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pipeline.py
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#!/usr/bin/env python
# coding: utf-8
#Author: Cong Zhu
import umap
from sklearn.datasets import load_digits
import pandas as pd
import numpy as np
import xgboost as xgb
from bayes_opt import BayesianOptimization
import matplotlib.pyplot as plt
import os
import seaborn as sns
from xgboost import plot_importance
from matplotlib import pyplot as plt
import shap
from xgboost import XGBClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from statistics import mean
from pandas import DataFrame
import matplotlib.pyplot as plt
# In[239]:
os.chdir("...")
date = "date"
seed = 222
n_try = 2
top20vs = '....'.format(date, seed, n_try)
selected_features = '....'.format(date,seed,n_try)
optimized_parms = '....'.format(date, seed,n_try)
# # Import data (clustered created; check R and notebook)
df_clusters = pd.read_excel('....')
cluster_list = ["cluster_2D_minsamp1_mincluster100"]
model_seed = 222
params = {'max_depth': 9,
'gamma': 1,
'n_estimators': 100,
'subsample': 0.8,
'eta': 0.01,
'min_child_weight':1,
"colsample_bytree":1,
'colsample_bylevel':1,
'colsample_bynode ':1,
'objective':'reg:logistic',
'eval_metric': 'auc'}
for n in [0,1]:
for i in [0,1]:
df = df_clusters[(df_clusters[cluster_method]==n) & (df_clusters["proton"]==i)]
X = df.iloc[:,2:-2]
y=df["G4RIL"]
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
print(n,i)
print(ratio_try)
print(np.sum(y == 1)/y.shape[0])
print("-------")
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio_try = float(np.sum(label == 0)) / np.sum(label == 1)
if ratio_try > 3:
ratio = 2
else:
ratio = 1
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
vs_results = {}
for cluster_method in cluster_list:
n_cluster = df_clusters[cluster_method].unique()
cluster = {}
for n in n_cluster:
rt_mod = {}
for i, mod in zip(range(2),["photon","proton"]):
print(cluster_method+": "+"cluster"+str(n)+ "- " +mod)
df = df_clusters[(df_clusters[cluster_method]==n) & (df_clusters["proton"]==i)]
X = df.iloc[:,2:-2]
y=df["G4RIL"]
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
if ratio_try>3:
ratio = 2
else:
ratio = 1.0
params['scale_pos_weight'] = ratio
'''top 20 important variables according to SHAP'''
model_temp = XGBClassifier(**params)
model_temp.fit(X, y)
shap_values = shap.TreeExplainer(model_temp).shap_values(X)
shap.summary_plot(shap_values, X,max_display=20)
vals= np.abs(shap_values).mean(0)
feature_importance = pd.DataFrame(list(zip(X.columns, vals)), columns=['var_name','importance'])
feature_importance.sort_values(by=['importance'], ascending=False,inplace=True)
feature_selected = feature_importance.head(20)
cv_logloss={}
cv_logloss.update({"fs20":feature_selected})
'''experiment with different number of feastures, selection based on the ranking of importannce'''
for j in range(5,20):
X_selected = X[feature_selected["var_name"][0:j]]
dtrain = xgb.DMatrix(X_selected, label = y)
cv = xgb.cv(params,
dtrain,
num_boost_round=200,
seed=model_seed,
folds=RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=model_seed),
metrics={'auc'},
early_stopping_rounds=30,
fpreproc = fpreproc)
logloss = round(cv['test-auc-mean'].tail(1).ravel()[0],3)
cv_logloss.update({j:logloss})
rt_mod.update({mod:cv_logloss})
