TODO 9Now let's wrap our one-hot code in a class that can be used with Sklearn's pipeline functionality. Finish the OneHotEncoding class which produces one-hot encodings and stores the columns names of the newly generated one-hot for reference later.In the transform() method, convert the input X into one-hot encodings using pd.get_dummies(). Store the output into the variable one_hot. It's similar to what you did in TODO 8.Store the names of the columns for our one-hot encoding one_hot so we can access them later if needed. Store the output into the class variable self.feature_namesThink about how you access the columns of a DataFrame!class OneHotEncoding(BaseEstimator, TransformerMixin): def __init__(self): self.feature_names = None def fit(self, X: pd.DataFrame, y: pd.DataFrame = None): # We don't need to set/learn any variables so # we just need to return a reference to the object with 'self' return self def transform(self, X: pd.DataFrame, y: pd.DataFrame = None): # TODO 9.1 one_hot = # TODO 9.2 self.feature_names = return one_hot

The task is to develop a class named "OneHotEncoding" that produces one-hot encodings of categorical…

Answered: TODO 9 Now let's wrap our one-hot code…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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TODO 9

Now let's wrap our one-hot code in a class that can be used with Sklearn's pipeline functionality. Finish the OneHotEncoding class which produces one-hot encodings and stores the columns names of the newly generated one-hot for reference later.

In the transform() method, convert the input X into one-hot encodings using pd.get_dummies(). Store the output into the variable one_hot. It's similar to what you did in TODO 8.
Store the names of the columns for our one-hot encoding one_hot so we can access them later if needed. Store the output into the class variable self.feature_names
1. Think about how you access the columns of a DataFrame!

class OneHotEncoding(BaseEstimator, TransformerMixin):
def __init__(self):
self.feature_names = None

def fit(self, X: pd.DataFrame, y: pd.DataFrame = None):
# We don't need to set/learn any variables so
# we just need to return a reference to the object with 'self'
return self

def transform(self, X: pd.DataFrame, y: pd.DataFrame = None):
# TODO 9.1
one_hot =

# TODO 9.2
self.feature_names =

return one_hot

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