Feature Union with Pandas: Properly Selecting Columns?

Introduction

In this article, we will explore feature union in the context of pandas and scikit-learn. Feature union is a technique used to combine multiple datasets into one dataset for training machine learning models. In our example, we have a dataframe df that contains a column number_col of numeric values, a column text_col of text values, and an outcome variable. We are using feature union to transform these columns before feeding them into a Support Vector Machine (SVM) classifier.

Understanding the Problem

The problem presented in the Stack Overflow question is related to the way the feature union is implemented in our code. Specifically, we have two pipelines: one for vectorizing text_col and another for standardizing number_col. The question states that when we call fit() on the pipeline, it drops off the columns from the original dataframe except for text_col. This raises a KeyError: 'text_col' because the transformer is not able to select the column.

What’s Going On?

Let’s analyze what’s happening behind the scenes. When we create the feature union in our code, we are using two pipelines:

('union', FeatureUnion(
    transformer_list=[
        # Pipeline for vectorizing text
        ('subject', Pipeline([
            ('selector', ItemSelector(key='text_col')),
            ('tfidf', TfidfVectorizer(min_df=50)),
        ])),
        # Pipeline for standardizing number_col
        ('body_bow', Pipeline([
            ('selector', ItemSelector(key='number_col')),
            ('std', StandardScaler()),
        ])),
    ]
)))

In the FeatureUnion, we are essentially saying: “I want to apply these two pipelines one after another on each column of my dataframe”. The first pipeline in our example is used for vectorizing text_col and the second pipeline is used for standardizing number_col.

However, when we call fit() on the pipeline, pandas applies the transformers one by one to each column in the dataframe. Since we are only selecting text_col in the transformer of interest (the first pipeline), it gets applied but the other columns do not.

The Issue

Here’s where things get interesting. When we apply a StandardScaler() on number_col, it does not actually standardize number_col. Instead, it creates a new scaled version of number_col which is then used as input for our SVM model.

In the context of feature union, this means that number_col gets processed by both pipelines: the first one (vectorization) and the second one (standardization). However, because we are applying standardization to number_col, it gets dropped out of consideration when creating the transformed dataframe. This is why our code raises a KeyError for text_col.

Solution

To fix this issue, we can apply feature selection before applying transformations. One way to do this is by using an ItemSelector() on each column in our feature union.

class ItemSelector(BaseEstimator, TransformerMixin):

    def __init__(self, key):
        self.key = key

    def fit(self, x, y=None):
        return self

    def transform(self, data_dict):
        return data_dict[self.key]

In this code, we’re using an ItemSelector() to select each column individually. This ensures that all columns are processed by both pipelines.

Here’s how you can use it in your feature union:

('union', FeatureUnion(
    transformer_list=[
        # Pipeline for vectorizing text
        ('subject', Pipeline([
            ('selector', ItemSelector(key='text_col')),
            ('tfidf', TfidfVectorizer(min_df=50)),
        ])),
        # Pipeline for standardizing number_col
        ('body_bow', Pipeline([
            ('selector', ItemSelector(key='number_col')),
            ('std', StandardScaler()),
        ])),
    ]
)))

In this case, text_col is selected by the first pipeline and processed before applying vectorization. Meanwhile, number_col is selected by the second pipeline and processed before standardization.

Finally, we can apply our SVM model to the transformed dataframe:

a.fit(df)
y_pred = a.predict(X_transformed)

Conclusion

Feature union with pandas can be a powerful tool for combining multiple datasets into one dataset. However, it requires careful consideration of how data is processed and transformed before feeding it into machine learning models.

In our example, we’ve explored the issue that arises when using feature union in conjunction with standardization and vectorization on different columns. By applying an ItemSelector() to each column individually, we can ensure that all columns are properly selected and processed by both pipelines.

Remember, in order to leverage the benefits of feature union for your own machine learning tasks, you’ll need to carefully consider the data processing steps involved and choose the right transformers to apply on each column.

Last modified on 2024-08-05