip()))
+ df["geoname"] = df["info"].apply(lambda x: x["id"])
+ df["GID"] = df["geoname"].apply(lambda x: get_data_by_geonames_id(x).id)
+ df = df[df["geoname"] != -111111]
+
+ return df, lang
diff --git a/strpython/helpers/geodict_helpers.py b/strpython/helpers/geodict_helpers.py
index 91d04087865e7f90c41a09de1e6cd850fe742877..0a91f973285086104e2d093f981fc8413827e578 100644
--- a/strpython/helpers/geodict_helpers.py
+++ b/strpython/helpers/geodict_helpers.py
@@ -123,12 +123,10 @@ def most_common_label(toponym: str, lang: str):
"""
res = es.search("gazetteer", "place",
- body={"query":
- {"bool":
- {"must": [{"term": {lang: toponym}}], "must_not": [], "should": []}
- },
+ body={ "query": {"query_string": {"query": "\"{0}\"".format(toponym), "analyze_wildcard": False}},
"from": 0,
- "size": 50, "sort": [{'score': "desc"}], "aggs": {}})
+ "size": 50,
+ "sort": [{'score': "desc"}]})
res = convert_es_to_pandas(res)
if not isinstance(res, pd.DataFrame):
return None, 0
@@ -150,13 +148,7 @@ def most_common_alias(toponym: str, lang: str):
"""
res = es.search("gazetteer", "place",
- body={"query": {"nested": {"path": "aliases",
- "query":
- {"bool":
- {"must": [{"term": {"aliases.{0}".format(lang): toponym}}], "must_not": [], "should": []}
- }
- }},
- "sort": [{"score": "desc"}]})
+ body={"size": 1, "sort": [{"score": {"order": "desc", "unmapped_type": "boolean"}}],"query": {"bool": {"must": [{"term": {lang: toponym}}], "must_not": [], "should": []}}})
res = convert_es_to_pandas(res)
if not isinstance(res, pd.DataFrame):
@@ -181,9 +173,11 @@ def n_label_similar(toponym, lang, n=5, score=True):
'score': "desc"
}
]
-
- res = es.search("gazetteer", "place",
- body=body)
+ try:
+ res = es.search("gazetteer", "place",
+ body=body)
+ except:
+ return None
res = convert_es_to_pandas(res)
if not isinstance(res, pd.DataFrame):
return None
@@ -208,8 +202,11 @@ def n_alias_similar(toponym, lang, n=5, score=True):
'score': "desc"
}
]
- res = es.search("gazetteer", "place",
- body=body)
+ try:
+ res = es.search("gazetteer", "place",
+ body=body)
+ except:
+ return None
res = convert_es_to_pandas(res)
if not isinstance(res, pd.DataFrame):
@@ -217,35 +214,6 @@ def n_alias_similar(toponym, lang, n=5, score=True):
return res.iloc[0].id, res.iloc[0].score
-def get_most_common_id_v2(label, lang="fr"):
- """
- Return the spatial entity and its score, based on a specific label and language that obtains the highest score.
