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docs(numpy): fix audits format

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eslopfer 2 years ago
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  1. 76
      subjects/ai/numpy/audit/README.md

76
subjects/ai/numpy/audit/README.md
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@ -1,6 +1,6 @@
#### Exercise 0: Environment and libraries #### Exercise 0: Environment and libraries
##### The exercice is validated if all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### Install the virtual environment with `requirements.txt` ##### Install the virtual environment with `requirements.txt`
@ -12,9 +12,9 @@
###### Does it print `Python 3.8.x`? x could be any number from 0 to 9 ###### Does it print `Python 3.8.x`? x could be any number from 0 to 9
##### Does `import jupyter` and `import numpy` run without any error ? ###### Does `import jupyter` and `import numpy` run without any error?
###### Have you used the followingthe command `jupyter notebook --port 8891` ? ###### Have you used the following command `jupyter notebook --port 8891`?
###### Is there a file named `Notebook_ex00.ipynb` in the working directory? ###### Is there a file named `Notebook_ex00.ipynb` in the working directory?
@ -54,19 +54,17 @@
##### Delete all the cells you added for the audit and restart the notebook ##### Delete all the cells you added for the audit and restart the notebook
TODO
--- ---
--- ---
#### Exercise 2: Zeros #### Exercise 2: Zeros
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated is the solution uses `np.zeros` and if the shape of the array is `(300,)` ###### For question 1, does the solution use `np.zeros` and is the shape of the array `(300,)`?
##### The question 2 is validated if the solution uses `reshape` and the shape of the array is `(3, 100)` ###### For question 2, does the solution use `reshape` and is the shape of the array `(3, 100)`?
--- ---
@ -74,15 +72,15 @@ TODO
#### Exercise 3: Slicing #### Exercise 3: Slicing
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if the solution doesn't involve a for loop or writing all integers from 1 to 100 and if the array is: `np.array([1,...,100])`. The list from 1 to 100 can be generated with an iterator: `range`. ###### For question 1, is validated if the solution doesn't involve a for loop or writing all integers from 1 to 100 and if the array is: `np.array([1,...,100])`. The list from 1 to 100 can be generated with an iterator: `range`. Were the previous requirements fulfilled?
##### The question 2 is validated if the solution is: `integers[::2]` ###### For question 2, is the solution `integers[::2]`?
##### The question 3 is validated if the solution is: `integers[::-2]` ###### For question 3, is the solution `integers[::-2]`?
##### The question 4 is validated if the array is: `np.array([0, 1,0,3,4,0,...,0,99,100])`. There are at least two ways to get this results without for loop. The first one uses `integers[1::3] = 0` and the second involves creating a boolean array that indexes the array: ###### For question 4, is the array `np.array([0, 1,0,3,4,0,...,0,99,100])`? There are at least two ways to get this results without for loop. The first one uses `integers[1::3] = 0` and the second involves creating a boolean array that indexes the array:
```python ```python
mask = (integers+1)%3 == 0 mask = (integers+1)%3 == 0
@ -95,15 +93,15 @@ integers[mask] = 0
#### Exercise 4: Random #### Exercise 4: Random
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### For this exercise, as the results may change depending on the version of the package or the OS, I give the code to correct the exercise. If the code is correct and the output is not the same as mine, it is accepted. ##### For this exercise, as the results may change depending on the version of the package or the OS, I give the code to correct the exercise. If the code is correct and the output is not the same as mine, it is accepted.
##### The question 1 is validated if the solution is: `np.random.seed(888)` ###### For question 1, is the solution `np.random.seed(888)`?
##### The question 2 is validated if the solution is: `np.random.randn(100)`. The value of the first element is `0.17620087373662233`. ###### For question 2, is the solution `np.random.randn(100)`? The value of the first element is `0.17620087373662233`.
##### The question 3 is validated if the solution is: `np.random.randint(1,11,(8,8))`. ###### For question 3, is the solution `np.random.randint(1,11,(8,8))`?
```console ```console
Given the NumPy version and the seed, you should have this output: Given the NumPy version and the seed, you should have this output:
@ -118,7 +116,7 @@ integers[mask] = 0
[ 4, 4, 9, 2, 8, 5, 9, 5]]) [ 4, 4, 9, 2, 8, 5, 9, 5]])
``` ```
##### The question 4 is validated if the solution is: `np.random.randint(1,18,(4,2,5))`. ###### For question 4, is the solution `np.random.randint(1,18,(4,2,5))`?
```console ```console
Given the NumPy version and the seed, you should have this output: Given the NumPy version and the seed, you should have this output:
@ -142,15 +140,15 @@ integers[mask] = 0
#### Exercise 5: Split, concatenate, reshape arrays #### Exercise 5: Split, concatenate, reshape arrays
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if the generated array is based on an iterator as `range` or `np.arange`. Check that 50 is part of the array. ###### For question 1, is the generated array based on an iterator as `range` or `np.arange`? Check that 50 is part of the array.
##### The question 2 is validated if the generated array is based on an iterator as `range` or `np.arange`. Check that 100 is part of the array. ###### For question 2, is the generated array based on an iterator as `range` or `np.arange`? Check that 100 is part of the array.
##### The question 3 is validated if the array is concatenated this way `np.concatenate(array1,array2)`. ###### For question 3, is the array concatenated this way `np.concatenate(array1,array2)`?
##### The question 4 is validated if the result is: ###### For question 4, is the result the following?
```console ```console
array([[ 1, ... , 10], array([[ 1, ... , 10],
@ -168,13 +166,13 @@ https://jakevdp.github.io/PythonDataScienceHandbook/ (section: The Basics of Num
#### Exercise 6: Broadcasting and Slicing #### Exercise 6: Broadcasting and Slicing
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if the output is the same as: ###### For question 1, is the output the same as the following?
`np.ones([9,9], dtype=np.int8)` `np.ones([9,9], dtype=np.int8)`
##### The question 2 is validated if the output is ###### For question 2, is the output the following?
```console ```console
array([[1, 1, 1, 1, 1, 1, 1, 1, 1], array([[1, 1, 1, 1, 1, 1, 1, 1, 1],
@ -205,9 +203,9 @@ Here is an example of a possible solution:
#### Exercise 7: NaN #### Exercise 7: NaN
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### This question is validated if, without having used a for loop or having filled the array manually, the output is: ###### Without having used a for loop or having filled the array manually, is the output the following?
```console ```console
[[ 7. 1. 7.] [[ 7. 1. 7.]
@ -244,13 +242,11 @@ There are two steps in this exercise:
#### Exercise 8: Wine #### Exercise 8: Wine
##### The exercice is validated is all questions of the exercice are validated ##### The exercise is validated if all questions of the exercise are validated
##### This question is validated if the text file has successfully been loaded in a NumPy array with
`genfromtxt('winequality-red.csv', delimiter=',')` and the reduced arrays weights **76800 bytes** ###### Has the text file successfully been loaded in a NumPy array with `genfromtxt('winequality-red.csv', delimiter=',')` and the reduced arrays weights **76800 bytes**?
##### This question is validated if the output is ###### Is the output the following?
```python ```python
array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. , array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. ,
@ -263,11 +259,11 @@ array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. ,
This slicing gives the answer `my_data[[1,6,11],:]`. This slicing gives the answer `my_data[[1,6,11],:]`.
##### This question is validated if the answer if False. There many ways to get the answer: find the maximum or check values greater than 20. ###### Is the answer False? There are many ways to get the answer: find the maximum or check values greater than 20.
##### This question is validated if the answer is 10.422983114446529. ###### Is the answer 10.422983114446529?
##### This question is validated if the answers is: ###### Is the answer the following?
```console ```console
pH stats pH stats
@ -281,9 +277,9 @@ This slicing gives the answer `my_data[[1,6,11],:]`.
> *Note: Using `percentile` or `median` may give different results depending on the duplicate values in the column. If you do not have my results please use `percentile`.* > *Note: Using `percentile` or `median` may give different results depending on the duplicate values in the column. If you do not have my results please use `percentile`.*
##### This question is validated if the answer is ~`5.2`. The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`. ###### Is the answer ~`5.2`? The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`.
##### This question is validated if the output for the best wines is: ###### Is the output for the best wines the following?
```python ```python
array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444, array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444,
@ -291,7 +287,7 @@ array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444,
12.09444444, 8. ]) 12.09444444, 8. ])
``` ```
##### This question is validated if the output for the bad wines is: ###### Is the output for the bad wines the following?
```python ```python
array([ 8.36 , 0.8845 , 0.171 , 2.635 , 0.1225 , 11. , array([ 8.36 , 0.8845 , 0.171 , 2.635 , 0.1225 , 11. ,
@ -306,7 +302,7 @@ This can be done in three steps: Get the max, create a boolean mask that indicat
#### Exercise 9: Football tournament #### Exercise 9: Football tournament
##### This exercise is validated if the output is: ###### Is the output the following?
```console ```console
[[0 3 1 2 4] [[0 3 1 2 4]

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