cluster.update({("cluster" + str(n)):rt_mod})
vs_results.update({cluster_method:cluster})
writer = pd.ExcelWriter(top20vs)
for cluster_method in vs_results.keys():
for cluster_number in vs_results[cluster_method].keys():
for rt_mod in vs_results[cluster_method][cluster_number].keys():
cluster_info = cluster_method.replace("_minsamp1","").replace ("cluster_","").replace("mincluster","mincl") + cluster_number.replace("cluster","_c")+"-"+rt_mod
print(cluster_info)
output_df = DataFrame(vs_results[cluster_method][cluster_number][rt_mod]['fs20'])
output_df.to_excel(writer, sheet_name=cluster_info)
writer.save()
import copy
vs_results2 = copy.deepcopy(vs_results)
# In[252]:
vs_results2['cluster_2D_minsamp1_mincluster100'].keys()
# In[253]:
vs_results2
writer = pd.ExcelWriter(selected_features)
for cluster_method in vs_results2.keys():
for cluster_number in vs_results2[cluster_method].keys():
for rt_mod in vs_results2[cluster_method][cluster_number].keys():
cluster_info = cluster_method.replace("_minsamp1","").replace ("cluster_","").replace("mincluster","mincl") + cluster_number.replace("cluster","_c")+"-"+rt_mod
print(cluster_info)
feature20 = vs_results2[cluster_method][cluster_number][rt_mod]['fs20']
vs_results3 = copy.deepcopy(vs_results2[cluster_method][cluster_number][rt_mod])
del vs_results3['fs20']
var_index = max(vs_results3,
key = vs_results3.get)
feature_selected = feature20[0:var_index]
print("Index:"+str(var_index))
output_df = DataFrame(feature_selected)
output_df.to_excel(writer, sheet_name=cluster_info)
writer.save()
output_df
# ### Bayesian opitmization
xlsx_names = ['2D_mincl100_c1-photon', '2D_mincl100_c1-proton', '2D_mincl100_c0-photon', '2D_mincl100_c0-proton']
c_name_list = ['2D_mincl100_c1_photon', '2D_mincl100_c1_proton', '2D_mincl100_c0_photon', '2D_mincl100_c0_proton']
fs_list = {}
for cluster_name1, cluster_name2 in zip(c_name_list,xlsx_names):
fs = cluster_name1+"_fs"
fs = pd.read_excel(selected_features,sheet_name = cluster_name2)
fs_list.update({cluster_name1:fs})
fs_list
bo_result={}
for cluster_method in cluster_list:
cluster = {}
for cluster_number in vs_results[cluster_method].keys():
mod = {}
for rt_mod in vs_results[cluster_method][cluster_number].keys():
print(cluster_method+":",cluster_number+"-",rt_mod)
if rt_mod=="proton":
i = 1
else:
i = 0
n = int(cluster_number[-1])
df = df_clusters[(df_clusters[cluster_method]==n) & (df_clusters["proton"]==i)]
X = df.iloc[:,2:-2]
y=df["G4RIL"]
'''use selected features for optimization'''
fs_c_name = "2D_mincl100_c"+str(n)+"_"+rt_mod
var_selected = fs_list[fs_c_name]["var_name"]
print(var_selected)
X_selected = X[var_selected]
dtrain = xgb.DMatrix(X_selected, label = y)
def bo_tune_xgb(max_depth, gamma, n_estimators ,subsample,eta,
colsample_bytree,
colsample_bylevel,
colsample_bynode,min_child_weight):
params = {'max_depth': int(max_depth),
'gamma': gamma,
'n_estimators': int(n_estimators),
'subsample': subsample,
'eta': eta,
'min_child_weight':min_child_weight,
'colsample_bytree':colsample_bytree,
'colsample_bylevel':colsample_bylevel,
'colsample_bynode':colsample_bynode,
'eval_metric': 'auc'}
cv_result = xgb.cv(params = params, dtrain = dtrain, num_boost_round=200,seed=model_seed,
folds=RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=model_seed),
early_stopping_rounds=30,
fpreproc=fpreproc)
