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myutils.py
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myutils.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 24 15:04:22 2017
@author: bychkov
"""
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import keras.backend as K
from sklearn.metrics import roc_auc_score
from lifelines.utils import concordance_index
#----------------------------------------------------------------------
# Plot losses:
#----------------------------------------------------------------------
def sub_plot_history(history, preds, data, idx, h_type=2, y_lim=()):
risk = data['h%s'%str(h_type)][idx].flatten()
labl = data['bin_t%s'%str(h_type)][idx].flatten()
low_i = labl == 0
high_i = labl == 1
plt.figure(figsize=(12,5))
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Training history')
plt.ylabel('loss'); plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper right')
c_ind = np.round(concordance_index(data['t2'][idx], preds),3)
if c_ind < 0.5:
c_ind = 1-c_ind
auc = np.round(roc_auc_score(labl, preds), 2)
if auc < 0.5:
auc = 1-auc
plt.subplot(1, 2, 2)
plt.plot(risk[high_i], preds[high_i], '.', c='r', alpha=0.75)
plt.plot(risk[low_i], preds[low_i], '.', c='b', alpha=0.75)
plt.title('C_ind: %s, AUC: %s' % (str(c_ind), str(auc)))
plt.xlabel('Risk')
plt.ylabel('Predicted Risk')
plt.legend(['Non-Survivors', 'Survivors'], loc=2)
if len(y_lim):
plt.ylim(y_lim)
plt.show()
def sub_plot_history2(history, preds, data, idx, h_type=2, y_lim=()):
risk = data['Ys'][idx,0].flatten()
labl = data['Ys'][idx,4].flatten()
low_i = labl == 0
high_i = labl == 1
plt.figure(figsize=(12,5))
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Training history')
plt.ylabel('loss'); plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper right')
c_ind = np.round(concordance_index(data['Ys'][idx,1], preds),3)
if c_ind < 0.5:
c_ind = 1-c_ind
auc = np.round(roc_auc_score(labl, preds), 2)
if auc < 0.5:
auc = 1-auc
plt.subplot(1, 2, 2)
plt.plot(risk[high_i], preds[high_i], '.', c='r', alpha=0.75)
plt.plot(risk[low_i], preds[low_i], '.', c='b', alpha=0.75)
plt.title('C_ind: %s, AUC: %s' % (str(c_ind), str(auc)))
plt.xlabel('Risk')
plt.ylabel('Predicted Risk')
plt.legend(['Non-Survivors', 'Survivors'], loc=2)
if len(y_lim):
plt.ylim(y_lim)
plt.show()
def plot_history(history):
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss'); plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper right')
plt.show()
class PlotLosses(object):
def __init__(self, figsize=(8,6)):
plt.plot([], [])
def __call__(self, nn, train_history):
train_loss = np.array([i["train_loss"] for i in nn.train_history_])
valid_loss = np.array([i["valid_loss"] for i in nn.train_history_])
plt.gca().cla()
plt.plot(train_loss, label="train")
plt.plot(valid_loss, label="test")
plt.grid()
plt.legend()
plt.draw()
#----------------------------------------------------------------------
# Batch iterator:
#----------------------------------------------------------------------
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
#----------------------------------------------------------------------
# Load Survival Samples:
#----------------------------------------------------------------------
def load_surv_samples(fname, sort=False):
dataset = pickle.load( open( fname, "rb" ) )
if not isinstance(dataset, dict):
print('Cannot load the data!')
