def main():
output_dir = os.path.dirname(__file__)
experiments = [
"G_Jul03_21_14_nr_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
"G_Jul05_00_24_nr_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
"G_Jul06_03_39_nr_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
"G_Jul07_06_38_nr_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
]
scoring_fn = alaska_weighted_auc
for metric in [
# "loss",
# "bauc",
"cauc"
]:
holdout_predictions_d4 = get_predictions_csv(experiments, metric, "holdout", "d4")
oof_predictions_d4 = get_predictions_csv(experiments, metric, "oof", "d4")
test_predictions_d4 = get_predictions_csv(experiments, metric, "test", "d4")
fnames_for_checksum = [x + f"{metric}" for x in experiments]
bin_pred_d4 = make_binary_predictions(holdout_predictions_d4)
y_true = bin_pred_d4[0].y_true_type.values
bin_pred_d4_score = scoring_fn(y_true, blend_predictions_mean(bin_pred_d4).Label)
cls_pred_d4 = make_classifier_predictions(holdout_predictions_d4)
cls_pred_d4_score = scoring_fn(y_true, blend_predictions_mean(cls_pred_d4).Label)
bin_pred_d4_cal = make_binary_predictions_calibrated(holdout_predictions_d4, oof_predictions_d4)
bin_pred_d4_cal_score = scoring_fn(y_true, blend_predictions_mean(bin_pred_d4_cal).Label)
cls_pred_d4_cal = make_classifier_predictions_calibrated(holdout_predictions_d4, oof_predictions_d4)
cls_pred_d4_cal_score = scoring_fn(y_true, blend_predictions_mean(cls_pred_d4_cal).Label)
prod_pred_d4_cal_score = scoring_fn(
y_true, blend_predictions_mean(cls_pred_d4_cal).Label * blend_predictions_mean(bin_pred_d4_cal).Label
)
print(metric, "Bin NC", "d4", bin_pred_d4_score)
print(metric, "Bin CL", "d4", cls_pred_d4_score)
print(metric, "Cls NC", "d4", bin_pred_d4_cal_score)
print(metric, "Cls CL", "d4", cls_pred_d4_cal_score)
print(metric, "Prod ", "d4", prod_pred_d4_cal_score)
max_score = max(
bin_pred_d4_score, cls_pred_d4_score, bin_pred_d4_cal_score, cls_pred_d4_cal_score, prod_pred_d4_cal_score
)
if bin_pred_d4_score == max_score:
predictions = make_binary_predictions(test_predictions_d4)
predictions = blend_predictions_mean(predictions)
predictions.to_csv(
os.path.join(output_dir, f"mean_{max_score:.4f}_bin_{compute_checksum_v2(fnames_for_checksum)}.csv"),
index=False,
)
if bin_pred_d4_cal_score == max_score:
predictions = make_binary_predictions_calibrated(test_predictions_d4, oof_predictions_d4)
predictions = blend_predictions_mean(predictions)
predictions.to_csv(
os.path.join(
output_dir, f"mean_{max_score:.4f}_bin_cal_{compute_checksum_v2(fnames_for_checksum)}.csv"
),
index=False,
)
if cls_pred_d4_score == max_score:
predictions = make_classifier_predictions(test_predictions_d4)
predictions = blend_predictions_mean(predictions)
predictions.to_csv(
os.path.join(output_dir, f"mean_{max_score:.4f}_cls_{compute_checksum_v2(fnames_for_checksum)}.csv"),
index=False,
)
if cls_pred_d4_cal_score == max_score:
predictions = make_classifier_predictions_calibrated(test_predictions_d4, oof_predictions_d4)
predictions = blend_predictions_mean(predictions)
predictions.to_csv(
os.path.join(
output_dir, f"mean_{max_score:.4f}_cls_cal_{compute_checksum_v2(fnames_for_checksum)}.csv"
),
index=False,
)
if prod_pred_d4_cal_score == max_score:
cls_predictions = make_classifier_predictions_calibrated(test_predictions_d4, oof_predictions_d4)
bin_predictions = make_binary_predictions_calibrated(test_predictions_d4, oof_predictions_d4)
predictions1 = blend_predictions_mean(cls_predictions)
predictions2 = blend_predictions_mean(bin_predictions)
predictions = predictions1.copy()
predictions.Label = predictions1.Label * predictions2.Label
predictions.to_csv(
os.path.join(
output_dir, f"mean_{max_score:.4f}_prod_cal_{compute_checksum_v2(fnames_for_checksum)}.csv"
),
index=False,
)
import pandas as pd
