TSR Univariate

In [1]:

from tslearn.datasets import UCR_UEA_datasets
import numpy as np 
import torch 
import seaborn as sns
import sklearn
from ClassificationModels.LSTM_T import LSTM
from ClassificationModels.CNN_T import ResNetBaseline, get_all_preds, fit, UCRDataset

from tslearn.datasets import UCR_UEA_datasets import numpy as np import torch import seaborn as sns import sklearn from ClassificationModels.LSTM_T import LSTM from ClassificationModels.CNN_T import ResNetBaseline, get_all_preds, fit, UCRDataset

/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

In [2]:

#Load Data 
dataset='GunPoint'
X_train,y_train, X_test, y_test=UCR_UEA_datasets().load_dataset(dataset)
train_x=X_train.reshape(-1,X_train.shape[-2],X_train.shape[-1])
test_x=X_test.reshape(-1,X_train.shape[-2],X_train.shape[-1])
train_y = y_train
test_y=y_test
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)) 

print(train_y)
#Load Model 
n_pred_classes =len(np.unique(train_y))
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)

#Load Data dataset='GunPoint' X_train,y_train, X_test, y_test=UCR_UEA_datasets().load_dataset(dataset) train_x=X_train.reshape(-1,X_train.shape[-2],X_train.shape[-1]) test_x=X_test.reshape(-1,X_train.shape[-2],X_train.shape[-1]) train_y = y_train test_y=y_test 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)) print(train_y) #Load Model n_pred_classes =len(np.unique(train_y)) 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)

[[0. 1.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [0. 1.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [0. 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(

In [3]:

model = LSTM(1, 10 ,2 ,0.1)#ResNetBaseline(in_channels=1, num_pred_classes=n_pred_classes)
#fit(model,train_loader,test_loader)
model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/LSTM'))

model.eval()

model = LSTM(1, 10 ,2 ,0.1)#ResNetBaseline(in_channels=1, num_pred_classes=n_pred_classes) #fit(model,train_loader,test_loader) model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/LSTM')) model.eval()

Out[3]:

LSTM(
  (drop): Dropout(p=0.1, inplace=False)
  (fc): Linear(in_features=10, out_features=2, bias=True)
  (rnn): LSTM(1, 10, batch_first=True)
)

In [4]:

item = np.array([test_x[0,:,:]],dtype=np.float64)
label =0

item = np.array([test_x[0,:,:]],dtype=np.float64) label =0

In [5]:

#TODO SLIDING WINDOW
from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR
int_mod=TSR(model, train_x.shape[-2],train_x.shape[-1], method='FO', mode='time')
print(item.shape)
exp= int_mod.explain(item,labels=label,TSR =True)

#TODO SLIDING WINDOW from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR int_mod=TSR(model, train_x.shape[-2],train_x.shape[-1], method='FO', mode='time') print(item.shape) exp= int_mod.explain(item,labels=label,TSR =True)

2023-09-18 12:55:40.118252: 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 12:55:41.436679: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

(1, 150, 1)

In [6]:

print(exp.shape)
print(np.array([test_x[0,:,:]]).shape)
int_mod.plot(np.array([test_x[0,:,:]]),exp, figsize=(30,30))

print(exp.shape) print(np.array([test_x[0,:,:]]).shape) int_mod.plot(np.array([test_x[0,:,:]]),exp, figsize=(30,30))

(150, 1)
(1, 150, 1)
time mode