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cop-mining-participants/code/scripts/find_experience_hamming.py
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find_experience_hamming.py
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import sys
import pandas as pd
# constants
max_distance = 1
names = {} # contains all the unique names and their experience
def hamming_distance(str1, str2, max_dist=None):
"""
Inspired by https://stackoverflow.com/questions/24624415/finding-minimum-hamming-distance-of-a-set-of-strings-in-python
Args:
str1 ([type]): [description]
str2 ([type]): [description]
max_dist ([type], optional): [description]. Defaults to None.
Returns:
[type]: [description]
"""
count = 0
if len(str1) != len(str2):
return max(len(str1), len(str2))
for c1, c2 in zip(str1, str2):
if c1 != c2:
count += 1
if max_dist is not None and count >= max_dist:
return count
return count
def compare_names(name1, name2):
# case: one starts with the other
if name2.startswith(name1) or name1.startswith(name2):
return True
# case: first name and last name inversed -> same set of names
if len(name1) == len(name2) and set(name2.split()) == set(name1.split()):
return True
dist = hamming_distance(name1, name2)
return dist <= max_distance
def get_experience(name):
for key, exp in names.items():
if compare_names(name, key):
names[key] += 1
return exp
names[name] = 1
return 0
complete_data = pd.read_csv("../results/complete_dataset.csv",
encoding="utf-8-sig")
complete_data_with_experience = pd.DataFrame(columns={
"meeting",
"name",
"gender",
"has_title",
"affiliation",
"affiliation_category",
"fossil_fuel_industry",
"role",
"description",
"experience"})
metadata = pd.read_csv("../data/meetings_metadata.csv")
#for label in metadata["label"]:
for label in ["cop24", "cop25"]:
print(label)
data = complete_data.loc[complete_data.meeting == label]
data["experience"] = data["name"].apply(get_experience)
complete_data_with_experience = complete_data_with_experience.append(data, ignore_index=True)
# generate the output file
complete_data_with_experience.to_csv("../results/complete_dataset_experience-hamming.csv",
encoding="utf-8-sig", index=False)
print(len(names))
# print the dictionary to a text file
f = open("experience_hamming_dict.txt", "w")
print(names)
f.write(str(names), encoding="utf-8")
f.close()
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