.ipynb .pdf

Colab

Contents

LAB 04.01 - Cleaning Data

!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/20201.xai4eng/master/content/init.py
import init; init.init(force_download=False); init.get_weblink()

init.endpoint

from local.lib.rlxmoocapi import submit, session
student = session.Session(init.endpoint).login( course_id=init.course_id, 
                                                session_id="UDEA", 
                                                lab_id="L04.01" )
init.get_weblink()

observe the following synthetic example with missing data

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import Image
import numpy as np
import seaborn as sns

n = 20
place = np.r_[["Medellin", "Bogota", "Madrid"]][(np.random.randint(3, size=n))]
age = np.random.randint(50, size=n)+10
children = np.r_[[(np.random.randint(2) if i<30 else (np.random.randint(4))) for i in age]]
risk = np.r_[[np.random.random()*(.2 if i=="Medellin" else .8) for i in place]].round(3)
risk[np.random.permutation(len(risk))[:5]]=np.nan

d01 = pd.DataFrame([age, risk, children, place], index=["age", "risk", "children", "place"]).T
d01.to_csv("risk.csv", index=False)
d01

	age	risk	children	place
0	59	0.112	2	Bogota
1	36	0.093	1	Medellin
2	25	0.638	1	Madrid
3	57	NaN	1	Madrid
4	59	0.641	0	Madrid
5	50	0.111	3	Bogota
6	58	0.633	2	Bogota
7	13	0.025	0	Medellin
8	10	NaN	1	Bogota
9	58	0.299	3	Madrid
10	52	0.004	1	Medellin
11	47	0.2	0	Madrid
12	55	NaN	3	Bogota
13	44	0.742	3	Madrid
14	27	NaN	0	Madrid
15	54	0.567	2	Bogota
16	28	0.028	0	Medellin
17	24	0.299	1	Bogota
18	57	NaN	0	Medellin
19	40	0.081	2	Medellin

observe, in particular, that risk in Medellín is usually lower than in Bogotá, so we will try to fix missing data using this fact.

k = d01[d01.place=="Bogota"]["risk"].dropna()
plt.scatter(k, [0]*len(k), label="Bogota")
k = d01[d01.place=="Medellin"]["risk"].dropna()
plt.scatter(k, [1]*len(k), label="Medellin")
k = d01[d01.place=="Madrid"]["risk"].dropna()
plt.scatter(k, [2]*len(k), label="Madrid")
plt.grid();
plt.xlabel("risk level")
plt.ylabel("city")
plt.legend()

<matplotlib.legend.Legend at 0x7f25b2ddf7d0>

Task 1. FillNA in risk with corresponding city average

Observe that the above dataframe has been stored in the file risk.csv. You will have to fill in the missing values in the risk column with the related city mean in the following way:

1. Download the file risk.csv

2. Compute the mean risk per city

3. Substitute any missing value in the risk column by the corresponding city mean

4. Create a new csv file named risk_fixed.csv, with the exact same structure but with the missing values replaced

5. Upload your risk_fixed.csv file to the notebook environment

6. Run the evaluation cell below

Use the tool of your choice

(Excel, Orange, your programming language, or even this notebook if you can program python)

For Python, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable r01

use three decimal places for precision

Example

for the following data

Image("local/imgs/cities.png", width=200)

you must create a file with the following content

Image("local/imgs/cities-riskfree.png", width=200)

your solution

r01 = pd.read_csv("risk_fixed.csv")
r01

submit your answer

student.submit_task(globals(), task_id="task_01");

Task 2. Standardize age so that min=0, max=1

Standardizing values is, in certain cases, a necesity for ML models, providing stability and increased performance.

In this task you will have to standardize the column age so that all values stay in the [0,1] interval. Given any value \(x_i\), its corresponsing stardardized value \(s_i\) will be:

\[s_i = \frac{s_i-min}{max-min}\]

where \(min\), \(max\) is the minimum and maximum ages respectively

You must use again the file risk.csv and create and upload a file named age_minmax.csv with your answer. You should only modify the age column, leaving the rest as you find them in the csv file.

For Python, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable r02

For the previous example, the correct answer would be

Image("local/imgs/cities-ageminmax.png")

load your file

r02 = pd.read_csv("age_minmax.csv")
r02

submit your answer

student.submit_task(globals(), task_id="task_02");

Task 3. Standardize age so that \(\mu=0\) and \(\sigma=1\)

In this task you will have to standardize the column age so that all values stay have zero mean and standard deviation of 1. Given any value \(x_i\), its corresponsing stardardized value \(s_i\) will be:

\[s_i = \frac{s_i-\mu}{\sigma}\]

where \(\mu\) is the mean of all age values, and \(\sigma\) is the standard deviation.

You must use again the file risk.csv and create and upload a file named age_meanstd.csv with your answer. You should only modify the age column, leaving the rest as you find them in the csv file.

For Python, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable r03

For the previous example, the correct answer would be

Image("local/imgs/cities-agemeanstd.png")

load your file

r03 = pd.read_csv("age_meanstd.csv")
r03

submit your answer

student.submit_task(globals(), task_id="task_03");

Task 4. Create a one-hot encoding for place

substitute the column place for three new columns with onehot encoding. You must use again the file risk.csv and create and upload a file named place_onehot.csv with your answer.

For Python, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable r04

The solution for the example above should look like this. Observe that you must name the columns as shown here:

Image("local/imgs/cities_onehot.png")

load your file

r04 = pd.read_csv("place_onehot.csv")
r04

submit your answer

student.submit_task(globals(), task_id="task_04");

04.04 - COMPARING SAMPLES LAB 04.02 - Buiding Datasets