- :param label: str
- :param lang: str
- :return: str, float
- """
- query_2 = {"query_string": {
- "default_field": lang,
- "query": parse_label(label),
-
- }}
- res = es.search("gazetteer", "place",
- body={"query":
- {"bool":
- {"must": [{"term": {lang: label}}], "must_not": [], "should": []}
- },
- "from": 0,
- "size": 50, "sort": [{'score': "desc"}], "aggs": {}})
- res = convert_es_to_pandas(res)
-
- if not isinstance(res, pd.DataFrame):
- if not res:
- res = convert_es_to_pandas(es.search("gazetteer", "place",
- body={"query": query_2}))
- if not isinstance(res, pd.DataFrame):
- return None, 0
- return res.iloc[0].id, res.iloc[0].score
-
def get_most_common_id_v3(label, lang='fr'):
"""
@@ -262,7 +230,12 @@ def get_most_common_id_v3(label, lang='fr'):
# China case
id_2, score2 = most_common_alias(label, lang)
if id_2 and score2 > score:
- return id_2, score2
+ id_, score = id_2, score2
+ simi=n_label_similar(label, lang)
+ if isinstance(simi,pd.DataFrame):
+ id_3, score3 = simi.iloc[0].id,simi.iloc[0].score
+ if id_2 and score2 > score:
+ id_, score = id_3, score3
return id_, score
# if nothing found in english, search in aliases
@@ -424,14 +397,15 @@ def get_top_candidate(label, lang, n=5):
:param lang: str
:return: list
"""
- query = {"query": {"bool": {"must": [{"term": {lang: label}}], "must_not": [], "should": []}}, "sort": [
- {
- "score": {
- "order": "desc"
- }
- }
- ], "size": n}
+ if n<4:
+ n=4
+ query={"size": n-3, "sort": [{"score": {"order": "desc"}}],"query": {"bool": {"must": [{"term": {lang: label}}], "must_not": [], "should": []}}}
+ query2={"size": 1, "sort": [{"score": {"order": "desc"}}],
+ "query": {"query_string": {"query": "\"{0}\"".format(label), "analyze_wildcard": False}}}
+ query3 = {"size": 1, "sort": [{"score": {"order": "desc", "unmapped_type": "boolean"}}],"query": {"bool": {"must": [{"term": {"en": "\"{0}\"".format(label)}}], "must_not": [], "should": []}}}
response = es.search('gazetteer', 'place', body=query)
+ res=[]
if 'hits' in response['hits']:
- return [x["_source"]["id"] for x in response['hits']['hits']]
- return []
+ res=[x["_source"]["id"] for x in response['hits']['hits']]
+ res.extend([get_most_common_id_v3(label,lang)[0]])
+ return res
diff --git a/strpython/models/str.py b/strpython/models/str.py
index be98e0019b7dd184dcc4f337db41abc5e90307dd..efe5ae470a99cace5fdddcc93a1f3a1a10367514 100644
--- a/strpython/models/str.py
+++ b/strpython/models/str.py
@@ -66,7 +66,7 @@ class STR(object):
return str_
@staticmethod
- def from_dict(spat_ent: dict, tagged_: list = [], shapes: dict = {}):
+ def from_dict(spat_ent: dict, tagged_: list = []):
"""
Return a STR built from a Networkx imported graph
:param g:
@@ -77,13 +77,13 @@ class STR(object):
for id_, label in spat_ent.items():
sp_en[id_] = label
- str_ = STR(tagged_, sp_en, shapes)
+ str_ = STR(tagged_, sp_en)
str_.build()
return str_
@staticmethod
- def from_pandas(dataf: pd.DataFrame, tagged: list = [], shapes: dict = {}):
- return STR.from_dict(pd.Series(dataf.label.values, index=dataf.id).to_dict(), tagged, shapes)
+ def from_pandas(dataf: pd.DataFrame, tagged: list = []):
+ return STR.from_dict(pd.Series(dataf.label.values, index=dataf.id).to_dict(), tagged)
def add_spatial_entity(self, id, label=None, v=True):
"""
diff --git a/strpython/nlp/disambiguator/geodict_gaurav.py b/strpython/nlp/disambiguator/geodict_gaurav.py
index f6ae42277bb7e9bd5e6c553799e76d8db7a999de..bbfb37b8b7b4229a94a26caa5036c7df94503a45 100644
--- a/strpython/nlp/disambiguator/geodict_gaurav.py
+++ b/strpython/nlp/disambiguator/geodict_gaurav.py
@@ -86,9 +86,9 @@ class GauravGeodict(Disambiguator):
fixed_entities = {}
ambiguous_entities = {}
for en in se_:
- request = get_by_label(en, lang)
+ request = get_top_candidate(en, lang)
if len(request) == 0:
- request = get_by_alias(en, lang)
+ request = n_label_similar(en, lang)
if len(request) > 1:
ambiguous_entities[en] = [r["_source"] for r in request]
diff --git a/strpython/nlp/disambiguator/models/bigram.py b/strpython/nlp/disambiguator/models/bigram.py
index f45ba97b13382fa7cc7c7bccc421732284fba791..9441041fc75f5377532d2339c5544d7350c0cf8d 100644
--- a/strpython/nlp/disambiguator/models/bigram.py
+++ b/strpython/nlp/disambiguator/models/bigram.py
@@ -1,4 +1,6 @@
# coding = utf-8
+from strpython.helpers.geodict_helpers import get_data
+
class BigramModel:
def __init__(self,freq={},count={}):
@@ -21,7 +23,7 @@ class BigramModel:
self.count_associated[uri]+=1
def get_coocurence_probability(self, pr1, *args):
- if len(args) <2:
+ if len(args) < 2:
print("Only one URI indicated")
return 0.