return (cv_result['test-auc-mean'].iloc[-1])
xgb_bo = BayesianOptimization(bo_tune_xgb,
{'max_depth': (3, 15),
'gamma': (0, 2),
'n_estimators':(50,250),
'subsample':(0.3,1),
'eta':(0.05,0.35),
'min_child_weight':(0,4),
'colsample_bytree':(0.3,1),
'colsample_bylevel':(0.3,1),
'colsample_bynode':(0.3,1)
},
random_state=model_seed)
xgb_bo.maximize(n_iter=60, init_points=5)
params = xgb_bo.max['params']
mod.update({rt_mod:params})
cluster.update({cluster_number:mod})
bo_result.update({cluster_method:cluster})
# ### Show results of BO model
auc_results = []
for cluster_method in bo_result.keys():
for cluster_number in bo_result[cluster_method].keys():
for rt_mod in bo_result[cluster_method][cluster_number].keys():
if rt_mod=="proton":
i = 1
else:
i = 0
n = int(cluster_number[-1])
df = df_clusters[(df_clusters[cluster_method]==n) & (df_clusters["proton"]==i)]
X = df.iloc[:,2:-2]
y=df["G4RIL"]
feature20 = vs_results2[cluster_method][cluster_number][rt_mod]['fs20']
vs_results3 = copy.deepcopy(vs_results2[cluster_method][cluster_number][rt_mod])
del vs_results3['fs20']
var_index = max(vs_results3,
key = vs_results3.get)
feature_selected = feature20[0:var_index]['var_name']
X_selected = X[feature_selected]
dtrain = xgb.DMatrix(X_selected, label = y)
'''select optimized hyperparameters'''
parms_selected = bo_result[cluster_method][cluster_number][rt_mod]
'''convert max_depth and n_estiamtors to integer'''
parms_selected['max_depth']= int(parms_selected['max_depth'])
parms_selected['n_estimators']= int(parms_selected['n_estimators'])
parms_selected['eval_metric']= 'auc'
'''repeated stratified k-fold CV'''
results = xgb.cv(params = parms_selected, dtrain = dtrain, num_boost_round=200,seed=model_seed,
folds=RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=model_seed),
early_stopping_rounds=30,
metrics={'auc'},
fpreproc=fpreproc)
auc_mean = round(results['test-auc-mean'].tail(1).ravel()[0],3)
auc_sd = round(results['test-auc-std'].tail(1).ravel()[0],4)
cluster_info = cluster_method.replace("_minsamp1","")+"_"+ cluster_number+"-"+rt_mod
print(cluster_info)
print("AUC: %.2f%% (%.2f%%)" % (auc_mean, auc_sd))
auc_results_tmp = [cluster_info, auc_mean, auc_sd]
auc_results +=[auc_results_tmp]
print("-------------------------------------------")
for cluster_method in bo_result.keys():
for cluster_number in bo_result[cluster_method].keys():
for rt_mod in bo_result[cluster_method][cluster_number].keys():
cluster_info = cluster_method.replace("_minsamp1","")+":"+ cluster_number+"-"+rt_mod
print(cluster_info)
print (bo_result[cluster_method][cluster_number][rt_mod])
print("----------------------------------------------")
date = "..."
seed = 222
n_try = 2
optimized_parms = '....'.format(date, seed,n_try)
model_performance = ".....".format(date, seed,n_try)
writer = pd.ExcelWriter(optimized_parms)
for cluster_method in bo_result.keys():
for cluster_number in bo_result[cluster_method].keys():
for rt_mod in bo_result[cluster_method][cluster_number].keys():
cluster_info = cluster_method.replace("_minsamp1","").replace ("cluster_","").replace("mincluster","mincl") + cluster_number.replace("cluster","_c")+"-"+rt_mod
output_df = bo_result[cluster_method][cluster_number][rt_mod]
output_df = pd.DataFrame.from_dict(output_df, orient='index',columns = ['value'])
output_df = DataFrame(output_df)
