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Exercise 5 Categorical variables
The goal of this exercise is to learn how to deal with Categorical variables with Ordinal Encoder, Label Encoder and One Hot Encoder. For this exercice I strongly suggest to use a recent version of sklearn >= 0.24.1 to avoid issues with the Ordinal Encoder.
Preliminary:
- Load the breast-cancer.csv file
- Drop
Classcolumn - Drop NaN values
- Split the data in a train set and test set (test set size = 20% of the total size) with
random_state=43.
-
Count the number of unique values per feature in the train set.
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Identify the variables ordinal variables, nominal variables and the target. Compute a One Hot Encoder transformation on the test set for all categorical features (no ordinal) in the following order
['node-caps' , 'breast', 'breast-quad', 'irradiat']. Here are the assumptions made on the variables:
age: Ordinal
['90-99' > '80-89' > '70-79' > '60-69' > '50-59' > '40-49' > '30-39' > '20-29' > '10-19']
menopause: Ordinal
['ge40'> 'premeno' >'lt40']
tumor-size: Ordinal
['55-59' > '50-54' > '45-49' > '40-44' > '35-39' > '30-34' > '25-29' > '20-24' > '15-19' > '10-14' > '5-9' > '0-4']
inv-nodes: Ordinal
['36-39' > '33-35' > '30-32' > '27-29' > '24-26' > '21-23' > '18-20' > '15-17' > '12-14' > '9-11' > '6-8' > '3-5' > '0-2']
node-caps: One Hot
['yes' 'no']
deg-malig: Ordinal
[3 > 2 > 1]
breast: One Hot
['left' 'right']
breast-quad: One Hot
['right_low' 'left_low' 'left_up' 'central' 'right_up']
irradiat: One Hot
['recurrence-events' 'no-recurrence-events']
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Fit on the train set
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Transform the test set
Example of expected output:
# One Hot encoder on: ['node-caps' , 'breast', 'breast-quad', 'irradiat']
input: ohe.transform(X_test[ohe_cols])[:10]
output:
array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0.],
[1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 1.]])
input: ohe.get_feature_names(ohe_cols)
output:
array(['node-caps_no', 'node-caps_yes', 'breast_left', 'breast_right',
'breast-quad_central', 'breast-quad_left_low',
'breast-quad_left_up', 'breast-quad_right_low',
'breast-quad_right_up', 'irradiat_no', 'irradiat_yes'],
dtype=object)
- Create one Ordinal encoder for all Ordinal features in the following order
["menopause", "age", "tumor-size","inv-nodes", "deg-malig"]on the test set. The documentation of Scikit-learn is not clear on how to perform this on many columns at the same time. Here's a hint:
If the ordinal data set is (subset of two columns but I keep all rows for this example):
| | menopause | deg-malig |
|---:|:--------------|------------:|
| 0 | premeno | 3 |
| 1 | ge40 | 1 |
| 2 | ge40 | 2 |
| 3 | premeno | 3 |
| 4 | premeno | 2 |
The first step is to create a dictionnary or a list - the most recent version of sklearn take as input lists:
dict_ = {0: ['lt40', 'premeno' , 'ge40'], 1:[1,2,3]}
Then to instantiate an OrdinalEncoder:
oe = OrdinalEncoder(dict_)
Now that you have enough information:
- Fit on the train set
- Transform the test set
- Use a
make_column_transformerto combine the two Encoders.
- Fit on the train set
- Transform the test set
Hint: Check the first ressource
Note: The version 0.22 of Scikit-learn can't handle get_feature_names on OrdinalEncoder. If the column transformer contains an OrdinalEncoder, the method returns this error:
AttributeError: Transformer ordinalencoder (type OrdinalEncoder) does not provide get_feature_names.
It means that if you want to use the Ordinal Encoder, you will have to create a variable that contains the columns name in the right order. This step is not required in that exercise
Ressources: