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simclr_embed_ood.py
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simclr_embed_ood.py
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from sklearn.metrics import roc_auc_score
import pandas as pd
import numpy as np
from classification.classification_module import ClassificationModule
from hydra import compose, initialize
from data_handling.mammo import modelname_map, EmbedOODDataModule
from evaluation.helper_functions import run_inference
import os
os.chdir("/vol/biomedic3/mb121/causal-contrastive/evaluation")
rev_model_map = {v: k for k, v in modelname_map.items()}
# Mapping from human readable run name to Weights&Biases run_id.
# Human readable name should be in format:
# for finetuning:
# {simclr/simclrcf/simclrcfaug}-{train_prop}-{seed}
# for linear probing
# {simclr/simclrcf/simclrcfaug}head-{train_prop}-{seed}
model_dict = {
"simclrcfaughead-0.1-33": "sfjtep0q",
"simclrcfhead-0.05-33": "cbfkz1sl",
"simclrhead-0.05-33": "x9l2xljv",
"simclrcfaughead-0.25-33": "x3ygrgw8",
"simclrcfhead-0.1-33": "3vz13efk",
"simclrhead-0.1-33": "bjpjjsf8",
"simclrcfaug-0.1-33": "7akh9trb",
"simclrcf-0.1-33": "dvb0d52s",
"simclrcfhead-0.25-33": "2q9vqnnf",
"simclrhead-0.25-33": "t8826i37",
"simclrcfaughead-0.5-33": "a2t86qgm",
"simclrcfaug-0.25-33": "pnucxhp6",
"simclrcf-0.25-33": "018sy9al",
"simclrcfhead-0.5-33": "ocuvdahw",
"simclrhead-0.5-33": "huze07kx",
"simclrcfaughead-1.0-33": "fq5i6et5",
"simclrcfaug-0.5-33": "3r963q78",
"simclrcf-0.5-33": "llmb6gq8",
"simclrhead-1.0-33": "yywbsyi5",
"simclrcf-1.0-33": "l2jlzbzt",
"simclr-0.1-33": "jyn1jzku",
"simclr-0.25-33": "hgcfhz1t",
"simclrcfhead-1.0-33": "in9dzbn3",
"simclrhead-0.05-22": "lnj3yydc",
"simclrcfaug-1.0-33": "1zwvsq0s",
"simclrcfaughead-0.05-22": "ymguomdu",
"simclrhead-0.1-22": "jugy7nw2",
"simclr-0.5-33": "g8aq5em5",
"simclrcfaug-0.05-22": "1cm0ul2o",
"simclrcfaughead-0.1-22": "5ubg393w",
"simclrcf-0.05-22": "kp8u3s2m",
"simclrhead-0.25-22": "n6osuheo",
"simclrcfhead-0.05-22": "nvelflfg",
"simclrcfaug-0.1-22": "fiixmtqq",
"simclrhead-0.5-22": "4z5nyl5u",
"simclrcfhead-0.1-22": "mqv4rv7l",
"simclrcfaughead-0.25-22": "1st31efx",
"simclrhead-1.0-22": "iqrs8bpw",
"simclrcfaug-0.25-22": "5gk0bf4h",
"simclrcf-0.1-22": "np0nb3fk",
"simclrhead-0.05-11": "pejx08s9",
"simclrhead-0.1-11": "jbun6pv1",
"simclrcfaug-0.5-22": "lymnrsi4",
"simclrcf-0.25-22": "y5d9twi9",
"simclrcfhead-0.25-22": "2w8dd9sl",
"simclrhead-0.25-11": "nzh0uxqw",
"simclrcfaughead-0.5-22": "ock5l9ii",
"simclrcfaug-1.0-22": "x0zkov3t",
"simclrcf-0.5-22": "8222inz6",
"simclr-1.0-33": "g57vd7lg",
"simclrhead-0.5-11": "8td74xhh",
"simclr-0.05-22": "jdh3xf7t",
"simclrcfhead-0.5-22": "tmxnvhml",
"simclrcfaug-0.05-11": "tdjrer5o",
"simclrhead-1.0-11": "by1h6jp3",
"simclrcf-1.0-22": "5dq2a3wx",
