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Poetry #17

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f4d630f
Upgrade to Tensorflow v2 and fix flake8 errors/warnings
Mar 3, 2025
50cb762
Fix more v1 to v2 issues
Mar 3, 2025
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3 changes: 3 additions & 0 deletions .flake8
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[flake8]
max-line-length = 88
extend-ignore = E203, E501
27 changes: 27 additions & 0 deletions .pre-commit-config.yaml
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# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.2.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files

- repo: https://github.com/pycqa/isort
rev: 5.13.2
hooks:
- id: isort
args: ["--profile", "black"]

- repo: https://github.com/psf/black
rev: 23.12.1
hooks:
- id: black
language_version: python3

- repo: https://github.com/PyCQA/flake8
rev: 7.0.0
hooks:
- id: flake8
323 changes: 213 additions & 110 deletions class_DeepHit.py
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262 changes: 162 additions & 100 deletions get_main.py
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'''
"""
This train DeepHit, and outputs the validation performance for random search.

INPUTS:
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OUTPUTS:
- the validation performance of the trained network
- save the trained network in the folder directed by "in_parser['out_path'] + '/itr_' + str(out_itr)"
'''
"""

_EPSILON = 1e-08

import os
import random

import numpy as np
import pandas as pd
import tensorflow as tf
import random
import os
# import sys

from termcolor import colored
from tensorflow.contrib.layers import fully_connected as FC_Net
from sklearn.metrics import brier_score_loss
from sklearn.model_selection import train_test_split

import utils_network as utils
from termcolor import colored

from class_DeepHit import Model_DeepHit
from utils_eval import c_index, brier_score, weighted_c_index, weighted_brier_score
from utils_eval import weighted_c_index

# import sys

_EPSILON = 1e-08


##### USER-DEFINED FUNCTIONS
# USER-DEFINED FUNCTIONS
def log(x):
return tf.log(x + 1e-8)
return tf.math.log(x + 1e-8)


def div(x, y):
return tf.div(x, (y + 1e-8))
return tf.compat.v1.div(x, (y + 1e-8))


def f_get_minibatch(mb_size, x, label, time, mask1, mask2):
idx = range(np.shape(x)[0])
idx = random.sample(idx, mb_size)

x_mb = x[idx, :].astype(np.float32)
k_mb = label[idx, :].astype(np.float32) # censoring(0)/event(1,2,..) label
k_mb = label[idx, :].astype(np.float32) # censoring(0)/event(1,2,..) label
t_mb = time[idx, :].astype(np.float32)
m1_mb = mask1[idx, :, :].astype(np.float32) #fc_mask
m2_mb = mask2[idx, :].astype(np.float32) #fc_mask
m1_mb = mask1[idx, :, :].astype(np.float32) # fc_mask
m2_mb = mask2[idx, :].astype(np.float32) # fc_mask
return x_mb, k_mb, t_mb, m1_mb, m2_mb


def get_valid_performance(DATA, MASK, in_parser, out_itr, eval_time=None, MAX_VALUE = -99, OUT_ITERATION=5, seed=1234):
##### DATA & MASK
(data, time, label) = DATA
(mask1, mask2) = MASK

x_dim = np.shape(data)[1]
_, num_Event, num_Category = np.shape(mask1) # dim of mask1: [subj, Num_Event, Num_Category]

ACTIVATION_FN = {'relu': tf.nn.relu, 'elu': tf.nn.elu, 'tanh': tf.nn.tanh}
def get_valid_performance(
DATA,
MASK,
in_parser,
out_itr,
eval_time=None,
MAX_VALUE=-99,
OUT_ITERATION=5,
seed=1234,
):
# DATA & MASK
(data, time, label) = DATA
(mask1, mask2) = MASK

