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Q2_Eval.py
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Q2_Eval.py
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import pickle
from tty import CFLAG
import matplotlib.pyplot as plt
from evaluation.WachterEtAl import wachter_recourse
from evaluation.metrics import yNN_timeseries
from models.CNN_TSNet import train
import seaborn as sns
import os
import pandas as pd
import numpy as np
import torch
from models.ResNet import ResNetBaseline
from tslearn.datasets import UCR_UEA_datasets
from deap import creator, base, algorithms, tools
from deap.benchmarks.tools import hypervolume, diversity, convergence
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMin,window=0)
def calculate_total_ynn():
data=[]
method=[]
value=[]
for file in ['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']:
if not file.endswith('csv') and not file.endswith('png'):
pop=[]
y=[]
cf=[]
for a in os.listdir(f'./Results/mutate_both/{file}'):
#print(a)
if a.startswith('Counter'):
cf=pickle.load(open(f'./Results/mutate_both/{file}/{a}','rb'))
y.append(np.argmax(cf[0].output))
#print(np.array(cf[0][0]).shape)
pop.append(np.array(cf[0]))
shape= np.array(cf).shape
#print(shape)
if shape[-2]==1:
train_x,train_y,_,_=UCR_UEA_datasets().load_dataset(file)
else:
train_x,train_y,_,_=UCR_UEA_datasets().load_dataset(file)
pass
train_x=np.nan_to_num(train_x, copy=True, nan=0.0)
#X_test=np.nan_to_num(test_x, copy=True, nan=0.0)
model = ResNetBaseline(in_channels=shape[-2], num_pred_classes=len(np.unique(train_y)))
model.load_state_dict(torch.load(f'./models/{file}/ResNet'))
model.eval()
wachter=pickle.load(open(f'./Results/Benchmarking/{file}/Wachter_cf.pkl','rb'))
n=[]
for a in wachter:
if not a is None:
n.append(a)
else:
print('None')
wachter=n
if len(wachter) != 0:
input_ = torch.from_numpy(np.array(wachter)).float().reshape(-1,shape[-2],shape[-1])
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
y_wachter = np.argmax(output,axis=1)
data.append(file)
if len(np.array(wachter).shape)==2:
cfg=np.array(wachter).reshape(1,shape[-2],shape[-1])
value.append(yNN_timeseries(wachter, model,train_x,5,labels=y_wachter))
else:
value.append(0)
method.append('wachter')
print('stert cfg')
if shape[-2]==1:
cfg=pickle.load(open(f'./Results/Benchmarking/{file}/cfg_cf.pkl','rb'))
n=[]
for a in cfg:
if not a is None:
n.append(a)
else:
print('None')
cfg=n
if len(cfg) != 0:
input_ = torch.from_numpy(np.array(cfg,dtype=np.float32)).float().reshape(-1,shape[-2],shape[-1])
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
cfg_y = np.argmax(output,axis=1)
#method.append('cfg')
if len(np.array(cfg).shape)==2:
cfg=np.array(cfg).reshape(1,shape[-2],shape[-1])
value.append(yNN_timeseries(cfg, model,train_x,5,labels=cfg_y))
else:
value.append(0)
method.append('cfg')
ib=pickle.load(open(f'./Results/Benchmarking/{file}/ib_cf.pkl','rb'))
n=[]
for a in ib:
if not a is None:
n.append(a)
ib=n
if len(ib) != 0:
input_ = torch.from_numpy(np.array(ib)).float().reshape(-1,shape[-2],shape[-1])
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
ib_y = np.argmax(output,axis=1)
#method.append('ib')
if len(np.array(ib).shape)==2:
ib=np.array(ib).reshape(1,shape[-2],shape[-1])
else:
ib=np.array(ib).reshape(-1,shape[-2],shape[-1])
value.append(yNN_timeseries(ib, model,train_x,5,labels=ib_y))
#print(len(cfg))