output_df.to_excel(writer, sheet_name=cluster_info)
writer.save()
auc_results2 = DataFrame(auc_results,columns = ["Cluster-Name","Mean AUC","SD AUC"])
auc_results2.to_excel(model_performance,sheet_name = "XGBoost")
df = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==0) & (df_clusters["proton"]==1)]
var_selected = fs_list['2D_mincl100_c0_proton']["var_name"]
X = df[var_selected]
y=df["G4RIL"]
dtrain = xgb.DMatrix(X, label = y)
params_temp = bo_result['cluster_2D_minsamp1_mincluster100']['cluster0']['proton']
'''get corresponding optimized variables'''
params_temp2 = {
'colsample_bylevel':params_temp['colsample_bylevel'],
'colsample_bynode':params_temp['colsample_bynode'],
'colsample_bytree':params_temp['colsample_bytree'],
'eta' : params_temp['eta'],
'gamma' : params_temp['gamma'],
'max_depth' : int(params_temp['max_depth']),
'min_child_weight': params_temp['min_child_weight'],
'n_estimators' : int(params_temp['n_estimators']),
'subsample' : params_temp['subsample']
}
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
if ratio_try>3:
ratio = 2
else:
ratio = 1.0
params_temp2['scale_pos_weight'] = ratio
model2 = XGBClassifier(**params_temp2)
model2.fit(X, y)
shap_values = shap.TreeExplainer(model2).shap_values(X)
vals= np.abs(shap_values).mean(0)
maxdisplay = 25
plt.rcParams["figure.figsize"] = (14, 25)
plt.rcParams.update({'font.size': 40,"font.weight" : "bold",
'axes.titlesize':40,"axes.labelweight":"bold"})
shap.summary_plot(shap_values, X, plot_type="bar",max_display=maxdisplay,show=True)
plt.rcParams["figure.figsize"] = (14, 25)
plt.rcParams.update({'font.size': 40,"font.weight" : "bold",
'axes.titlesize':40,"axes.labelweight":"bold"})
shap.summary_plot(shap_values, X,max_display=maxdisplay,show=True)
# # Visualizations
date = "..."
seed = 222
n_try = 2
top20vs = '....'.format(date, seed, n_try)
selected_features = '.....'.format(date,seed,n_try)
xl = pd.ExcelFile(selected_features)
xlsx_names = xl.sheet_names
xgb_params = pd.ExcelFile(optimized_parms)
xgb_params_grpname = xgb_params.sheet_names
c_name_list=[]
for cluster_name in xlsx_names:
cluster_name2 = cluster_name.replace('-',"_")
c_name_list +=[cluster_name2]
fs_list = {}
for cluster_name1, cluster_name2 in zip(c_name_list,xlsx_names):
fs = cluster_name1+"_fs"
fs = pd.read_excel(selected_features,sheet_name = cluster_name2)
fs_list.update({cluster_name1:fs})
xgbparams_list = {}
for cluster_name1, cluster_name2 in zip(c_name_list,xgb_params_grpname):
fs = cluster_name1+"_fs"
fs = pd.read_excel(optimized_parms,sheet_name = cluster_name2)
xgbparams_list.update({cluster_name1:fs})
group_list = ['2D_mincl100_c0_photon', '2D_mincl100_c0_proton',
'2D_mincl100_c1_photon', '2D_mincl100_c1_proton']
group_list2 = [['2D_mincl100_c0_photon', '2D_mincl100_c0_proton'],
['2D_mincl100_c1_photon', '2D_mincl100_c1_proton']]
class SHAP_vis:
def __init__(self,cluster_name,vername):
self.cluster_name = cluster_name
self.vername = vername
def plot_shap(self):
shap_dict = {}
for cluster in self.cluster_name:
c_number = int(cluster.split("_")[2][1])
rt_mod = cluster.split("_")[3]
if rt_mod=="proton":
rt_mod2 = 1
else:
rt_mod2 = 0
df = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==c_number) & (df_clusters["proton"]==rt_mod2)]
var_selected = fs_list[cluster]["var_name"]
X = df[var_selected]
