Added all files for slim version

Brain_Integ_Y.csv

@@ -0,0 +1 @@
+Censored,Survival0,1440,3931,4700,2110,6910,10240,3940,4240,3180,4690,4050,3840,7171,13221,14051,2180,12290,6320,8140,4140,4660,330,3800,1500,3821,9321,1810,5060,1331,9580,3570,1080,2540,1381,2681,2731,2600,830,1141,1871,1390,1591,2370,1640,450,36670,2241,2531,3911,1451,471,1451,1510,5480,1110,4630,3330,10620,4420,4540,620,410,880,8800,5430,1190,7721,2940,1480,1240,2130,2020,5230,2440,5750,1440,3680,540,4280,5110,4551,9531,2861,4521,1671,2411,710,1540,5050,3000,7370,12330,9140,7530,4800,771,370,5440,7130,4680,1420,3860,5150,1130,3830,5851,2720,2900,300,1350,3600,60,4600,3790,1051,6361,1321,4361,2280,1380,940,1110,2790,1000,820,5190,1650,2540,1470,1530,1140,7270,10480,5671,11010,491,35740,13350,11061,18281,12221,81,16310,1940,5380,8141,15681,14281,15191,14941,14261,37331,24931,22891,14581,22180,15471,27721,13541,11121,23811,14121,5731,9191,14211,12101,11391,10211,22191,9621,7360,7751,9161,1821,9551,8351,8461,8890,4381,5631,3681,3421,5441,9351,4920,6071,9681,19431,14011,7481,12791,8461,5231,5231,7061,5481,4431,5310,2051,4071,2571,2071,55461,5761,5321,4611,64230,15850,6820,14910,22860,14010,20000,370,26600,2140,3540,40680,6050,5120,28750,34701,2421,730,3490,19151,47520,46950,4560,32000,1990,2690,35711,27610,9331,8000,3150,2421,28690,5760,18860,7881,18340,7091,12771,840,1550,7220,4441,6291,6511,5821,5161,4711,4311,10121,9081,9641,7431,7961,7600,10331,7771,6561,6780,3470,8141,5220,6480,5471,25651,15670,2280,13510,11831,170,15250,20520,12510,24330,17620,40840,23790,29070,39780,28350,1110,5920,4920,6480,7270,15780,4660,9870,11200,44450,3980,22350,9841,12011,71,61,14530,7751,15880,11201,5711,4541,12941,12571,4671,5441,6861,6271,6111,8621,4161,4331,4871,6111,4551,6851,9921,11641,3260,5591,5691,7211,4491,6551,4571,6771,8681,3281,11151,6221,5671,1741,31,2030,3510,12201,41,11,5031,1141,5301,1991,7381,1941,12271,22871,29181,32531,52551,1221,1531,9081,7051,37251,1391,5260,961,6711,13991,17061,17520,71,4341,51,4941,231,13591,1841,13001,31,9641,4551,31,31,901,5331,2101,4421,12011,5851,11891,71,15401,151,7181,11301,101,41,1121,1621,10790,1131,3431,9561,501,4941,2860,19331,30,11521,10041,3721,1901,31,141,1691,4191,71,9001,10401,4781,7921,41,6151,21,31,821,5151,551,4911,5121,6511,5331,741,3201,3361,31,17211,13820,12621,13871,20781,21071,10780,12091,12171,12941,7580,9620,3881,6300,5781,13011,4381,01,01,71,2041,2431,721,701,2871,1791,2421,2311,391,71,3171,4031,3541,3331,2741,4141,2921,2301,3721,5841,1051,721,1341,771,3841,4821,4271,5881,4511,4421,4140,3721,4081,2791,4421,4971,5091,581,5021,5081,3371,3951,27021,28601,9931,9391,1661,3011,631,5960,18911,26501,5711,12451,10760,7421,7851,9921,10691,29181,22891,41131,26021,11731,8051,5671,4281,10990,2411,7151,6081,6091,2491,621,5641,8260,51661,9140,9611,15001,5661,4541,6501,5991,4861,13411,10261,7951,7871,-11,18680,8210,9541,13971,13141,4111,30,2401,11911,7141,11271,4871,7061,15531,13931,14261,1147

