File size: 7,422 Bytes
dae5c90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | import csv
import json
import os
import logging
import numpy as np
import pandas as pd
from scipy.special import softmax
from sklearn.metrics import (
confusion_matrix,
recall_score,
f1_score,
accuracy_score,
balanced_accuracy_score,
)
from fairlearn.metrics import (
count,
false_positive_rate,
false_negative_rate,
selection_rate,
demographic_parity_difference,
equal_opportunity_difference,
equalized_odds_difference,
)
from fairlearn.metrics import MetricFrame
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler()],
)
def logits_to_probs(logits, config):
# Posterioir probabilities are calculated differently in some experiments
if config.get("domain_independent_loss", False):
per_group = np.split(logits, config["num_groups"], axis=1)
marginalized = np.sum(per_group, axis=0)
return softmax(marginalized, axis=1)
if config.get("domain_discriminative_loss", False):
# Prior shift inference, train distribution
prior_shift_weight = np.array(
[
1088/1072, 1088/16, 17746/17515, 17746/231, 6454/6273, 6454/181, 850/834, 850/16
]
) / 100
probs_yd = softmax(logits, axis=1) * prior_shift_weight
per_group = np.split(probs_yd, config["num_groups"], axis=1)
marginalized = np.sum(per_group, axis=0)
# We shifted probs, apply softmax once more
return softmax(marginalized, axis=1)
return softmax(logits, axis=1)
# Fairlearn docs
def compute_error_metric(metric_value, sample_size):
"""Compute standard error of a given metric based on the assumption of
normal distribution.
Parameters:
metric_value: Value of the metric
sample_size: Number of data points associated with the metric
Returns:
The standard error of the metric
"""
metric_value = metric_value / sample_size
return 1.96 * np.sqrt(metric_value * (1.0 - metric_value)) / np.sqrt(sample_size)
def false_positive_error(y_true, y_pred):
"""Compute the standard error for the false positive rate estimate."""
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
return compute_error_metric(fp, tn + fp)
def false_negative_error(y_true, y_pred):
"""Compute the standard error for the false negative rate estimate."""
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
return compute_error_metric(fn, fn + tp)
def balanced_accuracy_error(y_true, y_pred):
"""Compute the standard error for the balanced accuracy estimate."""
fpr_error, fnr_error = false_positive_error(y_true, y_pred), false_negative_error(
y_true, y_pred
)
return np.sqrt(fnr_error**2 + fpr_error**2) / 2
if __name__ == "__main__":
root_dir = "C:\\Users\\Duje\\Desktop\\fer\\8. semestar\\lumen\\rezultati\\02 eksperimenti\\"
common_csv = "rezultati.csv"
disagg_csv = "disaggregated.csv"
experiments = [
"01 baseline 0304",
"02 recall ce 0304",
"04 cielab re based",
"05 cielab ohem",
"08 optim params large",
"10 transformer\\normal",
"11 transformer ohem",
"12 domain discriminative\\new",
"13 oversampler",
"15 focal loss\\new",
"14 domain independent\\new",
"16 efficient m\\new",
"17 masked\\new",
"18 efficient l\\new",
"19 oversampler trio\\1 base",
"19 oversampler trio\\2 ifw, recall_ce",
"19 oversampler trio\\3 ifw, ohem",
"20 dino\\new",
"21 dino oversample",
"22 dino undersample",
"23 long train 04"
"24 dd transformer"
]
logging.info(f"Collecting metrics for {len(experiments)} experiments")
for exp in experiments:
eval_dir = os.path.join(root_dir, exp, "eval")
chkpt = next(os.walk(eval_dir))[1][0] # checkpoint folder
logging.info(f"Evaluating checkpoint {chkpt} for experiment {exp}")
with open(os.path.join(root_dir, exp, "config.json")) as f:
config = json.load(f)
y_true = np.load(os.path.join(eval_dir, chkpt, "y_true.npy"))
logits = np.load(os.path.join(eval_dir, chkpt, "logits.npy"))
groups = np.load(os.path.join(eval_dir, chkpt, "groups.npy"))
y_prob = logits_to_probs(logits, config)
y_pred = np.argmax(y_prob, axis=1)
prob_path = os.path.join(eval_dir, chkpt, "probs.npy")
np.save(prob_path, y_prob)
logging.info(f"Saved posteriror probabilities to {prob_path}")
metrics = dict(
count=count,
f1=f1_score,
recall=recall_score,
accuracy=accuracy_score,
selection_rate=selection_rate,
balanced_accuracy=balanced_accuracy_score,
balanced_acc_error=balanced_accuracy_error,
false_positive_rate=false_positive_rate,
false_positive_error=false_positive_error,
false_negative_rate=false_negative_rate,
false_negative_error=false_negative_error,
)
mf = MetricFrame(
metrics=metrics,
y_true=y_true,
y_pred=y_pred,
sensitive_features=groups,
)
dpd = demographic_parity_difference(
y_true, y_pred, sensitive_features=groups
).item()
eq_odds = equalized_odds_difference(y_true, y_pred, sensitive_features=groups)
eq_opp = equal_opportunity_difference(
y_true, y_pred, sensitive_features=groups
).item()
diffs = mf.difference()[
[
"f1",
"recall",
"accuracy",
"balanced_accuracy",
"false_positive_rate",
"false_negative_rate",
]
]
diffs = diffs.rename(
dict(
f1="f1_diff",
recall="recall_diff",
accuracy="accuracy_diff",
balanced_accuracy="balanced_acc_diff",
false_positive_rate="fpr_diff",
false_negative_rate="fnr_diff",
)
)
# One row for each experiment
fair = pd.Series([dpd, eq_odds, eq_opp], index=["dpd", "eq_odds", "eq_opp"])
fair = fair.add(diffs, fill_value=0)
result = mf.overall.add(fair, fill_value=0)
header = ["experiment"] + result.keys().to_list()
if not os.path.isfile(common_csv):
with open(common_csv, "a", newline="") as f:
writer = csv.writer(f)
writer.writerow(header)
row = [exp] + result.to_list()
with open(common_csv, "a", newline="") as f:
writer = csv.writer(f)
writer.writerow(row)
logging.info(f"Added row to {common_csv}")
# Disaggregated metrics
group = mf.by_group
group = group.rename(columns={"sensitive_feature_0": "group"})
group.to_csv(os.path.join(eval_dir, chkpt, disagg_csv))
logging.info(f"Saved disaggregated metrics to {disagg_csv}")
logging.info("Done")
|