from scipy.stats import spearmanr
from sklearn.metrics import matthews_corrcoef
from alaska2.submissions import blend_predictions_ranked, blend_predictions_mean
submission_v25_xl_NR_moreTTA = pd.read_csv(
"submission_v25_xl_NR_moreTTA.csv").sort_values(by="Id")
stacked_b6_xgb_cv = pd.read_csv(
"662cfbbddf616db0df6f59ee2a96cc20_xgb_cv_0.9485.csv")
print(spearmanr(submission_v25_xl_NR_moreTTA.Label, stacked_b6_xgb_cv.Label))
blend_1_ranked = blend_predictions_ranked(
[submission_v25_xl_NR_moreTTA, stacked_b6_xgb_cv])
blend_1_ranked.to_csv("blend_1_ranked.csv", index=False)
blend_1_mean = blend_predictions_mean(
[submission_v25_xl_NR_moreTTA, stacked_b6_xgb_cv])
blend_1_mean.to_csv("blend_1_mean.csv", index=False)
import pandas as pd
from scipy.stats import spearmanr
from sklearn.metrics import matthews_corrcoef
from alaska2.submissions import blend_predictions_ranked, blend_predictions_mean
submission_v25_xl_NR_moreTTA = pd.read_csv(
"submission_v25_xl_NR_moreTTA.csv").sort_values(by="Id").reset_index()
submission_b6_mean_calibrated = pd.read_csv(
"662cfbbddf616db0df6f59ee2a96cc20_best_cauc_blend_cls_mean_calibrated_0.9422.csv"
)
# Force 1.01 value of OOR values in my submission
oor_mask = submission_v25_xl_NR_moreTTA.Label > 1.0
submission_b6_mean_calibrated.loc[oor_mask, "Label"] = 1.01
print(
spearmanr(submission_v25_xl_NR_moreTTA.Label,
submission_b6_mean_calibrated.Label))
blend_3_ranked = blend_predictions_ranked(
[submission_v25_xl_NR_moreTTA, submission_b6_mean_calibrated])
blend_3_ranked.to_csv(
"blend_3_ranked_from_v25_xl_NR_moreTTA_and_b6_cauc_mean_calibrated.csv",
index=False)
blend_3_mean = blend_predictions_mean(
[submission_v25_xl_NR_moreTTA, submission_b6_mean_calibrated])
blend_3_mean.to_csv(
"blend_3_mean_from_v25_xl_NR_moreTTA_and_b6_cauc_mean_calibrated.csv",
index=False)
def main():
output_dir = os.path.dirname(__file__)
experiments = [
# A models trained on old folds without holdout, so it will have a leak if evaluated.
# "A_May24_11_08_ela_skresnext50_32x4d_fold0_fp16",
# "A_May15_17_03_ela_skresnext50_32x4d_fold1_fp16",
# "A_May21_13_28_ela_skresnext50_32x4d_fold2_fp16",
# "A_May26_12_58_ela_skresnext50_32x4d_fold3_fp16",
#
# "B_Jun05_08_49_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
# "B_Jun09_16_38_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
# "B_Jun11_08_51_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
# "B_Jun11_18_38_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
#
# "C_Jun24_22_00_rgb_tf_efficientnet_b2_ns_fold2_local_rank_0_fp16",
# #
# "D_Jun18_16_07_rgb_tf_efficientnet_b7_ns_fold1_local_rank_0_fp16",
# "D_Jun20_09_52_rgb_tf_efficientnet_b7_ns_fold2_local_rank_0_fp16",
# #
# # "E_Jun18_19_24_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
# "E_Jun21_10_48_rgb_tf_efficientnet_b6_ns_fold0_istego100k_local_rank_0_fp16",
# #
# "F_Jun29_19_43_rgb_tf_efficientnet_b3_ns_fold0_local_rank_0_fp16",
#
"G_Jul03_21_14_nr_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
"G_Jul05_00_24_nr_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
"G_Jul06_03_39_nr_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
"G_Jul07_06_38_nr_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
]
test_predictions_d4 = get_predictions_csv(experiments, "cauc", "test",
"d4")
classes = []
for x in test_predictions_d4:
df = pd.read_csv(x)
df = df.rename(columns={"image_id": "Id"})
df["classes"] = df["pred_modification_type"].apply(parse_and_softmax)
classes.append(df["classes"].tolist())
classes = np.mean(classes, axis=0)
print("Class distribution", np.bincount(classes.argmax(axis=1)))
bin_probas = np.stack([classes[:, 0], 1 - classes[:, 0]])
bin_classes = bin_probas.argmax(axis=0)
classes_cp = classes.copy()
classes_cp[bin_classes == 1, 0] = 0
