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aaai_coxph_func.py
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import numpy as np
import ConfigSpace.hyperparameters as CSH
import ConfigSpace as CS
from ray import tune
from ray.tune.suggest.bohb import TuneBOHB
from ray.tune.schedulers import HyperBandForBOHB
from ray.tune.logger import CSVLogger, JsonLogger, MLFLowLogger
from sksurv.linear_model import CoxPHSurvivalAnalysis
from sksurv.metrics import concordance_index_censored
from sklearn.utils import shuffle
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
import mlflow
from mlflow.tracking import MlflowClient
from datasets import get_flchain, get_whas500, get_DBCD, get_NWTCO
def trainer(config, data_split=None, data=None, labels=None, cont_labels=None):
print(f"Alpha: {config['alpha']}")
alpha = config["alpha"]
epochs = config["epochs"]
mlflow.log_param("epochs", epochs)
mlflow.log_param("alpha", alpha)
try:
train_scores = []
val_scores = []
test_scores = []
for train_index, val_index, test_index in data_split:
data_train, data_val, data_test = data[train_index], data[val_index], data[test_index]
labels_train, labels_val, labels_test = labels[train_index], labels[val_index], labels[test_index]
cont_labels_train, cont_labels_val, cont_labels_test = cont_labels[train_index], cont_labels[val_index], cont_labels[test_index]
scaler = StandardScaler().fit(data_train)
data_train = scaler.transform(data_train)
data_val = scaler.transform(data_val)
data_test = scaler.transform(data_test)
lifetime_train = labels_train[:, 0]
censor_train = labels_train[:, 1]
cont_lifetime_train = cont_labels_train[:, 0]
cont_lifetime_val = cont_labels_val[:, 0]
censor_val = cont_labels_val[:, 1]
cont_lifetime_test = cont_labels_test[:, 0]
censor_test = cont_labels_test[:, 1]
data_surv_train = np.zeros(censor_train.shape[0], dtype={'names': ('censor', 'time'),
'formats': (bool, float)})
data_surv_train["censor"] = censor_train
data_surv_train["time"] = lifetime_train
print("fitting...")
model = CoxPHSurvivalAnalysis(alpha=alpha,
ties="efron",
n_iter=epochs,
verbose=1).fit(data_train, data_surv_train)
print("done fitting...")
train_risk = model.predict(data_train)
c_index_train = concordance_index_censored(censor_train.astype(bool), cont_lifetime_train, train_risk)[0]
print('Train C-index: {:.6f}'.format(c_index_train))
train_scores.append(c_index_train)
val_risk = model.predict(data_val)
c_index_val = concordance_index_censored(censor_val.astype(bool), cont_lifetime_val, val_risk)[0]
print('Validation C-index: {:.6f}'.format(c_index_val))
val_scores.append(c_index_val)
test_risk = model.predict(data_test)
c_index = concordance_index_censored(censor_test.astype(bool), cont_lifetime_test, test_risk)[0]
print('Test C-index: {:.6f}'.format(c_index))
test_scores.append(c_index)
result_dict = {"mean-test-C-index": np.mean(test_scores),
"max-test-C-index": max(test_scores),
"min-test-C-index": min(test_scores),
"mean-val-C-index": np.mean(val_scores),
"max-val-C-index": max(val_scores),
"min-val-C-index": min(val_scores),
"mean-train-C-index": np.mean(train_scores),
"max-train-C-index": max(train_scores),
"min-train-C-index": min(train_scores)}
except ValueError as e:
print(e)
result_dict = {"mean-test-C-index": 0,
"max-test-C-index": 0,
"min-test-C-index": 0,
"mean-val-C-index": 0,
"max-val-C-index": 0,
"min-val-C-index": 0,
"mean-train-C-index": 0,
"max-train-C-index": 0,
"min-train-C-index": 0}
return tune.report(**result_dict)
def run_experiment(dataset, num_bins):
data, labels, name = dataset()
print(f"data nan: {np.isnan(data).any()}")
print(f"data inf: {np.isinf(data).any()}")
print(f"labels nan: {np.isnan(labels).any()}")
print(f"labels inf: {np.isinf(labels).any()}")
data, labels = shuffle(data, labels, random_state=0)
cont_labels = labels.copy()
def get_data_split(folds=None):
if folds:
kf = KFold(n_splits=folds, random_state=0)
data_split = list(kf.split(data))
else:
data_split = [(range(0, int(round(data.shape[0] * 0.6))),
range(int(round(data.shape[0] * 0.6)), int(round(data.shape[0] * 0.8))),
range(int(round(data.shape[0] * 0.8)), data.shape[0])), ]
return data_split
def discritization(num_bins):
bins = np.linspace(np.min(labels[:, 0]), np.max(labels[:, 0]), num=num_bins)
bins = [(bins[i], bins[i + 1]) for i in range(len(bins) - 1)]
for bin_lower, bin_upper in bins:
mid = (bin_lower + bin_upper) / 2
loc = np.where((bin_lower <= labels[:, 0]) & (labels[:, 0] <= bin_upper))
labels[loc, 0] = mid
if num_bins != 0:
discritization(num_bins=num_bins)
data_split = get_data_split()
epochs = CSH.UniformIntegerHyperparameter(name=f"epochs", lower=20, upper=1000, log=False)
alphas = CSH.UniformFloatHyperparameter(name=f"alpha", lower=10000, upper=10000000.0, log=True)
config_space = CS.ConfigurationSpace(seed=1234)
config_space.add_hyperparameters([alphas, epochs])
experiment_metrics = dict(metric="mean-val-C-index", mode="max")
bohb_hyperband = HyperBandForBOHB(
time_attr="training_iteration", max_t=1, **experiment_metrics)
bohb_search = TuneBOHB(
config_space, max_concurrent=10, **experiment_metrics)
NAME = f"{name}_coxph_num_bins_{num_bins}"
client = MlflowClient("./mlruns")
experiments = client.list_experiments()
experiment_id = None
for experiment in experiments:
if experiment.name == NAME:
experiment_id = experiment.experiment_id
if not experiment_id:
experiment_id = client.create_experiment(NAME)
analysis = tune.run(
tune.with_parameters(trainer, data=data, labels=labels, cont_labels=cont_labels, data_split=data_split),
name=NAME,
scheduler=bohb_hyperband,
search_alg=bohb_search,
num_samples=100,
resources_per_trial={"cpu": 1},
loggers=[CSVLogger, JsonLogger, MLFLowLogger],
config={
"logger_config": {
"mlflow_experiment_id": experiment_id
},
},
local_dir="./tune-results",
)
if __name__ == "__main__":
for dataset_ in [get_flchain, get_whas500, get_DBCD, get_NWTCO]:
for num_bins_ in [0, 5, 10, 15, 20, 25]:
run_experiment(dataset_, num_bins_)