In [1]:

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

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

/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

Parameters¶

In [2]:

dataset='BasicMotions'

dataset='BasicMotions'

In [3]:

tslearn.datasets.CachedDatasets().list_datasets()

tslearn.datasets.CachedDatasets().list_datasets()

Out[3]:

['Trace']

Load Data¶

In [4]:

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 [5]:

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

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

(40, 100, 6)
(40, 100, 6)

In [6]:

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_tf.pkl','wb'))

train_y=enc1.transform(train_y.reshape(-1,1))
test_y=enc1.transform(test_y.reshape(-1,1))

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_tf.pkl','wb')) train_y=enc1.transform(train_y.reshape(-1,1)) test_y=enc1.transform(test_y.reshape(-1,1))

/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(

Load Model¶

In [7]:

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

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

2023-09-18 14:42:58.962386: 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 14:42:59.585681: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-09-18 14:43:00.261652: 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 14:43:00.262132: 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...

In [8]:

label_pred=np.argmax(model.predict(test_x),axis=1)
label_pred

label_pred=np.argmax(model.predict(test_x),axis=1) label_pred

2/2 [==============================] - 0s 2ms/step

Out[8]:

array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Explanation Algo¶

In [9]:

item=test_x[0].reshape(1,test_x.shape[1],test_x.shape[2])
shape=item.shape
y_target= model.predict(item)

item=test_x[0].reshape(1,test_x.shape[1],test_x.shape[2]) shape=item.shape y_target= model.predict(item)

1/1 [==============================] - 0s 16ms/step

In [10]:

y_target

y_target

Out[10]:

array([[2.8015620e-05, 1.2882984e-03, 9.9654043e-01, 6.2079956e-03]],
      dtype=float32)

In [11]:

from TSInterpret.InterpretabilityModels.counterfactual.COMTECF import COMTECF

exp_model= COMTECF(model,(train_x,train_y),mode='time', backend='TF', method= 'brute')

from TSInterpret.InterpretabilityModels.counterfactual.COMTECF import COMTECF exp_model= COMTECF(model,(train_x,train_y),mode='time', backend='TF', method= 'brute')

In [12]:

exp = exp_model.explain(item)

exp = exp_model.explain(item)

1/1 [==============================] - 0s 21ms/step
1/1 [==============================] - 0s 14ms/step
2/2 [==============================] - 0s 2ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 13ms/step

In [13]:

array, label=exp

array, label=exp

In [14]:

%matplotlib inline
org_label=np.argmax(y_target)
cf_label=label[0]
exp=array
print(exp.shape)
exp_model.plot_in_one(np.array(item[0]),org_label,np.array(exp[0]),cf_label)

%matplotlib inline org_label=np.argmax(y_target) cf_label=label[0] exp=array print(exp.shape) exp_model.plot_in_one(np.array(item[0]),org_label,np.array(exp[0]),cf_label)

(1, 100, 6)