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WRF_EV.py
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WRF_EV.py
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import argparse as ap
import pandas as pd
import numpy as np
import sys
from sklearn import preprocessing
def read_params(args):
parser = ap.ArgumentParser(description='Specify the probability')
arg = parser.add_argument
arg('-fn', '--fn', type=str, help='datasets')
# arg('-ns', '--ns', type=str, help='number of select biomarkers')
arg('-ts', '--ts', type=str, help='the ratio of test data')
arg('-rs', '--rs', type=str, help='repeat times')
return vars(parser.parse_args())
def read_files(file_name):
# file_name='Karlsson_T2D'
known = pd.read_csv("data/" + file_name+'_known.csv', index_col=0)
unknown = pd.read_csv("data/" + file_name+'_unknown.csv', index_col=0)
y = pd.read_csv("data/" + file_name+'_y.csv', index_col=0)
le = preprocessing.LabelEncoder()
y = np.array(y).ravel()
y = le.fit_transform(y)
return known, unknown, y
def WRF_eva(known, unknown,y, ts, rs, file_name ):
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
import time
import os
from scipy import stats
from numpy.random import seed
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
start = time.time()
known_X = np.array(known)
unknown_X = np.array(unknown)
knownX_train, knownX_test, unknownX_train, unknownX_test, y_train, y_test = train_test_split(
known_X, unknown_X, y, test_size=ts, random_state=4489)
ave_auc = []
repeat_seed = rs
path = file_name+'_WRF_ev.txt'
if os.path.exists(path):
os.remove(path)
file = open(path, 'a')
feature_imp = []
kweight = []
for i in range(repeat_seed):
print('Round ' + str(i + 1))
seed(i)
y_predpro = []
paramsampler = {'max_features': stats.uniform(0, 1.0),
'max_depth': stats.randint(1, 10), "n_estimators": stats.randint(100, 2000)}
clf = RandomizedSearchCV(
RandomForestClassifier(oob_score=True),
param_distributions=paramsampler,
cv=5, n_jobs=-1)
clf.fit(knownX_train, y_train)
clfz = RandomizedSearchCV(
RandomForestClassifier(oob_score=True),
param_distributions=paramsampler,
cv=5, n_jobs=-1)
clfz.fit(unknownX_train, y_train)
known_oob = clf.best_score_
unknown_oob = clfz.best_score_
known_weight = known_oob / (known_oob + unknown_oob)
unknown_weight = 1 - known_weight
kweight.append(known_weight)
print('known weight: %.4f' % known_weight, 'unknown weight: %.4f' % unknown_weight)
combinedX_train = np.hstack((known_weight * knownX_train, unknown_weight * unknownX_train))
combinedX_test = np.hstack((known_weight * knownX_test, unknown_weight * unknownX_test))
clfc = RandomizedSearchCV(
RandomForestClassifier(),
param_distributions=paramsampler,
cv=5, n_jobs=-1)
clfc.fit(combinedX_train, y_train)
importances = clfc.best_estimator_.feature_importances_
feature_imp.append(importances)
pred_prob = clfc.predict_proba(combinedX_test)[:, 1]
y_predpro.extend(pred_prob)
auc = roc_auc_score(y_test, pred_prob)
ave_auc.append(auc)
print('AUC : %.4f' % auc)
print('Mean AUC : %.4f' % np.mean(ave_auc))
print('Ave Known Weight : %.4f' % np.mean(kweight))
print('Ave unKnown Weight : %.4f' % (1 - np.mean(kweight)))
end = time.time()
running_time = end - start
print('Time cost : %.5f s' % running_time)
meanauc=np.mean(ave_auc)
mkweight=np.mean(kweight)
mukweight=1 - np.mean(kweight)
print('====================')
file.write('Mean AUCs: ' + str(meanauc) + "\n")
file.write('Mean Known weights: ' + str(mkweight) + "\n")
file.write('Mean Unknown weights: ' + str(mukweight) + "\n")
file.write('Time for '+str(repeat_seed)+' rounds running: ' + str(running_time) + "s")
par = read_params(sys.argv)
file_name = str(par['fn'])
ts = float(par['ts'])
rs = int(par['rs'])
known, unknown,y=read_files(file_name)
WRF_eva(known, unknown,y, ts, rs, file_name )