def calcSurvHazardCat(df: pd.DataFrame, *, hazardcol: str = "hazard",) -> pd.DataFrame:
"""
Calculate cumulative hazard survived for each individual patient, as an alternative
to raw (and often censored) survival time.
Parameters
----------
df
A data frame with two compulsory columns: time and event.
hazardcol
Column name for the survived hazard.
Returns
-------
The input dataframe, with an extra column of hazards.
"""
### Fit survival Nelson-Aalen Estimator of Hazard on survival data
T = df["time"]
E = df["event"]
naf = NelsonAalenFitter()
naf.fit(T, E)
df[hazardcol] = naf.predict(T).tolist()
return df
naf = NelsonAalenFitter()
#Fit our data into the object:
naf.fit(data["time"], event_observed=data["dead"])
#Print the cumulative hazard:
naf.cumulative_hazard_
#Plot the cumulative hazard grpah:
naf.plot_cumulative_hazard()
plt.title("Cumulative Probability for Event of Interest")
plt.xlabel("Number of days")
plt.ylabel("Cumulative Probability of person's death")
#We can predict the value at a certain point :
print("Time = 500 days: ",naf.predict(500))
print("Time = 1022 days: ",naf.predict(1022))
#Cumulative hazard with confidence interval:
naf.confidence_interval_
#Plot cumulative hazard with confidence interval:
confidence_interval = naf.confidence_interval_
plt.plot(confidence_interval["NA_estimate_lower_0.95"],label="Lower")
plt.plot(confidence_interval["NA_estimate_upper_0.95"],label="Upper")
plt.title("Cumulative hazard With Confidence Interval")
plt.xlabel("Number of days")
plt.ylabel("Cumulative hazard")
plt.legend()
#Plot the cumulative_hazard and cumulative density:
def test(ini_file):
''' Performs training according to .ini file
:param ini_file: (String) the path of .ini file
:return best_c_index: the best c-index
'''
# reads configuration from .ini file
config = read_config(ini_file)
# builds network|criterion|optimizer based on configuration
model = DeepSurv(config['network']).to(device)
criterion = NegativeLogLikelihood(config['network'], device).to(device)
# cph = CoxPHFitter()
# constructs data loaders based on configuration
train_dataset = SurvivalDataset(config['train']['h5_file'],
is_train=True,
device=device)
test_dataset = SurvivalDataset(config['train']['h5_file'],
is_train=False,
device=device)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_dataset.__len__())
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=test_dataset.__len__())
test_df = pd.read_csv(
r'H:\project\DeepSurv\DeepSurv.pytorch-master\ours_test.csv',
index_col=['PatientID'])
train_df = pd.read_csv(
r'H:\project\DeepSurv\DeepSurv.pytorch-master\ours_train.csv',
index_col=['PatientID'])
# train step
best_c_index = 0
# kmf = KaplanMeierFitter()
naf = NelsonAalenFitter()
# wf = WeibullFitter()
naf.fit(test_df['Time_d'], event_observed=test_df['Event'])
timeline = np.arange(0, 25000)
base_risk = naf.predict(timeline)
i = timeline[-1]
while i > 0:
base_risk[i] = base_risk[i] - base_risk[i - 1]
i -= 1
np.savetxt('temp.txt', base_risk, '%.17f')
# base_risk.to_csv('test_base_risk.csv', header=True)
model.load_state_dict(
torch.load(os.path.join(models_dir,
ini_file.split('\\')[-1] + '.pth'))['model'])
model.eval()
for X, y, e in test_loader:
with torch.no_grad():
risk_pred = model(X)
valid_loss = criterion(risk_pred, y, e, model)
print(valid_loss)
valid_c = c_index(-risk_pred, y, e)
best_c_index = valid_c
R = risk_pred.detach().cpu().numpy()[:, 0]
for test_index in range(len(R)):
# test_index = 120 # people
_r = R[test_index]
_y = y.detach().cpu().numpy()[test_index, 0]
_e = e.detach().cpu().numpy()[test_index, 0]
t0 = naf.predict(_y)
risk = t0 * np.exp(_r)
# print(np.exp(_r))
print(risk, int(_e))
# pre_y = 0.
# m = np.min(np.where(p > 0.5))
# print(int(_y), m, _e, p[int(_y)] >= 0.5)
# if (p[int(_y)] >= 0.5) == bool(_e):
# ture += 1
# print(ture/len(R))
# if _e == pre_y:
# ture += 1
# plt.plot(p)
# plt.show()
naf.plot()
plt.show()
return best_c_index
