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import pandas as pd
import json
import numpy as np
import warnings
# Suprimir SettingWithCopyWarning
warnings.simplefilter(action='ignore', category=pd.errors.SettingWithCopyWarning)
def evaluate_results(lang, gold, test):
def normalize_labels(df):
# Define a function that checks if each narrative is present and assigns "yes" or "no"
def convert_narratives(row):
country_code = row['country'][:2].upper() # Get the country code ('RU', 'CH', etc.)
narratives = row['narratives'] # List of narratives for that row
# For each N1 to N6, check if it appears in the list of narratives
for i in range(1, 7):
narrative_code = f"{country_code}{i}"
row[f"N{i}"] = 'yes' if narrative_code in narratives else 'no'
return row
# Apply the function to each row of the DataFrame
data = df.apply(convert_narratives, axis=1)
# Drop the original 'narratives' column if no longer needed
data.drop(columns=['narratives', 'tweet_id'], inplace=True)
return data
def get_gold_lists_for_evaluation(gold_list, test_list):
gold_strict=[]
gold_lenient=[]
for i in range(0,6):
g=gold_list[i]
t=test_list[i]
g = 1 if g == 'yes' else 2 if g == 'no' else g
t = 1 if t == 'yes' else 2 if t == 'no' else t
if g==t:
gold_strict.append(g)
gold_lenient.append(g)
elif g!=t:
if g in [2, 1]:
gold_strict.append(g)
gold_lenient.append(g)
else:
gold_strict.append(2)
gold_lenient.append(t)
return gold_strict, gold_lenient
def gen_dic(lang):
narratives_list=['CH1', 'CH2', 'CH3', 'CH4', 'CH5', 'CH6', 'CH_micro', 'RU1', 'RU2', 'RU3', 'RU4', 'RU5', 'RU6', 'RU_micro', 'EU1', 'EU2', 'EU3', 'EU4', 'EU5', 'EU6', 'EU_micro', 'US1', 'US2', 'US3', 'US4', 'US5', 'US6', 'US_micro']
countries_dic={'China':'CH', 'Russia':'RU', 'EU':'EU', 'USA':'US'}
dic = {}
dic[lang] = {}
for ev in ['strict', 'lenient']:
if ev not in dic[lang]:
dic[lang][ev] = {}
for narr in narratives_list:
dic[lang][ev][narr] = {'scores': {'precision': 0., 'recall': 0., 'f1-score': 0.}, 'raw_data': []}
for code in countries_dic.values():
dic[lang][ev][f'{code}_micro'] = {'scores': {'precision': 0., 'recall': 0., 'f1-score': 0}, 'raw_data': []}
dic[lang][ev]['micro'] = {'scores': {'precision': 0., 'recall': 0., 'f1-score': 0}, 'raw_data': []}
return dic
def convert_labels(values):
return np.array([
[1 if v == 'yes' else 2 if v == 'no' else 3 for v in row]
for row in values
])
def convert_floats(dic):
for key, value in dic.items():
if isinstance(value, np.float64):
dic[key] = float(value)
elif isinstance(value, dict): # If the value is another dictionary, apply recursion
convert_floats(value)
elif isinstance(value, list): # If the value is a list, convert individual elements
dic[key] = [float(v) if isinstance(v, np.float64) else v for v in value]
dic=gen_dic(lang)
countries_dic={'China':'CH', 'Russia':'RU', 'EU':'EU', 'USA':'US'}
cols=[f'N{i}' for i in range(1,7)]
df_gold=pd.DataFrame(gold)
df_gold["country"] = df_gold["country"].replace("European Union", "EU")
df_gold.drop_duplicates(subset=['id', 'lang'], keep='last', inplace=True)
df=df_gold[df_gold['lang']==lang]
df.reset_index(inplace=True, drop=True)