print("Class distribution", np.bincount(classes_cp.argmax(axis=1)))
plt.figure()
plt.hist(classes[:, 0], bins=100, alpha=0.25, label="Cover")
plt.hist(classes[:, 1], bins=100, alpha=0.25, label="JMiPOD")
plt.hist(classes[:, 2], bins=100, alpha=0.25, label="JUNIWARD")
plt.hist(classes[:, 3], bins=100, alpha=0.25, label="UERD")
plt.yscale("log")
plt.legend()
plt.show()
holdout_predictions_d4 = get_predictions_csv(experiments, "cauc",
"holdout", "d4")
holdout_predictions_d4 = make_product_predictions(holdout_predictions_d4)
y_true_type = holdout_predictions_d4[0].y_true_type
holdout_predictions_d4 = blend_predictions_mean(holdout_predictions_d4)
scores = evaluate_wauc_shakeup_using_bagging(holdout_predictions_d4,
y_true_type, 10000)
plt.figure()
plt.hist(scores,
bins=100,
alpha=0.5,
label=f"{np.mean(scores):.5f} +- {np.std(scores):.6f}")
plt.legend()
plt.show()
print(np.mean(scores), np.std(scores))
def main():
output_dir = os.path.dirname(__file__)
experiments = [
"B_Jun05_08_49_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
"B_Jun09_16_38_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
"B_Jun11_08_51_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
"B_Jun11_18_38_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
"G_Jul03_21_14_nr_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
"G_Jul05_00_24_nr_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
"G_Jul06_03_39_nr_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
"G_Jul07_06_38_nr_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
]
for metric in [
# "loss",
# "bauc",
"cauc"
]:
holdout_predictions_d4 = get_predictions_csv(experiments, metric, "holdout", "d4")
oof_predictions_d4 = get_predictions_csv(experiments, metric, "oof", "d4")
test_predictions_d4 = get_predictions_csv(experiments, metric, "test", "d4")
hld_bin_pred_d4 = make_binary_predictions(holdout_predictions_d4)
hld_y_true = hld_bin_pred_d4[0].y_true_type.values
oof_bin_pred_d4 = make_binary_predictions(oof_predictions_d4)
hld_cls_pred_d4 = make_classifier_predictions(holdout_predictions_d4)
oof_cls_pred_d4 = make_classifier_predictions(oof_predictions_d4)
bin_pred_d4_cal = make_binary_predictions_calibrated(holdout_predictions_d4, oof_predictions_d4)
cls_pred_d4_cal = make_classifier_predictions_calibrated(holdout_predictions_d4, oof_predictions_d4)
print(
" ", " ", " ", " OOF", " OOF 5K", " OOF 1K", " HLD", " HLD 5K", " HLD 1K"
)
print(
metric,
"Bin NC",
"{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}".format(
np.mean([alaska_weighted_auc(x.y_true_type, x.Label) for x in oof_bin_pred_d4]),
np.mean([shaky_wauc(x.y_true_type, x.Label) for x in oof_bin_pred_d4]),
np.mean([shaky_wauc_public(x.y_true_type, x.Label) for x in oof_bin_pred_d4]),
alaska_weighted_auc(hld_y_true, blend_predictions_mean(hld_bin_pred_d4).Label),
shaky_wauc(hld_y_true, blend_predictions_mean(hld_bin_pred_d4).Label),
shaky_wauc_public(hld_y_true, blend_predictions_mean(hld_bin_pred_d4).Label),
),
)
print(
metric,
"Cls NC",
"{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}".format(
np.mean([alaska_weighted_auc(x.y_true_type, x.Label) for x in oof_cls_pred_d4]),
np.mean([shaky_wauc(x.y_true_type, x.Label) for x in oof_cls_pred_d4]),
np.mean([shaky_wauc_public(x.y_true_type, x.Label) for x in oof_cls_pred_d4]),
alaska_weighted_auc(hld_y_true, blend_predictions_mean(hld_cls_pred_d4).Label),
shaky_wauc(hld_y_true, blend_predictions_mean(hld_cls_pred_d4).Label),
shaky_wauc_public(hld_y_true, blend_predictions_mean(hld_cls_pred_d4).Label),
),
)
print(
metric,
"Bin CL",
" {:.6f}\t{:.6f}\t{:.6f}".format(
alaska_weighted_auc(hld_y_true, blend_predictions_mean(bin_pred_d4_cal).Label),
shaky_wauc(hld_y_true, blend_predictions_mean(bin_pred_d4_cal).Label),
shaky_wauc_public(hld_y_true, blend_predictions_mean(bin_pred_d4_cal).Label),
