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from glob import glob
from sklearn.metrics import accuracy_score
import os
import pandas as pd
def get_merged_df(results_path, skip_samples = False):
results = glob(os.path.join(results_path, "*.json"))
dfs = []
for r in results:
if skip_samples and 'sample_result' in r:
continue
df = pd.read_json(r, lines = True)
if df.isna().values.any():
print(f"Missing values in {r}")
else:
dfs.append(df)
full_df = pd.concat(dfs)
return full_df
def map_df(full_df):
gnd_truth_mapping = {'full fake': 0,
'half fake': 0,
'real': 1}
pred_mapping = {'fake':0, 'real': 1}
full_df['label'] = full_df['ground_truth'].map(gnd_truth_mapping)
full_df['pred'] = full_df['type'].map(pred_mapping)
return full_df
def get_scores(df):
columns = ['Under 25s', '26s - 55s', '56s - 125s', 'Overall' ]
samples_tested = []
acc_scores = []
for c in columns:
if c == 'Overall':
mask = df.label == 0
elif c == 'Under 26 s':
mask = (df.label == 0) & (df.duration < 26)
elif c == '55 s':
mask = (df.label == 0) & (df.duration >= 26) & (df.duration < 56)
elif c == '125 s':
mask = (df.label == 0) & (df.duration >= 56) & (df.duration < 126)
else:
raise ValueError
sel_df = df[mask]
samples_tested.append(len(sel_df))
acc_scores.append(round(accuracy_score(sel_df.label.values, sel_df.pred.values), 3))
lb = pd.DataFrame({"Sample": columns, "Num Samples": samples_tested, "Accuracy": acc_scores})
return lb
def build_leaderboard(results_path = 'results'):
full_df = get_merged_df(results_path)
full_df_mapped = map_df(full_df)
leaderboard = get_scores(full_df_mapped)
return leaderboard
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