optimize_metabric.py

import pandas as pd
import numpy as np
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
tf.compat.v1.random.set_random_seed(42)

from tensorflow_probability import distributions as tfd
from tensorflow.keras.layers import Input, Dense, Activation, Concatenate, BatchNormalization, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

from sksurv.metrics import concordance_index_ipcw, integrated_brier_score, cumulative_dynamic_auc
from sksurv.util import Surv
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

from model import MDN
from utils import NLLLoss,CensoredNLLLoss,AlternativeNLLLoss
from utils import concordance_index_ipcw_scorer,integrated_brier_scorer,cumulative_dynamic_auc_scorer,root_mean_squared_error_scorer

import optuna

from optimizer import Optimizer
from sklearn.preprocessing import MinMaxScaler

import json

from sksurv.datasets import load_veterans_lung_cancer
from sksurv.column import encode_categorical

name = "MDN_metabric_data"

# Load and Preprocess
df = pd.read_csv("Data/metabric.tsv",sep="\t",header=4).drop(["PATIENT_ID","VITAL_STATUS","RFS_MONTHS"],axis=1)
cell = []
for c in df.CELLULARITY:
    if c == "Low":
        cell.append(0.0)
    elif c == "Moderate":
        cell.append(0.5)
    elif c == "High":
        cell.append(1.0)
    else:
        cell.append(np.NaN)
df.CELLULARITY = cell
df = df.dropna()
df = encode_categorical(df)
cont_features = ["LYMPH_NODES_EXAMINED_POSITIVE","NPI","CELLULARITY","AGE_AT_DIAGNOSIS"]
scaler = MinMaxScaler()
df[cont_features] = scaler.fit_transform(df[cont_features])
time_scaler = MinMaxScaler()
df.OS_MONTHS = time_scaler.fit_transform(df.OS_MONTHS.to_numpy().reshape(-1, 1))
X = df.drop(["OS_MONTHS","OS_STATUS=1:DECEASED"],axis = 1)
x_size = len(X.columns)
X = np.float32(X.to_numpy())
t = np.float32(df.OS_MONTHS.to_numpy())
delta = df["OS_STATUS=1:DECEASED"].to_numpy().astype(np.float32)
y = np.stack([t,delta],axis = 1)

# Run Optimizer
print("Running Optimizer with LogRank binary scoring")
opt = Optimizer(X,y,name,num_epochs=200,batch_size=128,use_kfold=True,use_logrank=True)
best_val,best_params = opt(1000)
best_params['UnoC_LR']=best_val
print(best_params)
with open(f"Logs/{name}.json", "w") as write_file:
    json.dump(best_params, write_file)
    
print("Running Optimizer normaly")
opt = Optimizer(X,y,name,num_epochs=200,batch_size=128,use_kfold=True,use_logrank=False)
best_val,best_params = opt(1000)
best_params['UnoC']=best_val
print(best_params)
with open(f"Logs/{name}_no_logrank.json", "w") as write_file:
    json.dump(best_params, write_file)