# 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/river_data/SMSSpam/SMSSpamCollection
URL https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip
Size 466.71 KB
Downloaded True
```

```
from pprint import pprint
X_y = datasets.SMSSpam()
for x, y in X_y:
pprint(x)
print(f'Spam: {y}')
break
```

```
{'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
X_y = datasets.SMSSpam()
model = (
feature_extraction.TFIDF(on='body') |
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: 93.00%
```

The confusion matrix:

```
cm
```

```
False True
False 4,809 17
True 102 645
```

The results are quite good with this first model.

Since we are working with an imbalanced 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 = (
feature_extraction.TFIDF(on='body') |
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: 94.61%
```

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 4,570 255
True 41 706
```

```
model
```

TFIDF

```
TFIDF (
normalize=True
on="body"
strip_accents=True
lowercase=True
preprocessor=None
tokenizer=None
ngram_range=(1, 1)
)
```

RandomUnderSampler

```
RandomUnderSampler (
classifier=BernoulliNB (
alpha=0
true_threshold=0.
)
desired_dist={0: 0.5, 1: 0.5}
seed=42
)
```

BernoulliNB

```
BernoulliNB (
alpha=0
true_threshold=0.
)
```

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 = (
feature_extraction.TFIDF(on='body') |
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: 93.80%
```

The confusion matrix:

```
cm
```

```
False True
False 4,584 243
True 55 692
```

```
model
```

TFIDF

```
TFIDF (
normalize=True
on="body"
strip_accents=True
lowercase=True
preprocessor=None
tokenizer=None
ngram_range=(1, 1)
)
```

Normalizer

```
Normalizer (
order=2
)
```

RandomUnderSampler

```
RandomUnderSampler (
classifier=LogisticRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.9
)
)
loss=Log (
weight_pos=1.
weight_neg=1.
)
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
desired_dist={0: 0.5, 1: 0.5}
seed=42
)
```

LogisticRegression

```
LogisticRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.9
)
)
loss=Log (
weight_pos=1.
weight_neg=1.
)
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
```

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, on: str):
self.on = on
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[self.on]).vector)}
```

Let's train our logistic regression:

```
X_y = datasets.SMSSpam()
model = (
Embeddings(on='body') |
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
```

```
ROCAUC: 91.31%
```

The confusion matrix:

```
cm
```

```
False True
False 4,537 290
True 85 662
```

```
model
```

Embeddings

```
Embeddings (
on="body"
)
```

Normalizer

```
Normalizer (
order=2
)
```

RandomOverSampler

```
RandomOverSampler (
classifier=LogisticRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.5
)
)
loss=Log (
weight_pos=1.
weight_neg=1.
)
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
desired_dist={0: 0.5, 1: 0.5}
seed=42
)
```

LogisticRegression

```
LogisticRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.5
)
)
loss=Log (
weight_pos=1.
weight_neg=1.
)
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
```

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.