In [1]:

import pickle
import numpy as np 
import matplotlib.pyplot as plt
import seaborn as snst
from tslearn.datasets import UCR_UEA_datasets
import tensorflow as tf

import pickle import numpy as np import matplotlib.pyplot as plt import seaborn as snst from tslearn.datasets import UCR_UEA_datasets import tensorflow as tf

/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
2023-09-18 12:06:18.348758: 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 12:06:19.269948: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Parameters¶

In [2]:

dataset='Coffee'

dataset='Coffee'

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)

Model Training¶

In [4]:

import tensorflow as tf 


#model = Classifier_CNN(f'./ClassificationModels/models/{dataset}', (train_x.shape[1], train_x.shape[2]), train_y.shape[-1], True)
#y_true = np.argmax(test_y, axis=1)
#model.fit(train_x,train_y,test_x,test_y)

model_to_explain = tf.keras.models.load_model(f'../../ClassificationModels/models/{dataset}/cnn/last_model.hdf5')

import tensorflow as tf #model = Classifier_CNN(f'./ClassificationModels/models/{dataset}', (train_x.shape[1], train_x.shape[2]), train_y.shape[-1], True) #y_true = np.argmax(test_y, axis=1) #model.fit(train_x,train_y,test_x,test_y) model_to_explain = tf.keras.models.load_model(f'../../ClassificationModels/models/{dataset}/cnn/last_model.hdf5')

2023-09-18 12:06:20.361423: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-09-18 12:06:20.362034: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

Explanation Algo¶

In [5]:

from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR
int_mod=TSR(model_to_explain, train_x.shape[-2],train_x.shape[-1], method='IG',mode='time')

from TSInterpret.InterpretabilityModels.Saliency.TSR import TSR int_mod=TSR(model_to_explain, train_x.shape[-2],train_x.shape[-1], method='IG',mode='time')

Mode in TF Saliency time

In [6]:

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

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

(1, 286, 1)

In [7]:

item= np.array([test_x[0,:,:]])
label=int(np.argmax(test_y[0]))

item= np.array([test_x[0,:,:]]) label=int(np.argmax(test_y[0]))

In [8]:

exp=int_mod.explain(item,labels=label,TSR =True)

exp=int_mod.explain(item,labels=label,TSR =True)

Plot Item¶

In [9]:

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

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

time mode

In [10]:

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

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

time mode