20200410

configs/gaussian.ini

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+[train]
+h5_file = 'data/gaussian/gaussian_survival_data.h5'
+epochs = 500
+learning_rate = 3.194e-3
+lr_decay_rate = 3.173e-4
+optimizer = 'Adam'
+
+[network]
+drop = 0.401
+norm = True
+dims = [10, 17, 17, 17, 1]
+activation = 'ReLU'
+l2_reg = 0

configs/gbsg.ini

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+[train]
+h5_file = 'data/gbsg/gbsg_cancer_train_test.h5'
+epochs = 500
+learning_rate = 0.154
+lr_decay_rate = 5.667e-3
+optimizer = 'Adam'
+
+[network]
+drop = 0.661
+norm = True
+dims = [7, 8, 1]
+activation = 'SELU'
+l2_reg = 0

configs/linear.ini

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+[train]
+h5_file = 'data/linear/linear_survival_data.h5'
+epochs = 500
+learning_rate = 2.922e-3
+lr_decay_rate = 3.579e-4
+optimizer = 'Adam'
+
+[network]
+drop = 0.375
+norm = True
+dims = [10, 4, 1]
+activation = 'SELU'
+l2_reg = 0

configs/metabric.ini

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+[train]
+h5_file = 'data/metabric/metabric_IHC4_clinical_train_test.h5'
+epochs = 500
+learning_rate = 0.010
+lr_decay_rate = 4.169e-3
+optimizer = 'Adam'
+
+[network]
+drop = 0.160
+norm = True
+dims = [9, 41, 1]
+activation = 'SELU'
+l2_reg = 0

configs/support.ini

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+[train]
+h5_file = 'data/support/support_train_test.h5'
+epochs = 500
+learning_rate = 0.047
+lr_decay_rate = 2.573e-3
+optimizer = 'Adam'
+
+[network]
+drop = 0.255
+norm = True
+dims = [14, 44, 1]
+activation = 'SELU'
+l2_reg = 0

configs/treatment.ini

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+[train]
+h5_file = 'data/treatment/sim_treatment_dataset.h5'
+epochs = 500
+learning_rate = 0.026
+lr_decay_rate = 1.636e-4
+optimizer = 'Adam'
+
+[network]
+drop = 0.109
+norm = True
+dims = [11, 45, 1]
+activation = 'SELU'
+L2_reg = 0

configs/whas.ini

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+[train]
+h5_file = 'data/whas/whas_train_test.h5'
+epochs = 500
+learning_rate = 0.067
+lr_decay_rate = 6.494e-4
+optimizer = 'Adam'
+
+[network]
+drop = 0.147
+norm = True
+dims = [6, 48, 48, 1]
+activation = 'ReLU'
+l2_reg = 0

datasets.py

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+# ------------------------------------------------------------------------------
+# --coding='utf-8'--
+# Written by czifan (czifan@pku.edu.cn)
+# ------------------------------------------------------------------------------
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import h5py
+import numpy as np
+import pandas as pd
+import torch
+
+from torch.utils.data import Dataset
+
+class SurvivalDataset(Dataset):
+    ''' The dataset class performs loading data from .h5 file. '''
+    def __init__(self, h5_file, is_train):
+        ''' Loading data from .h5 file based on (h5_file, is_train).
+
+        :param h5_file: (String) the path of .h5 file
+        :param is_train: (bool) which kind of data to be loaded?
+                is_train=True: loading train data
+                is_train=False: loading test data
+        '''
+        # loads data
+        self.X, self.e, self.y = \
+            self._read_h5_file(h5_file, is_train)
+        # normalizes data
+        self._normalize()
+
+        print('=> load {} samples'.format(self.X.shape[0]))
+
+    def _read_h5_file(self, h5_file, is_train):
+        ''' The function to parsing data from .h5 file.
+
+        :return X: (np.array) (n, m)
+            m is features dimension.
+        :return e: (np.array) (n, 1)
+            whether the event occurs? (1: occurs; 0: others)
+        :return y: (np.array) (n, 1)
+            the time of event e.
+        '''
+        split = 'train' if is_train else 'test'
+        with h5py.File(h5_file, 'r') as f:
+            X = f[split]['x'][()]
+            e = f[split]['e'][()].reshape(-1, 1)
+            y = f[split]['t'][()].reshape(-1, 1)
+        return X, e, y
+
+    def _normalize(self):
+        ''' Performs normalizing X data. '''
+        self.X = (self.X-self.X.min(axis=0)) / \
+            (self.X.max(axis=0)-self.X.min(axis=0))
+
+    def __getitem__(self, item):
+        ''' Performs constructing torch.Tensor object'''
+        # gets data with index of item
+        X_item = self.X[item] # (m)
+        e_item = self.e[item] # (1)
+        y_item = self.y[item] # (1)
+        # constructs torch.Tensor object
+        X_tensor = torch.from_numpy(X_item)
+        e_tensor = torch.from_numpy(e_item)
+        y_tensor = torch.from_numpy(y_item)
+        return X_tensor, y_tensor, e_tensor
+
+    def __len__(self):
+        return self.X.shape[0]

logs/2020-04-10-16-21-55.log

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+2020-04-10 16:21:55,239 - utils - INFO - Running Simulated Linear(linear.ini)...
+2020-04-10 16:23:38,320 - utils - INFO - The best valid c-index: 0.7787218145430287
+2020-04-10 16:23:38,320 - utils - INFO - 
+2020-04-10 16:23:38,320 - utils - INFO - Running Simulated Nonlinear(gaussian.ini)...
+2020-04-10 16:24:17,832 - utils - INFO - The best valid c-index: 0.5005123415610407
+2020-04-10 16:24:17,833 - utils - INFO - 
+2020-04-10 16:24:17,833 - utils - INFO - Running WHAS(whas.ini)...
+2020-04-10 16:24:53,124 - utils - INFO - The best valid c-index: 0.8449728673025717
+2020-04-10 16:24:53,125 - utils - INFO - 
+2020-04-10 16:24:53,125 - utils - INFO - Running SUPPORT(support.ini)...
+2020-04-10 16:33:32,915 - utils - INFO - The best valid c-index: 0.6173979652937414
+2020-04-10 16:33:32,924 - utils - INFO - 
+2020-04-10 16:33:32,924 - utils - INFO - Running METABRIC(metabric.ini)...
+2020-04-10 16:33:47,937 - utils - INFO - The best valid c-index: 0.6371630149173443
+2020-04-10 16:33:47,938 - utils - INFO - 
+2020-04-10 16:33:47,938 - utils - INFO - Running Simulated Treatment(treatment.ini)...
+2020-04-10 16:34:41,391 - utils - INFO - The best valid c-index: 0.5599653102068045
+2020-04-10 16:34:41,391 - utils - INFO - 
+2020-04-10 16:34:41,391 - utils - INFO - Running Rotterdam & GBSG(gbsg.ini)...
+2020-04-10 16:35:21,385 - utils - INFO - The best valid c-index: 0.670171035229049
+2020-04-10 16:35:21,386 - utils - INFO - 
+2020-04-10 16:35:21,386 - utils - INFO - +------------------+---------------------+----------+----------+----------+---------------------+------------------+
+| Simulated Linear | Simulated Nonlinear |   WHAS   | SUPPORT  | METABRIC | Simulated Treatment | Rotterdam & GBSG |
++------------------+---------------------+----------+----------+----------+---------------------+------------------+
+|     0.778722     |       0.500512      | 0.844973 | 0.617398 | 0.637163 |       0.559965      |     0.670171     |
++------------------+---------------------+----------+----------+----------+---------------------+------------------+