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rf.py
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import time
import warnings
import sys
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectPercentile, f_classif, SelectFromModel
from sklearn.metrics import roc_auc_score, confusion_matrix, precision_recall_curve, auc, mean_squared_error, \
r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor, XGBClassifier
import multiprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
import argparse
#parser.add_argument('--device', type=int, default=6, help='which gpu to use if any (default: 0)')
#parser.add_argument('--embed', type=str, default = 'GM', help='Embedding method: None, LE, LIFM, GM, SM, IFM.')
#parser.add_argument('--epochs', type=int, default=300, help='running epochs')
#parser.add_argument('--batch_size', type=int, default=128, help='batchsize')
parser = argparse.ArgumentParser(description='PyTorch implementation')
parser.add_argument('--data_label', type=str, default = 'esol', help='dataset.')
parser.add_argument('--runseed', type=int, default=43, help = "Seed for minibatch selection, random initialization.")
parser.add_argument('--patience', type=int, default=50, help='Patience')
parser.add_argument('--opt_iters', type=int, default=50, help='Optimization_iters')
parser.add_argument('--repetitions', type=int, default=50, help='Splitting repetitions')
parser.add_argument('--num_pools', type =int , default=5 , help= 'The number of poolings')
args = parser.parse_args()
#set np.random seed
np.random.seed(args.runseed)
start = time.time()
warnings.filterwarnings("ignore")
def standardize(col):
return (col - np.mean(col)) / np.std(col)
# the metrics for classification
def statistical(y_true, y_pred, y_pro):
c_mat = confusion_matrix(y_true, y_pred)
tn, fp, fn, tp = list(c_mat.flatten())
se = tp / (tp + fn)
sp = tn / (tn + fp)
acc = (tp + tn) / (tn + fp + fn + tp)
mcc = (tp * tn - fp * fn) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn) + 1e-8)
auc_prc = auc(precision_recall_curve(y_true, y_pro, pos_label=1)[1],
precision_recall_curve(y_true, y_pro, pos_label=1)[0])
auc_roc = roc_auc_score(y_true, y_pro)
return tn, fp, fn, tp, se, sp, acc, mcc, auc_prc, auc_roc
def all_one_zeros(data):
if (len(np.unique(data)) == 2):
flag = False
else:
flag = True
return flag
feature_selection = False
tasks_dic = {'qm7':['u0_atom'],
'freesolv': ['activity'], 'esol': ['activity'], 'lipop': ['activity'], 'bace': ['activity'],
'bbbp': ['activity'], 'hiv': ['activity'],
'clintox': ['FDA_APPROVED', 'CT_TOX'],
'sider': ['SIDER1', 'SIDER2', 'SIDER3', 'SIDER4', 'SIDER5', 'SIDER6', 'SIDER7', 'SIDER8', 'SIDER9',
'SIDER10', 'SIDER11', 'SIDER12', 'SIDER13', 'SIDER14', 'SIDER15', 'SIDER16', 'SIDER17',
'SIDER18', 'SIDER19', 'SIDER20', 'SIDER21', 'SIDER22', 'SIDER23', 'SIDER24', 'SIDER25',
'SIDER26', 'SIDER27'],
'tox21': ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', 'NR-ER-LBD', 'NR-PPAR-gamma', 'SR-ARE',
'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53'],
'muv': [
"MUV-466", "MUV-548", "MUV-600", "MUV-644", "MUV-652", "MUV-689", "MUV-692", "MUV-712", "MUV-713",
"MUV-733", "MUV-737", "MUV-810", "MUV-832", "MUV-846", "MUV-852", "MUV-858", "MUV-859"
],
'toxcast': ['ACEA_T47D_80hr_Negative', 'ACEA_T47D_80hr_Positive',
'APR_HepG2_CellCycleArrest_24h_dn',
'APR_HepG2_CellCycleArrest_72h_dn', 'APR_HepG2_CellLoss_24h_dn',
'APR_HepG2_CellLoss_72h_dn', 'APR_HepG2_MicrotubuleCSK_72h_up',