else:
value.append(0)
data.append(file)
data.append(file)
method.append('ib')
value.append(0)
data.append(file)
method.append('ates')
else:
ates=pickle.load(open(f'./Results/Benchmarking/{file}/ates_cf.pkl','rb'))
n=[]
for a in ates:
if not a is None:
n.append(a)
ates=n
if len(ates) != 0:
input_ = torch.from_numpy(np.array(ates)).float().reshape(-1,shape[-2],shape[-1])
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
ates_y = np.argmax(output,axis=1)
#method.append('ates')
if len(np.array(ates).shape)==2:
ates=np.array(ates).reshape(1,shape[-2],shape[-1])
else:
ates=np.array(ates).reshape(-1,shape[-2],shape[-1])
value.append(yNN_timeseries(ates, model,train_x,5,labels=ates_y))
#print(len(cfg))
else:
value.append(0)
data.append(file)
method.append('ates')
value.append(0)
value.append(0)
data.append(file)
data.append(file)
method.append('ib')
method.append('cfg')
#PUT ATES IN HERE
pass
data.append(file)
method.append('TSEvo')
value.append(yNN_timeseries(pop, model,train_x,5,labels=y))
results=pd.DataFrame([])
print(len(data))
print(len(method))
print(len(value))
results['Dataset']=data
results['Method']=method
results['yNN']=value
results.to_csv('./Results/Benchmarking/yNN_base_UCR.csv')
def yNN_to_latex():
data= pd.read_csv('./Results/Benchmarking/yNN_base_UCR.csv')
string=''
for dataset in ['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']:
d=data[data['Dataset']==dataset]
print(type(d[d['Method']=='TSEvo']['yNN'].values[0]))
evo= round(float(d[d['Method']=='TSEvo']['yNN'].values[0]),4)
w=round(float(d[d['Method']=='wachter']['yNN'].values[0]),4)
print(dataset)
ins=round(float(d[d['Method']=='ib']['yNN'].values[0]),4)
grad=round(float(d[d['Method']=='cfg']['yNN'].values[0]),4)
ates=round(float(d[d['Method']=='ates']['yNN'].values[0]),4)
string+= f' {dataset}& ${evo}$ & ${w}$ & ${ins}$ & ${grad}$ & ${ates}$ \\\\ \hline'
print(string)
def calculate_total_dis():
data=[]
value_std=[]
method=[]
value=[]
data2=[]
value_std2=[]
method2=[]
value2=[]
for file in ['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']: #os.listdir('./Results/Benchmarking'):
if not file.endswith('csv') and not file.endswith('png'):
d1= pickle.load(open(f'./Results/Benchmarking/{file}/Dis1.pkl','rb'))
d2= pickle.load(open(f'./Results/Benchmarking/{file}/Dis2.pkl','rb'))
for c in d1.keys():
value.append(np.mean(d1[c]))
value_std.append(np.std(d1[c]))
data.append(file)
method.append(c)
value2.append(np.mean(d2[c]))
value_std2.append(np.std(d2[c]))
data2.append(file)
method2.append(c)
results=pd.DataFrame([])
results['Dataset']=data
results['Method']=method
results['Sparsity']=value
results['Sparsity_STD']=value_std
results['Proximity']=value2
results['Proximity_STD']=value_std2
results.to_csv('./Results/Benchmarking/dis_base.csv')
def dis_to_latex():
rem=pd.read_csv('./Results/Benchmarking/dis_base.csv')
for d in np.unique(rem['Dataset']):
line=d
data=rem[rem['Dataset']==d]
line=line +'&$'+str(data[data['Method']=='our']['Sparsity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='our']['Sparsity_STD'].round(4).values[0])+'$&$'+str(data[data['Method']=='our']['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='our']['Proximity_STD'].round(4).values[0])+'$&'