##### HYPER-PARAMETERS
mb_size = in_parser['mb_size']
x_dim = np.shape(data)[1]
_, num_Event, num_Category = np.shape(
mask1
) # dim of mask1: [subj, Num_Event, Num_Category]

iteration = in_parser['iteration']
ACTIVATION_FN = {"relu": tf.nn.relu, "elu": tf.nn.elu, "tanh": tf.nn.tanh}

keep_prob = in_parser['keep_prob']
lr_train = in_parser['lr_train']
# HYPER-PARAMETERS
mb_size = in_parser["mb_size"]

iteration = in_parser["iteration"]

alpha = in_parser['alpha'] #for log-likelihood loss
beta = in_parser['beta'] #for ranking loss
gamma = in_parser['gamma'] #for RNN-prediction loss
parameter_name = 'a' + str('%02.0f' %(10*alpha)) + 'b' + str('%02.0f' %(10*beta)) + 'c' + str('%02.0f' %(10*gamma))
keep_prob = in_parser["keep_prob"]
lr_train = in_parser["lr_train"]

initial_W = tf.contrib.layers.xavier_initializer()
alpha = in_parser["alpha"] # for log-likelihood loss
beta = in_parser["beta"] # for ranking loss
gamma = in_parser["gamma"] # for RNN-prediction loss

initial_W = tf.compat.v1.keras.initializers.VarianceScaling(
scale=1.0, mode="fan_avg", distribution="uniform"
)

##### MAKE DICTIONARIES
# MAKE DICTIONARIES
# INPUT DIMENSIONS
input_dims = { 'x_dim' : x_dim,
'num_Event' : num_Event,
'num_Category' : num_Category}
input_dims = {"x_dim": x_dim, "num_Event": num_Event, "num_Category": num_Category}

# NETWORK HYPER-PARMETERS
network_settings = { 'h_dim_shared' : in_parser['h_dim_shared'],
'num_layers_shared' : in_parser['num_layers_shared'],
'h_dim_CS' : in_parser['h_dim_CS'],
'num_layers_CS' : in_parser['num_layers_CS'],
'active_fn' : ACTIVATION_FN[in_parser['active_fn']],
'initial_W' : initial_W }


file_path_final = in_parser['out_path'] + '/itr_' + str(out_itr)

#change parameters...
if not os.path.exists(file_path_final + '/models/'):
os.makedirs(file_path_final + '/models/')


print (file_path_final + ' (a:' + str(alpha) + ' b:' + str(beta) + ' c:' + str(gamma) + ')' )

##### CREATE DEEPFHT NETWORK
tf.reset_default_graph()

config = tf.ConfigProto()
network_settings = {
"h_dim_shared": in_parser["h_dim_shared"],
"num_layers_shared": in_parser["num_layers_shared"],
"h_dim_CS": in_parser["h_dim_CS"],
"num_layers_CS": in_parser["num_layers_CS"],
"active_fn": ACTIVATION_FN[in_parser["active_fn"]],
"initial_W": initial_W,
}

file_path_final = in_parser["out_path"] + "/itr_" + str(out_itr)

# change parameters...
if not os.path.exists(file_path_final + "/models/"):
os.makedirs(file_path_final + "/models/")

print(
file_path_final
+ " (a:"
+ str(alpha)
+ " b:"
+ str(beta)
+ " c:"
+ str(gamma)
+ ")"
)

# CREATE DEEPFHT NETWORK
tf.compat.v1.reset_default_graph()

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess = tf.compat.v1.Session(config=config)

model = Model_DeepHit(sess, "DeepHit", input_dims, network_settings)
saver = tf.train.Saver()

sess.run(tf.global_variables_initializer())

saver = tf.compat.v1.train.Saver()

sess.run(tf.compat.v1.global_variables_initializer())

# TRAINING-TESTING SPLIT
(
tr_data,
te_data,
tr_time,
te_time,
tr_label,
te_label,
tr_mask1,
te_mask1,
tr_mask2,
te_mask2,
) = train_test_split(
data, time, label, mask1, mask2, test_size=0.20, random_state=seed
)

(
tr_data,
va_data,
tr_time,
va_time,
tr_label,
va_label,
tr_mask1,
va_mask1,
tr_mask2,
va_mask2,
) = train_test_split(
tr_data,
tr_time,
tr_label,
tr_mask1,
tr_mask2,
test_size=0.20,
random_state=seed,
)

### TRAINING-TESTING SPLIT
(tr_data,te_data, tr_time,te_time, tr_label,te_label,
tr_mask1,te_mask1, tr_mask2,te_mask2) = train_test_split(data, time, label, mask1, mask2, test_size=0.20, random_state=seed)