'APR_HepG2_MitoMass_24h_dn', 'APR_HepG2_MitoMass_72h_dn',
'APR_HepG2_MitoMembPot_24h_dn', 'APR_HepG2_MitoMembPot_72h_dn',
'APR_HepG2_MitoticArrest_24h_up', 'APR_HepG2_MitoticArrest_72h_up',
'APR_HepG2_OxidativeStress_24h_up',
'APR_HepG2_OxidativeStress_72h_up',
'APR_HepG2_StressKinase_72h_up', 'APR_HepG2_p53Act_24h_up',
'APR_HepG2_p53Act_72h_up', 'ATG_AP_1_CIS_up', 'ATG_Ahr_CIS_up',
'ATG_BRE_CIS_up', 'ATG_CMV_CIS_up', 'ATG_CRE_CIS_up',
'ATG_DR4_LXR_CIS_dn', 'ATG_DR5_CIS_up', 'ATG_EGR_CIS_up',
'ATG_ERE_CIS_up', 'ATG_ERa_TRANS_up', 'ATG_E_Box_CIS_dn',
'ATG_HIF1a_CIS_up', 'ATG_HSE_CIS_up', 'ATG_IR1_CIS_dn',
'ATG_ISRE_CIS_dn', 'ATG_MRE_CIS_up', 'ATG_NRF2_ARE_CIS_up',
'ATG_Oct_MLP_CIS_up', 'ATG_PBREM_CIS_up', 'ATG_PPARg_TRANS_up',
'ATG_PPRE_CIS_up', 'ATG_PXRE_CIS_dn', 'ATG_PXRE_CIS_up',
'ATG_PXR_TRANS_up', 'ATG_Pax6_CIS_up', 'ATG_RORE_CIS_up',
'ATG_RXRb_TRANS_up', 'ATG_SREBP_CIS_up', 'ATG_Sp1_CIS_up',
'ATG_TCF_b_cat_CIS_dn', 'ATG_VDRE_CIS_up', 'ATG_Xbp1_CIS_up',
'ATG_p53_CIS_dn', 'BSK_3C_Eselectin_down', 'BSK_3C_HLADR_down',
'BSK_3C_ICAM1_down', 'BSK_3C_IL8_down', 'BSK_3C_MCP1_down',
'BSK_3C_MIG_down', 'BSK_3C_Proliferation_down', 'BSK_3C_SRB_down',
'BSK_3C_Thrombomodulin_up', 'BSK_3C_TissueFactor_down',
'BSK_3C_VCAM1_down', 'BSK_3C_Vis_down', 'BSK_3C_uPAR_down',
'BSK_4H_Eotaxin3_down', 'BSK_4H_MCP1_down',
'BSK_4H_Pselectin_down', 'BSK_4H_SRB_down', 'BSK_4H_VCAM1_down',
'BSK_4H_VEGFRII_down', 'BSK_4H_uPAR_down', 'BSK_BE3C_HLADR_down',
'BSK_BE3C_IL1a_down', 'BSK_BE3C_IP10_down', 'BSK_BE3C_MIG_down',
'BSK_BE3C_MMP1_down', 'BSK_BE3C_MMP1_up', 'BSK_BE3C_PAI1_down',
'BSK_BE3C_SRB_down', 'BSK_BE3C_TGFb1_down', 'BSK_BE3C_tPA_down',
'BSK_BE3C_uPAR_down', 'BSK_BE3C_uPA_down', 'BSK_CASM3C_HLADR_down',
'BSK_CASM3C_IL6_down', 'BSK_CASM3C_IL8_down',
'BSK_CASM3C_LDLR_down', 'BSK_CASM3C_MCP1_down',
'BSK_CASM3C_MCSF_down', 'BSK_CASM3C_MIG_down',
'BSK_CASM3C_Proliferation_down', 'BSK_CASM3C_SAA_down',
'BSK_CASM3C_SRB_down', 'BSK_CASM3C_Thrombomodulin_up',
'BSK_CASM3C_TissueFactor_down', 'BSK_CASM3C_VCAM1_down',
'BSK_CASM3C_uPAR_down', 'BSK_KF3CT_ICAM1_down',
'BSK_KF3CT_IL1a_down', 'BSK_KF3CT_IP10_down',
'BSK_KF3CT_MCP1_down', 'BSK_KF3CT_MMP9_down', 'BSK_KF3CT_SRB_down',
'BSK_KF3CT_TGFb1_down', 'BSK_KF3CT_TIMP2_down',
'BSK_KF3CT_uPA_down', 'BSK_LPS_CD40_down',
'BSK_LPS_Eselectin_down', 'BSK_LPS_IL1a_down', 'BSK_LPS_IL8_down',
'BSK_LPS_MCP1_down', 'BSK_LPS_MCSF_down', 'BSK_LPS_PGE2_down',
'BSK_LPS_SRB_down', 'BSK_LPS_TNFa_down',
'BSK_LPS_TissueFactor_down', 'BSK_LPS_VCAM1_down',
'BSK_SAg_CD38_down', 'BSK_SAg_CD40_down', 'BSK_SAg_CD69_down',
'BSK_SAg_Eselectin_down', 'BSK_SAg_IL8_down', 'BSK_SAg_MCP1_down',
'BSK_SAg_MIG_down', 'BSK_SAg_PBMCCytotoxicity_down',
'BSK_SAg_Proliferation_down', 'BSK_SAg_SRB_down',
'BSK_hDFCGF_CollagenIII_down', 'BSK_hDFCGF_IL8_down',
'BSK_hDFCGF_IP10_down', 'BSK_hDFCGF_MCSF_down',
'BSK_hDFCGF_MIG_down', 'BSK_hDFCGF_MMP1_down',
'BSK_hDFCGF_PAI1_down', 'BSK_hDFCGF_Proliferation_down',
'BSK_hDFCGF_SRB_down', 'BSK_hDFCGF_TIMP1_down',
'BSK_hDFCGF_VCAM1_down', 'CEETOX_H295R_11DCORT_dn',
'CEETOX_H295R_ANDR_dn', 'CEETOX_H295R_CORTISOL_dn',
'CEETOX_H295R_ESTRONE_dn', 'CEETOX_H295R_ESTRONE_up',
'NHEERL_ZF_144hpf_TERATOSCORE_up', 'NVS_NR_bER', 'NVS_NR_hER',
'NVS_NR_hPPARg', 'NVS_NR_hPXR', 'NVS_NR_mERa', 'OT_AR_ARSRC1_0960',
'OT_ER_ERaERb_0480', 'OT_ER_ERaERb_1440', 'OT_ER_ERbERb_0480',
'OT_ER_ERbERb_1440', 'OT_ERa_EREGFP_0120', 'OT_FXR_FXRSRC1_0480',