SurvivalAnalysis.py

@@ -0,0 +1,97 @@
+import numpy as np
+
+
+class SurvivalAnalysis(object):
+    """ This class contains methods used in survival analysis.
+    """
+
+    def c_index(self, risk, T, C):
+        """Calculate concordance index to evaluate model prediction.
+
+        C-index calulates the fraction of all pairs of subjects whose predicted
+        survival times are correctly ordered among all subjects that can actually
+		be ordered, i.e. both of them are uncensored or the uncensored time of
+		one is smaller than the censored survival time of the other.
+        
+        Parameters
+        ----------
+        risk: numpy.ndarray
+           m sized array of predicted risk (do not confuse with predicted survival time)
+        T: numpy.ndarray
+           m sized vector of time of death or last follow up
+        C: numpy.ndarray
+           m sized vector of censored status (do not confuse with observed status)
+
+        Returns
+        -------
+        A value between 0 and 1 indicating concordance index. 
+        """
+        n_orderable = 0.0
+        score = 0.0
+        for i in range(len(T)):
+            for j in range(i+1,len(T)):
+                if(C[i] == 0 and C[j] == 0):
+                    n_orderable = n_orderable + 1
+                    if(T[i] > T[j]):
+                        if(risk[j] > risk[i]):
+                            score = score + 1
+                    elif(T[j] > T[i]):
+                        if(risk[i] > risk[j]):
+                            score = score + 1
+                    else:
+                        if(risk[i] == risk[j]):
+                            score = score + 1
+                elif(C[i] == 1 and C[j] == 0):
+                    if(T[i] >= T[j]):
+                        n_orderable = n_orderable + 1
+                        if(T[i] > T[j]):
+                            if(risk[j] > risk[i]):
+                                score = score + 1
+                elif(C[j] == 1 and C[i] == 0):
+                    if(T[j] >= T[i]):
+                        n_orderable = n_orderable + 1
+                        if(T[j] > T[i]):
+                            if(risk[i] > risk[j]):
+                                score = score + 1
+
+        #print score to screen
+        return score / n_orderable
+
+    def calc_at_risk(self, X, T, O):
+        """Calculate the at risk group of all patients.
+		
+		For every patient i, this function returns the index of the first 
+		patient who died after i, after sorting the patients w.r.t. time of death.
+        Refer to the definition of Cox proportional hazards log likelihood for
+		details: https://goo.gl/k4TsEM
+        
+        Parameters
+        ----------
+        X: numpy.ndarray
+           m*n matrix of input data
+        T: numpy.ndarray
+           m sized vector of time of death
+        O: numpy.ndarray
+           m sized vector of observed status (1 - censoring status)
+
+        Returns
+        -------
+        X: numpy.ndarray
+           m*n matrix of input data sorted w.r.t time of death
+        T: numpy.ndarray
+           m sized sorted vector of time of death
+        O: numpy.ndarray
+           m sized vector of observed status sorted w.r.t time of death
+        at_risk: numpy.ndarray
+           m sized vector of starting index of risk groups
+        """
+        tmp = list(T)
+        T = np.asarray(tmp).astype('float64')
+        order = np.argsort(T)
+        sorted_T = T[order]
+        at_risk = np.asarray([list(sorted_T).index(x) for x in sorted_T]).astype('int32')
+        T = np.asarray(sorted_T)
+        O = O[order]
+        X = X[order]
+
+        return X, T, O, at_risk

ckpt_dir/checkpoint

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+model_checkpoint_path: "model.ckpt-0"
+all_model_checkpoint_paths: "model.ckpt-0"