if not len(data[data['Method']=='Wachter']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='Wachter']['Sparsity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='Wachter']['Sparsity_STD'].round(4).values[0])+'$&$'+str(data[data['Method']=='Wachter']['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='Wachter']['Proximity_STD'].round(4).values[0])+'$&'
else:
line=line+'&'+'&'
if not len(data[data['Method']=='ib']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='ib']['Sparsity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='ib']['Sparsity_STD'].round(4).values[0])+'$&$'+str(data[data['Method']=='ib']['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='ib']['Proximity_STD'].round(4).values[0])+'$&'
else:
line=line+'&'+'&'
if not len(data[data['Method']=='cfg']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='cfg']['Sparsity'].round(4).values[0])+' \pm ' +str(data[data['Method']=='cfg']['Sparsity_STD'].round(4).values[0])+'$&$'+str(data[data['Method']=='cfg']['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='cfg']['Proximity_STD'].round(4).values[0])+'$&'
else:
line=line+'&'+'&'
if not len(data[data['Method']=='Ates']['Sparsity'].values)==0:
line=line +'$'+str(data[data['Method']=='Ates']['Sparsity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='Ates']['Sparsity_STD'].round(4).values[0])+'$&$'+str(data[data['Method']=='Ates']['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Method']=='Ates']['Proximity_STD'].round(4).values[0])+'$ \\'+'\\'+' \hline'
else:
line=line+'&'+'\\'+'\\'+' \hline'
print(line)
def dis_to_latex_2_Tables():
rem=pd.read_csv('./Results/Benchmarking/dis_base.csv')
print('Distance')
for d in np.unique(rem['Dataset']):
line=d
data=rem[rem['Dataset']==d]
line=line +'&$'+str(data[data['Method']=='our']['Proximity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='our']['Proximity_STD'].round(2).values[0])+'$&'
if not len(data[data['Method']=='Wachter']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='Wachter']['Proximity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='Wachter']['Proximity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='ib']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='ib']['Proximity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='ib']['Proximity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='cfg']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='cfg']['Proximity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='cfg']['Proximity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='Ates']['Sparsity'].values)==0:
line=line +'$'+str(data[data['Method']=='Ates']['Proximity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='Ates']['Proximity_STD'].round(2).values[0])+'$ \\'+'\\'+' \hline'
else:
line=line+'\\'+'\\'+' \hline'
print(line)
print('Sparsity')
for d in np.unique(rem['Dataset']):
line=d
data=rem[rem['Dataset']==d]
line=line +'&$'+str(data[data['Method']=='our']['Sparsity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='our']['Sparsity_STD'].round(2).values[0])+'$&'
if not len(data[data['Method']=='Wachter']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='Wachter']['Sparsity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='Wachter']['Sparsity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='ib']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='ib']['Sparsity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='ib']['Sparsity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='cfg']['Sparsity'].values)==0:
