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2_lococv_on_GDSC.py
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import argparse
import os
import math
import numpy as np
import pandas as pd
import skopt
from datetime import datetime
from sklearn.metrics import accuracy_score, average_precision_score, roc_auc_score
from sklearn.model_selection import LeaveOneGroupOut, train_test_split
from refdnn.model import REFDNN
from refdnn.dataset import DATASET, read_cancertypeFile
def get_args():
parser = argparse.ArgumentParser()
## positional
parser.add_argument('responseFile', type=str, help="A filepath of drug response data for TRAINING")
parser.add_argument('expressionFile', type=str, help="A filepath of gene expression data for TRAINING")
parser.add_argument('fingerprintFile', type=str, help="A filepath of fingerprint data for TRAINING")
parser.add_argument('annotationFile', type=str, help="A filepath of cellline annotation data for TRAINING")
## optional
parser.add_argument('-o', metavar='outputdir', type=str, default='output_2', help="A directory path for saving outputs (default:'output_2')")
parser.add_argument('-b', metavar='batchsize', type=int, default=64, help="A size of batch on training process. The small size is recommended if an available size of RAM is small (default: 64)")
parser.add_argument('-t', metavar='numtrainingsteps', type=int, default=5000, help="Number of training steps on training process. It is recommended that the steps is larger than (numpairs / batchsize) (default: 5000)")
parser.add_argument('-v', metavar='verbose', type=int, default=1, help="0:No logging, 1:Basic logging to check process, 2:Full logging for debugging (default:1)")
return parser.parse_args()
def main():
args = get_args()
outputdir = args.o
verbose = args.v
if verbose > 0:
print('[START]')
if verbose > 1:
print('[ARGUMENT] RESPONSEFILE: {}'.format(args.responseFile))
print('[ARGUMENT] EXPRESSIONFILE: {}'.format(args.expressionFile))
print('[ARGUMENT] FINGERPRINTFILE: {}'.format(args.fingerprintFile))
print('[ARGUMENT] OUTPUTDIR: {}'.format(args.o))
print('[ARGUMENT] VERBOSE: {}'.format(args.v))
## output directory
if not os.path.exists(outputdir):
os.mkdir(outputdir)
checkpointdir = os.path.join(outputdir, "checkpoint")
if not os.path.exists(checkpointdir):
os.mkdir(checkpointdir)
########################################################
## 1. Read data
########################################################
responseFile = args.responseFile
expressionFile = args.expressionFile
fingerprintFile = args.fingerprintFile
annotationFile = args.annotationFile
dataset = DATASET(responseFile, expressionFile, fingerprintFile)
if verbose > 0:
print('[DATA] NUM_PAIRS: {}'.format(len(dataset)))
print('[DATA] NUM_DRUGS: {}'.format(len(dataset.get_drugs(unique=True))))
print('[DATA] NUM_CELLS: {}'.format(len(dataset.get_cells(unique=True))))
print('[DATA] NUM_GENES: {}'.format(len(dataset.get_genes())))
print('[DATA] NUM_SENSITIVITY: {}'.format(np.count_nonzero(dataset.get_labels()==0)))
print('[DATA] NUM_RESISTANCE: {}'.format(np.count_nonzero(dataset.get_labels()==1)))
cell2cancer = read_cancertypeFile(annotationFile)
cancertypes = [cell2cancer.get(cell, 'others') for cell in dataset.get_cells()]
## time log
timeformat = '[TIME] [{0}] {1.year}-{1.month}-{1.day} {1.hour}:{1.minute}:{1.second}'
if verbose > 0:
print(timeformat.format(1, datetime.now()))
#######################################################
## 2. Train RefDNN using the best hyperparameters
########################################################
batchsize = args.b
numtrainingsteps = args.t
## 2-1) init lists for metrics
ACCURACY_outer = []
AUCROC_outer = []
AUCPR_outer = []
CANCER_outer = []
kf = LeaveOneGroupOut()
n_splits = kf.get_n_splits(groups=cancertypes)