'OT_NURR1_NURR1RXRa_0480', 'TOX21_ARE_BLA_agonist_ratio',
'TOX21_AR_BLA_Antagonist_ratio', 'TOX21_AR_LUC_MDAKB2_Antagonist',
'TOX21_AR_LUC_MDAKB2_Antagonist2', 'TOX21_AhR_LUC_Agonist',
'TOX21_Aromatase_Inhibition', 'TOX21_ERa_BLA_Antagonist_ratio',
'TOX21_ERa_LUC_BG1_Agonist', 'TOX21_FXR_BLA_antagonist_ratio',
'TOX21_MMP_ratio_down', 'TOX21_TR_LUC_GH3_Antagonist',
'TOX21_p53_BLA_p1_ratio', 'TOX21_p53_BLA_p2_ch2',
'TOX21_p53_BLA_p2_ratio', 'TOX21_p53_BLA_p2_viability',
'TOX21_p53_BLA_p3_ratio', 'TOX21_p53_BLA_p4_ratio',
'TOX21_p53_BLA_p5_ratio', 'Tanguay_ZF_120hpf_AXIS_up',
'Tanguay_ZF_120hpf_ActivityScore', 'Tanguay_ZF_120hpf_JAW_up',
'Tanguay_ZF_120hpf_MORT_up', 'Tanguay_ZF_120hpf_PE_up',
'Tanguay_ZF_120hpf_SNOU_up', 'Tanguay_ZF_120hpf_YSE_up']}
sm_num=10
dataset_label =args.data_label
file_name = './dataset/'+dataset_label+'_moe_pubsubfp.csv'
if dataset_label=='esol' or dataset_label== 'freesolv' or dataset_label== 'lipop':
task_type='reg'
else:
task_type='cla'
tasks = tasks_dic[dataset_label]
OPT_ITERS = args.opt_iters
repetitions = args.repetitions
num_pools = args.num_pools
space_ = {'n_estimators': hp.choice('n_estimators', [10, 50, 100, 200, 300, 400, 500]),
'max_depth': hp.choice('max_depth', range(3, 12)),
'min_samples_leaf': hp.choice('min_samples_leaf', [1, 3, 5, 10, 20, 50]),
'min_impurity_decrease': hp.uniform('min_impurity_decrease', 0, 0.01),
'max_features': hp.choice('max_features', ['sqrt', 'log2', 0.7, 0.8, 0.9])
}
n_estimators_ls = [10, 50, 100, 200, 300, 400, 500]
max_depth_ls = range(3, 12)
min_samples_leaf_ls = [1, 3, 5, 10, 20, 50]
max_features_ls = ['sqrt', 'log2', 0.7, 0.8, 0.9]
dataset = pd.read_csv(file_name)
# if dataset_label == 'freesolv':
# dataset.drop(columns=['vsa_pol', 'h_emd', 'a_donacc'], inplace=True)
# elif dataset_label == 'esol':
# dataset.drop(columns=['logS', 'h_logS', 'SlogP'], inplace=True)
# else:
# dataset.drop(columns=['SlogP', 'h_logD', 'logS'], inplace=True)
pd_res = []
def hyper_runing(subtask):
cols = [subtask]
cols.extend(dataset.columns[(len(tasks) + 1):])
sub_dataset = dataset[cols]
# detect the na in the subtask (y cloumn)
rm_index = sub_dataset[subtask][sub_dataset[subtask].isnull()].index
sub_dataset.drop(index=rm_index, inplace=True)
# remove the features with na
if dataset_label != 'hiv':
sub_dataset = sub_dataset.dropna(axis=1)
else:
sub_dataset = sub_dataset.dropna(axis=0)
# *******************
# demension reduction
# *******************
# Removing features with low variance
# threshold = 0.05
data_fea_var = sub_dataset.iloc[:, 2:].var()
del_fea1 = list(data_fea_var[data_fea_var <= 0.05].index)
sub_dataset.drop(columns=del_fea1, inplace=True)
# pair correlations
# threshold = 0.95
data_fea_corr = sub_dataset.iloc[:, 2:].corr()
del_fea2_col = []
del_fea2_ind = []
length = data_fea_corr.shape[1]
for i in range(length):
for j in range(i + 1, length):
if abs(data_fea_corr.iloc[i, j]) >= 0.95:
del_fea2_col.append(data_fea_corr.columns[i])
del_fea2_ind.append(data_fea_corr.index[j])
sub_dataset.drop(columns=del_fea2_ind, inplace=True)
# standardize the features
cols_ = list(sub_dataset.columns)[2:]
sub_dataset[cols_] = sub_dataset[cols_].apply(standardize, axis=0)
# get the attentivefp data splits
sub_datasett=pd.read_csv('./dataset/dataset_used_for_modeling/'+dataset_label+'.csv')
data_tr = sub_dataset[sub_datasett['group'] == 'train']
data_va = sub_dataset[sub_datasett['group'] == 'valid']