(tr_data,va_data, tr_time,va_time, tr_label,va_label,
tr_mask1,va_mask1, tr_mask2,va_mask2) = train_test_split(tr_data, tr_time, tr_label, tr_mask1, tr_mask2, test_size=0.20, random_state=seed)

max_valid = -99
stop_flag = 0

if eval_time is None:
eval_time = [int(np.percentile(tr_time, 25)), int(np.percentile(tr_time, 50)), int(np.percentile(tr_time, 75))]

eval_time = [
int(np.percentile(tr_time, 25)),
int(np.percentile(tr_time, 50)),
int(np.percentile(tr_time, 75)),
]

### TRAINING - MAIN
print( "MAIN TRAINING ...")
print( "EVALUATION TIMES: " + str(eval_time))
# TRAINING - MAIN
print("MAIN TRAINING ...")
print("EVALUATION TIMES: " + str(eval_time))

avg_loss = 0
for itr in range(iteration):
if stop_flag > 5: #for faster early stopping
if stop_flag > 5: # for faster early stopping
break
else:
x_mb, k_mb, t_mb, m1_mb, m2_mb = f_get_minibatch(mb_size, tr_data, tr_label, tr_time, tr_mask1, tr_mask2)
x_mb, k_mb, t_mb, m1_mb, m2_mb = f_get_minibatch(
mb_size, tr_data, tr_label, tr_time, tr_mask1, tr_mask2
)
DATA = (x_mb, k_mb, t_mb)
MASK = (m1_mb, m2_mb)
PARAMETERS = (alpha, beta, gamma)
_, loss_curr = model.train(DATA, MASK, PARAMETERS, keep_prob, lr_train)
avg_loss += loss_curr/1000

if (itr+1)%1000 == 0:
print('|| ITR: ' + str('%04d' % (itr + 1)) + ' | Loss: ' + colored(str('%.4f' %(avg_loss)), 'yellow' , attrs=['bold']))
avg_loss += loss_curr / 1000

if (itr + 1) % 1000 == 0:
print(
"|| ITR: "
+ str("%04d" % (itr + 1))
+ " | Loss: "
+ colored(str("%.4f" % (avg_loss)), "yellow", attrs=["bold"])
)
avg_loss = 0

### VALIDATION (based on average C-index of our interest)
if (itr+1)%1000 == 0:
### PREDICTION
# VALIDATION (based on average C-index of our interest)
if (itr + 1) % 1000 == 0:
# PREDICTION
pred = model.predict(va_data)

### EVALUATION
# EVALUATION
va_result1 = np.zeros([num_Event, len(eval_time)])

for t, t_time in enumerate(eval_time):
eval_horizon = int(t_time)

if eval_horizon >= num_Category:
print('ERROR: evaluation horizon is out of range')
va_result1[:, t] = va_result2[:, t] = -1
print("ERROR: evaluation horizon is out of range")
va_result1[:, t] = -1
else:
risk = np.sum(pred[:,:,:(eval_horizon+1)], axis=2) #risk score until eval_time
risk = np.sum(
pred[:, :, : (eval_horizon + 1)], axis=2
) # risk score until eval_time
for k in range(num_Event):
# va_result1[k, t] = c_index(risk[:,k], va_time, (va_label[:,0] == k+1).astype(int), eval_horizon) #-1 for no event (not comparable)
va_result1[k, t] = weighted_c_index(tr_time, (tr_label[:,0] == k+1).astype(int), risk[:,k], va_time, (va_label[:,0] == k+1).astype(int), eval_horizon)
va_result1[k, t] = weighted_c_index(
tr_time,
(tr_label[:, 0] == k + 1).astype(int),
risk[:, k],
va_time,
(va_label[:, 0] == k + 1).astype(int),
eval_horizon,
)
tmp_valid = np.mean(va_result1)


if tmp_valid > max_valid:
if tmp_valid > max_valid:
stop_flag = 0
max_valid = tmp_valid
print( 'updated.... average c-index = ' + str('%.4f' %(tmp_valid)))
print("updated.... average c-index = " + str("%.4f" % (tmp_valid)))

if max_valid > MAX_VALUE:
saver.save(sess, file_path_final + '/models/model_itr_' + str(out_itr))
saver.save(
sess, file_path_final + "/models/model_itr_" + str(out_itr)
)
else:
stop_flag += 1

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