Merge pull request Novartis#88 from Novartis/87-remove-comprehensive-…

…example-from-joss-paper

rm comprehensive example JOSS paper

paper/paper.md

@@ -153,115 +153,6 @@ cindex.p_value(alternative='greater')  # pvalue, H0:c=0.5, HA:c>0.5
 cindex.compare(cindex_other)  # pvalue, H0:c1=c2, HA:c1>c2
 ```
 
-# Comprehensive Example: Fitting a Cox Proportional Hazards Model with TorchSurv
-
-In this section, we provide a reproducible code example to demonstrate how to use `TorchSurv` for fitting a Cox proportional hazards model. We simulate data where each observations is associated with 10 features, a time-to-event that depends linearly on these features, and a time-to-censoring. The observable time is the minimum between the time-to-event and time-to-censoring, representing the first event that occurs. Subsequently, we fit a Cox proportional hazards model using maximum likelihood estimation and assess the model's predictive performance through the AUC and the C-index. To facilitate rapid execution, we use a simple linear backend model in PyTorch to define the log relative hazards. For more comprehensive examples using real data, we encourage readers to visit the `Torchsurv` website.
-
-
-
-```python
-import torch
-from torch.utils.data import DataLoader, Dataset
-from torchsurv.loss import cox
-from torchsurv.metrics.cindex import ConcordanceIndex
-from torchsurv.metrics.auc import Auc
-
-torch.manual_seed(42)
-
-# 1. Simulate response
-n_features = 10  # int, number of features per observation
-time_end = torch.tensor(
-    2000.0
-)  # float, end of observational period after which all observations are censored
-weights = (
-    torch.randn(n_features) * 5
-)  # float, weights associated with the features ~ normal(0, 5^2)
-
-# Define the survival response generator function
-def tte_generator(batch_size: int):
-    while True:
-        x = torch.randn(batch_size, n_features)  # features
-        mean_event_time, mean_censoring_time = 1000.0 + x @ weights, 1000.0
-
-        event_time = (
-            mean_event_time + torch.randn(batch_size) * 50
-        )  # event time ~ normal(mean_event_time, 50^2)
-        censoring_time = torch.distributions.Exponential(
-            1 / mean_censoring_time
-        ).sample(
-            (batch_size,)
-        )  # censoring time ~ Exponential(mean = mean_censoring_time)
-        censoring_time = torch.minimum(
-            censoring_time, time_end
-        )  # truncate censoring time to time_end
-
-        event = (event_time <= censoring_time).bool()  # event indicator
-        time = torch.minimum(event_time, censoring_time)  # observed time
-
-        yield x, event, time
-
-# 2. Define the PyTorch dataset class
-class TTE_dataset(Dataset):
-    def __init__(self, generator: callable, batch_size: int):
-        self.batch_size = batch_size
-        self.generatated_data = generator(batch_size=batch_size)
-
-    def __len__(self):
-        return self.batch_size
-
-    def __getitem__(self, index):
-        return next(self.generatated_data)
-
-# 3. Define the backbone model on the log hazards.
-class MyPyTorchCoxModel(torch.nn.Module):
-    def __init__(self):
-        super(MyPyTorchCoxModel, self).__init__()
-        self.fc = torch.nn.Linear(n_features, 1, bias=False)  # Simple linear model
-
-    def forward(self, x):
-        return self.fc(x)
-
-# 4. Instantiate the model, optimizer, dataset and dataloader
-cox_model = MyPyTorchCoxModel()
-optimizer = torch.optim.Adam(cox_model.parameters(), lr=0.01)
-batch_size = 64  # int, batch size
-dataset = TTE_dataset(tte_generator, batch_size=batch_size)
-dataloader = DataLoader(
-    dataset, batch_size=1, shuffle=True
-)  # Batch size of 1 because dataset yields batches
-
-# 5. Training loop
-for epoch in range(100):
-    for i, batch in enumerate(dataloader):
-        x, event, time = [t.squeeze() for t in batch]  
-        optimizer.zero_grad()
-        log_hzs = cox_model(x)  # torch.Size([batch_size, 1])
-        loss = cox.neg_partial_log_likelihood(log_hzs, event, time)
-        loss.backward()
-        optimizer.step()
-
-# 6. Evaluate the model
-n_samples_test = 1000  # int, number of observations in test set
-data_test = next(tte_generator(batch_size=n_samples_test))
-x, event, time = [t.squeeze() for t in data_test]  # test set
-log_hzs = cox_model(x)  # log relative hazards evaluated on test set
-
-# AUC at time point 1000
-auc = Auc()
-print(
-    "AUC:", auc(log_hzs, event, time, new_time=torch.tensor(1000.0))
-)  # tensor([0.5902])
-print("AUC Confidence Interval:", auc.confidence_interval())  # tensor([0.5623, 0.6180])
-print("AUC p-value:", auc.p_value(alternative="greater"))  # tensor([0.])
-
-# C-index
-cindex = ConcordanceIndex()
-print("C-Index:", cindex(log_hzs, event, time))  # tensor(0.5774)
-print(
-    "C-Index Confidence Interval:", cindex.confidence_interval()
-)  # tensor([0.5086, 0.6463])
-print("C-Index p-value:", cindex.p_value(alternative="greater"))  # tensor(0.0138)
-```
 
 # Conflicts of interest