Content from Using Markdown

Last updated on 2022-04-22 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • How do you write a lesson using Markdown and sandpaper?

Objectives

  • Explain how to use markdown with The Carpentries Workbench
  • Demonstrate how to include pieces of code, figures, and nested challenge blocks

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files and R Markdown for dynamic files that can render code into output. Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.

Inline instructor notes can help inform instructors of timing challenges associated with the lessons. They appear in the “Instructor View”

Challenge 1: Can you do it?

What is the output of this command?

R 

paste("This", "new", "lesson", "looks", "good")

OUTPUT 

[1] "This new lesson looks good"

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

You can use standard markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
You belong in The Carpentries!

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally

Content from Exploring the dataset

Last updated on 2022-08-31 | Edit this page

Estimated time: 40 minutes

Overview

Questions

  • What should I look for when exploring a dataset for machine learning?
  • How do I split the dataset using scikitlearn?

Objectives

  • Get to know the weather prediction dataset
  • Know the steps in the machine learning workflow
  • Know how to do exploratory data analysis
  • Know how to split the data in train and test set

Supervised versus unsupervised machine learning

Remember we make the following distinction:

  • Supervised models try to predict a (dependent) variable, called the target, that is available during training time
  • Unsupervised models try to find structure or patterns in the data, without a specific target

Challenge: Supervised or unsupervised

For the following problems, do you think you need a supervised or unsupervised approach?

  1. Find numerical representations for words in a language (word vectors) that contain semantic information on the word
  2. Determine whether a tumor is benign or malign, based on an MRI-scan
  3. Predict the age of a patient, based on an EEG-scan
  4. Cluster observations of plants into groups of individuals that have similar properties, possibly belonging to the same species
  5. Your own problem and dataset
  1. Unsupervised
  2. Supervised
  3. Supervised
  4. Unsupervised
  5. Discuss!

Machine learning workflow

For most machine learning approaches, we have to take the following steps:

  1. Data cleaning and preperation
  2. Split data into train and test set
  3. Optional: Feature selection
  4. Use cross validation to:
    • Train one or more ML models on the train set
    • Choose optimal model / parameter settings based on some metric
  5. Calculate final model performance on the test set

Discussion

Why is it important to reserve part of your data as test set? What can go wrong in choosing a test set?

Importing packages

We import pandas for tabular data processing and seaborn for data visualization.

PYTHON 

import seaborn as sns
import pandas as pd

Weather prediction dataset

Here we want to work with the weather prediction dataset. It contains daily weather observations from 18 different European cities or places through the years 2000 to 2010. For all locations the data contains the variables ‘mean temperature’, ‘max temperature’, and ‘min temperature’. In addition, for multiple of the following variables are provided: ‘cloud_cover’, ‘wind_speed’, ‘wind_gust’, ‘humidity’, ‘pressure’, ‘global_radiation’, ‘precipitation’, ‘sunshine’, but not all of them are provided for all locations. A more extensive description of the dataset including the different physical units is given in accompanying metadata file.

There are several tasks that one could think of given this data. For now, we are intested in the question: how likely is it to be nice weather for a barbecue tomorrow (that is, sunny and no rain) in Basel, given the weather conditions in all cities today?

Challenge: What kind of task?

What kind of machine learning task is this?

A supervised binary classification task.

Loading the data

We load the data directly from a URL.

PYTHON 

url_features = 'https://zenodo.org/record/5071376/files/weather_prediction_dataset.csv?download=1'
url_labels = 'https://zenodo.org/record/5071376/files/weather_prediction_bbq_labels.csv?download=1'
weather_features = pd.read_csv(url_features)
weather_labels = pd.read_csv(url_labels)

Let’s take a look at the first 5 rows of the features:

PYTHON 

weather_features.head()

And let’s look at the labels data:

PYTHON 

weather_labels.head()

We can inspect the shape of the data:

PYTHON 

print(weather_labels.shape)

OUTPUT 

(3654, 18)

Let’s print all the column names:

PYTHON 

for c in weather_features.columns:
    print(c)

OUTPUT 

    DATE
    MONTH
    BASEL_cloud_cover
    BASEL_humidity
    ...

Challenge: Explore weather dataset

Explore the dataset with pandas:

  1. How many features do we have to predict the BBQ weather?
  2. How many samples does this dataset have?
  3. What datatype is the target label stored in?

1. & 2. How many features and samples??

PYTHON 

# Nr of columns, nr of rows:
weather_features.shape

OUTPUT 

(3654, 165)

So we have 3654 samples. Of the 165 columns, the first column is the date, we ommit this column for prediction. The second column is Month, this could be a useful feature but we will only use the numerical features for now. So we have 163 features left.

