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test.py
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from model import S_L_R
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import argparse
from sklearn.preprocessing import StandardScaler
import os
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument("--state",type=str,default=False,required=False)
parser.add_argument("--path_data", type = str, default = False,required=False)
parser.add_argument("--n_clus", type = int, default = False,required=False)
parser.add_argument("--alpha", type = float, default = False,required=False)
parser.add_argument("--option", type = str, default = False,required=False)
parser.add_argument("--threshold", type = float, default = False,required=False)
parser.add_argument("--min_iters", type = int, default = False,required=False)
parser.add_argument("--max_iters",type=int,default=False,required=False)
parser.add_argument("--max_loop",type=int,default=False,required=False)
args = parser.parse_args()
path=os.getcwd()+args.path_data
print("Data path: ",path)
df = pd.read_csv(path)
df.head()
n_v=len(df.columns[:-1])+1 # nunber of variables
df.dropna(inplace=True)
df.shape
X = df[df.columns[:-1]]
y = df[df.columns[-1]]
# Split the X, y data into training/validation/testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 ,random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=0) # 0.25 x 0.8 = 0.2
# #Standard data
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
X_val = sc_X.transform(X_val)
X_train = pd.DataFrame(data=X_train, columns=X.columns)
X_test = pd.DataFrame(data=X_test, columns=X.columns)
X_val = pd.DataFrame(data=X_val, columns=X.columns)
def run_train(X_train,X_test,X_val,y_train,y_test,y_val,n_c,n_v,alpha,option,max_loop,threshold=None):
slr=S_L_R()
weight_all,list_k,acc_s,f1_s,p_s,r_s,g_w,epsi,mA,ret=slr.train(X_train,X_val,X_val,y_train,y_test,y_val,n_c,n_v,alpha,option,max_loop,threshold)
if ret:
index= np.argmax(acc_s)
g_w=np.reshape(np.array(g_w),(len(g_w),1))
w_x=[]
for i in range(0,n_c):
w_x.append(np.dot(mA[i,0:n_v],np.array(weight_all[index])).tolist())
c=w_x-epsi[index]
acc_g = slr.accuracy(slr.predict(weight_all[index],X_val),y_val)
f1_g = slr.f1_score(slr.predict(g_w,X_val),y_val)
p_g = slr.precision_score(slr.predict(g_w,X_val),y_val)
r_g = slr.recall_score(slr.predict(g_w,X_val),y_val)
print("beta : ",list_k[index])
print("accuracy of SLR on testset: ",slr.accuracy(slr.predict(weight_all[index],X_test),y_test))
print("accuracy of SLR on valset: ",slr.accuracy(slr.predict(weight_all[index],X_val),y_val))
print("accuracy of GLR on valset: ",slr.accuracy(slr.predict(g_w,X_val),y_val))
print("accuracy of GLR on testset: ",slr.accuracy(slr.predict(g_w,X_test),y_test))
print("f1_score of SLR on testset: ",slr.f1_score(slr.predict(weight_all[index],X_test),y_test))
print("f1_score of SLR on valset: ",slr.f1_score(slr.predict(weight_all[index],X_val),y_val))
print("f1_score of GLR on valset: ",slr.f1_score(slr.predict(g_w,X_val),y_val))
print("f1_score of GLR on testset: ",slr.f1_score(slr.predict(g_w,X_test),y_test))
print("precision of SLR on testset: ",slr.precision_score(slr.predict(weight_all[index],X_test),y_test))
print("precision of SLR on valset: ",slr.precision_score(slr.predict(weight_all[index],X_val),y_val))
print("precision of GLR on valset: ",slr.precision_score(slr.predict(g_w,X_val),y_val))
print("precision of GLR on testset: ",slr.precision_score(slr.predict(g_w,X_test),y_test))
print("recall of SLR on testset: ",slr.recall_score(slr.predict(weight_all[index],X_test),y_test))
