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correct mispell word

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%% Cell type:markdown id: tags:
<img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
# <!-- TITLE --> [IMDB1] - Sentiment alalysis with text embedding
# <!-- TITLE --> [IMDB1] - Sentiment analysis with text embedding
<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
## Objectives :
- The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text.
- Understand the management of **textual data** and **sentiment analysis**
Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)**
Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/)
For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
## What we're going to do :
- Retrieve data
- Preparing the data
- Build a model
- Train the model
- Evaluate the result
%% Cell type:markdown id: tags:
## Step 1 - Init python stuff
%% Cell type:code id: tags:
``` python
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.datasets.imdb as imdb
import matplotlib.pyplot as plt
import matplotlib
import os,sys,h5py,json
from importlib import reload
sys.path.append('..')
import fidle.pwk as pwk
datasets_dir = pwk.init('IMDB1')
```
%% Output
**FIDLE 2020 - Practical Work Module**
Version : 0.6.1 DEV
Notebook id : IMDB1
Run time : Friday 18 December 2020, 17:52:06
TensorFlow version : 2.0.0
Keras version : 2.2.4-tf
Datasets dir : /home/pjluc/datasets/fidle
Running mode : full
Update keras cache : False
Save figs : True
Path figs : ./run/figs
%% Cell type:markdown id: tags:
## Step 2 - Retrieve data
IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
Note : Due to their nature, textual data can be somewhat complex.
### 2.1 - Data structure :
The dataset is composed of 2 parts:
- **reviews**, this will be our **x**
- **opinions** (positive/negative), this will be our **y**
There are also a **dictionary**, because words are indexed in reviews
```
<dataset> = (<reviews>, <opinions>)
with : <reviews> = [ <review1>, <review2>, ... ]
<opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer
where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary
<ratei> = int 0 for negative opinion, 1 for positive
<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]
with : <wordi> = word
<wi> = int
```
%% Cell type:markdown id: tags:
### 2.2 - Get dataset
For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data)
However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text)
**Load dataset :**
%% Cell type:code id: tags:
``` python
vocab_size = 10000
# ----- Retrieve x,y
# Uncomment this if you want to load dataset directly from keras (small size <20M)
#
(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,
skip_top = 0,
maxlen = None,
seed = 42,
start_char = 1,
oov_char = 2,
index_from = 3, )
# To load a h5 version of the dataset :
#
# with h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:
# x_train = f['x_train'][:]
# y_train = f['y_train'][:]
# x_test = f['x_test'][:]
# y_test = f['y_test'][:]
```
%% Output
/home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:129: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
/home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:130: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
%% Cell type:markdown id: tags:
**About this dataset :**
%% Cell type:code id: tags:
``` python
print(" Max(x_train,x_test) : ", pwk.rmax([x_train,x_test]) )
print(" x_train : {} y_train : {}".format(x_train.shape, y_train.shape))
print(" x_test : {} y_test : {}".format(x_test.shape, y_test.shape))
print('\nReview example (x_train[12]) :\n\n',x_train[12])
```
%% Output
Max(x_train,x_test) : 9999
x_train : (25000,) y_train : (25000,)
x_test : (25000,) y_test : (25000,)
Review example (x_train[12]) :
[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]
%% Cell type:markdown id: tags:
### 2.3 - Have a look for humans (optional)
When we loaded the dataset, we asked for using \<start\> as 1, \<unknown word\> as 2
So, we shifted the dataset by 3 with the parameter index_from=3
**Load dictionary :**
%% Cell type:code id: tags:
``` python
# ---- Retrieve dictionary {word:index}, and encode it in ascii
#
word_index = imdb.get_word_index()
# ---- Shift the dictionary from +3
#
word_index = {w:(i+3) for w,i in word_index.items()}
# ---- Add <pad>, <start> and unknown tags
#
word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )
# ---- Create a reverse dictionary : {index:word}
#
index_word = {index:word for word,index in word_index.items()}
# ---- Add a nice function to transpose :
#
def dataset2text(review):
return ' '.join([index_word.get(i, '?') for i in review])
```
%% Cell type:markdown id: tags:
**Have a look :**
%% Cell type:code id: tags:
``` python
print('\nDictionary size : ', len(word_index))
for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )
pwk.subtitle('Review example :')
print(x_train[12])
pwk.subtitle('After translation :')
print(dataset2text(x_train[12]))
```
%% Output
Dictionary size : 88587
440 : hope
441 : entertaining
442 : she's
443 : mr
444 : overall
445 : evil
446 : called
447 : loved
