In [1]:

import sklearn
import pickle
import numpy as np 
import torch 
from ClassificationModels.CNN_T import ResNetBaseline, get_all_preds, fit, UCRDataset
from ClassificationModels.LSTM_T import LSTM
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
import pandas as pd
import os 
from tslearn.datasets import UCR_UEA_datasets

import sklearn import pickle import numpy as np import torch from ClassificationModels.CNN_T import ResNetBaseline, get_all_preds, fit, UCRDataset from ClassificationModels.LSTM_T import LSTM import matplotlib.pyplot as plt import seaborn as sns from sklearn.metrics import confusion_matrix, accuracy_score, classification_report import pandas as pd import os from tslearn.datasets import UCR_UEA_datasets

/home/jacqueline/.local/share/virtualenvs/TSInterpret-x4eqnPOt/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Parameters¶

In [2]:

#dataset='GunPoint'f
dataset='BasicMotions'

#dataset='GunPoint'f dataset='BasicMotions'

Load Data¶

In [3]:

train_x,train_y, test_x, test_y=UCR_UEA_datasets().load_dataset(dataset)

train_x,train_y, test_x, test_y=UCR_UEA_datasets().load_dataset(dataset)

In [4]:

enc1=sklearn.preprocessing.OneHotEncoder(sparse=False).fit(np.vstack((train_y.reshape(-1,1),test_y.reshape(-1,1))))
pickle.dump(enc1,open(f'../../ClassificationModels/models/{dataset}/OneHotEncoder.pkl','wb'))

train_y=enc1.transform(train_y.reshape(-1,1))
test_y=enc1.transform(test_y.reshape(-1,1))

enc1=sklearn.preprocessing.OneHotEncoder(sparse=False).fit(np.vstack((train_y.reshape(-1,1),test_y.reshape(-1,1)))) pickle.dump(enc1,open(f'../../ClassificationModels/models/{dataset}/OneHotEncoder.pkl','wb')) train_y=enc1.transform(train_y.reshape(-1,1)) test_y=enc1.transform(test_y.reshape(-1,1))

/home/jacqueline/.local/share/virtualenvs/TSInterpret-x4eqnPOt/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:972: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
  warnings.warn(

Model Training¶

In [5]:

n_pred_classes =train_y.shape[1]
train_dataset = UCRDataset(train_x.astype(np.float64),train_y.astype(np.int64))
test_dataset = UCRDataset(test_x.astype(np.float64),test_y.astype(np.int64))
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=16,shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=1,shuffle=False)
device='cpu'
hidden_size=10
rnn=0.1
model = LSTM(6, hidden_size ,n_pred_classes,rnndropout=rnn).to(device)
#fit(model,train_loader,test_loader)
if dataset in os.listdir('../../ClassificationModels/models/'):
    print('Folder exists')
else: 
    os.mkdir(f'../../ClassificationModels/models/{dataset}')
#torch.save(model.state_dict(), f'../../ClassificationModels/models/{dataset}/LSTM')
model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/LSTM'))

model.eval()
test_preds, ground_truth = get_all_preds(model, test_loader)
ground_truth=np.argmax(ground_truth,axis=1)

sns.set(rc={'figure.figsize':(5,4)})
heatmap=confusion_matrix(ground_truth, test_preds)
sns.heatmap(heatmap, annot=True)
plt.savefig(f'../../ClassificationModels/models/{dataset}/LSTM_confusion_matrix.png')
plt.close()
acc= accuracy_score(ground_truth, test_preds)
a = classification_report(ground_truth, test_preds, output_dict=True)
dataframe = pd.DataFrame.from_dict(a)
dataframe.to_csv(f'../../ClassificationModels/models/{dataset}/LSTMclassification_report.csv', index = False)

n_pred_classes =train_y.shape[1] train_dataset = UCRDataset(train_x.astype(np.float64),train_y.astype(np.int64)) test_dataset = UCRDataset(test_x.astype(np.float64),test_y.astype(np.int64)) train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=16,shuffle=True) test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=1,shuffle=False) device='cpu' hidden_size=10 rnn=0.1 model = LSTM(6, hidden_size ,n_pred_classes,rnndropout=rnn).to(device) #fit(model,train_loader,test_loader) if dataset in os.listdir('../../ClassificationModels/models/'): print('Folder exists') else: os.mkdir(f'../../ClassificationModels/models/{dataset}') #torch.save(model.state_dict(), f'../../ClassificationModels/models/{dataset}/LSTM') model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/LSTM')) model.eval() test_preds, ground_truth = get_all_preds(model, test_loader) ground_truth=np.argmax(ground_truth,axis=1) sns.set(rc={'figure.figsize':(5,4)}) heatmap=confusion_matrix(ground_truth, test_preds) sns.heatmap(heatmap, annot=True) plt.savefig(f'../../ClassificationModels/models/{dataset}/LSTM_confusion_matrix.png') plt.close() acc= accuracy_score(ground_truth, test_preds) a = classification_report(ground_truth, test_preds, output_dict=True) dataframe = pd.DataFrame.from_dict(a) dataframe.to_csv(f'../../ClassificationModels/models/{dataset}/LSTMclassification_report.csv', index = False)

Folder exists

Explanation Algo¶

In [6]:

item=test_x[0].reshape(1,-1,6)
shape=item.shape
_item=  torch.from_numpy(item).float()
model.eval()
y_target= model(_item).detach().numpy()
#y_target = torch.nn.functional.softmax(model(_item)).detach().numpy()

item=test_x[0].reshape(1,-1,6) shape=item.shape _item= torch.from_numpy(item).float() model.eval() y_target= model(_item).detach().numpy() #y_target = torch.nn.functional.softmax(model(_item)).detach().numpy()

In [7]:

from TSInterpret.InterpretabilityModels.Saliency.SaliencyMethods_PTY import Saliency_PTY
int_mod=Saliency_PTY(model,NumTimeSteps=train_x.shape[-2],NumFeatures=train_x.shape[-1], method='FA', mode ='time')

from TSInterpret.InterpretabilityModels.Saliency.SaliencyMethods_PTY import Saliency_PTY int_mod=Saliency_PTY(model,NumTimeSteps=train_x.shape[-2],NumFeatures=train_x.shape[-1], method='FA', mode ='time')

2023-09-18 13:59:41.345000: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-09-18 13:59:42.368222: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

In [8]:

print(np.array([test_x[0,:,:]]).shape)

print(np.array([test_x[0,:,:]]).shape)

(1, 100, 6)

In [9]:

exp=int_mod.explain(np.array([test_x[0,:,:]]),labels =1 ,TSR = True)

exp=int_mod.explain(np.array([test_x[0,:,:]]),labels =1 ,TSR = True)

In [10]:

int_mod.plot(np.array([test_x[0,:,:]]),exp)

int_mod.plot(np.array([test_x[0,:,:]]),exp)

time mode

In [11]:

int_mod.plot(np.array([test_x[0,:,:]]),exp, heatmap = True)

int_mod.plot(np.array([test_x[0,:,:]]),exp, heatmap = True)

time mode