In [1]:

import numpy as np 
from tslearn.datasets import UCR_UEA_datasets

import numpy as np from tslearn.datasets import UCR_UEA_datasets

Parameters¶

In [2]:

dataset='ElectricDevices'

dataset='ElectricDevices'

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)

(8926, 96, 1)
(7711, 96, 1)

In [5]:

train_x.shape

train_x.shape

Out[5]:

(8926, 96, 1)

In [6]:

train_y.shape

train_y.shape

Out[6]:

(8926,)

Load Model¶

In [7]:

import tensorflow as tf 
model_to_explain = tf.keras.models.load_model(f'../../ClassificationModels/models/{dataset}/cnn/best_model.hdf5')

import tensorflow as tf model_to_explain = tf.keras.models.load_model(f'../../ClassificationModels/models/{dataset}/cnn/best_model.hdf5')

2023-06-15 11:04:50.284047: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-15 11:04:50.505469: W tensorflow/compiler/xla/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
2023-06-15 11:04:50.505494: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-06-15 11:04:51.418759: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-06-15 11:04:51.418867: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-06-15 11:04:51.418879: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2023-06-15 11:04:52.395804: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2023-06-15 11:04:52.395839: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2023-06-15 11:04:52.395861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jacqueline-ThinkPad-P53): /proc/driver/nvidia/version does not exist
2023-06-15 11:04:52.396059: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

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

In [10]:

leftist = LEFTIST(model_to_explain,(test_x, test_y),mode='time',backend='TF',transform_name='straight_line',learning_process_name='Lime')
#,'straight_line',segmentator,,learning_process_name

leftist = LEFTIST(model_to_explain,(test_x, test_y),mode='time',backend='TF',transform_name='straight_line',learning_process_name='Lime') #,'straight_line',segmentator,,learning_process_name

In [11]:

explanations = leftist.explain(np.array(explained_instance))

explanations = leftist.explain(np.array(explained_instance))

32/32 [==============================] - 0s 5ms/step

In [13]:

leftist.plot(np.array([explained_instance]), np.array([explanations[1]]))

leftist.plot(np.array([explained_instance]), np.array([explanations[1]]))

time mode
(1, 96)
(1, 96)
-1
1
0