line=line +'$' +str(data[data['Method']=='cfg']['Sparsity'].round(2).values[0])+' \pm ' +str(data[data['Method']=='cfg']['Sparsity_STD'].round(2).values[0])+'$&'
else:
line=line+'&'
if not len(data[data['Method']=='Ates']['Sparsity'].values)==0:
line=line +'$'+str(data[data['Method']=='Ates']['Sparsity'].round(2).values[0]) +' \pm ' +str(data[data['Method']=='Ates']['Sparsity_STD'].round(2).values[0])+'$ \\'+'\\'+' \hline'
else:
line=line+'\\'+'\\'+' \hline'
print(line)
def dis_to_latex_t():
rem=pd.read_csv('./Results/Benchmarking/dis_base.csv')
for d in np.unique(rem['Method']):
line2=d
line=d
data=rem[rem['Method']==d]
for name in sorted(np.unique(rem['Dataset'])):
if not len(data[data['Dataset']==name]['Sparsity'].values)==0:
line=line+'&$'+ str(data[data['Dataset']==name]['Sparsity'].round(4).values[0]) +' \pm ' +str(data[data['Dataset']==name]['Sparsity_STD'].round(4).values[0]) +'$'
line2=line2+'&$'+ str(data[data['Dataset']==name]['Proximity'].round(4).values[0]) +' \pm ' +str(data[data['Dataset']==name]['Proximity_STD'].round(4).values[0]) +'$'
else:
line=line+'&'
line2=line2+'&'
line=line+'&'+'\\'+'\\'+' \hline'
line2=line2+'&'+'\\'+'\\'+' \hline'
print(line)
print(line2)
def make_table():
#TODO still to do
dataFrame=pd.DataFrame([])
for data in ['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']:
if not data.endswith('.png'):
bench=pd.read_csv(f'./Results/Benchmarking/{data}/BenchmarkMetrics.csv')
line=data
for i in range(0,4):
line = line +'& $' +str(bench['ynn_timeseries'][i]) + ' \pm ' +str(bench['ynn_timeseries_std'][i])+'$ & $'+str(bench['red'][i]) + ' \pm ' +str(bench['red_std'][i])+ '$&$'+str(bench['sparsity'][i]) + ' \pm ' +str(bench['sparsity_std'][i])+'$&$'+str(bench['dis'][i]) + ' \pm ' +str(bench['dis_std'][i])+'$ & $'+str(bench['validity'][i])
line=line+'\\'
print(line)
return
def make_table_split():
#TODO still to do
dataFrame=pd.DataFrame([])
print('yNN')
for data in ['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']:
if not data.endswith('.png'):
bench=pd.read_csv(f'./Results/Benchmarking/{data}/BenchmarkMetrics.csv')
line_a=data
for i in range(0,4):
line_a = line_a +'& $' +str(bench['ynn_timeseries'][i].round(4)) + ' \pm ' +str(bench['ynn_timeseries_std'][i].round(4))+'$'
line_a=line_a+'\\'+'\\'
print(line_a)
def build_figure(k=0):
#TODO check if item is none firsr
j=1
#k=0
for dataset in ['GunPoint','CBF','Coffee','ECG5000','ElectricDevices','FordA']:#os.listdir('./Benchmarking'):#'CBF','Coffee','ECG5000','ElectricDevices','FordA',
mi=5
ma=-5
X_train,train_y,X_test,test_y=UCR_UEA_datasets().load_dataset(dataset)
train_x=X_train.reshape(-1,X_train.shape[-1],X_train.shape[-2])
test_x=X_test.reshape(-1,X_train.shape[-1],X_train.shape[-2])
#test = pd.read_csv(os.path.abspath(f'/media/jacqueline/Data/UCRArchive_2018/{dataset}/{dataset}_TEST.tsv'), sep='\t', header=None)
#test_y, test_x = test.loc[:, 0].apply(lambda x: x-1).to_numpy(), test.loc[:, 1:].to_numpy()
original=test_x[k]
model = ResNetBaseline(in_channels=1, num_pred_classes=len(np.unique(test_y)))
model.load_state_dict(torch.load(f'./models/{dataset}/ResNet'))
model.eval()
l=original.shape[-1]
enc1=pickle.load(open(f'./models/{dataset}/OneHotEncoder.pkl','rb'))
test_y=enc1.transform(test_y.reshape(-1,1))
label=np.argmax( test_y[k])
#input_ = torch.from_numpy(original).float().reshape(1,-1,l)
