Purpose on making transformer

 See this code

from sklearn.base import BaseEstimator, TransformerMixin

rooms_ix, bedrooms_ix, population_ix, households_ix = 3,4,5,6

class CombinedAttributesAdder(BaseEstimator, TransformerMixin):

    def __init__(self, add_bedrooms_per_room = True):

        self.add_bedrooms_per_room = add_bedrooms_per_room

    def fit(self, X, y=None):

        return self 

    def transform(self, X, y=None):

        rooms_per_household = X[:,rooms_ix]/X[:,households_ix]

        population_per_household = X[:, population_ix]/X[:,households_ix]

        if self.add_bedrooms_per_room:

            bedrooms_per_room = X[:,bedrooms_ix]/X[:,rooms_ix]

            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]

        else:

            return np.c_[X, rooms_per_household,population_per_household]

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)

housing_extra_attribs = attr_adder.transform(housing.values)

what does it mean? 

Sure! Here is a clear, simple line-by-line explanation of your transformer code.

I’ll explain every row in a beginner-friendly way.

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📌 

Line-by-line explanation

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1. Import classes needed to create a custom transformer

from sklearn.base import BaseEstimator, TransformerMixin

• BaseEstimator → makes your transformer compatible with scikit-learn (e.g., it can be used in pipelines).

• TransformerMixin → gives the class a default .fit_transform() behavior.

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2. Indices of the columns in the data

rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6

This means:

• Column 3 = total rooms

• Column 4 = total bedrooms

• Column 5 = population

• Column 6 = households

These are index positions inside your housing.values NumPy array.

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3. Start defining a new transformer class

class CombinedAttributesAdder(BaseEstimator, TransformerMixin):

You are creating a new class that can:

• fit

• transform

data like scikit-learn transformers do.

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4. Constructor (runs when the object is created)

    def __init__(self, add_bedrooms_per_room=True):
        self.add_bedrooms_per_room = add_bedrooms_per_room

• Saves whether you want to add the new column bedrooms_per_room.

• Setting it here makes the transformer work well inside pipelines.

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5. The fit method

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

• This transformer does not need to learn anything from the data.

• So .fit() simply returns itself.

• Required by scikit-learn’s interface.

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6. The transform method (creates new features)

    def transform(self, X, y=None):

This method is run when you call .transform() or inside a pipeline.

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7. Compute rooms per household

        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]

• Takes column 3 (rooms)

• Divides by column 6 (households)

  → A new, more helpful feature.

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8. Compute population per household

        population_per_household = X[:, population_ix] / X[:, households_ix]

Same idea:

• Column 5 (population)

• Divided by column 6 (households)

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9. If user wants bedrooms_per_room, calculate it

        if self.add_bedrooms_per_room:
            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]

Computes:

• Column 4 (bedrooms)

• Divided by column 3 (rooms)

This shows how many bedrooms each room has → a useful density measure.

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10. Return X plus the new columns

            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]

np.c_[] = concatenate columns side-by-side.

So you return:

• original data

• • rooms_per_household

• • population_per_household

• • bedrooms_per_room

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11. If bedrooms_per_room is disabled, return fewer columns

        else:
            return np.c_[X, rooms_per_household, population_per_household]

Only returns two engineered features.

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12. Create a transformer object

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)

• Creates the transformer

• Tells it not to add the bedrooms_per_room column.

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13. Apply the transformer to the housing data

housing_extra_attribs = attr_adder.transform(housing.values)

• housing.values converts the DataFrame to a NumPy array

• Transformer adds new features

• Returns a new expanded dataset containing the extra columns

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🎉 

In one simple sentence

This transformer adds new useful columns (rooms per household, population per household, and optionally bedrooms per room) to improve the model’s learning.

Here’s a simple, visual “before and after” table that shows exactly what your transformer does to the data.

I’ll use small sample numbers so you can clearly see how new features are created.

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🟦 

Before: Original Columns (from housing.values)

Index	Total Rooms	Total Bedrooms	Population	Households
3	6	1	150	1
3	8	2	200	2
3	10	3	300	2

(Only showing the 4 relevant columns.)

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🟩 

After: New Columns Added by Transformer

New columns created:

• rooms_per_household = rooms / households

• population_per_household = population / households

• bedrooms_per_room = bedrooms / rooms (only if enabled)

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If 

add_bedrooms_per_room=False

(Your current setting)

Total Rooms	Total Bedrooms	Population	Households	rooms_per_household	population_per_household
6	1	150	1	6/1 = 6	150/1 = 150
8	2	200	2	8/2 = 4	200/2 = 100
10	3	300	2	10/2 = 5	300/2 = 150

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If 

add_bedrooms_per_room=True

(Only if you turn it on)

Total Rooms	Total Bedrooms	Population	Households	rooms_per_household	population_per_household	bedrooms_per_room
6	1	150	1	6	150	1/6 = 0.167
8	2	200	2	4	100	2/8 = 0.25
10	3	300	2	5	150	3/10 = 0.3

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🎉 

What you can see clearly

Your transformer:

✔ keeps all original columns

✔ adds useful “ratio” columns

✔ improves model quality by giving it more informative patterns