data_te = sub_dataset[sub_datasett['group'] == 'test']
# prepare data for training
# training set
data_tr_y = data_tr[subtask].values.reshape(-1, 1)
data_tr_x = np.array(data_tr.iloc[:, 2:].values)
# validation set
data_va_y = data_va[subtask].values.reshape(-1, 1)
data_va_x = np.array(data_va.iloc[:, 2:].values)
# test set
data_te_y = data_te[subtask].values.reshape(-1, 1)
data_te_x = np.array(data_te.iloc[:, 2:].values)
if feature_selection:
# univariate feature selection
trans1 = SelectPercentile(f_classif, percentile=80)
trans1.fit(data_tr_x, data_tr_y)
data_tr_x = trans1.transform(data_tr_x)
data_va_x = trans1.transform(data_va_x)
data_te_x = trans1.transform(data_te_x)
# select from model
clf = XGBClassifier(n_jobs=12, random_state=1)
clf = clf.fit(data_tr_x, data_tr_y)
trans2 = SelectFromModel(clf, prefit=True)
data_tr_x = trans2.transform(data_tr_x)
data_va_x = trans2.transform(data_va_x)
data_te_x = trans2.transform(data_te_x)
num_fea = data_tr_x.shape[1]
print('the num of retained features for the ' + dataset_label + ' ' + subtask + ' is:', num_fea)
def hyper_opt(args):
model = RandomForestClassifier(**args, n_jobs=6, random_state=1, verbose=0, class_weight='balanced') \
if task_type == 'cla' else RandomForestRegressor(**args, n_jobs=6, random_state=1, verbose=0)
model.fit(data_tr_x, data_tr_y)
val_preds = model.predict_proba(data_va_x) if task_type == 'cla' else model.predict(data_va_x)
loss = 1 - roc_auc_score(data_va_y, val_preds[:, 1]) if task_type == 'cla' else np.sqrt(
mean_squared_error(data_va_y, val_preds))
return {'loss': loss, 'status': STATUS_OK}
# start hyper-parameters optimization
trials = Trials()
best_results = fmin(hyper_opt, space_, algo=tpe.suggest, max_evals=OPT_ITERS, trials=trials, show_progressbar=False)
print('the best hyper-parameters for ' + dataset_label + ' ' + subtask + ' are: ', best_results)
best_model = RandomForestClassifier(n_estimators=n_estimators_ls[best_results['n_estimators']],
max_depth=max_depth_ls[best_results['max_depth']],
min_samples_leaf=min_samples_leaf_ls[best_results['min_samples_leaf']],
max_features=max_features_ls[best_results['max_features']],
min_impurity_decrease=best_results['min_impurity_decrease'],
n_jobs=6, random_state=1, verbose=0, class_weight='balanced') \
if task_type == 'cla' else RandomForestRegressor(
n_estimators=n_estimators_ls[best_results['n_estimators']],
max_depth=max_depth_ls[best_results['max_depth']],
min_samples_leaf=min_samples_leaf_ls[best_results['min_samples_leaf']],
max_features=max_features_ls[best_results['max_features']],
min_impurity_decrease=best_results['min_impurity_decrease'],
n_jobs=6, random_state=1, verbose=0)
best_model.fit(data_tr_x, data_tr_y)
num_of_compounds = len(sub_dataset)
if task_type == 'cla':
# training set
tr_pred = best_model.predict_proba(data_tr_x)
tr_results = [dataset_label, subtask, 'tr', num_fea, num_of_compounds, data_tr_y[data_tr_y == 1].shape[0],
data_tr_y[data_tr_y == 0].shape[0],
data_tr_y[data_tr_y == 0].shape[0] / data_tr_y[data_tr_y == 1].shape[0],
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']]]
tr_results.extend(statistical(data_tr_y, np.argmax(tr_pred, axis=1), tr_pred[:, 1]))
# validation set
va_pred = best_model.predict_proba(data_va_x)
va_results = [dataset_label, subtask, 'va', num_fea, num_of_compounds, data_va_y[data_va_y == 1].shape[0],
data_va_y[data_va_y == 0].shape[0],
data_va_y[data_va_y == 0].shape[0] / data_va_y[data_va_y == 1].shape[0],
