# Create Custom Transformer Create a custom transformer, just as we did in the lecture video entitled "Custom Transformers", that performs two computations: 1. Adds an attribute to the end of the numerical data (i.e. new last column) that is equal to $\frac{x_1^3}{x_5}$ for each observation. In other words, for each instance, you will cube the $x_1$ column and then divide by the $x_5$ column. 2. Drops the entire $x_4$ feature column if the passed function argument `drop_x4` is `True` and doesn't drop the column if `drop_x4` is `False`. (See further instructions below.) You must name your custom transformer class `Assignment4Transformer`. Your class should include an input parameter called `drop_x4` with a default value of `True` that deletes the $x_4$ feature column when its value is `True`, but preserves the $x_4$ feature column when you pass a value of `False`. This transformer will be used in a pipeline. In that pipeline, an imputer will be run before this transformer. Keep in mind that the imputer will output an array, so this transformer must be written to accept an array. This is very important and a cause of many errors that students encounter. In other words, think about using NumPy in your transformer instead of Pandas. Additionally, this transformer will ONLY be given the numerical features of the data. The categorical feature will be handled elsewhere in the full pipeline. This means that your code for this transformer must reflect the absence of the categorical $x_3$ column when indexing data structures. Again this is very important and a cause of the second most number of errors that students encounter.

EBK JAVA PROGRAMMING

9th Edition

ISBN:9781337671385

Author:FARRELL

Publisher:FARRELL

Chapter8: Arrays

Section: Chapter Questions

Problem 9PE

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# Create Custom Transformer

Create a custom transformer, just as we did in the lecture video entitled "Custom Transformers", that performs two computations:

1. Adds an attribute to the end of the numerical data (i.e. new last column) that is equal to $\frac{x_1^3}{x_5}$ for each observation. In other words, for each instance, you will cube the $x_1$ column and then divide by the $x_5$ column.

2. Drops the entire $x_4$ feature column if the passed function argument `drop_x4` is `True` and doesn't drop the column if `drop_x4` is `False`. (See further instructions below.)

You must name your custom transformer class `Assignment4Transformer`. Your class should include an input parameter called `drop_x4` with a default value of `True` that deletes the $x_4$ feature column when its value is `True`, but preserves the $x_4$ feature column when you pass a value of `False`.

This transformer will be used in a pipeline. In that pipeline, an imputer will be run *before* this transformer. Keep in mind that the imputer will output an array, so **this transformer must be written to accept an array.** This is very important and a cause of many errors that students encounter. In other words, think about using NumPy in your transformer instead of Pandas.

Additionally, this transformer will ONLY be given the numerical features of the data. The categorical feature will be handled elsewhere in the full pipeline. This means that your code for this transformer **must reflect the absence of the categorical $x_3$ column** when indexing data structures. Again this is very important and a cause of the second most number of errors that students encounter.

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