df_test=pd.DataFrame(test)
df_test["country"] = df_test["country"].replace("European Union", "EU")
df_test=normalize_labels(df_test)
df_test.drop_duplicates(subset=['id', 'language'], keep='last', inplace=True)
df_test.reset_index(inplace=True, drop=True)
df_strict=df.copy()
df_lenient=df.copy()
for i in range(len(df)):
lang=df['lang'].iloc[i]
id=df['id'].iloc[i]
gold_values=df[cols].iloc[i].values
dft=df_test[(df_test['language']==lang) & (df_test['id']==id)]
test_values=dft[cols].iloc[0].values
df_strict.loc[i, cols], df_lenient.loc[i, cols]=get_gold_lists_for_evaluation(gold_values, test_values)
countries=['China', 'Russia', 'EU', 'USA']
df_lang=df[(df['lang']==lang)]
df_test_lang=df_test[(df_test['language']==lang)]
df_strict_lang=df_strict[df_strict['lang']==lang]
df_lenient_lang=df_lenient[df_lenient['lang']==lang]
#F1 per narrative
for country in countries:
df_dup_t=df[(df['country']==country) & (df['lang']==lang)]
df_strict_t=df_strict_lang[df_strict_lang['country']==country]
df_lenient_t=df_lenient_lang[df_lenient_lang['country']==country]
dft=df_test_lang[(df_test_lang['country']==country)]
real_strict=[]
real_lenient=[]
real=[]
pred=[]
for i in range(len(df_strict_t)):
id=df_strict_t['id'].iloc[i]
dft2=dft[dft['id']==id]
if len(dft2)!=0:
real_strict.append(df_strict_t[cols].iloc[i].values)
real_lenient.append(df_lenient_t[cols].iloc[i].values)
pred.append(dft2[cols].iloc[0].values)
real.append(df_dup_t[df_dup_t['id']==id][cols].iloc[0].values)
real_strict=np.array(real_strict)
real_lenient=np.array(real_lenient)
real = convert_labels(real)
pred = convert_labels(pred)
for i in range(0, 6):
raw_matrix = np.zeros((2, 3), dtype=int) # 2 filas (pred), 3 columnas (real)
pred_options = [1, 2] # 1 -> 'yes', 2 -> 'no'
real_options = [1, 3, 2] # 1
p=pred[:,i]
r=real[:,i]
for p, r in zip(p, r):
pred_index = pred_options.index(p)
real_index = real_options.index(r)
raw_matrix[pred_index, real_index] += 1
tp=raw_matrix[0,0]
yl=raw_matrix[0,1]
fp=raw_matrix[0,2]
fn=raw_matrix[1,0]
nl=raw_matrix[1,1]
tn=raw_matrix[1,2]
dic[lang]['lenient'][f'{countries_dic[country]}{i+1}']['raw_data']=raw_matrix.tolist()
precision=(tp+yl)/(tp+yl+fp) if (tp+yl+fp)!=0 else 0
recall=(tp+yl)/(tp+fn+yl) if (tp+fn+yl)!=0 else 0
dic[lang]['lenient'][f'{countries_dic[country]}{i+1}']['scores']['precision']=precision
dic[lang]['lenient'][f'{countries_dic[country]}{i+1}']['scores']['recall']=recall
dic[lang]['lenient'][f'{countries_dic[country]}{i+1}']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
dic[lang]['strict'][f'{countries_dic[country]}{i+1}']['raw_data']=raw_matrix.tolist()
precision=tp/(tp+fp+yl) if (tp+fp+yl)!=0 else 0
recall=tp/(tp+fn) if (tp+fn)!=0 else 0
dic[lang]['strict'][f'{countries_dic[country]}{i+1}']['scores']['precision']=precision
dic[lang]['strict'][f'{countries_dic[country]}{i+1}']['scores']['recall']=recall
dic[lang]['strict'][f'{countries_dic[country]}{i+1}']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
#F1 Micro
real_strict=[]
real_lenient=[]
pred=[]
not_match=[]
real=[]
for i in range(len(df_lang)):
id=df_lang['id'].iloc[i]
