TSR Univariate ResNet

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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

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#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[-1],X_train.shape[-2])
test_x=X_test.reshape(-1,X_train.shape[-1],X_train.shape[-2])
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[-1],X_train.shape[-2])
test_x=X_test.reshape(-1,X_train.shape[-1],X_train.shape[-2])
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)

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/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(

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model =ResNetBaseline(in_channels=1, num_pred_classes=n_pred_classes)
model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/ResNet'))

model.eval()
model =ResNetBaseline(in_channels=1, num_pred_classes=n_pred_classes)
model.load_state_dict(torch.load(f'../../ClassificationModels/models/{dataset}/ResNet'))

model.eval()

Out[3]:

ResNetBaseline(
  (layers): Sequential(
    (0): ResNetBlock(
      (layers): Sequential(
        (0): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(1, 64, kernel_size=(8,), stride=(1,))
            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (1): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(64, 64, kernel_size=(5,), stride=(1,))
            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (2): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(64, 64, kernel_size=(3,), stride=(1,))
            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
      )
      (residual): Sequential(
        (0): Conv1dSamePadding(1, 64, kernel_size=(1,), stride=(1,))
        (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): ResNetBlock(
      (layers): Sequential(
        (0): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(64, 128, kernel_size=(8,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (1): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(128, 128, kernel_size=(5,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (2): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(128, 128, kernel_size=(3,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
      )
      (residual): Sequential(
        (0): Conv1dSamePadding(64, 128, kernel_size=(1,), stride=(1,))
        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (2): ResNetBlock(
      (layers): Sequential(
        (0): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(128, 128, kernel_size=(8,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (1): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(128, 128, kernel_size=(5,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
        (2): ConvBlock(
          (layers): Sequential(
            (0): Conv1dSamePadding(128, 128, kernel_size=(3,), stride=(1,))
            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): ReLU()
          )
        )
      )
    )
  )
  (final): Linear(in_features=128, out_features=2, bias=True)
)

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

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from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR
int_mod=TSR(model, train_x.shape[-1],train_x.shape[-2], method='FO', \
    mode='feat')
exp= int_mod.explain(item,labels=label,TSR =True)
from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR
int_mod=TSR(model, train_x.shape[-1],train_x.shape[-2], method='FO', \
    mode='feat')
exp= int_mod.explain(item,labels=label,TSR =True)

2023-09-18 12:47:33.775890: 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 12:47:34.578751: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

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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))

(1, 150)
(1, 1, 150)
NOT Time mode