return False
if sort:
# Sort Training Data for Accurate Likelihood
sort_idx = np.argsort(dataset['t'])[::-1]
for k in dataset.keys():
if type(dataset[k]) == np.ndarray:
if dataset[k].shape[0] == dataset['t'].shape[0]:
dataset[k] = dataset[k][sort_idx]
# (N, d)
data_x = dataset['x']
# (N, 3)
data_y = np.column_stack((
dataset['h'],
dataset['t'],
dataset['e'],
dataset['id'] ))
print('Loading data from: %s' % fname)
return (data_x, data_y)
#----------------------------------------------------------------------
# Losses:
#----------------------------------------------------------------------
def efron_estimator_tf(y_true, y_pred):
sort_idx = tf.nn.top_k(y_true[:,1], k=tf.shape(y_pred)[0], sorted=True).indices
risk = tf.gather(y_pred, sort_idx)
risk_exp = tf.exp(risk)
events = tf.gather(y_true[:,2], sort_idx)
ftimes = tf.gather(y_true[:,1], sort_idx)
ftimes_cens = ftimes * events
# Get unique failure times & Exclude zeros
# NOTE: this assumes that falure times start from > 0 (greater than zero)
unique = tf.unique(ftimes_cens).y
unique_ftimes = tf.boolean_mask(unique, tf.greater(unique, 0) )
m = tf.shape(unique_ftimes)[0]
# Define key variables:
log_lik = tf.Variable(0., dtype=tf.float32, validate_shape=True, trainable=False)
E_ti = tf.Variable([], dtype=tf.int32, validate_shape=True, trainable=False)
risk_phi = tf.Variable([], dtype=tf.float32, validate_shape=True, trainable=False)
tie_phi = tf.Variable([], dtype=tf.float32, validate_shape=True, trainable=False)
cum_risk = tf.Variable([], dtype=tf.float32, validate_shape=True, trainable=False)
cum_sum = tf.cumsum(risk_exp)
# -----------------------------------------------------------------
# Prepare for looping:
# -----------------------------------------------------------------
i = tf.constant(0, tf.int32)
def loop_cond(i, *args):
return i < m
# Step for loop # 1:
def loop_1_step(i, E, Rp, Tp, Cr, Cs):
n = tf.shape(Cs)[0]
idx_b = tf.logical_and(
tf.equal(ftimes, unique_ftimes[i]),
tf.equal(events, tf.ones_like(events)) )
idx_i = tf.cast(
tf.boolean_mask(
tf.lin_space(0., tf.cast(n-1,tf.float32), n),
tf.greater(tf.cast(idx_b, tf.int32),0)
), tf.int32 )
E = tf.concat([E, [tf.reduce_sum(tf.cast(idx_b, tf.int32))]], 0)
Rp = tf.concat([Rp, [tf.reduce_sum(tf.gather(risk, idx_i))]], 0)
Tp = tf.concat([Tp, [tf.reduce_sum(tf.gather(risk_exp, idx_i))]], 0)
idx_i = tf.cast(
tf.boolean_mask(
tf.lin_space(0., tf.cast(n-1,tf.float32), n),
tf.greater(tf.cast(tf.equal(ftimes, unique_ftimes[i]), tf.int32),0)
), tf.int32 )
Cr = tf.concat([Cr, [tf.reduce_max(tf.gather( Cs, idx_i))]], 0)
return i + 1, E, Rp, Tp, Cr, Cs
# Step for loop # 1:
def loop_2_step(i, E, Rp, Tp, Cr, likelihood):
l = E_ti[i]
J = tf.lin_space(0., tf.cast(l-1,tf.float32), l) / tf.cast(l, tf.float32)
Dm = Cr[i] - J * Tp[i]
likelihood = likelihood + Rp[i] - tf.reduce_sum(tf.log(Dm))
return i + 1, E, Rp, Tp, Cr, likelihood
# -----------------------------------------------------------------
# Loop # 1:
_, E_ti, risk_phi, tie_phi, cum_risk, _ = loop_1 = tf.while_loop(
loop_cond, loop_1_step,
loop_vars = [i, E_ti, risk_phi, tie_phi, cum_risk, cum_sum],
shape_invariants = [i.get_shape(),tf.TensorShape([None]),tf.TensorShape([None]),tf.TensorShape([None]),tf.TensorShape([None]),cum_sum.get_shape()]
)
# Loop # 2:
loop_2 = tf.while_loop(
loop_cond, loop_2_step,
loop_vars = [i, E_ti, risk_phi, tie_phi, cum_risk, log_lik],
shape_invariants = [i.get_shape(),tf.TensorShape([None]),tf.TensorShape([None]),tf.TensorShape([None]),tf.TensorShape([None]),log_lik.get_shape()]
)
log_lik = loop_2[5]
# TODO: Normalize by the number of EVENTS in the batch,
# NOT number of samples in the batch FIXIT!!
log_lik = log_lik / tf.cast(tf.shape(y_pred)[0], tf.float32)
return tf.negative(log_lik)
def my_mse(y_true, y_pred):
return K.mean(K.square(y_pred - y_true[:,0]))
# def partial_likelihood(y_true, y_pred):
# ''' Returns the Negative Partial Log Likelihood
# of the parameters given ordered hazards [estimated by a NN];
# This method cannot handle tied observations.
#
# Parameters:
# risk (N,): a vector of hazard values for each of N samples.
# Samples (=> risk vector) are ordered according to failure
# time from largest to smallest.
# events (N,): a vector (ordered in the same fashion) of event
# indicators, where 1 - is event; 0 - is censored.
# '''
# #y_true = theano.tensor.fmatrix()
# #y_pred = theano.tensor.fvector()
#
# # first sort by time for Accurate Likelihood:
# sort_idx = np.argsort( y_true[:,1] )[::-1]
#
# risk = y_pred[sort_idx]
# events = y_true[:,2][sort_idx]
#
# hazard_ratio = T.exp(risk)
# log_cum_risk = T.log(T.extra_ops.cumsum(hazard_ratio))
# uncencored_likelihood = risk.T - log_cum_risk
# censored_likelihood = uncencored_likelihood * events
# neg_likelihood = -T.sum( censored_likelihood )
#
# return neg_likelihood
#----------------------------------------------------------------------
# Done.
#----------------------------------------------------------------------