print("recall of SLR on valset: ",slr.recall_score(slr.predict(weight_all[index],X_val),y_val))
print("recall of GLR on valset: ",slr.recall_score(slr.predict(g_w,X_val),y_val))
print("recall of GLR on testset: ",slr.recall_score(slr.predict(g_w,X_test),y_test))
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(list_k, acc_g*np.ones(len(acc_s)), label = "ACC_GLR",linewidth=4,ls="--")
plt.plot(list_k, acc_s, label = "ACC_SLR",linewidth=4)
plt.xlabel("Beta")
plt.ylabel("Accuracy")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(list_k, f1_g*np.ones(len(f1_s)), label = "F1Score_GLR",linewidth=4,ls="--")
plt.plot(list_k, f1_s, label = "F1Score_SLR",linewidth=4)
plt.xlabel("Beta")
plt.ylabel("F1-score")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(list_k, p_g*np.ones(len(p_s)), label = "Pre_GLR",linewidth=4,ls="--")
plt.plot(list_k, p_s, label = "Pre_SLR",linewidth=4)
plt.xlabel("Beta")
plt.ylabel("Precision-score")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(list_k, r_g*np.ones(len(r_s)), label = "Re_GLR",linewidth=4,ls="--")
plt.plot(list_k, r_s, label = "Re_SLR",linewidth=4)
plt.xlabel("Beta")
plt.ylabel("Recall-score")
plt.legend()
plt.grid()
plt.show()
def run_eval(X_train,X_test,X_val,y_train,y_test,y_val,n_v,alpha,min_iters,max_iters):
slr=S_L_R()
acc_s,f1_s,p_s,r_s,acc_g,f1_g,p_g,r_g,num,time_run=slr.eval(X_train,X_test,X_val,y_train,y_test,y_val,n_v,alpha,"Kmean",min_iters,max_iters)
print(num)
X_val=X_val.drop('bias',axis=1)
acc_s_q,f1_s_q,p_s_q,r_s_q,_,_,_,_,_,time_run_q=slr.eval(X_train,X_test,X_val,y_train,y_test,y_val,n_v,alpha,"Quantile",min_iters,num)
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(num, acc_g*np.ones(len(acc_s)), label = "ACC_GLR",linewidth=4)
plt.plot(num, acc_s, label = "ACC_SLR_kmean",linewidth=4,ls="--")
plt.plot(num, acc_s_q, label = "ACC_SLR_quantile",linewidth=4,ls="-.")
plt.xlabel("num_cluters(level_quantile)")
plt.ylabel("Accuracy")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(num, f1_g*np.ones(len(f1_s)), label = "F1Score_GLR",linewidth=4)
plt.plot(num, f1_s, label = "F1Score_SLR_kmean",linewidth=4,ls="--")
plt.plot(num, f1_s_q, label = "F1Score_SLR_quantile",linewidth=4,ls="-.")
plt.xlabel("num_cluters(level_quantile)")
plt.ylabel("F1-score")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(num, p_g*np.ones(len(p_s)), label = "Pre_GLR",linewidth=4)
plt.plot(num, p_s, label = "Pre_SLR_kmean",linewidth=4,ls="--")
plt.plot(num, p_s_q, label = "Pre_SLR_quantile",linewidth=4,ls="-.")
plt.xlabel("num_cluters(level_quantile)")
plt.ylabel("Precision-score")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(num, r_g*np.ones(len(r_s)), label = "Re_GLR",linewidth=4)
plt.plot(num, r_s, label = "Re_SLR_kmean",linewidth=4,ls="--")
plt.plot(num, r_s_q, label = "Re_SLR_quantile",linewidth=4,ls="-.")
plt.xlabel("num_cluters(level_quantile)")
plt.ylabel("Recall-score")
plt.legend()
plt.grid()
plt.show()
plt.figure(figsize=(10,5))
plt.rcParams.update({'font.size': 16})
plt.plot(num, time_run, label = "Time_training_kmean",linewidth=4,ls="--")
plt.plot(num, time_run_q, label = "Time_training_quantile",linewidth=4,ls="-.")
plt.xlabel("num_cluters(level_quantile)")
plt.ylabel("Time training")
plt.legend()
plt.grid()
plt.show()
if __name__=='__main__':
if args.state == "train":
run_train(X_train,X_test,X_val,y_train,y_test,y_val,args.n_clus,n_v,args.alpha,args.option,args.max_loop,args.threshold)
elif args.state == "eval":
run_eval(X_train,X_test,X_val,y_train,y_test,y_val,n_v,args.alpha,args.min_iters,args.max_iters)