#output = torch.nn.functional.softmax(model(input_)).detach().numpy()
#label = output.argmax()
test_y=np.argmax(test_y,axis=1)
highlight_differences = True
'''Get Data'''
pop=pickle.load(open(f'./Results/mutate_both/{dataset}/Counterfactuals_{k}.pkl','rb'))
if mi>np.min(np.array(pop).reshape(-1),axis=0):
mi=np.min(np.array(pop).reshape(-1),axis=0)
if ma<np.max(np.array(pop).reshape(-1),axis=0):
ma=np.max(np.array(pop).reshape(-1),axis=0)
try:
wa = pickle.load(open(f'./Results/Benchmarking/{dataset}/Wachter_cf.pkl','rb'))[0]
if wa is None:
#wa=np.zeros_like(original)
wa=None
except:
print('Wachter is None')
wa=np.zeros_like(original)
if wa is not None:
if mi>np.min(wa.reshape(-1),axis=0):
mi=np.min(wa.reshape(-1),axis=0)
if ma<np.max(wa.reshape(-1),axis=0):
ma=np.max(wa.reshape(-1),axis=0)
try:
cfg= pickle.load(open(f'./Results/Benchmarking/{dataset}/cfg_cf.pkl','rb'))[0]
if cfg is None:
cfg=None
#cfg=np.zeros_like(original)
print('CFG is None')
except:
cfg=np.zeros_like(original)
if cfg is not None:
if mi>np.min(cfg.reshape(-1),axis=0):
mi=np.min(cfg.reshape(-1))
if ma<np.max(cfg.reshape(-1),axis=0):
ma=np.max(cfg.reshape(-1))
try:
ib = pickle.load(open(f'./Results/Benchmarking/{dataset}/ib_cf.pkl','rb'))[0]
if ib is None:
ib=None
#ib=np.zeros_like(original)
except:
print('IB is None')
ib=np.zeros_like(original)
if ib is not None:
if mi> np.min(ib.reshape(-1),axis=0):
mi=np.min(ib.reshape(-1),axis=0)
if ma<np.max(ib.reshape(-1),axis=0):
ma=np.max(ib.reshape(-1),axis=0)
#print('Test_Y ',test_y)
#print('Label',label)
#print(test_y[test_y != label])
data= test_x[np.where(test_y != label)]
timeline_max=[]
y=test_y[test_y != label]
mi_max=5
i= 0
i_max=0
for timeline in data:
mi = np.sum(np.abs(timeline.reshape(-1)- original.reshape(-1) ))/150
if mi <mi_max:
mi_max=mi
timeline_max= timeline
i_max=i
i = i+1
if mi> np.min(timeline_max.reshape(-1),axis=0):
mi=np.min(timeline_max.reshape(-1),axis=0)
if ma<np.max(timeline_max.reshape(-1),axis=0):
ma=np.max(timeline_max.reshape(-1),axis=0)
print('MI',mi)
print(np.min(original.reshape(-1),axis=0))
if mi> np.min(original.reshape(-1),axis=0):
mi=np.min(original.reshape(-1),axis=0)
if ma<np.max(original.reshape(-1),axis=0):
ma=np.max(original.reshape(-1),axis=0)
#print(mi)
#print(ma)
mi =mi -0.2
ma=ma+0.2
ax011 = plt.subplot(6,1,1,)
ax012 = ax011.twinx()
ax011.set_ylim(mi,ma)
ax012.set_ylim(mi,ma)
sns.heatmap(np.zeros_like(original.reshape(1,-1)), fmt="g", cmap='viridis', cbar=False, ax=ax011, yticklabels=False)
p=sns.lineplot(x=range(l), y=original.flatten(), label= str(label), color='white',ax=ax012,legend=False)
p.set_ylabel("Original")
plt.legend(loc='upper right')
ax021 = plt.subplot(6,1,3)
ax022 = ax021.twinx()
ax021.set_ylim(mi,ma)
ax022.set_ylim(mi,ma)
if highlight_differences:
sal_01= np.abs(original.reshape(-1)-np.array(pop)[0][0].reshape(-1)).reshape(1,-1)
sns.heatmap(sal_01, fmt="g", cmap='viridis', cbar=False, ax=ax021, yticklabels=False)
p=sns.lineplot(x=range(l), y=np.array(pop)[0][0].flatten(), label= str(np.argmax(pop[0].output)), color='white', ax=ax022,legend=False)
ax022.set(xlabel=None)
p.set_ylabel("TSEvo")
#ax021.set(xlabel='TSEvo')
plt.legend(loc='upper right')
if wa is not None:
input_ = torch.from_numpy(wa).float().reshape(1,-1,l)
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
idx = output.argmax()
else:
idx= 'No CF!'