3. What datatype is the target label stored in?

The target class stored in the BASEL_BBQ_weather column of the labels dataset, and its type is a boolean:

PYTHON 

type(weather_labels['BASEL_BBQ_weather'][0])

OUTPUT 

numpy.bool_

Data selection

For the purposes of this lesson, we select only the first 3 years. We also remove all columns that cannot be used for prediction, and merge the features and labels into one dataframe.

PYTHON 

nr_rows = 365*3

weather_3years = weather_features.drop(columns=['DATE', 'MONTH'])
weather_3years = weather_3years[:nr_rows]

# Need to take next day as target!
weather_3years['BASEL_BBQ_weather'] = list(weather_labels[1:nr_rows+1]['BASEL_BBQ_weather'])

print(weather_3years.shape)

OUTPUT 

(1095, 164)

Let’s look at the head of the data we just created:

PYTHON 

weather_3years.head()

Split into train and test

Before doing further exploration of the data, we held out part of the data as test set for later. This way, no information from the test set will leak to the model we are going to create

PYTHON 

from sklearn.model_selection import train_test_split

PYTHON 

data_train, data_test = train_test_split(weather_3years, test_size=0.3, random_state=0)

PYTHON 

len(data_train), len(data_test)

OUTPUT 

(766, 329)

We write the train and test set to csv files because we will be needing them later. We create the data directory if it does not exist.

PYTHON 

import os

if not os.path.exists('data'):
    os.mkdir('data')
data_train.to_csv('data/weather_train.csv', index=False)
data_test.to_csv('data/weather_test.csv', index=False)

Some more data exploration using visualization

Let’s visualize the numerical feature columns. One nice visualization for datasets with relatively few attributes is the Pair Plot. This can be created using sns.pairplot(...). It shows a scatterplot of each attribute plotted against each of the other attributes. By using the hue='BASEL_BBQ_weather' setting for the pairplot the graphs on the diagonal are layered kernel density estimate plots.

Because we have so many features, we plot the features only for Basel itself (not the other cities).

Note that we use a list comprehension here, a short notation for creating a new list based on values in an existing list

PYTHON 

columns_basel = [c for c in data_train.columns if c.startswith('BASEL')]
data_plot = data_train[columns_basel]

PYTHON 

sns.pairplot(data_plot, hue='BASEL_BBQ_weather')

Output:

Exercise: Observe the pairplot

Discuss what you see in the scatter plots and write down any observations. Think of:

  • Are the classes easily separable based on these features?
  • What are potential difficulties for a classification algorithm?
  • What do you note about the units of the different attributes?

Key Points

  • Use the machine learning workflow to tackle a machine learning problem in a structured way
  • Get to know the dataset before diving into a machine learning task
  • Use an independent testset for evaluating your results

Content from Preparing your data for machine learning

Last updated on 2022-08-31 | Edit this page

Estimated time: 20 minutes

Overview

Questions

  • How do I check for missing data?
  • How can I scale the data?

Objectives

  • Know how to check for missing data
  • Be able to scale your data using scikitlearn’s scalers

Missing data

We splitted our data into train and test, but did not make any other modifications. To make our data fit for machine learning, we need to:

  • Handle missing, corrupt or incorrect data
  • Do feature normalization

Let’s start by looking how much missing data we have:

PYTHON 

import pandas as pd
weather_train = pd.read_csv('data/weather_train.csv')
weather_train.isna().sum().sum()

OUTPUT 

0

We have no missing values so we can continue.

It could also be that we have corrupt data, leading e.g. to outliers in the data set. The pair plot in the previous episode could have hinted to this. For this dataset, we don’t need to do anything about outliers.

Feature normalization

As we saw in the pairplot, the magnitudes of the different features are not directly comparable with each other. Some of the features are in mm, others in degrees celcius, and the scales are different.

Most Machine Learning algorithms regard all features together in one multi-dimensional space. To do calculations in this space that make sense, the features should be comparable to each other, e.g. they should be scaled. There are two options for scaling:

  • Normalization (Min-Max scaling)
  • Standardization (scale by mean and variance)

In this case, we choose min_max scaling, because we do not know much about the distribution of our features. If we know that (some) features have a normal distribution, it makes more sense to do standardization.

PYTHON 

import sklearn.preprocessing
min_max_scaler = sklearn.preprocessing.MinMaxScaler()

feature_names = weather_train.columns[:-1]

weather_train_scaled = weather_train.copy()
weather_train_scaled[feature_names] = min_max_scaler.fit_transform(weather_train[feature_names])

Exercise: Comparing distributions

Compare the distributions of the numerical features before and after scaling. What do you notice?

Let’s look at the statistics before scaling:

PYTHON 

weather_train.describe()

And after scaling:

PYTHON 

weather_train_scaled.describe()

We save the data to use in our next notebook

PYTHON 

weather_train_scaled.to_csv('data/weather_train_scaled.csv', index=False)

Key Points

  • You can check for missing data using pandas .isna() method
  • You can scale your data using a scaler from the sklearn.preprocessing module