Sklearn custom transformers with pipeline: all the input array dimensions for the concatenation axis must match exactly

Question

I am learning about sklearn custom transformers and read about the two core ways to create custom transformers:

1. by setting up a custom class that inherits from BaseEstimator and TransformerMixin, or
2. by creating a transformation method and passing it to FunctionTransformer.

I wanted to compare these two approaches by implementing a "meta-vectorizer" functionality: a vectorizer that supports either CountVectorizer or TfidfVectorizer and transforms the input data according to the specified vectorizer type.

However, I can't seem to get any of the two work when passing them to a sklearn.pipeline.Pipeline. I am getting the following error message in the fit_transform() step:

ValueError: all the input array dimensions for the concatenation axis must match 
exactly, but along dimension 0, the array at index 0 has size 6 and the array 
at index 1 has size 1

My code for option 1 (using a custom class):

class Vectorizer(BaseEstimator, TransformerMixin):
    def __init__(self, vectorizer:Callable=CountVectorizer(), ngram_range:tuple=(1,1)) -> None:
        super().__init__()
        self.vectorizer = vectorizer
        self.ngram_range = ngram_range
    def fit(self, X, y=None):
        return self 
    def transform(self, X, y=None):
        X_vect_ = self.vectorizer.fit_transform(X.copy())
        return X_vect_.toarray()

pipe = Pipeline([
    ('column_transformer', ColumnTransformer([
        ('lesson_type_category', OneHotEncoder(), ['Type']),
        ('comment_text_vectorizer', Vectorizer(), ['Text'])],
        remainder='drop')),
    ('model', LogisticRegression())])

param_dict = {'column_transformer__comment_text_vectorizer__vectorizer': \
[CountVectorizer(), TfidfVectorizer()]
}

randsearch = GridSearchCV(pipe, param_dict, cv=2, scoring='f1',).fit(X_train, y_train)

And my code for option 2 (creating a custom transformer from a function using FunctionTransformer):

def vectorize_text(X, vectorizer: Callable):
    X_vect_ = vectorizer.fit_transform(X)
    return X_vect_.toarray()

vectorizer_transformer = FunctionTransformer(vectorize_text, kw_args={'vectorizer': TfidfVectorizer()})

pipe = Pipeline([
    ('column_transformer', ColumnTransformer([
        ('lesson_type_category', OneHotEncoder(), ['Type']),
        ('comment_text_vectorizer', vectorizer_transformer, ['Text'])],
        remainder='drop')),
    ('model', LogisticRegression())])

param_dict = {'column_transformer__comment_text_vectorizer__kw_args': \
    [{'vectorizer':CountVectorizer()}, {'vectorizer': TfidfVectorizer()}]
}

randsearch = GridSearchCV(pipe, param_dict, cv=2, scoring='f1').fit(X_train, y_train)

Imports and sample data:

import pandas as pd 
from typing import Callable
import sklearn
from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV

df = pd.DataFrame([
    ['A99', 'hi i love python very much', 'c', 1],
    ['B07', 'which programming language should i learn', 'b', 0],
    ['A12', 'what is the difference between python django flask', 'b', 1],
    ['A21', 'i want to be a programmer one day', 'c', 0],
    ['B11', 'should i learn java or python', 'b', 1],
    ['C01', 'how much can i earn as a programmer with python', 'a', 0]
], columns=['Src', 'Text', 'Type', 'Target'])

Notes:

• As recommended in this question, I transformed all sparse matrices to dense arrays after the vectorization, as you can see in both cases: X_vect_.toarray().

Answer 1

The issue is that both CountVectorizer and TfidfVectorizer require their input to be 1D (and not 2D). In such cases the doc of ColumnTransformer states that parameter columns of the transformers tuple should be passed as a string rather than as a list.

columns: str, array-like of str, int, array-like of int, array-like of bool, slice or callable

Indexes the data on its second axis. Integers are interpreted as positional columns, while strings can reference DataFrame columns by name. A scalar string or int should be used where transformer expects X to be a 1d array-like (vector), otherwise a 2d array will be passed to the transformer. A callable is passed the input data X and can return any of the above. To select multiple columns by name or dtype, you can use make_column_selector.

Therefore, the following will work in your case (i.e. changing ['Text'] into 'Text').

class Vectorizer(BaseEstimator, TransformerMixin):
    def __init__(self, vectorizer:Callable=CountVectorizer(), ngram_range:tuple=(1,1)) -> None:
        super().__init__()
        self.vectorizer = vectorizer
        self.ngram_range = ngram_range
    def fit(self, X, y=None):
        return self 
    def transform(self, X, y=None):
        X_vect_ = self.vectorizer.fit_transform(X.copy())
        return X_vect_.toarray()

pipe = Pipeline([
    ('column_transformer', ColumnTransformer([
        ('lesson_type_category', OneHotEncoder(handle_unknown='ignore'), ['Type']),
        ('comment_text_vectorizer', Vectorizer(), 'Text')], remainder='drop')),
    ('model', LogisticRegression())])

param_dict = {'column_transformer__comment_text_vectorizer__vectorizer': [CountVectorizer(), TfidfVectorizer()]
}

randsearch = GridSearchCV(pipe, param_dict, cv=2, scoring='f1',).fit(X_train, y_train)

You can adjust the example with FunctionTransformer accordingly. Observe, as a final remark, that I had to pass handle_unknown='ignore' to OneHotEncoder to prevent the possibility that an error would have arisen in case of unknown categories seen during the test phase of your cross-validation (and not seen during the training phase).