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']]]
va_results.extend(statistical(data_va_y, np.argmax(va_pred, axis=1), va_pred[:, 1]))
# test set
te_pred = best_model.predict_proba(data_te_x)
te_results = [dataset_label, subtask, 'te', num_fea, num_of_compounds, data_te_y[data_te_y == 1].shape[0],
data_te_y[data_te_y == 0].shape[0],
data_te_y[data_te_y == 0].shape[0] / data_te_y[data_te_y == 1].shape[0],
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']]]
te_results.extend(statistical(data_te_y, np.argmax(te_pred, axis=1), te_pred[:, 1]))
else:
# training set
tr_pred = best_model.predict(data_tr_x)
tr_results = [dataset_label, subtask, 'tr', num_fea, num_of_compounds,
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']],
np.sqrt(mean_squared_error(data_tr_y, tr_pred)), r2_score(data_tr_y, tr_pred),
mean_absolute_error(data_tr_y, tr_pred)]
# validation set
va_pred = best_model.predict(data_va_x)
va_results = [dataset_label, subtask, 'va', num_fea, num_of_compounds,
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']],
np.sqrt(mean_squared_error(data_va_y, va_pred)), r2_score(data_va_y, va_pred),
mean_absolute_error(data_va_y, va_pred)]
# test set
te_pred = best_model.predict(data_te_x)
te_results = [dataset_label, subtask, 'te', num_fea, num_of_compounds,
n_estimators_ls[best_results['n_estimators']],
max_depth_ls[best_results['max_depth']],
min_samples_leaf_ls[best_results['min_samples_leaf']],
best_results['min_impurity_decrease'],
max_features_ls[best_results['max_features']],
np.sqrt(mean_squared_error(data_te_y, te_pred)), r2_score(data_te_y, te_pred),
mean_absolute_error(data_te_y, te_pred)]
return tr_results, va_results, te_results
pool = multiprocessing.Pool(num_pools)
res = pool.starmap(hyper_runing, zip(tasks))
pool.close()
pool.join()
for item in res:
for i in range(3):
pd_res.append(item[i])
if task_type == 'cla':
best_hyper = pd.DataFrame(pd_res, columns=['dataset', 'subtask', 'set',
'num_of_retained_feature',
'num_of_compounds', 'postives',
'negtives', 'negtives/postives',
'n_estimators', 'max_depth', 'min_samples_leaf',
'min_impurity_decrease', 'max_features',
'tn', 'fp', 'fn', 'tp', 'se', 'sp',
'acc', 'mcc', 'auc_prc', 'auc_roc'])
else:
best_hyper = pd.DataFrame(pd_res, columns=['dataset', 'subtask', 'set',
'num_of_retained_feature',
'num_of_compounds', 'n_estimators', 'max_depth', 'min_samples_leaf',
'min_impurity_decrease', 'max_features', 'rmse', 'r2', 'mae'])
best_hyper.to_csv('./stat_res/' + dataset_label + '_moe_pubsub_rf_hyperopt_info.csv', index=0)
if task_type == 'cla':
print('train', best_hyper[best_hyper['set'] == 'tr']['auc_roc'].mean(), best_hyper[best_hyper['set'] == 'tr']['auc_prc'].mean())
print('valid', best_hyper[best_hyper['set'] == 'va']['auc_roc'].mean(), best_hyper[best_hyper['set'] == 'va']['auc_prc'].mean())
print('test', best_hyper[best_hyper['set'] == 'te']['auc_roc'].mean(), best_hyper[best_hyper['set'] == 'te']['auc_prc'].mean())
else:
print('train', best_hyper[best_hyper['set'] == 'tr']['rmse'].mean(), best_hyper[best_hyper['set'] == 'tr']['r2'].mean(), best_hyper[best_hyper['set'] == 'tr']['mae'].mean())
print('valid', best_hyper[best_hyper['set'] == 'va']['rmse'].mean(), best_hyper[best_hyper['set'] == 'va']['r2'].mean(), best_hyper[best_hyper['set'] == 'va']['mae'].mean())
print('test', best_hyper[best_hyper['set'] == 'te']['rmse'].mean(), best_hyper[best_hyper['set'] == 'te']['r2'].mean(), best_hyper[best_hyper['set'] == 'te']['mae'].mean())