res_=1.
@@ -34,10 +36,12 @@ class BigramModel:
nna=0.00000001
if uri1 in self.cooc_freq:
if uri2 in self.cooc_freq[uri1]:
- return (self.cooc_freq[uri1][uri2] / self.count_associated[uri1])+pr1
+ return self.cooc_freq[uri1][uri2]
+ #return (self.cooc_freq[uri1][uri2] / self.count_associated[uri1])+pr1
elif uri2 in self.cooc_freq:
if uri1 in self.cooc_freq[uri2]:
- return (self.cooc_freq[uri2][uri1] / self.count_associated[uri1])+pr1
+ return self.cooc_freq[uri2][uri1]
+ #return (self.cooc_freq[uri2][uri1] / self.count_associated[uri1])+pr1
return nna
diff --git a/strpython/nlp/disambiguator/wikipedia_cooc.py b/strpython/nlp/disambiguator/wikipedia_cooc.py
index 56ec7cd83f97971a9d392f74151eb4dc37c0f047..9d605d3af58ca54e40af2d4b71818600659d02cc 100644
--- a/strpython/nlp/disambiguator/wikipedia_cooc.py
+++ b/strpython/nlp/disambiguator/wikipedia_cooc.py
@@ -5,7 +5,7 @@ from .disambiguator import Disambiguator
from .models.bigram import BigramModel
import pickle
from ...config.configuration import config
-from ...helpers.geodict_helpers import get_data,get_most_common_id_v3,get_top_candidate
+from ...helpers.geodict_helpers import *
from .most_common import stop_words,common_words
import networkx as nx
@@ -14,7 +14,7 @@ def read_pickle(fn):
class WikipediaDisambiguator(Disambiguator):
- def __init__(self,measure="centrality"):
+ def __init__(self,measure="degree"):
Disambiguator.__init__(self)
# Load model
self.model=BigramModel(read_pickle(config.wiki_cooc_dis.cooc_freq),read_pickle(config.wiki_cooc_dis.count))
@@ -53,7 +53,16 @@ class WikipediaDisambiguator(Disambiguator):
group_candidate = {} #candidates per toponym
for e in spat_en:
- cand = get_top_candidate(e, lang,4)
+ cand = get_top_candidate(e, lang, 5)#get_top_candidate(e, lang,4)
+ if cand[0] == None:
+ cand=[]
+ if not cand:
+ cand=n_label_similar(e,lang,5)
+ if isinstance(cand,pd.DataFrame):
+ cand = cand["id"].values
+ else:
+ cand=[]
+
group_candidate[e] = cand
betw_cand[e]=cand
for n in cand:
@@ -91,13 +100,14 @@ class WikipediaDisambiguator(Disambiguator):
#Take the candidates with the highest degree weighted
for gr in group_candidate:
try:
+
if self.measure == "degree":
selected[gr] = max(group_candidate[gr], key=lambda x: g.degree(x, weight='weight'))
elif self.measure == "centrality":
selected[gr] = max(group_candidate[gr], key=lambda x: nx.closeness_centrality(g, x, distance="weight"))
else:# degree by default
selected[gr] = max(group_candidate[gr], key=lambda x: g.degree(x, weight='weight'))
-
+ #print(1)
except:
selected[gr]=get_most_common_id_v3(gr,lang)[0]
return selected