448 : based
449 : oh
450 : several
451 : fans
452 : mother
453 : drama
454 : beginning
<br>**Review example :**
[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]
<br>**After translation :**
<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint
%% Cell type:markdown id: tags:
### 2.4 - Have a look for NN
%% Cell type:code id: tags:
``` python
sizes=[len(i) for i in x_train]
plt.figure(figsize=(16,6))
plt.hist(sizes, bins=400)
plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)),
xlabel='Size', ylabel='Density', xlim=[0,1500])
pwk.save_fig('01-stats-sizes')
plt.show()
```
%% Output
%% Cell type:markdown id: tags:
## Step 3 - Preprocess the data (padding)
In order to be processed by an NN, all entries must have the **same length.**
We chose a review length of **review_len**
We will therefore complete them with a padding (of \<pad\>\)
%% Cell type:code id: tags:
``` python
review_len = 256
x_train = keras.preprocessing.sequence.pad_sequences(x_train,
value = 0,
padding = 'post',
maxlen = review_len)
x_test = keras.preprocessing.sequence.pad_sequences(x_test,
value = 0 ,
padding = 'post',
maxlen = review_len)
pwk.subtitle('After padding :')
print(x_train[12])
pwk.subtitle('In real words :')
print(dataset2text(x_train[12]))
```
%% Output
<br>**After padding :**
[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436
363 108 7 14 432 14 22 9 1055 34 8599 2 5 381
3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587
4 280 725 6 58 11 2714 201 4 206 16 702 5 5176
19 480 5920 157 13 64 219 4 2 11 107 665 1212 39
4 206 4 65 410 16 565 5 24 43 343 17 5602 8
169 101 85 206 108 8 3008 14 25 215 168 18 6 2579
1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633
115 4363 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0]
<br>**In real words :**
<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>
%% Cell type:markdown id: tags:
**Save dataset and dictionary (For future use but not mandatory)**
%% Cell type:code id: tags:
``` python
# ---- Write dataset in a h5 file, could be usefull
#
output_dir = './data'
pwk.mkdir(output_dir)
with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:
f.create_dataset("x_train", data=x_train)
f.create_dataset("y_train", data=y_train)
f.create_dataset("x_test", data=x_test)
f.create_dataset("y_test", data=y_test)
with open(f'{output_dir}/word_index.json', 'w') as fp:
json.dump(word_index, fp)
with open(f'{output_dir}/index_word.json', 'w') as fp:
json.dump(index_word, fp)
print('Saved.')
```
%% Output
Saved.
%% Cell type:markdown id: tags:
## Step 4 - Build the model
Few remarks :
- We'll choose a dense vector size for the embedding output with **dense_vector_size**
- **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)
In other words: we average the set of vectors/words of a sentence
- L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)
Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer)
ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks)
More documentation about this model functions :
- [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)
- [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)
%% Cell type:code id: tags:
``` python
def get_model(dense_vector_size=32):
model = keras.Sequential()
model.add(keras.layers.Embedding(input_dim = vocab_size,
output_dim = dense_vector_size,
input_length = review_len))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(dense_vector_size, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
return model
```
%% Cell type:markdown id: tags:
## Step 5 - Train the model
### 5.1 - Get it
%% Cell type:code id: tags:
``` python
model = get_model(32)
model.summary()
```
%% Output
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 256, 32) 320000
_________________________________________________________________
global_average_pooling1d (Gl (None, 32) 0
_________________________________________________________________
dense (Dense) (None, 32) 1056
_________________________________________________________________
dense_1 (Dense) (None, 1) 33
=================================================================
Total params: 321,089
Trainable params: 321,089
Non-trainable params: 0
_________________________________________________________________
%% Cell type:markdown id: tags:
### 5.2 - Add callback
%% Cell type:code id: tags:
``` python
os.makedirs('./run/models', mode=0o750, exist_ok=True)
save_dir = "./run/models/best_model.h5"
savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)
```
%% Cell type:markdown id: tags:
### 5.1 - Train it
%% Cell type:code id: tags:
``` python
%%time
n_epochs = 30
batch_size = 512
history = model.fit(x_train,
y_train,
epochs = n_epochs,
batch_size = batch_size,
validation_data = (x_test, y_test),
verbose = 1,
callbacks = [savemodel_callback])
```
%% Output
Train on 25000 samples, validate on 25000 samples
Epoch 1/30
25000/25000 [==============================] - 1s 57us/sample - loss: 0.6881 - accuracy: 0.6431 - val_loss: 0.6782 - val_accuracy: 0.7408
Epoch 2/30
25000/25000 [==============================] - 1s 32us/sample - loss: 0.6506 - accuracy: 0.7618 - val_loss: 0.6168 - val_accuracy: 0.7650