# 50 repetitions based on thr best hypers
#dataset.drop(columns=['group'], inplace=True)
pd_res = []
def best_model_runing(split):
seed = split
if task_type == 'cla':
while True:
training_data, data_te = train_test_split(sub_dataset, test_size=0.1, random_state=seed)
# the training set was further splited into the training set and validation set
data_tr, data_va = train_test_split(training_data, test_size=0.1, random_state=seed)
if (all_one_zeros(data_tr[subtask]) or all_one_zeros(data_va[subtask]) or all_one_zeros(data_te[subtask])):
print(
'\ninvalid random seed {} due to one class presented in the {} splitted sets...'.format(seed,
subtask))
print('Changing to another random seed...\n')
seed = np.random.randint(50, 999999)
else:
print('random seed used in repetition {} is {}'.format(split, seed))
break
else:
training_data, data_te = train_test_split(sub_dataset, test_size=0.1, random_state=seed)
# the training set was further splited into the training set and validation set
data_tr, data_va = train_test_split(training_data, test_size=0.1, random_state=seed)
# prepare data for training
# training set
data_tr_y = data_tr[subtask].values.reshape(-1, 1)
data_tr_x = np.array(data_tr.iloc[:, 1:].values)
# validation set
data_va_y = data_va[subtask].values.reshape(-1, 1)
data_va_x = np.array(data_va.iloc[:, 1:].values)
# test set
data_te_y = data_te[subtask].values.reshape(-1, 1)
data_te_x = np.array(data_te.iloc[:, 1:].values)
if feature_selection:
# univariate feature selection
trans1 = SelectPercentile(f_classif, percentile=80)
trans1.fit(data_tr_x, data_tr_y)
data_tr_x = trans1.transform(data_tr_x)
data_va_x = trans1.transform(data_va_x)
data_te_x = trans1.transform(data_te_x)
# select from model
clf = XGBClassifier(n_jobs=6, random_state=1)
clf = clf.fit(data_tr_x, data_tr_y)
trans2 = SelectFromModel(clf, prefit=True)
data_tr_x = trans2.transform(data_tr_x)
data_va_x = trans2.transform(data_va_x)
data_te_x = trans2.transform(data_te_x)
num_fea = data_tr_x.shape[1]
model = RandomForestClassifier(n_estimators=best_hyper[best_hyper.subtask == subtask].iloc[0,]['n_estimators'],
max_depth=best_hyper[best_hyper.subtask == subtask].iloc[0,]['max_depth'],
min_samples_leaf=best_hyper[best_hyper.subtask == subtask].iloc[0,][
'min_samples_leaf'],
max_features=best_hyper[best_hyper.subtask == subtask].iloc[0,]['max_features'],
min_impurity_decrease=best_hyper[best_hyper.subtask == subtask].iloc[0,][
'min_impurity_decrease'],
n_jobs=6, random_state=1, verbose=0, class_weight='balanced') \
if task_type == 'cla' else RandomForestRegressor(
n_estimators=best_hyper[best_hyper.subtask == subtask].iloc[0,]['n_estimators'],
max_depth=best_hyper[best_hyper.subtask == subtask].iloc[0,]['max_depth'],
min_samples_leaf=best_hyper[best_hyper.subtask == subtask].iloc[0,]['min_samples_leaf'],
max_features=best_hyper[best_hyper.subtask == subtask].iloc[0,]['max_features'],
min_impurity_decrease=best_hyper[best_hyper.subtask == subtask].iloc[0,]['min_impurity_decrease'],
n_jobs=6, random_state=1, verbose=0)
model.fit(data_tr_x, data_tr_y)
num_of_compounds = sub_dataset.shape[0]
if task_type == 'cla':
# training set
tr_pred = model.predict_proba(data_tr_x)
tr_results = [split, dataset_label, subtask, 'tr', num_fea, num_of_compounds,
data_tr_y[data_tr_y == 1].shape[0],
data_tr_y[data_tr_y == 0].shape[0],