X = X.rename(columns={'oxaliplatin_concurrent':'Oxaliplatin Concurrent',
'CRT0ALC':'Baseline ALC',
"CRT0NLR": "Baseline NLR",
"CRT0WBC":"Baseline WBC",
"CRT0RBC":"Baseline RBC",
"Total_blood_volume_litres_Nadlerformula":'Total Blood Volume',
"TAXANE_concurrent": "TAXANE Concurrent Chemo",
"Tumor_length": "Tumor Length",
"CRT0monocyte_absolute_count_KuL":"Baseline Monocyte Count",
"CRT0eosinophil_absolute_count_KuL":"Baseline Eosinophil Count",
"meanheartdose":"Mean Heart Dose",
"CRT0Hb":"Baseline Hemoglobin",
"CRT0PLC":"Baseline Platelet"
})
y=df["G4RIL"]
dtrain = xgb.DMatrix(X, label = y)
params_list = xgbparams_list[cluster]['value']
'''get corresponding optimized variables'''
param_bo = {
'colsample_bylevel':params_list[0],
'colsample_bynode':params_list[1],
'colsample_bytree':params_list[2],
'eta' : params_list[3],
'gamma' : params_list[4],
'max_depth' : int(params_list[5]),
'min_child_weight': params_list[6],
'n_estimators' : int(params_list[7]),
'subsample' : params_list[8]}
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
if ratio_try>3:
#ratio = min(ratio_try,1.5)
ratio = 2
else:
ratio = 1.0
param_bo['scale_pos_weight'] = ratio
model2 = XGBClassifier(**param_bo)
model2.fit(X, y)
shap_values = shap.TreeExplainer(model2).shap_values(X)
vals= np.abs(shap_values).mean(0)
shap_values_pd = pd.DataFrame(shap_values)
neg_val = shap_values_pd[shap_values_pd<0].mean(0)
pos_val = shap_values_pd[shap_values_pd>=0].mean(0)
feature_importance = pd.DataFrame(list(zip(X.columns, vals,neg_val,pos_val)),
columns=['var_name','overall importance',
"negative effect", "positive effect"])
feature_importance = pd.DataFrame(list(zip(X.columns, vals)),
columns=['var_name','overall importance'])
feature_importance.sort_values(by=['overall importance'], ascending=False,inplace=True)
shap_dict.update({cluster:feature_importance})
print(cluster)
maxdisplay = 25
plt.rcParams["figure.figsize"] = (14, 25)
plt.rcParams.update({'font.size': 40,"font.weight" : "bold",
'axes.titlesize':40,"axes.labelweight":"bold"})
shap.summary_plot(shap_values, X, plot_type="bar",max_display=maxdisplay,show=False)
plt.savefig('output/SHAP_plots/SHAP_bar_{}_final_{}.png'.format(cluster,self.vername),
dpi=400,bbox_inches='tight',transparent = True)
plt.clf()
plt.rcParams["figure.figsize"] = (14, 25)
plt.rcParams.update({'font.size': 40,"font.weight" : "bold",
'axes.titlesize':40,"axes.labelweight":"bold"})
shap.summary_plot(shap_values, X,max_display=maxdisplay,show=False)
plt.savefig('output/SHAP_plots/SHAP_dot_{}_final_{}.png'.format(cluster,self.vername),
dpi=400,bbox_inches='tight',transparent = True)
plt.clf()
print("----------------------------------------------------")
#return (shap_dict, shap_values)
return (shap_dict)
def dec_plot(self):
plt.rcParams["figure.figsize"] = (200, 150)
for dua_cluster,cluster_rank in zip(self.cluster_name,['cluster1','cluster2']):
for i, cluster in zip(range(1,3),dua_cluster):
c_number = int(cluster.split("_")[2][1])
rt_mod = cluster.split("_")[3]
if rt_mod=="proton":
rt_mod2 = 1
else:
rt_mod2 = 0
df = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==c_number) & (df_clusters["proton"]==rt_mod2)]
var_selected = fs_list[cluster]["var_name"]
X = df[var_selected]
X = X.rename(columns={'oxaliplatin_concurrent':'Oxaliplatin Concurrent',
'CRT0ALC':'Baseline ALC',
"CRT0NLR": "Baseline NLR",
