modified: models/cph/__init__.py

	modified:   models/cph/dcph_utilities.py

auton_survival/models/cph/__init__.py

@@ -21,41 +21,286 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+r""" Deep Cox Proportional Hazards Model"""
 
-r"""
-`dsm` includes extended functionality for survival analysis as part
-of `dsm.contrib`.
+import torch
+import numpy as np
 
-Contributed Modules
---------------------
-This submodule incorporates contributed survival analysis methods.
+from .dcph_torch import DeepCoxPHTorch, DeepRecurrentCoxPHTorch
+from .dcph_utilities import train_dcph, predict_survival
 
+from auton_survival.models.dsm.utilities import _get_padded_features
+from auton_survival.models.dsm.utilities import _get_padded_targets
 
-Deep Cox Mixtures
-------------------
 
-The Cox Mixture involves the assumption that the survival function
-of the individual to be a mixture of K Cox Models. Conditioned on each
-subgroup Z=k; the PH assumptions are assumed to hold and the baseline
-hazard rates is determined non-parametrically using an spline-interpolated
-Breslow's estimator.
+class DeepCoxPH:
+  """A Deep Cox Proportional Hazards model.
 
-For full details on Deep Cox Mixture, refer to the paper [1].
+  This is the main interface to a Deep Cox Proportional Hazards model.
+  A model is instantiated with approporiate set of hyperparameters and
+  fit on numpy arrays consisting of the features, event/censoring times
+  and the event/censoring indicators.
 
-References
-----------
-[1] <a href="https://arxiv.org/abs/2101.06536">Deep Cox Mixtures
-for Survival Regression. Machine Learning in Health Conference (2021)</a>
+  For full details on Deep Cox Proportional Hazards, refer [1], [2].
 
-```
-  @article{nagpal2021dcm,
-  title={Deep Cox mixtures for survival regression},
-  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
-  journal={arXiv preprint arXiv:2101.06536},
-  year={2021}
-  }
-```
+  References
+  ----------
+  [1] <a href="https://arxiv.org/abs/1606.00931">DeepSurv: personalized
+  treatment recommender system using a Cox proportional hazards
+  deep neural network. BMC medical research methodology (2018)</a>
 
-"""
+  [2] <a href="https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.4780140108">
+  A neural network model for survival data. Statistics in medicine (1995)</a>
 