print("LeaveOneGroupOut.get_n_splits: {}".format(n_splits))
for k, (idx_train, idx_test) in enumerate(kf.split(X=np.zeros(len(dataset)), groups=cancertypes)):
## 2-2) Check a cancer type in test
test_cancertype = np.unique([cancertypes[x] for x in idx_test])[0]
CANCER_outer.append(test_cancertype)
print('[{}/{}] TEST_CANCER: {}'.format(k+1, n_splits, test_cancertype))
## 2-3) Set the best values of hyperparameters
BEST_HIDDEN_UNITS = 49
BEST_LEARNING_RATE_FTRL = 7.94581095185585e-06
BEST_LEARNING_RATE_ADAM = 0.0004067851789088527
BEST_L1_REGULARIZATION_STRENGTH = 0.001
BEST_L2_REGULARIZATION_STRENGTH = 66.7516541409175
## 2-4) Dataset
idx_train_train, idx_train_valid = train_test_split(idx_train, test_size=0.2, stratify=dataset.get_drugs()[idx_train])
base_drugs = np.unique(dataset.get_drugs()[idx_train_train])
X_train = dataset.make_xdata(idx_train_train)
S_train = dataset.make_sdata(base_drugs, idx_train_train)
I_train = dataset.make_idata(base_drugs, idx_train_train)
Y_train = dataset.make_ydata(idx_train_train)
X_valid = dataset.make_xdata(idx_train_valid)
S_valid = dataset.make_sdata(base_drugs, idx_train_valid)
I_valid = dataset.make_idata(base_drugs, idx_train_valid)
Y_valid = dataset.make_ydata(idx_train_valid)
X_test = dataset.make_xdata(idx_test)
S_test = dataset.make_sdata(base_drugs, idx_test)
Y_test = dataset.make_ydata(idx_test)
## 2-5) Create a model using the best parameters
if verbose > 0:
print('[{}/{}] NOW TRAINING THE MODEL WITH BEST PARAMETERS...'.format(k+1, n_splits))
checkpoint_path = "RefDNN_lococv_{}.ckpt".format(test_cancertype)
checkpoint_path = os.path.join(checkpointdir, checkpoint_path)
clf = REFDNN(hidden_units=BEST_HIDDEN_UNITS,
learning_rate_ftrl=BEST_LEARNING_RATE_FTRL,
learning_rate_adam=BEST_LEARNING_RATE_ADAM,
l1_regularization_strength=BEST_L1_REGULARIZATION_STRENGTH,
l2_regularization_strength=BEST_L2_REGULARIZATION_STRENGTH,
batch_size=batchsize,
training_steps=numtrainingsteps,
checkpoint_path=checkpoint_path)
## 2-6) Fit a model
history = clf.fit(X_train, S_train, I_train, Y_train,
X_valid, S_valid, I_valid, Y_valid,
verbose=verbose)
## 2-7) Compute the metric
Pred_test = clf.predict(X_test, S_test, verbose=verbose)
Prob_test = clf.predict_proba(X_test, S_test, verbose=verbose)
ACCURACY_outer_k = accuracy_score(Y_test, Pred_test)
ACCURACY_outer.append(ACCURACY_outer_k)
AUCROC_outer_k = roc_auc_score(Y_test, Prob_test)
AUCROC_outer.append(AUCROC_outer_k)
AUCPR_outer_k = average_precision_score(Y_test, Prob_test)
AUCPR_outer.append(AUCPR_outer_k)
if verbose > 0:
print('[{}/{}] BEST_TEST_ACCURACY : {:.3f}'.format(k+1, n_splits, ACCURACY_outer_k))
print('[{}/{}] BEST_TEST_AUCROC : {:.3f}'.format(k+1, n_splits, AUCROC_outer_k))
print('[{}/{}] BEST_TEST_AUCPR : {:.3f}'.format(k+1, n_splits, AUCPR_outer_k))
## time log
if verbose > 0:
print(timeformat.format(3, datetime.now()))
#######################################################
## 3. Save the results
########################################################
res = pd.DataFrame.from_dict({'CANCERTYPE':CANCER_outer,
'ACCURACY':ACCURACY_outer,
'AUCROC':AUCROC_outer,
'AUCPR':AUCPR_outer,
'Hidden_units':BEST_HIDDEN_UNITS,
'Learning_rate_ftrl':BEST_LEARNING_RATE_FTRL,
'Learning_rate_adam':BEST_LEARNING_RATE_ADAM,
'L1_regularization_strength':BEST_L1_REGULARIZATION_STRENGTH,
'L2_regularization_strength':BEST_L2_REGULARIZATION_STRENGTH})
res = res[['CANCERTYPE', 'ACCURACY', 'AUCROC', 'AUCPR', 'Hidden_units', 'Learning_rate_ftrl', 'Learning_rate_adam', 'L1_regularization_strength', 'L2_regularization_strength']]
res.to_csv(os.path.join(outputdir, 'metrics_hyperparameters.csv'), sep=',')
## time log
if verbose > 0:
print(timeformat.format(4, datetime.now()))
if verbose > 0:
print('[FINISH]')
if __name__=="__main__":
main()