forked from chrismattmann/tika-similarity
/
edit-value-similarity.py
253 lines (189 loc) · 12 KB
/
edit-value-similarity.py
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#!/usr/bin/env python2.7
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
from tika import parser
import os, editdistance, itertools, argparse, csv
from requests import ConnectionError
from time import sleep
import json
def stringify(attribute_value):
if isinstance(attribute_value, list):
return str((", ".join(attribute_value)).encode('utf-8').decode('utf-8').strip())
else:
return str(attribute_value.encode('utf-8').decode('utf-8').strip())
def computeScores(inputDir, outCSV, acceptTypes, allKeys):
na_metadata = ["resourceName"]
with open(outCSV, "wb") as outF:
a = csv.writer(outF, delimiter=',')
a.writerow(["x-coordinate","y-coordinate","Similarity_score"])
filename_list = []
for root, dirnames, files in os.walk(inputDir):
dirnames[:] = [d for d in dirnames if not d.startswith('.')]
for filename in files:
if not filename.startswith('.'):
filename_list.append(os.path.join(root, filename))
try:
filename_list = [filename for filename in filename_list if "metadata" in parser.from_file(filename)]
except ConnectionError:
sleep(1)
if acceptTypes:
filename_list = [filename for filename in filename_list if str(parser.from_file(filename)['metadata']['Content-Type'].encode('utf-8').decode('utf-8')).split('/')[-1] in acceptTypes]
else:
print("Accepting all MIME Types.....")
files_tuple = itertools.combinations(filename_list, 2)
for file1, file2 in files_tuple:
try:
row_edit_distance = [file1, file2]
file1_parsedData = parser.from_file(file1)
file2_parsedData = parser.from_file(file2)
intersect_features = set(file1_parsedData["metadata"].keys()) & set(file2_parsedData["metadata"].keys())
intersect_features = [feature for feature in intersect_features if feature not in na_metadata ]
file_edit_distance = 0.0
for feature in intersect_features:
file1_feature_value = stringify(file1_parsedData["metadata"][feature])
file2_feature_value = stringify(file2_parsedData["metadata"][feature])
if len(file1_feature_value) == 0 and len(file2_feature_value) == 0:
feature_distance = 0.0
else:
feature_distance = float(editdistance.eval(file1_feature_value, file2_feature_value))/(len(file1_feature_value) if len(file1_feature_value) > len(file2_feature_value) else len(file2_feature_value))
file_edit_distance += feature_distance
if allKeys:
file1_only_features = set(file1_parsedData["metadata"].keys()) - set(intersect_features)
file1_only_features = [feature for feature in file1_only_features if feature not in na_metadata]
file2_only_features = set(file2_parsedData["metadata"].keys()) - set(intersect_features)
file2_only_features = [feature for feature in file2_only_features if feature not in na_metadata]
file_edit_distance += len(file1_only_features) + len(file2_only_features) # increment by 1 for each disjunct feature in (A-B) & (B-A), file1_disjunct_feature_value/file1_disjunct_feature_value = 1
file_edit_distance /= float(len(intersect_features) + len(file1_only_features) + len(file2_only_features))
else:
file_edit_distance /= float(len(intersect_features)) #average edit distance
row_edit_distance.append(1-file_edit_distance)
a.writerow(row_edit_distance)
except ConnectionError:
sleep(1)
except KeyError:
continue
def compute_score2(json_input_list, outCSV, acceptTypes, allKeys):
na_metadata = ["resourceName"]
with open(outCSV, "wb") as outF:
a = csv.writer(outF, delimiter=',')
a.writerow(["x-coordinate","y-coordinate","Similarity_score"])
json_list = []
for each in json_input_list:
with open(each) as json_input_file:
json_list.extend(json.load(json_input_file))
# each object in json_list contains a key as file name and a value: as metadata JSON object
metadata_dict = {}
for entry in json_list:
key = list(entry.keys())[0]
metadata_dict[key] = entry[key]
files_tuple = itertools.combinations(list(metadata_dict.keys()), 2)
for file1, file2 in files_tuple:
try:
row_edit_distance = [file1, file2]
file1_metadata = metadata_dict[file1]
file2_metadata = metadata_dict[file2]
intersect_features = set(file1_metadata.keys()) & set(file2_metadata.keys())
intersect_features = [feature for feature in intersect_features if feature not in na_metadata ]
file_edit_distance = 0.0
for feature in intersect_features:
file1_feature_value = stringify(file1_metadata[feature])
file2_feature_value = stringify(file2_metadata[feature])
if len(file1_feature_value) == 0 and len(file2_feature_value) == 0:
feature_distance = 0.0
else:
feature_distance = float(editdistance.eval(file1_feature_value, file2_feature_value))/(len(file1_feature_value) if len(file1_feature_value) > len(file2_feature_value) else len(file2_feature_value))
