Sentence classification¶
In this tutorial we will try to predict whether an SMS is a spam or not. To train our model, we will use the SMSSpam dataset. This dataset is unbalanced, there is only 13.4% spam. Let's look at the data:
from river import datasets
datasets.SMSSpam()
SMS Spam Collection dataset.
The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent
13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.
Name SMSSpam
Task Binary classification
Samples 5,574
Features 1
Sparse False
Path /Users/max.halford/river_data/SMSSpam/SMSSpamCollection
URL https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip
Size 466.71 KB
Downloaded False
from pprint import pprint
X_y = datasets.SMSSpam()
for x, y in X_y:
pprint(x)
print(f'Spam: {y}')
break
Downloading https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip (198.65 KB)
Uncompressing into /Users/max.halford/river_data/SMSSpam
{'body': 'Go until jurong point, crazy.. Available only in bugis n great world '
'la e buffet... Cine there got amore wat...\n'}
Spam: False
Let's start by building a simple model like a Naive Bayes classifier. We will first preprocess the sentences with a TF-IDF transform that our model can consume. Then, we will measure the accuracy of our model with the AUC metric. This is the right metric to use when the classes are not balanced. In addition, the Naive Bayes models can perform very well on unbalanced datasets and can be used for both binary and multi-class classification problems.
from river import feature_extraction
from river import naive_bayes
from river import metrics
def extract_body(x):
"""Extract the body of the sms."""
return x['body']
X_y = datasets.SMSSpam()
model = (
extract_body |
feature_extraction.TFIDF() |
naive_bayes.BernoulliNB(alpha=0)
)
metric = metrics.ROCAUC()
cm = metrics.ConfusionMatrix()
for x, y in X_y:
y_pred = model.predict_one(x)
if y_pred is not None:
metric.update(y_pred=y_pred, y_true=y)
cm.update(y_pred=y_pred, y_true=y)
model.learn_one(x, y)
metric
ROCAUC: 0.929296
The confusion matrix:
cm
False True
False 4809 17
True 103 644
The results are quite good with this first model.
Since we are working with an unbalanced dataset, we can use the imblearn module to rebalance the classes of our dataset. For more information about the imblearn module, you can find a dedicated tutorial here.
from river import imblearn
X_y = datasets.SMSSpam()
model = (
extract_body |
feature_extraction.TFIDF() |
imblearn.RandomUnderSampler(
classifier=naive_bayes.BernoulliNB(alpha=0),
desired_dist={0: .5, 1: .5},
seed=42
)
)
metric = metrics.ROCAUC()
cm = metrics.ConfusionMatrix()
for x, y in X_y:
y_pred = model.predict_one(x)
if y_pred is not None:
metric.update(y_pred=y_pred, y_true=y)
cm.update(y_pred=y_pred, y_true=y)
model.learn_one(x, y)
metric
ROCAUC: 0.951728
The imblearn module improved our results. Not bad! We can visualize the pipeline to understand how the data is processed.
The confusion matrix:
cm
False True
False 4624 201
True 41 706
model.draw()
Now let's try to use logistic regression to classify messages. We will use different tips to make my model perform better. As in the previous example, we rebalance the classes of our dataset. The logistics regression will be fed from a TF-IDF.
from river import linear_model
from river import optim
from river import preprocessing
X_y = datasets.SMSSpam()
model = (
extract_body |
feature_extraction.TFIDF() |
preprocessing.Normalizer() |
imblearn.RandomUnderSampler(
classifier=linear_model.LogisticRegression(
optimizer=optim.SGD(.9),
loss=optim.losses.Log()
),
desired_dist={0: .5, 1: .5},
seed=42
)
)
metric = metrics.ROCAUC()
cm = metrics.ConfusionMatrix()
for x, y in X_y:
y_pred = model.predict_one(x)
metric.update(y_pred=y_pred, y_true=y)
cm.update(y_pred=y_pred, y_true=y)
model.learn_one(x, y)
metric
ROCAUC: 0.946039
The confusion matrix:
cm
False True
False 4655 172
True 54 693
model.draw()
The results of the logistic regression are quite good but still inferior to the naive Bayes model.
Let's try to use word embeddings to improve our logistic regression. Word embeddings allow you to represent a word as a vector. Embeddings are developed to build semantically rich vectors. For instance, the vector which represents the word python should be close to the vector which represents the word programming. We will use spaCy to convert our sentence to vectors. spaCy converts a sentence to a vector by calculating the average of the embeddings of the words in the sentence.
You can download pre-trained embeddings in many languages. We will use English pre-trained embeddings as our SMS are in English.
The command below allows you to download the pre-trained embeddings that spaCy makes available. More informations about spaCy and its installation may be found here here.
python -m spacy download en_core_web_sm
Here, we create a custom transformer to convert an input sentence to a dict of floats. We will integrate this transformer into our pipeline.
import spacy
from river.base import Transformer
class Embeddings(Transformer):
"""My custom transformer, word embedding using spaCy."""
def __init__(self):
self.embeddings = spacy.load('en_core_web_sm')
def transform_one(self, x, y=None):
return {dimension: xi for dimension, xi in enumerate(self.embeddings(x).vector)}
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-11-f2a8877fc890> in <module>
----> 1 import spacy
2
3 from river.base import Transformer
4
5 class Embeddings(Transformer):
ModuleNotFoundError: No module named 'spacy'
Let's train our logistic regression:
X_y = datasets.SMSSpam()
model = (
extract_body |
Embeddings() |
preprocessing.Normalizer() |
imblearn.RandomOverSampler(
classifier=linear_model.LogisticRegression(
optimizer=optim.SGD(.5),
loss=optim.losses.Log()
),
desired_dist={0: .5, 1: .5},
seed=42
)
)
metric = metrics.ROCAUC()
cm = metrics.ConfusionMatrix()
for x, y in X_y:
y_pred = model.predict_one(x)
metric.update(y_pred=y_pred, y_true=y)
cm.update(y_pred=y_pred, y_true=y)
model.learn_one(x, y)
metric
The confusion matrix:
cm
model
The results of the logistic regression using spaCy embeddings are lower than those obtained with TF-IDF values. We could surely improve the results by cleaning up the text. We could also use embeddings more suited to our dataset. However, on this problem, the logistic regression is not better than the Naive Bayes model. No free lunch today.