Python Machine Learning - Code Examples¶
Chapter 9 - Embedding a Machine Learning Model into a Web Application¶
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,nltk
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
Overview¶
The code for the Flask web applications can be found in the following directories:
1st_flask_app_1/: A simple Flask web app1st_flask_app_2/:1st_flask_app_1extended with flexible form validation and renderingmovieclassifier/: The movie classifier embedded in a web applicationmovieclassifier_with_update/: same asmovieclassifierbut with update from sqlite database upon start
To run the web applications locally, cd into the respective directory (as listed above) and execute the main-application script, for example,
cd ./1st_flask_app_1
python3 app.py
Now, you should see something like
* Running on http://127.0.0.1:5000/
* Restarting with reloader
in your terminal. Next, open a web browsert and enter the address displayed in your terminal (typically http://127.0.0.1:5000/) to view the web application.
Link to a live example application built with this tutorial: http://raschkas.pythonanywhere.com/.
from IPython.display import Image
Chapter 8 recap - Training a model for movie review classification¶
This section is a recap of the logistic regression model that was trained in the last section of Chapter 6. Execute the folling code blocks to train a model that we will serialize in the next section.
import numpy as np
import re
from nltk.corpus import stopwords
stop = stopwords.words('english')
porter = PorterStemmer()
def tokenizer(text):
text = re.sub('<[^>]*>', '', text)
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text.lower())
text = re.sub('[\W]+', ' ', text.lower()) + ' '.join(emoticons).replace('-', '')
tokenized = [w for w in text.split() if w not in stop]
return tokenized
def stream_docs(path):
with open(path, 'r') as csv:
next(csv) # skip header
for line in csv:
text, label = line[:-3], int(line[-2])
yield text, label
next(stream_docs(path='./movie_data.csv'))
Note¶
If you haven't created the movie_data.csv file in the previous chapter, you can find a download a zip archive at
https://github.com/rasbt/python-machine-learning-book/tree/master/code/datasets/movie
def get_minibatch(doc_stream, size):
docs, y = [], []
try:
for _ in range(size):
text, label = next(doc_stream)
docs.append(text)
y.append(label)
except StopIteration:
return None, None
return docs, y
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
vect = HashingVectorizer(decode_error='ignore',
n_features=2**21,
preprocessor=None,
tokenizer=tokenizer)
clf = SGDClassifier(loss='log', random_state=1, n_iter=1)
doc_stream = stream_docs(path='./movie_data.csv')
import pyprind
pbar = pyprind.ProgBar(45)
classes = np.array([0, 1])
for _ in range(45):
X_train, y_train = get_minibatch(doc_stream, size=1000)
if not X_train:
break
X_train = vect.transform(X_train)
clf.partial_fit(X_train, y_train, classes=classes)
pbar.update()
X_test, y_test = get_minibatch(doc_stream, size=5000)
X_test = vect.transform(X_test)
print('Accuracy: %.3f' % clf.score(X_test, y_test))
clf = clf.partial_fit(X_test, y_test)
Serializing fitted scikit-learn estimators¶
After we trained the logistic regression model as shown above, we know save the classifier along woth the stop words, Porter Stemmer, and HashingVectorizer as serialized objects to our local disk so that we can use the fitted classifier in our web application later.
import pickle
import os
dest = os.path.join('movieclassifier', 'pkl_objects')
if not os.path.exists(dest):
os.makedirs(dest)
pickle.dump(stop, open(os.path.join(dest, 'stopwords.pkl'), 'wb'), protocol=4)
pickle.dump(clf, open(os.path.join(dest, 'classifier.pkl'), 'wb'), protocol=4)
Next, we save the HashingVectorizer as in a separate file so that we can import it later.
%%writefile movieclassifier/vectorizer.py
from sklearn.feature_extraction.text import HashingVectorizer
import re
import os
import pickle
cur_dir = os.path.dirname(__file__)
stop = pickle.load(open(
os.path.join(cur_dir,
'pkl_objects',
'stopwords.pkl'), 'rb'))
def tokenizer(text):
text = re.sub('<[^>]*>', '', text)
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)',
text.lower())
text = re.sub('[\W]+', ' ', text.lower()) \
+ ' '.join(emoticons).replace('-', '')
tokenized = [w for w in text.split() if w not in stop]
return tokenized
vect = HashingVectorizer(decode_error='ignore',
n_features=2**21,
preprocessor=None,
tokenizer=tokenizer)
After executing the preceeding code cells, we can now restart the IPython notebook kernel to check if the objects were serialized correctly.
First, change the current Python directory to movieclassifer:
import os
os.chdir('movieclassifier')
import pickle
import re
import os
from vectorizer import vect
clf = pickle.load(open(os.path.join('pkl_objects', 'classifier.pkl'), 'rb'))
import numpy as np
label = {0:'negative', 1:'positive'}
example = ['I love this movie']
X = vect.transform(example)
print('Prediction: %s\nProbability: %.2f%%' %\
(label[clf.predict(X)[0]], clf.predict_proba(X).max()*100))
Setting up a SQLite database for data storage¶
Before you execute this code, please make sure that you are currently in the movieclassifier directory.
import sqlite3
conn = sqlite3.connect('reviews.sqlite')
c = conn.cursor()
c.execute('CREATE TABLE review_db (review TEXT, sentiment INTEGER, date TEXT)')
example1 = 'I love this movie'
c.execute("INSERT INTO review_db (review, sentiment, date) VALUES (?, ?, DATETIME('now'))", (example1, 1))
example2 = 'I disliked this movie'
c.execute("INSERT INTO review_db (review, sentiment, date) VALUES (?, ?, DATETIME('now'))", (example2, 0))
conn.commit()
conn.close()
conn = sqlite3.connect('reviews.sqlite')
c = conn.cursor()
c.execute("SELECT * FROM review_db WHERE date BETWEEN '2015-01-01 10:10:10' AND DATETIME('now')")
results = c.fetchall()
conn.close()
print(results)
Image(filename='./images/09_01.png', width=700)
Developing a web application with Flask¶
...
Our first Flask web application¶
...
Form validation and rendering¶
Image(filename='./images/09_02.png', width=400)
Image(filename='./images/09_03.png', width=400)
Turning the movie classifier into a web application¶
Image(filename='./images/09_04.png', width=400)
Image(filename='./images/09_05.png', width=400)
Image(filename='./images/09_06.png', width=400)
Image(filename='./images/09_07.png', width=200)
Deploying the web application to a public server¶
Image(filename='./images/09_08.png', width=600)