Epoch 3/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.5590 - accuracy: 0.8060 - val_loss: 0.5135 - val_accuracy: 0.8203
Epoch 4/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.4460 - accuracy: 0.8512 - val_loss: 0.4198 - val_accuracy: 0.8487
Epoch 5/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.3606 - accuracy: 0.8758 - val_loss: 0.3636 - val_accuracy: 0.8609
Epoch 6/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.3074 - accuracy: 0.8894 - val_loss: 0.3322 - val_accuracy: 0.8676
Epoch 7/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.2719 - accuracy: 0.9016 - val_loss: 0.3127 - val_accuracy: 0.8734
Epoch 8/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.2457 - accuracy: 0.9103 - val_loss: 0.3007 - val_accuracy: 0.8765
Epoch 9/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.2257 - accuracy: 0.9178 - val_loss: 0.2933 - val_accuracy: 0.8795
Epoch 10/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.2083 - accuracy: 0.9249 - val_loss: 0.2888 - val_accuracy: 0.8818
Epoch 11/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1938 - accuracy: 0.9310 - val_loss: 0.2874 - val_accuracy: 0.8824
Epoch 12/30
25000/25000 [==============================] - 1s 31us/sample - loss: 0.1818 - accuracy: 0.9352 - val_loss: 0.2867 - val_accuracy: 0.8826
Epoch 13/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1703 - accuracy: 0.9406 - val_loss: 0.2879 - val_accuracy: 0.8828
Epoch 14/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1605 - accuracy: 0.9451 - val_loss: 0.2922 - val_accuracy: 0.8815
Epoch 15/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.1520 - accuracy: 0.9480 - val_loss: 0.2945 - val_accuracy: 0.8828
Epoch 16/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1435 - accuracy: 0.9524 - val_loss: 0.2986 - val_accuracy: 0.8821
Epoch 17/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1359 - accuracy: 0.9551 - val_loss: 0.3042 - val_accuracy: 0.8803
Epoch 18/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.1290 - accuracy: 0.9581 - val_loss: 0.3100 - val_accuracy: 0.8783
Epoch 19/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1223 - accuracy: 0.9609 - val_loss: 0.3169 - val_accuracy: 0.8772
Epoch 20/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.1167 - accuracy: 0.9632 - val_loss: 0.3245 - val_accuracy: 0.8747
Epoch 21/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.1116 - accuracy: 0.9654 - val_loss: 0.3318 - val_accuracy: 0.8756
Epoch 22/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.1064 - accuracy: 0.9670 - val_loss: 0.3409 - val_accuracy: 0.8743
Epoch 23/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.1007 - accuracy: 0.9704 - val_loss: 0.3478 - val_accuracy: 0.8728
Epoch 24/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.0963 - accuracy: 0.9718 - val_loss: 0.3577 - val_accuracy: 0.8718
Epoch 25/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.0914 - accuracy: 0.9743 - val_loss: 0.3728 - val_accuracy: 0.8670
Epoch 26/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.0887 - accuracy: 0.9744 - val_loss: 0.3746 - val_accuracy: 0.8697
Epoch 27/30
25000/25000 [==============================] - 1s 29us/sample - loss: 0.0842 - accuracy: 0.9777 - val_loss: 0.3835 - val_accuracy: 0.8683
Epoch 28/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.0796 - accuracy: 0.9788 - val_loss: 0.3937 - val_accuracy: 0.8670
Epoch 29/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.0763 - accuracy: 0.9804 - val_loss: 0.4032 - val_accuracy: 0.8658
Epoch 30/30
25000/25000 [==============================] - 1s 30us/sample - loss: 0.0729 - accuracy: 0.9819 - val_loss: 0.4156 - val_accuracy: 0.8648
CPU times: user 1min 42s, sys: 7.41 s, total: 1min 50s
Wall time: 23.4 s
%% Cell type:markdown id: tags:
## Step 6 - Evaluate
### 6.1 - Training history
%% Cell type:code id: tags:
``` python
pwk.plot_history(history, save_as='02-history')
```
%% Output
%% Cell type:markdown id: tags:
### 6.2 - Reload and evaluate best model
%% Cell type:code id: tags:
``` python
model = keras.models.load_model('./run/models/best_model.h5')
# ---- Evaluate
score = model.evaluate(x_test, y_test, verbose=0)
print('x_test / loss : {:5.4f}'.format(score[0]))
print('x_test / accuracy : {:5.4f}'.format(score[1]))
values=[score[1], 1-score[1]]
pwk.plot_donut(values,["Accuracy","Errors"], title="#### Accuracy donut is :", save_as='03-donut')
# ---- Confusion matrix
y_sigmoid = model.predict(x_test)
y_pred = y_sigmoid.copy()
y_pred[ y_sigmoid< 0.5 ] = 0
y_pred[ y_sigmoid>=0.5 ] = 1
pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))
pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')
```
%% Output
x_test / loss : 0.2867
x_test / accuracy : 0.8826
#### Accuracy donut is :
#### Confusion matrix is :
%% Cell type:code id: tags:
``` python
pwk.end()
```
%% Output
End time is : Friday 18 December 2020, 17:52:43
Duration is : 00:00:37 585ms
This notebook ends here
%% Cell type:markdown id: tags:
---
<img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>
......
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