dft=df_test_lang[df_test_lang['id']==id][cols]
if len(dft)!=0:
real_strict.extend(df_strict_lang[cols].iloc[i].values)
real_lenient.extend(df_strict_lang[cols].iloc[i].values)
pred.extend(df_test_lang[df_test_lang['id']==id][cols].iloc[0].values)
real.extend(df_lang[df_lang['id']==id][cols].iloc[0].values)
else:
not_match.append(id)
real = convert_labels([real])[0]
pred = convert_labels([pred])[0]
raw_matrix=np.zeros((2,3), dtype=int)
pred_options = [1, 2] # 1 -> 'yes', 2 -> 'no'
real_options = [1, 3, 2] # 1
raw_matrix = np.zeros((2, 3), dtype=int)
for p, r in zip(pred, real):
pred_index = pred_options.index(p)
real_index = real_options.index(r)
raw_matrix[pred_index, real_index] += 1
tp=raw_matrix[0,0]
yl=raw_matrix[0,1]
fp=raw_matrix[0,2]
fn=raw_matrix[1,0]
nl=raw_matrix[1,1]
tn=raw_matrix[1,2]
dic[lang]['lenient']['micro']['raw_data']=raw_matrix.tolist()
precision=(tp+yl)/(tp+yl+fp) if (tp+yl+fp)!=0 else 0
recall=(tp+yl)/(tp+fn+yl) if (tp+fn+yl)!=0 else 0
dic[lang]['lenient']['micro']['scores']['precision']=precision
dic[lang]['lenient']['micro']['scores']['recall']=recall
dic[lang]['lenient']['micro']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
dic[lang]['strict']['micro']['raw_data']=raw_matrix.tolist()
precision=tp/(tp+fp+yl) if (tp+yl+fp)!=0 else 0
recall=tp/(tp+fn) if (tp+fn)!=0 else 0
dic[lang]['strict']['micro']['scores']['precision']=precision
dic[lang]['strict']['micro']['scores']['recall']=recall
dic[lang]['strict']['micro']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
#Micro-Countries
for country in countries_dic.values():
raw_matrix = np.sum([np.array(dic[f'{lang}']['strict'][f'{country}{i}']['raw_data']) for i in range(1, 7)], axis=0)
tp=raw_matrix[0,0]
yl=raw_matrix[0,1]
fp=raw_matrix[0,2]
fn=raw_matrix[1,0]
nl=raw_matrix[1,1]
tn=raw_matrix[1,2]
precision=(tp+yl)/(tp+yl+fp) if (tp+yl+fp)!=0 else 0
recall=(tp+yl)/(tp+fn+yl) if (tp+fn+yl)!=0 else 0
dic[lang]['lenient'][f'{country}_micro']['scores']['precision']=precision
dic[lang]['lenient'][f'{country}_micro']['scores']['recall']=recall
dic[lang]['lenient'][f'{country}_micro']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
dic[lang]['lenient'][f'{country}_micro']['raw_data']=raw_matrix.tolist()
precision=tp/(tp+fp+yl) if (tp+yl+fp)!=0 else 0
recall=tp/(tp+fn) if (tp+fn)!=0 else 0
dic[lang]['strict'][f'{country}_micro']['scores']['precision']=precision
dic[lang]['strict'][f'{country}_micro']['scores']['recall']=recall
dic[lang]['strict'][f'{country}_micro']['scores']['f1-score']=(2*precision*recall)/(precision+recall) if (precision+recall)!=0 else 0
dic[lang]['strict'][f'{country}_micro']['raw_data']=raw_matrix.tolist()
convert_floats(dic[lang])
return dic[lang]
"""
strict
narrative_country (e.g. CH1)
scores
precision
recall
f1-score
raw_data
country_micro (e.g. CH_micro)
scores
precision
recall
f1-score
raw_data
micro (global micro)
scores
precision
recall
f1-score
raw_data
lenient
narrative_country (e.g. CH1)
scores
precision
recall
f1-score
raw_data
country_micro (e.g. CH_micro)
scores
precision
recall
f1-score
raw_data
micro (global micro)
scores
precision
recall
f1-score
raw_data""" |