Understanding the Issue with ColumnTransformer and Spacy Vectorizer

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In this article, we’ll explore why using a custom class of Spacy to create a Glove vectorizer in scikit-learn’s ColumnTransformer results in a ValueError. We will go through the issue step-by-step, exploring how to fix it.

Understanding the Components of the Problem

To tackle this problem, we need to understand each component involved:

• Scikit-learn’s Pipeline: A way to combine multiple estimators and transformers in a single object.
• ColumnTransformer: Used to split input data into subsets based on column names.
• Spacy Vectorizer: A custom class that uses Spacy for text processing and Glove embeddings.

The Code

We’ll start by reviewing the code that was provided:

import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.base import BaseEstimator, TransformerMixin
import spacy

class SpacyVectorTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, nlp):
        self.nlp = nlp
        self.dim = 300

    def fit(self, X, y):
        return self

    def transform(self, X):
        return pd.DataFrame([self.nlp(text).vector for text in X])

# Load the Spacy NLP model
nlp = spacy.load("en_core_web_sm")

# Create the ColumnTransformer with the custom vectorizer
col_preprocessor = ColumnTransformer(
    [
        ('title_glove', SpacyVectorTransformer(nlp), 'title'),
        ('description_glove', SpacyVectorTransformer(nlp), 'description'),
    ],
    remainder='drop',
    n_jobs=1
)

# Create a pipeline with the custom column transformer and Logistic Regression
pipeline_glove = Pipeline([
    ('col_preprocessor', col_preprocessor), 
    ('classifier', LogisticRegression())
])

# Fit the pipeline to some data
df = pd.DataFrame({'title': ['Sample Title'], 'description': ['Sample Description']})
X = df[['title', 'description']]
y = [0]

pipeline_glove.fit(X, y)

The Problem: Incompatible Output Types

The ValueError message tells us that the output of the ’title_glove’ transformer should be 2D (scipy matrix, array, or pandas DataFrame). However, we’re returning a list from our custom vectorizer.

Fixing the Code

To fix this issue, we need to modify our custom vectorizer so it returns a compatible output type. One way to do this is by turning the list into a pandas DataFrame:

import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.base import BaseEstimator, TransformerMixin
import spacy

class SpacyVectorTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, nlp):
        self.nlp = nlp
        self.dim = 300

    def fit(self, X, y):
        return self

    def transform(self, X):
        # Turn the list into a pandas DataFrame
        return pd.DataFrame([self.nlp(text).vector for text in X])

By making this change, we ensure that our custom vectorizer returns a compatible output type, which should resolve the ValueError.

Additional Advice

• Please provide a minimal, reproducible example when asking for help with your problem. In this case, it would be helpful to include the necessary imports and some sample data.
• Always make sure you’re running the latest version of scikit-learn and Spacy.

By understanding how to fix this issue, we can write more effective custom transformers that work seamlessly within scikit-learn pipelines.

Last modified on 2024-03-14