ax031 = plt.subplot(6,1,4)
ax032 = ax031.twinx()
ax031.set_ylim(mi,ma)
ax032.set_ylim(mi,ma)
if highlight_differences:
if wa is not None:
sal_02= np.abs(original.reshape(-1)-np.array(wa).reshape(-1)).reshape(1,-1)
else:
sal_02=np.zeros_like(original.reshape(1,-1))
sns.heatmap(sal_02, fmt="g", cmap='viridis', cbar=False, ax=ax031, yticklabels=False)
if wa is not None:
wa=wa.flatten()
p=sns.lineplot(x=range(l), y=wa, label=str(idx), color='white', ax=ax032,legend=False)
p.set_ylabel("W-CF")
plt.legend(loc='upper right')
if cfg is not None:
input_ = torch.from_numpy(cfg).float().reshape(1,-1,l)
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
idx = output.argmax()
else:
idx= 'No CF!'
ax041 = plt.subplot(6,1,5)
ax042 = ax041.twinx()
ax041.set_ylim(mi,ma)
ax042.set_ylim(mi,ma)
if highlight_differences:
if cfg is not None:
sal_02= np.abs(original.reshape(-1)-np.array(cfg).reshape(-1)).reshape(1,-1)
else:
sal_02=np.zeros_like(original.reshape(1,-1))
sns.heatmap(sal_02, fmt="g", cmap='viridis', cbar=False, ax=ax041, yticklabels=False)
if cfg is not None:
cfg=cfg.flatten()
p=sns.lineplot(x=range(l), y=cfg, label= str(idx), color='white', ax=ax042,legend=False)
p.set_ylabel("Nun-Cf")
plt.legend(loc='upper right')
if ib is not None:
input_ = torch.from_numpy(ib).float().reshape(1,-1,l)
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
idx = output.argmax()
else:
idx= 'No CF!'
ax051 = plt.subplot(6,1,6)
ax052 = ax051.twinx()
ax051.set_ylim(mi,ma)
ax052.set_ylim(mi,ma)
if highlight_differences:
if ib is not None:
sal_02= np.abs(original.reshape(-1)-np.array(ib).reshape(-1)).reshape(1,-1)
else:
sal_02=np.zeros_like(original.reshape(1,-1))
sns.heatmap(sal_02, fmt="g", cmap='viridis', cbar=False, ax=ax051, yticklabels=False)
if ib is not None:
ib=ib.flatten()
p=sns.lineplot(x=range(l), y=ib, label=str(idx), color='white', ax=ax052,legend=False)
p.set_ylabel("NUN-Grad")
plt.legend(loc='upper right')
ax061 = plt.subplot(6,1,2)
ax062 = ax061.twinx()
ax061.set_ylim(mi,ma)
ax062.set_ylim(mi,ma)
if highlight_differences:
sal_02= np.abs(original.reshape(-1)-np.array(timeline_max).reshape(-1)).reshape(1,-1)
sns.heatmap(sal_02, fmt="g", cmap='viridis', cbar=False, ax=ax061, yticklabels=False)
p=sns.lineplot(x=range(l), y=timeline_max.flatten(), label=str(y[i_max]), color='white', ax=ax062,legend=False)
plt.legend(loc='upper right')
p.set_ylabel("Sample")
print(mi)
print(ma)
ax011.set(xlabel=None)
ax012.set(xlabel=None)
ax021.set(xlabel=None)
ax022.set(xlabel=None)
ax031.set(xlabel=None)
ax032.set(xlabel=None)
ax041.set(xlabel=None)
ax042.set(xlabel=None)
ax051.set(xlabel=None)
ax052.set(xlabel=None)
ax011.set(xticklabels=[])
ax012.set(xticklabels=[])
ax021.set(xticklabels=[])
ax022.set(xticklabels=[])
ax031.set(xticklabels=[])
ax032.set(xticklabels=[])
ax041.set(xticklabels=[])
ax042.set(xticklabels=[])
ax061.set(xticklabels=[])
ax062.set(xticklabels=[])
plt.tight_layout()
plt.savefig(f'./Results/Benchmarking/{dataset}_summary_{k}.png',transparent=True)
plt.close()
def plot_dis(k):
'''#TODO'''