"CRT0WBC":"Baseline WBC",
"CRT0RBC":"Baseline RBC",
"Total_blood_volume_litres_Nadlerformula":'Total Blood Volume',
"TAXANE_concurrent": "TAXANE Concurrent Chemo",
"Tumor_length": "Tumor Length",
"CRT0monocyte_absolute_count_KuL":"Baseline Monocyte Count",
"CRT0eosinophil_absolute_count_KuL":"Baseline Eosinophil Count",
"meanheartdose":"Mean Heart Dose",
"CRT0Hb":"Baseline Hemoglobin",
"CRT0PLC":"Baseline Platelet"
})
y=df["G4RIL"]
params_list = xgbparams_list[cluster]['value']
'''get corresponding optimized variables'''
param_bo = {
'colsample_bylevel':params_list[0],
'colsample_bynode':params_list[1],
'colsample_bytree':params_list[2],
'eta' : params_list[3],
'gamma' : params_list[4],
'max_depth' : int(params_list[5]),
'min_child_weight': params_list[6],
'n_estimators' : int(params_list[7]),
'subsample' : params_list[8]}
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
if ratio_try>3:
#ratio = min(ratio_try,2)
ratio = 2
else:
ratio = 1.0
param_bo['scale_pos_weight'] = ratio
model = XGBClassifier(**param_bo)
model.fit(X, y)
explainer = shap.TreeExplainer(model)
expected_value = explainer.expected_value
shap_values2 = shap.TreeExplainer(model).shap_values(X)
fs_index = np.full((shap_values2.shape[0]), False, dtype=bool)
if cluster=='2D_mincl100_c0_proton':
pt_index=58
else:
pt_index = 5
fs_index[pt_index] = True
plt.rcParams.update({'font.size': 15,"font.weight" : "bold",
'axes.titlesize':15,"axes.labelweight":"bold"})
ax1 = plt.subplot(1,2,1)
stand = shap.decision_plot(expected_value, shap_values2, X, link='logit', return_objects=True ,show = False)
ax2 = plt.subplot(1,2,2, sharey = ax1)
shap.decision_plot(expected_value, shap_values2[fs_index], X[fs_index],feature_order=stand.feature_idx,
link='logit', highlight=0,show = False)
#ax1.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)
plt.tight_layout(pad=3, w_pad=0.9, h_pad=1)
plt.rcParams.update({'font.size': 5,"font.weight" : "bold",
'axes.titlesize':5,"axes.labelweight":"bold",
'axes.labelsize': 5,'axes.titlesize':5,
'legend.fontsize': 5, 'xtick.labelsize': 5, 'ytick.labelsize': 5,
'figure.subplot.wspace':0.6,'figure.subplot.hspace':0.6})
plt.savefig('output/SHAP_plots/SHAP_decision_{}_{}.png'.format(cluster,self.vername),
dpi=500,transparent = True,
bbox_inches='tight')
plt.clf()
shap_vis1 = SHAP_vis(group_list,"...")
shap_dict = shap_vis1.plot_shap()
SHAP_vis(group_list2, '....').dec_plot()
plt.rcParams["figure.figsize"] = (24, 41)
plt.rcParams.update({'font.size': 40,"font.weight" : "bold",
'axes.titlesize':40,"axes.labelweight":"bold"})
def hist(x, ax=None):
cm = plt.cm.get_cmap("seismic")
ax = ax or plt.gca()
_, bins, patches = ax.hist(x,color="r",bins=30)
bin_centers = 0.5*(bins[:-1]+bins[1:])
maxi = np.abs(bin_centers).max()
norm = plt.Normalize(-maxi,maxi)
for c, p in zip(bin_centers, patches):
plt.setp(p, "facecolor", cm(norm(c)))
class counterfact_plot:
def __init__(self,cluster_name):
self.cluster_name = cluster_name
def prob_switch(self):
prob_switch_list = []
prob_orig_list = []
df_prob = []
for cluster in self.cluster_name:
c_number = int(cluster.split("_")[2][1])
c_modality = cluster.split("_")[3]
if c_modality == "proton":
c_modality_index = 1
c_modality_index2 = 0
else:
c_modality_index = 0
c_modality_index2 = 1
'''get the subgroup data'''
df = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==c_number) & (df_clusters["proton"]==c_modality_index)]
var_selected = fs_list[cluster]["var_name"]
X = df[var_selected]
y=df["G4RIL"]
params_list = xgbparams_list[cluster]['value']
'''get corresponding optimized variables'''
param_bo = {
'colsample_bylevel':params_list[0],
'colsample_bynode':params_list[1],
'colsample_bytree':params_list[2],
'eta' : params_list[3],
'gamma' : params_list[4],
'max_depth' : int(params_list[5]),
'min_child_weight': params_list[6],
'n_estimators' : int(params_list[7]),
'subsample' : params_list[8]
}
ratio_try = float(np.sum(y == 0)) / np.sum(y == 1)
if ratio_try>3:
ratio = 2
else:
ratio = 1.0
param_bo['scale_pos_weight'] = ratio
model = XGBClassifier(**param_bo)
model.fit(X, y)
prob_orig = model.predict_proba(X)[:, 1]
'''counterfactual: switch modality'''
orig_mod= cluster.split("_")
if orig_mod[3]=='proton':
orig_mod[3] = 'photon'
else:
orig_mod[3] = "proton"
switch_mod = "_".join(orig_mod)
'''same patients but with different predictors'''
'''train the model use the patients with opposing modality in the same cluster'''
df2 = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==c_number) & (df_clusters["proton"]==c_modality_index2)]
var_switch = fs_list[switch_mod]["var_name"]
X_opp = df2[var_switch]
y_opp = df2["G4RIL"]
X_switch = df[var_switch]
params_list = xgbparams_list[switch_mod]['value']
'''get corresponding optimized variables'''
param_bo_counter = {
'colsample_bylevel':params_list[0],
'colsample_bynode':params_list[1],
'colsample_bytree':params_list[2],
'eta' : params_list[3],
'gamma' : params_list[4],
'max_depth' : int(params_list[5]),
'min_child_weight': params_list[6],
'n_estimators' : int(params_list[7]),
'subsample' : params_list[8]
}
ratio_try2 = float(np.sum(y_opp == 0)) / np.sum(y_opp == 1)
if ratio_try2>3:
ratio2 = 2
else:
ratio2 = 1.0
param_bo_counter['scale_pos_weight'] = ratio2
model2 = XGBClassifier(**param_bo_counter)
#model2 = XGBClassifier()
model2.fit(X_opp, y_opp)
prob_switch = model2.predict_proba(X_switch)[:, 1]
prob_change = (prob_switch - prob_orig)*100
prob_switch_list+=[prob_change]
prob_orig_list+=[prob_orig]
df["orig_risk"] = prob_orig*100
df["counter_risk"] = prob_switch*100
df["risk_change"] = prob_change
df_prob +=[df]
return(prob_switch_list,prob_orig_list,df_prob)
cal_prob = counterfact_plot(group_list)
prob_switch,prob_orig_list, df_riskcal = cal_prob.prob_switch()
def dvh_dep(n,i,dvh,vername):
df = df_clusters[(df_clusters["cluster_2D_minsamp1_mincluster100"]==n) & (df_clusters["proton"]==i)]
if i == 1:
rt_mod = "proton"
else:
rt_mod = 'photon'
cluster_number1 = "2D_mincl100_c"+str(n)+"_"+rt_mod
cluster_number2 = "cluster"+str(n)
var_selected = fs_list[cluster_number1]["var_name"]
X = df[var_selected]
X = X.rename(columns={'oxaliplatin_concurrent':'Oxaliplatin Concurrent',
'CRT0ALC':'Baseline ALC',
"CRT0NLR": "Baseline NLR",
"CRT0WBC":"Baseline WBC",
"CRT0RBC":"Baseline RBC",
"Total_blood_volume_litres_Nadlerformula":'Total Blood Volume',
"TAXANE_concurrent": "TAXANE Concurrent Chemo",
"Tumor_length": "Tumor Length",
"CRT0monocyte_absolute_count_KuL":"Baseline Monocyte Count",
"CRT0eosinophil_absolute_count_KuL":"Baseline Eosinophil Count",
"meanheartdose":"Mean Heart Dose",
"CRT0Hb":"Baseline Hemoglobin",
"CRT0PLC":"Baseline Platelet",
"meanspleendose":"Mean Spleen Dose",