file_edit_distance += feature_distance
if allKeys:
file1_only_features = set(file1_metadata.keys()) - set(intersect_features)
file1_only_features = [feature for feature in file1_only_features if feature not in na_metadata]
file2_only_features = set(file2_metadata.keys()) - set(intersect_features)
file2_only_features = [feature for feature in file2_only_features if feature not in na_metadata]
file_edit_distance += len(file1_only_features) + len(file2_only_features) # increment by 1 for each disjunct feature in (A-B) & (B-A), file1_disjunct_feature_value/file1_disjunct_feature_value = 1
file_edit_distance /= float(len(intersect_features) + len(file1_only_features) + len(file2_only_features))
else:
file_edit_distance /= float(len(intersect_features)) #average edit distance
row_edit_distance.append(1-file_edit_distance)
a.writerow(row_edit_distance)
except ConnectionError:
sleep(1)
except KeyError:
continue
return
def compute_scores(json_file, outCSV, acceptTypes, json_key, allKeys):
na_metadata = ["resourceName"]
with open(outCSV, "wb") as outF:
a = csv.writer(outF, delimiter=',')
a.writerow(["x-coordinate","y-coordinate","Similarity_score"])
json_list = []
with open(json_file) as json_input_file:
json_list.extend(json.load(json_input_file)[json_key])
#json_list has list of JSON objects read from json file
print((len(json_list)))
metadata_dict = {}
for entry in json_list:
metadata_dict[entry['id']]=entry
files_tuple = itertools.combinations(list(metadata_dict.keys()), 2)
for record1, record2 in files_tuple:
try:
row_edit_distance = [record1, record2]
record1_metadata = metadata_dict[record1]
record2_metadata = metadata_dict[record2]
intersect_features = set(record1_metadata.keys()) & set(record2_metadata.keys())
intersect_features = [feature for feature in intersect_features if feature not in na_metadata ]
record_edit_distance = 0.0
for feature in intersect_features:
record1_feature_value = stringify(record1_metadata[feature])
record2_feature_value = stringify(record2_metadata[feature])
if len(record1_feature_value) == 0 and len(record2_feature_value) == 0:
feature_distance = 0.0
else:
feature_distance = float(editdistance.eval(record1_feature_value, record2_feature_value))/(len(record1_feature_value) if len(record1_feature_value) > len(record2_feature_value) else len(record2_feature_value))
record_edit_distance += feature_distance
if allKeys:
record1_only_features = set(record1_metadata.keys()) - set(intersect_features)
record1_only_features = [feature for feature in record1_only_features if feature not in na_metadata]
record2_only_features = set(record2_metadata.keys()) - set(intersect_features)
record2_only_features = [feature for feature in record2_only_features if feature not in na_metadata]
record_edit_distance += len(file1_only_features) + len(file2_only_features) # increment by 1 for each disjunct feature in (A-B) & (B-A), file1_disjunct_feature_value/file1_disjunct_feature_value = 1
record_edit_distance /= float(len(intersect_features) + len(record_only_features) + len(record2_only_features))
else:
record_edit_distance /= float(len(intersect_features)) #average edit distance
row_edit_distance.append(1-record_edit_distance)
a.writerow(row_edit_distance)
except ConnectionError:
sleep(1)
except KeyError:
continue
return
if __name__ == "__main__":
argParser = argparse.ArgumentParser('Edit Distance Similarity based on Metadata values')
argParser.add_argument('--inputDir', required=False, help='path to directory containing files')
argParser.add_argument('--outCSV', required=True, help='path to directory for storing the output CSV File, containing pair-wise Similarity Scores based on edit distance')
argParser.add_argument('--json', nargs='+', required=False, help='several paths to JSON file containing certain metadata')
argParser.add_argument('--accept', nargs='+', type=str, help='Optional: compute similarity only on specified IANA MIME Type(s)')
argParser.add_argument('--allKeys', action='store_true', help='compute edit distance across all keys')
argParser.add_argument('--fileInput',required=False, help='Set to 1 to compute edit distance for JSON objects in the file')
argParser.add_argument('--jsonKey',required=False, help='JSON object list key')
args = argParser.parse_args()
if args.fileInput=='1' and args.json and args.jsonKey:
compute_scores(args.json[0],args.outCSV, args.accept,args.jsonKey, args.allKeys)
elif args.inputDir and args.outCSV:
computeScores(args.inputDir, args.outCSV, args.accept, args.allKeys)
else:
if args.json:
compute_score2(args.json, args.outCSV, args.accept, args.allKeys)