print('Start')
df_plot=pd.DataFrame([])
d=[]
dis=[]
meth=[]
df_plot2=pd.DataFrame([])
d2=[]
dis2=[]
meth2=[]
for dataset in ['CBF','Coffee','ECG5000','ElectricDevices','FordA','GunPoint']:
df= pickle.load(open(f'./Results/Benchmarking/{dataset}/Dis1.pkl','rb'))
df2= pickle.load(open(f'./Results/Benchmarking/{dataset}/Dis2.pkl','rb'))
print(dataset)
print(df)
for a in df.keys():
print(a)
dis.extend(df[a])
meth.extend(np.repeat(a, len(df[a])))
#print(len(dis))
#print(len(meth))
d.extend( np.full(shape=len(df[a]),fill_value=dataset))
for a in df2.keys():
print(a)
dis2.extend(df2[a])
meth2.extend(np.repeat(a, len(df2[a])))
#print(len(dis))
#print(len(meth))
d2.extend( np.full(shape=len(df2[a]),fill_value=dataset))
#print(len(d))
#print(len(dis))
#print(len(meth))
#print(d)
#print('dataframe')
df_plot2['proximity']=dis2
df_plot2['dataset']=d2
df_plot2['method']=meth2
df_plot['sparsity']=dis
df_plot['dataset']=d
df_plot['method']=meth
print(np.unique(df_plot2['dataset']))
df_plot2['method']=df_plot2['method'].str.replace('our','TsEvo')
df_plot2['method']=df_plot2['method'].str.replace('wachter','W-CF')
df_plot2['method']=df_plot2['method'].str.replace('ib','NUN-CF')
df_plot2['method']=df_plot2['method'].str.replace('cfg','NUN-Grad')
df_plot['method']=df_plot['method'].str.replace('our','TsEvo')
df_plot['method']=df_plot['method'].str.replace('wachter','W-CF')
df_plot['method']=df_plot['method'].str.replace('ib','NUN-CF')
df_plot['method']=df_plot['method'].str.replace('cfg','NUN-Grad')
#print('finished')
#sns.boxplot(x="dis", y="meth", hue="dataset", data=df_plot, palette="Pastel1")
#sns.violinplot(x="dis", y="meth", hue="dataset", data=df_plot, palette="Pastel1")
#sns.violinplot(x=dis, y=meth, hue=dataset, data=df_plot, palette="Pastel1")
#print('C')
#plt.savefig(f'./Results/Benchmarking/D_{k}.png',transparent=True)
f, axes = plt.subplots(1, 2)
sns.boxplot(x="method", y="sparsity", hue="dataset", data=df_plot, palette="Pastel1",ax=axes[0])
sns.boxplot(x="method", y="proximity", hue="dataset", data=df_plot2, palette="Pastel1",ax=axes[1])
axes[0].get_legend().remove()
axes[1].get_legend().remove()
handles, labels = axes[0].get_legend_handles_labels()
f.legend(handles, labels, loc='upper right', ncol=5, bbox_to_anchor=(.75, 0.98))
plt.tight_layout()
#plt.savefig('myimage.svg', format='svg', dpi=1200)
plt.show()
#plt.close()
#plt.show()
#plt.close()
#def calculate_validity():
# data = []
# wachter=[]
# ib=[]
# cfg=[]
# ates=[]
# for file in os.listdir('./Results/Benchmarking'):
# if not file.endswith('.csv') and not file.endswith('.png') and file != '.':
# if 'ates_cf.pkl'in os.listdir(f'./Results/Benchmarking/{file}'):
# w=pickle.load(open(f'./Results/Benchmarking/{file}/ates_cf.pkl','rb'))
# da= []
# for a in w:
# if a is None:
# pass
# else:
# da.append(a)
# print(a)
# ates.append(len(w))
# if 'ib_cf.pkl'in os.listdir(f'./Results/Benchmarking/{file}'):
# w=pickle.load(open(f'./Results/Benchmarking/{file}/ib_cf.pkl','rb'))
# da= []
# for a in w:
# if a is None:
# pass
# else:
# da.append(a)