"simclrcfaughead-1.0-22": "3jhxlv5v",
"simclr-0.1-22": "8tja5u0s",
"simclrcfaug-0.1-11": "c6652p4m",
"simclrcf-0.05-11": "u8mkxtk2",
"simclrcf-0.1-11": "3nca3s09",
"simclrcfaughead-0.05-11": "fdvbf3yj",
"simclrcfhead-1.0-22": "fr6nh3px",
"simclrcfaug-0.5-11": "d5usym29",
"simclrcfhead-0.05-11": "4rbg4h1d",
"simclrcf-0.25-11": "8tp6vnjr",
"simclr-0.25-22": "kx3owiy4",
"simclrcfaughead-0.1-11": "apwbewir",
"simclrcfhead-0.1-11": "sas2eztu",
"simclrcfaughead-0.25-11": "cjxptv82",
"simclrcfaug-1.0-11": "952t5sbz",
"simclrcf-0.5-11": "2agax8fw",
"simclrcfhead-0.25-11": "6an1yp1y",
"simclrcfaughead-0.5-11": "6euv46vl",
"simclr-0.5-22": "vum2olh1",
"simclrcf-1.0-11": "mtcukk2x",
"supervised-0.05-33": "01doh1to",
"simclrcfhead-0.5-11": "2cfjko4w",
"supervised-0.1-33": "vy8qveyt",
"simclr-0.05-11": "lpeu81ya",
"supervised-0.25-33": "j46z0201",
"simclr-1.0-11": "fd4r9de5",
"supervised-1.0-33": "trj4rjoh",
"simclr-0.1-11": "7y72cm3b",
"simclrcfaughead-1.0-11": "zcefxazw",
"simclrcfhead-1.0-11": "tzwrpyqf",
"simclr-1.0-22": "kcloy93c",
"supervised-0.05-22": "ckyww6k4",
"simclr-0.05-33": "m4i603ks",
"simclr-0.25-11": "xala1ohh",
"supervised-0.1-22": "k4utvrmu",
"supervised-1.0-11": "a7brvlk5",
"supervised-0.25-11": "yhvlr7ww",
"supervised-0.1-11": "ma2ss7jh",
"supervised-0.05-11": "8ttg7aty",
"supervised-1.0-22": "dixs9enj",
}
with initialize(version_base=None, config_path="../configs"):
cfg = compose(
config_name="config.yaml",
overrides=["experiment=base_density", "data=embed_ood", "data.cache=False"],
)
print(cfg)
data_module = EmbedOODDataModule(config=cfg)
test_loader = data_module.test_dataloader()
df = pd.read_csv(f"../outputs/classification_{cfg.data.label}_results_ood.csv")
for run_name, run_id in model_dict.items():
already_in_df = run_name in df.run_name.values
if run_id != "" and not already_in_df:
print(run_name)
model_to_evaluate = f"../outputs/run_{run_id}/best.ckpt"
classification_model = ClassificationModule.load_from_checkpoint(
model_to_evaluate, map_location="cuda"
).model.eval()
classification_model.cuda()
# ID evaluation
inference_results = run_inference(test_loader, classification_model)
scanners = np.argmax(inference_results["scanners"], 1)
for i in np.unique(scanners):
print(f"\nEvaluating scanner {i}")
sc_idx = np.where(scanners == i)
targets = inference_results["targets"][sc_idx]
preds = np.argmax(inference_results["confs"], 1)[sc_idx]
confs = inference_results["confs"][sc_idx]
res = {}
res["N_test"] = [targets.shape[0]]
res["ROC"] = [roc_auc_score(targets, confs, multi_class="ovr")]
res["Model Name"] = [rev_model_map[i]]
res["run_name"] = run_name
print(res)
df = pd.concat([df, pd.DataFrame(res, index=[0])], ignore_index=True)
df.to_csv(
f"../outputs/classification_{cfg.data.label}_results_ood.csv",
index=False,
)