-from .dcph_api import DeepCoxPH, DeepRecurrentCoxPH
+  Parameters
+  ----------
+  k: int
+      The number of underlying Cox distributions.
+  layers: list
+      A list of integers consisting of the number of neurons in each
+      hidden layer.
+  Example
+  -------
+  >>> from auton_survival import DeepCoxPH
+  >>> model = DeepCoxPH()
+  >>> model.fit(x, t, e)
+
+  """
+
+  def __init__(self, layers=None):
+
+    self.layers = layers
+    self.fitted = False
+
+  def __call__(self):
+    if self.fitted:
+      print("A fitted instance of the Deep Cox PH model")
+    else:
+      print("An unfitted instance of the Deep Cox PH model")
+
+    print("Hidden Layers:", self.layers)
+
+  def _preprocess_test_data(self, x):
+    return torch.from_numpy(x).float()
+
+  def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state):
+
+    idx = list(range(x.shape[0]))
+
+    np.random.seed(random_state)
+    np.random.shuffle(idx)
+
+    x_train, t_train, e_train = x[idx], t[idx], e[idx]
+
+    x_train = torch.from_numpy(x_train).float()
+    t_train = torch.from_numpy(t_train).float()
+    e_train = torch.from_numpy(e_train).float()
+
+    if val_data is None:
+
+      vsize = int(vsize*x_train.shape[0])
+      x_val, t_val, e_val = x_train[-vsize:], t_train[-vsize:], e_train[-vsize:]
+
+      x_train = x_train[:-vsize]
+      t_train = t_train[:-vsize]
+      e_train = e_train[:-vsize]
+
+    else:
+
+      x_val, t_val, e_val = val_data
+
+      x_val = torch.from_numpy(x_val).float()
+      t_val = torch.from_numpy(t_val).float()
+      e_val = torch.from_numpy(e_val).float()
+
+    return (x_train, t_train, e_train, x_val, t_val, e_val)
+
+  def _gen_torch_model(self, inputdim, optimizer):
+    """Helper function to return a torch model."""
+    return DeepCoxPHTorch(inputdim, layers=self.layers,
+                          optimizer=optimizer)
+
+  def fit(self, x, t, e, vsize=0.15, val_data=None,
+          iters=1, learning_rate=1e-3, batch_size=100,
+          optimizer="Adam", random_state=100):
+
+    r"""This method is used to train an instance of the DSM model.
+
+    Parameters
+    ----------
+    x: np.ndarray
+        A numpy array of the input features, \( x \).
+    t: np.ndarray
+        A numpy array of the event/censoring times, \( t \).
+    e: np.ndarray
+        A numpy array of the event/censoring indicators, \( \delta \).
+        \( \delta = 1 \) means the event took place.
+    vsize: float
+        Amount of data to set aside as the validation set.
+    val_data: tuple
+        A tuple of the validation dataset. If passed vsize is ignored.
+    iters: int
+        The maximum number of training iterations on the training dataset.
+    learning_rate: float
+        The learning rate for the `Adam` optimizer.
+    batch_size: int
+        learning is performed on mini-batches of input data. this parameter
+        specifies the size of each mini-batch.
+    optimizer: str
+        The choice of the gradient based optimization method. One of
+        'Adam', 'RMSProp' or 'SGD'.
+    random_state: float
+        random seed that determines how the validation set is chosen.
+    """
+
+    processed_data = self._preprocess_training_data(x, t, e,
+                                                    vsize, val_data,
+                                                    random_state)
+
+    x_train, t_train, e_train, x_val, t_val, e_val = processed_data
+
+    #Todo: Change this somehow. The base design shouldn't depend on child
+
+    inputdim = x_train.shape[-1]
+
+    model = self._gen_torch_model(inputdim, optimizer)
+
+    model, _ = train_dcph(model,
+                          (x_train, t_train, e_train),
+                          (x_val, t_val, e_val),
+                          epochs=iters,
+                          lr=learning_rate,
+                          bs=batch_size,
+                          return_losses=True)
+
+    self.torch_model = (model[0].eval(), model[1])
+    self.fitted = True
+
+    return self
+
+  def predict_risk(self, x, t=None):
+
+    if self.fitted:
+      return 1-self.predict_survival(x, t)
+    else:
+      raise Exception("The model has not been fitted yet. Please fit the " +
+                      "model using the `fit` method on some training data " +
+                      "before calling `predict_risk`.")
+
+  def predict_survival(self, x, t=None):
+    r"""Returns the estimated survival probability at time \( t \),
+      \( \widehat{\mathbb{P}}(T > t|X) \) for some input data \( x \).
+
+    Parameters
+    ----------
+    x: np.ndarray
+        A numpy array of the input features, \( x \).
+    t: list or float
+        a list or float of the times at which survival probability is
+        to be computed
+    Returns:
+      np.array: numpy array of the survival probabilites at each time in t.
+
+    """
+    if not self.fitted:
+      raise Exception("The model has not been fitted yet. Please fit the " +
+                      "model using the `fit` method on some training data " +
+                      "before calling `predict_survival`.")
+
+    x = self._preprocess_test_data(x)
+
+    if t is not None:
+      if not isinstance(t, list):
+        t = [t]
+
+    scores = predict_survival(self.torch_model, x, t)
+    return scores
+
+
+class DeepRecurrentCoxPH(DeepCoxPH):
+  r"""A deep recurrent Cox PH model.
+
+  This model is based on the paper:
+  <a href="https://aclanthology.org/2021.naacl-main.358.pdf"> Leveraging
+  Deep Representations of Radiology Reports in Survival Analysis for
+  Predicting Heart Failure Patient Mortality. NAACL (2021)</a>
+
+  Parameters
+  ----------
+  k: int
+      The number of underlying Cox distributions.
+  layers: list
+      A list of integers consisting of the number of neurons in each
+      hidden layer.
+  Example
+  -------
+  >>> from dsm.contrib import DeepRecurrentCoxPH
+  >>> model = DeepRecurrentCoxPH()
+  >>> model.fit(x, t, e)
+
+  """
+
+  def __init__(self, layers=None, hidden=None, typ="LSTM"):
+
+    super(DeepRecurrentCoxPH, self).__init__(layers=layers)
+
+    self.typ = typ
+    self.hidden = hidden
+
+  def __call__(self):
+    if self.fitted:
+      print("A fitted instance of the Recurrent Deep Cox PH model")
+    else:
+      print("An unfitted instance of the Recurrent Deep Cox PH model")
+
+    print("Hidden Layers:", self.layers)
+
+  def _gen_torch_model(self, inputdim, optimizer):
+    """Helper function to return a torch model."""
+    return DeepRecurrentCoxPHTorch(inputdim, layers=self.layers,
+                                   hidden=self.hidden,
+                                   optimizer=optimizer, typ=self.typ)
+
+  def _preprocess_test_data(self, x):
+    return torch.from_numpy(_get_padded_features(x)).float()
+
+  def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state):
+    """RNNs require different preprocessing for variable length sequences"""
+
+    idx = list(range(x.shape[0]))
+    np.random.seed(random_state)
+    np.random.shuffle(idx)
+
+    x = _get_padded_features(x)
+    t = _get_padded_targets(t)
+    e = _get_padded_targets(e)
+
+    x_train, t_train, e_train = x[idx], t[idx], e[idx]
+
+    x_train = torch.from_numpy(x_train).float()
+    t_train = torch.from_numpy(t_train).float()
+    e_train = torch.from_numpy(e_train).float()
+
+    if val_data is None:
+
+      vsize = int(vsize*x_train.shape[0])
+
+      x_val, t_val, e_val = x_train[-vsize:], t_train[-vsize:], e_train[-vsize:]
+
+      x_train = x_train[:-vsize]
+      t_train = t_train[:-vsize]
+      e_train = e_train[:-vsize]
+
+    else:
+
+      x_val, t_val, e_val = val_data
+
+      x_val = _get_padded_features(x_val)
+      t_val = _get_padded_features(t_val)
+      e_val = _get_padded_features(e_val)
+
+      x_val = torch.from_numpy(x_val).float()
+      t_val = torch.from_numpy(t_val).float()
+      e_val = torch.from_numpy(e_val).float()
+
+    return (x_train, t_train, e_train, x_val, t_val, e_val)

auton_survival/models/cph/dcph_utilities.py

@@ -33,7 +33,7 @@ def partial_ll_loss(lrisks, tb, eb, eps=1e-3):
   return -pll
 
 def fit_breslow(model, x, t, e):
-  return BreslowEstimator().fit(model(x).detach().cpu().numpy(), 
+  return BreslowEstimator().fit(model(x).detach().cpu().numpy(),
                                 e.numpy(), t.numpy())
 
 def train_step(model, x, t, e, optimizer, bs=256, seed=100):
@@ -165,4 +165,4 @@ def __interpolate_missing_times(survival_predictions, times):
 
   for idx in not_in_index:
     survival_predictions.loc[idx] = nans
-  return survival_predictions.sort_index(axis=0).interpolate().interpolate(method='bfill').T[times].values
+  return survival_predictions.sort_index(axis=0).interpolate(method='bfill').T[times].values