In [1]:

import pickle
import numpy as np 
from tslearn.datasets import UCR_UEA_datasets

import pickle import numpy as np from tslearn.datasets import UCR_UEA_datasets

Parameters¶

In [2]:

dataset='GunPoint'

dataset='GunPoint'

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]:

print(train_x.shape)
print(test_x.shape)

print(train_x.shape) print(test_x.shape)

(50, 150, 1)
(150, 150, 1)

In [5]:

train_x.shape

train_x.shape

Out[5]:

(50, 150, 1)

In [6]:

train_y.shape

train_y.shape

Out[6]:

(50,)

Load Model¶

In [7]:

model= pickle.load(open(f'../../ClassificationModels/models/GunPoint/GunPoint_SVM.sav','rb')).fit(train_x,train_y)
#/media/jacqueline/Data/InterpretabiltyTimeSeries/ClassificationModels/models/GunPoint/GunPoint_SVM.sav

model= pickle.load(open(f'../../ClassificationModels/models/GunPoint/GunPoint_SVM.sav','rb')).fit(train_x,train_y) #/media/jacqueline/Data/InterpretabiltyTimeSeries/ClassificationModels/models/GunPoint/GunPoint_SVM.sav

Explanation Algo¶

In [8]:

explained_instance = train_x[0]
nb_interpretable_feature = 10
explanation_size = 5
nb_neighbors = 1000
learning_process_name = 'Lime'
transform_name = 'straight_line'

explained_instance = train_x[0] nb_interpretable_feature = 10 explanation_size = 5 nb_neighbors = 1000 learning_process_name = 'Lime' transform_name = 'straight_line'

In [9]:

from TSInterpret.InterpretabilityModels.leftist.leftist import LEFTIST

from TSInterpret.InterpretabilityModels.leftist.leftist import LEFTIST

2022-10-27 11:59:39.218998: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-10-27 11:59:39.219023: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

In [10]:

model_to_explain = model
random_state=0

model_to_explain = model random_state=0

In [11]:

leftist = LEFTIST(model_to_explain,(test_x,None),mode='time', backend='SK')

leftist = LEFTIST(model_to_explain,(test_x,None),mode='time', backend='SK')

In [12]:

explanations = leftist.explain(np.array(explained_instance),1000,explanation_size=explanation_size)

explanations = leftist.explain(np.array(explained_instance),1000,explanation_size=explanation_size)

In [13]:

%matplotlib inline
leftist.plot(np.array([explained_instance]).reshape(1,-1,1), np.array([explanations[0]]))

%matplotlib inline leftist.plot(np.array([explained_instance]).reshape(1,-1,1), np.array([explanations[0]]))

time mode
(1, 150)
(1, 150)
-1
1
0