public/subjects/ai/pipeline/audit

Exercise 0: Environment and libraries

The exercise is validated if all questions of the exercise 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

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

---

---

Exercise 1: Imputer 1

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

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

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

For question 2, is the filled train set the following?

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

For question 3, is the filled test set the following?

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

---

---

Exercise 2: Scaler

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

For question 1, is the scaled train set 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]])

For question 2, is the scaled test set the following?

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

---

---

Exercise 3: One hot Encoder

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

For question 1, is the output the following?

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

For question 2, is the output the following?

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

---

---

Exercise 4: Ordinal Encoder

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

For question 1, is the output of the Ordinal Encoder on the train set the following?

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

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

For question 2, is the output of the Ordinal Encoder on the test set the following?

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

---

---

Exercise 5: Categorical variables

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

For question 1, are the number of unique values per feature outputted the following?

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

For question 2, is the transformed test set by the OneHotEncoder fitted on the train set 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.]])

For question 3, is the transformed test set by the OrdinalEncoder fitted on the train set 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.]])

For question 4, is the column transformer transformed that is fitted on the X_train, transformed the X_test as below?

# 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.]])

---

---

Exercise 6: Pipeline

For question 1, are the predictions on the test set the following?

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 is the score on the test set 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!