# #print(da)
# ib.append(len(da))
# if 'cfg_cf.pkl'in os.listdir(f'./Results/Benchmarking/{file}'):
# w=pickle.load(open(f'./Results/Benchmarking/{file}/cfg_cf.pkl','rb'))
# da= []
# for a in w:
# if a is None:
# pass
# else:
# da.append(a)
# #print(da)
# cfg.append(len(da))
# if 'Wachter_cf.pkl'in os.listdir(f'./Results/Benchmarking/{file}'):
# w=pickle.load(open(f'./Results/Benchmarking/{file}/Wachter_cf.pkl','rb'))
# da= []
# for a in w:
# if a is None:
# pass
# else:
# da.append(a)
# #print(da)
# wachter.append(len(da))
# print(ates)
# print(ib)
# print(cfg)
# print(wachter)
#print(np.mean(data,axis=1))
#print(np.std(data,axis=1))
def validity():
counter=0
sum_wachter=0
sum_ates=0
sum_cfg=0
sum_out=0
sum_ng=0
string=''
for a in['GunPoint','Coffee','CBF','ElectricDevices','ECG5000','FordA','CharacterTrajectories','Heartbeat', 'UWaveGestureLibrary','NATOPS']:#,'Heartbeat','PenDigits', 'UWaveGestureLibrary','NATOPS']: #os.listdir('./Results/Benchmarking'):
if os.path.isdir(f'./Results/Benchmarking/{a}'):
print(a)
counter += counter
#print('Counter', counter)
data = pd.read_csv(f'./Results/Benchmarking/{a}/BenchmarkMetrics.csv')
#print(data[data['method']== 'TS_Evo']['validity'].values[0])
sum_out=data[data['method']== 'TS_Evo']['validity'].values[0]
sum_wachter=data[data['method']== 'Wachter']['validity'].values[0]
try:
sum_cfg=data[data['method']== 'NG_DBN']['validity'].values[0]
sum_ng=data[data['method']== 'NG_GradCam']['validity'].values[0]
sum_ates='-'
except:
not_valid=0
X_train,train_y,X_test,test_y=UCR_UEA_datasets().load_dataset(a)
train_x=X_train.reshape(-1,X_train.shape[-1],X_train.shape[-2])
test_x=X_test.reshape(-1,X_train.shape[-1],X_train.shape[-2])
enc1=pickle.load(open(f'./models/{a}/OneHotEncoder.pkl','rb'))
test_y=enc1.transform(test_y.reshape(-1,1))
n_classes = test_y.shape[1]
model = ResNetBaseline(in_channels=train_x.shape[-2], num_pred_classes=n_classes)
model.load_state_dict(torch.load(f'./models/{a}/ResNet'))
model.eval()
for i, item in enumerate(test_x):
pop=pickle.load(open( f'./Results/mutate_both/{a}/Counterfactuals_{i}.pkl', "rb" ))
input_ = torch.from_numpy(np.array(item).reshape(1,test_x.shape[-2],test_x.shape[-1])).float()
output = torch.nn.functional.softmax(model(input_)).detach().numpy()
y_target =np.argmax(output,axis=1)[0]
counterfactuals = pop
if y_target == np.argmax(counterfactuals[0].output):
not_valid=not_valid+1
if i==19:
break
sum_out= 1-not_valid/20#data[data['method']== 'TS_Evo']['validity'].values[0]
sum_ates=data[data['method']== 'Ates']['validity'].values[0]
sum_cfg='-'
sum_ng='-'
string +=f' {a} & ${sum_out}$ & ${sum_wachter}$ & ${sum_cfg}$ & ${sum_ng}$ &${sum_ates}$ \\\\ \hline'
#print(f'Our {sum_out/counter} , Wachter {sum_wachter/counter}, NG_DBM {sum_cfg/counter}, NG_GradCAm {sum_ng/counter}' )
#TODO Multivariate is still TODO
#sum_ates+=data[data['methods']== 'TS_Evo']['validity'].values[0]
print(string)
pass
if __name__=='__main__':
calculate_total_ynn()
calculate_total_dis()
yNN_to_latex()
validity()
build_figure(0)