data_tr_y[data_tr_y == 0].shape[0] / data_tr_y[data_tr_y == 1].shape[0]]
tr_results.extend(statistical(data_tr_y, np.argmax(tr_pred, axis=1), tr_pred[:, 1]))
# validation set
va_pred = model.predict_proba(data_va_x)
va_results = [split, dataset_label, subtask, 'va', num_fea, num_of_compounds,
data_va_y[data_va_y == 1].shape[0],
data_va_y[data_va_y == 0].shape[0],
data_va_y[data_va_y == 0].shape[0] / data_va_y[data_va_y == 1].shape[0]]
va_results.extend(statistical(data_va_y, np.argmax(va_pred, axis=1), va_pred[:, 1]))
# test set
te_pred = model.predict_proba(data_te_x)
te_results = [split, dataset_label, subtask, 'te', num_fea, num_of_compounds,
data_te_y[data_te_y == 1].shape[0],
data_te_y[data_te_y == 0].shape[0],
data_te_y[data_te_y == 0].shape[0] / data_te_y[data_te_y == 1].shape[0]]
te_results.extend(statistical(data_te_y, np.argmax(te_pred, axis=1), te_pred[:, 1]))
else:
# training set
tr_pred = model.predict(data_tr_x)
tr_results = [split, dataset_label, subtask, 'tr', num_fea, num_of_compounds,
np.sqrt(mean_squared_error(data_tr_y, tr_pred)), r2_score(data_tr_y, tr_pred),
mean_absolute_error(data_tr_y, tr_pred)]
# validation set
va_pred = model.predict(data_va_x)
va_results = [split, dataset_label, subtask, 'va', num_fea, num_of_compounds,
np.sqrt(mean_squared_error(data_va_y, va_pred)), r2_score(data_va_y, va_pred),
mean_absolute_error(data_va_y, va_pred)]
# test set
te_pred = model.predict(data_te_x)
te_results = [split, dataset_label, subtask, 'te', num_fea, num_of_compounds,
np.sqrt(mean_squared_error(data_te_y, te_pred)), r2_score(data_te_y, te_pred),
mean_absolute_error(data_te_y, te_pred)]
return tr_results, va_results, te_results
for subtask in tasks:
cols = [subtask]
cols.extend(dataset.columns[(len(tasks) + 1):])
# cols.extend(dataset.columns[(617+1):])
sub_dataset = dataset[cols]
# detect the NA in the subtask (y cloumn)
rm_index = sub_dataset[subtask][sub_dataset[subtask].isnull()].index
sub_dataset.drop(index=rm_index, inplace=True)
# remove the features with na
if dataset_label != 'hiv':
sub_dataset = sub_dataset.dropna(axis=1)
else:
sub_dataset = sub_dataset.dropna(axis=0)
# *******************
# demension reduction
# *******************
# Removing features with low variance
# threshold = 0.05
data_fea_var = sub_dataset.iloc[:, 1:].var()
del_fea1 = list(data_fea_var[data_fea_var <= 0.05].index)
sub_dataset.drop(columns=del_fea1, inplace=True)
# pair correlations
# threshold = 0.95
data_fea_corr = sub_dataset.iloc[:, 1:].corr()
del_fea2_col = []
del_fea2_ind = []
length = data_fea_corr.shape[1]
for i in range(length):
for j in range(i + 1, length):
if abs(data_fea_corr.iloc[i, j]) >= 0.95:
del_fea2_col.append(data_fea_corr.columns[i])
del_fea2_ind.append(data_fea_corr.index[j])
sub_dataset.drop(columns=del_fea2_ind, inplace=True)
# standardize the features
cols_ = list(sub_dataset.columns)[1:]
sub_dataset[cols_] = sub_dataset[cols_].apply(standardize, axis=0)
# for split in range(1, splits+1):
pool = multiprocessing.Pool(num_pools)
res = pool.starmap(best_model_runing, zip(range(1, repetitions + 1)))
pool.close()
pool.join()
for item in res:
for i in range(3):
pd_res.append(item[i])
if task_type == 'cla':
stat_res = pd.DataFrame(pd_res, columns=['split', 'dataset', 'subtask', 'set',
'num_of_retained_feature',
'num_of_compounds', 'postives',
'negtives', 'negtives/postives',
'tn', 'fp', 'fn', 'tp', 'se', 'sp',
'acc', 'mcc', 'auc_prc', 'auc_roc'])
else:
stat_res = pd.DataFrame(pd_res, columns=['split', 'dataset', 'subtask', 'set',
'num_of_retained_feature',
'num_of_compounds', 'rmse', 'r2', 'mae'])
stat_res.to_csv('./stat_res/' + dataset_label + '_rf_statistical_results_split50_20200622.csv', index=0)
# single tasks
if len(tasks) == 1:
args = {'data_label': dataset_label, 'metric': 'auc_roc' if task_type == 'cla' else 'rmse', 'model': 'RF'}
print('{}_{}: the mean {} for the training set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'tr'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'tr'][args['metric']])))
print(
'{}_{}: the mean {} for the validation set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'va'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'va'][args['metric']])))
print('{}_{}: the mean {} for the test set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'te'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'te'][args['metric']])))
args = {'data_label': dataset_label, 'metric': 'auc_roc' if task_type == 'cla' else 'mae', 'model': 'RF'}
print('{}_{}: the mean {} for the training set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'tr'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'tr'][args['metric']])))
print(
'{}_{}: the mean {} for the validation set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'va'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'va'][args['metric']])))
print('{}_{}: the mean {} for the test set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'te'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'te'][args['metric']])))
args = {'data_label': dataset_label, 'metric': 'auc_roc' if task_type == 'cla' else 'r2', 'model': 'RF'}
print('{}_{}: the mean {} for the training set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'tr'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'tr'][args['metric']])))
print(
'{}_{}: the mean {} for the validation set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'va'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'va'][args['metric']])))
print('{}_{}: the mean {} for the test set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(
stat_res[stat_res['set'] == 'te'][args['metric']]), np.std(
stat_res[stat_res['set'] == 'te'][args['metric']])))
# multi-tasks
else:
args = {'data_label': dataset_label, 'metric': 'auc_roc' if dataset_label != 'muv' else 'auc_prc', 'model': 'RF'}
tr_acc = np.zeros(repetitions)
va_acc = np.zeros(repetitions)
te_acc = np.zeros(repetitions)
for subtask in tasks:
tr = stat_res[stat_res['set'] == 'tr']
tr_acc = tr_acc + tr[tr['subtask'] == subtask][args['metric']].values
va = stat_res[stat_res['set'] == 'va']
va_acc = va_acc + va[va['subtask'] == subtask][args['metric']].values
te = stat_res[stat_res['set'] == 'te']
te_acc = te_acc + te[te['subtask'] == subtask][args['metric']].values
tr_acc = tr_acc / len(tasks)
va_acc = va_acc / len(tasks)
te_acc = te_acc / len(tasks)
print('{}_{}: the mean {} for the training set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(tr_acc),
np.std(tr_acc)))
print(
'{}_{}: the mean {} for the validation set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(va_acc),
np.std(va_acc)))
print('{}_{}: the mean {} for the test set is {:.3f} with std {:.3f}'.format(args['data_label'], args['model'],
args['metric'], np.mean(te_acc),
np.std(te_acc)))
end = time.time() # get the end time
print('the elapsed time is:', (end - start), 'S')