),
)
print(
metric,
"Cls CL",
" {:.6f}\t{:.6f}\t{:.6f}".format(
alaska_weighted_auc(hld_y_true, blend_predictions_mean(cls_pred_d4_cal).Label),
shaky_wauc(hld_y_true, blend_predictions_mean(cls_pred_d4_cal).Label),
shaky_wauc_public(hld_y_true, blend_predictions_mean(cls_pred_d4_cal).Label),
),
)
print(
metric,
"Prd NC",
"{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}".format(
np.mean(
[
alaska_weighted_auc(x.y_true_type, x.Label * y.Label)
for (x, y) in zip(oof_bin_pred_d4, oof_cls_pred_d4)
]
),
np.mean(
[shaky_wauc(x.y_true_type, x.Label * y.Label) for (x, y) in zip(oof_bin_pred_d4, oof_cls_pred_d4)]
),
np.mean(
[
shaky_wauc_public(x.y_true_type, x.Label * y.Label)
for (x, y) in zip(oof_bin_pred_d4, oof_cls_pred_d4)
]
),
alaska_weighted_auc(
hld_y_true,
blend_predictions_mean(bin_pred_d4_cal).Label * blend_predictions_mean(cls_pred_d4_cal).Label,
),
shaky_wauc(
hld_y_true,
blend_predictions_mean(bin_pred_d4_cal).Label * blend_predictions_mean(cls_pred_d4_cal).Label,
),
shaky_wauc_public(
hld_y_true,
blend_predictions_mean(bin_pred_d4_cal).Label * blend_predictions_mean(cls_pred_d4_cal).Label,
),
),
)
def main():
output_dir = os.path.dirname(__file__)
experiments = [
"G_Jul03_21_14_nr_rgb_tf_efficientnet_b6_ns_fold0_local_rank_0_fp16",
"G_Jul05_00_24_nr_rgb_tf_efficientnet_b6_ns_fold1_local_rank_0_fp16",
"G_Jul06_03_39_nr_rgb_tf_efficientnet_b6_ns_fold2_local_rank_0_fp16",
"G_Jul07_06_38_nr_rgb_tf_efficientnet_b6_ns_fold3_local_rank_0_fp16",
#
"H_Jul12_18_42_nr_rgb_tf_efficientnet_b7_ns_mish_fold1_local_rank_0_fp16",
"H_Jul11_16_37_nr_rgb_tf_efficientnet_b7_ns_mish_fold2_local_rank_0_fp16",
#
"K_Jul17_17_09_nr_rgb_tf_efficientnet_b6_ns_mish_fold0_local_rank_0_fp16",
"K_Jul18_16_41_nr_rgb_tf_efficientnet_b6_ns_mish_fold3_local_rank_0_fp16",
#
"J_Jul19_20_10_nr_rgb_tf_efficientnet_b7_ns_mish_fold1_local_rank_0_fp16",
]
holdout_predictions = get_predictions_csv(experiments, "cauc", "holdout", "d4")
test_predictions = get_predictions_csv(experiments, "cauc", "test", "d4")
fnames_for_checksum = np.array(
[x + "cauc_bin" for x in experiments]
# + [x + "loss_bin" for x in experiments]
+ [x + "cauc_cls" for x in experiments]
# + [x + "loss_cls" for x in experiments]
)
X = make_binary_predictions(holdout_predictions) + make_classifier_predictions(holdout_predictions)
y_true = X[0].y_true_type.values
X = np.array([x.Label.values for x in X])
assert len(fnames_for_checksum) == X.shape[0]
X_test = make_binary_predictions(test_predictions) + make_classifier_predictions(test_predictions)
indices = np.arange(len(X))
for r in range(2, 8):
best_comb = None
best_auc = 0
combs = list(itertools.combinations(indices, r))
for c in tqdm(combs, desc=f"{r}"):
avg_preds = X[np.array(c)].mean(axis=0)
score_averaging = alaska_weighted_auc(y_true, avg_preds)
if score_averaging > best_auc:
best_auc = score_averaging
best_comb = c
print(r, best_auc, best_comb)
checksum = compute_checksum_v2(fnames_for_checksum[np.array(best_comb)])
test_preds = [X_test[i] for i in best_comb]
test_preds = blend_predictions_mean(test_preds)
test_preds.to_csv(os.path.join(output_dir, f"cmb_mean_{best_auc:.4f}_{r}_{checksum}.csv"), index=False)
for r in range(2, 8):
best_comb = None
best_auc = 0
combs = list(itertools.combinations(indices, r))
for c in tqdm(combs, desc=f"{r}"):
rnk_preds = rankdata(X[np.array(c)], axis=1).mean(axis=0)
score_averaging = alaska_weighted_auc(y_true, rnk_preds)
if score_averaging > best_auc:
best_auc = score_averaging
best_comb = c
print(r, best_auc, best_comb)
checksum = compute_checksum_v2(fnames_for_checksum[np.array(best_comb)])
test_preds = [X_test[i] for i in best_comb]
test_preds = blend_predictions_mean(test_preds)
test_preds.to_csv(os.path.join(output_dir, f"cmb_rank_{best_auc:.4f}_{r}_{checksum}.csv"), index=False)
