Branch-AI/subjects/ai/machine-learning-pipeline/audit/README.md

Exercise 0: Environment and libraries

The exercice is validated is all questions of the exercice are validated.

Activate the virtual environment. If you used conda run conda activate your_env.

Run python --version.

Does it print Python 3.x? x >= 8

Does import jupyter, import numpy, import pandas, import matplotlib and import sklearn run without any error?

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Exercise 1: Imputer 1

The exercise is validated is all questions of the exercise are validated.

The question 1 is validated if the imp_mean.statistics_ returns:

    array([ 4., 13.,  6.])

The question 2 is validated if the filled train set is:

    array([[ 7.,  6.,  5.],
        [ 4., 13.,  5.],
        [ 1., 20.,  8.]])

The question 3 is validated if the filled test set is:

    array([[ 4.,  1.,  2.],
        [ 7., 13.,  9.],
        [ 4.,  2.,  4.]])

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Exercise 2: Scaler

The exercise is validated is all. questions of the exercise are validated.

The question 1 is validated if the scaled train set is as below. And by definition, the mean on the axis 0 should be array([0., 0., 0.]) and the standard deviation on the axis 0 should be array([1., 1., 1.]).

array([[ 0.        , -1.22474487,  1.33630621],
       [ 1.22474487,  0.        , -0.26726124],
       [-1.22474487,  1.22474487, -1.06904497]])

The question 2 is validated if the scaled test set is:

array([[ 1.22474487, -1.22474487,  0.53452248],
       [ 2.44948974,  3.67423461, -1.06904497],
       [ 0.        ,  1.22474487,  0.53452248]])

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Exercise 3: One hot Encoder

The exercise is validated is all questions of the exercise are validated.

The question 1 is validated if the output is:

|    |   ('C++',) |   ('Java',) |   ('Python',) |
|---:|-----------:|------------:|--------------:|
|  0 |          0 |           0 |             1 |
|  1 |          0 |           1 |             0 |
|  2 |          0 |           1 |             0 |
|  3 |          1 |           0 |             0 |

The question 2 is validated if the output is:

|    |   ('C++',) |   ('Java',) |   ('Python',) |
|---:|-----------:|------------:|--------------:|
|  0 |          0 |           0 |             1 |
|  1 |          0 |           1 |             0 |
|  2 |          0 |           0 |             0 |
|  3 |          1 |           0 |             0 |

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Exercise 4: Ordinal Encoder

The exercise is validated is all questions of the exercise are validated

The question 1 is validated if the output of the Ordinal Encoder on the train set is:

array([[2.],
       [0.],
       [1.]])

Check that enc.categories_ returns[array(['bad', 'neutral', 'good'], dtype=object)].

The question 2 is validated if the output of the Ordinal Encoder on the test set is:

array([[2.],
       [2.],
       [0.]])

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Exercise 5: Categorical variables

The exercise is validated is all questions of the exercise are validated

The question 1 is validated if the number of unique values per feature outputted are:

age             6
menopause       3
tumor-size     11
inv-nodes       6
node-caps       2
deg-malig       3
breast          2
breast-quad     5
irradiat        2
dtype: int64

The question 2 is validated if the transformed test set by the OneHotEncoder fitted on the train set is as below. Make sure the transformer takes as input a dataframe with the columns in the order defined ['node-caps' , 'breast', 'breast-quad', 'irradiat'] :

#First 10 rows:

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.]])

The question 3 is validated if the transformed test set by the OrdinalEncoder fitted on the train set is as below with the columns ordered as ["menopause", "age", "tumor-size","inv-nodes", "deg-malig"]:

#First 10 rows:

array([[1., 2., 5., 0., 1.],
   [1., 3., 4., 0., 1.],
   [1., 2., 4., 0., 1.],
   [1., 3., 2., 0., 1.],
   [1., 4., 3., 0., 1.],
   [1., 4., 5., 0., 0.],
   [2., 5., 4., 0., 1.],
   [2., 5., 8., 0., 1.],
   [0., 2., 3., 0., 2.],
   [1., 3., 6., 4., 2.]])

The question 4 is validated if the column transformer transformed that is fitted on the X_train, transformed the X_test as:

# First 2 rows:

array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 2., 5., 0., 1.],
       [1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 3., 4., 0., 1.]])

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Exercise 6: Pipeline

The question 1 is validated if the prediction on the test set are:

array([0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 2, 2, 0, 0,
       0, 2, 2, 2, 0, 1, 0, 0, 1, 0, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2,
       0, 1, 1, 1, 1, 1])

and the score on the test set is 98%.

Note: Keep in mind that having a 98% accuracy is not common when working with real life data. Every time you have a score > 97% check that there's no leakage in the data. On financial data set, the ratio signal to noise is low. Trying to forecast stock prices is a difficult problem. Having an accuracy higher than 70% should be interpreted as a warning to check data leakage!