Orange3 Educational Add-on

Widgets in Educational Add-on demonstrate several key data mining and machine learning procedures. The widgets are useful for beginners to understand the inner working of key algorithms in the data mining and for teachers to be able to visually explain various methods in a classroom.

Widgets

Google Sheets

Read data from a Google Sheets spreadsheet.

Outputs

• Data: data set from the Google Sheets service.

Description

The widget reads data from the Google Sheets service. To use the widget, click the Share button in a selected spreadsheet, copy the provided link and paste it into the widget’s URL line. Press enter to load the data. To observe the data in real time, use the Reload function.

1. Enter the link to the spreadsheet. Press Enter to load the data. Set reload if you wish to observe the updates in real time.
2. Information on the data set: name and attributes.
3. If Commit Automatically is ticked, the data will be automatically communicated downstream. Alternatively, press Commit.

Example

This widget is used for loading the data. We have used the link from the Google Sheets: https://goo.gl/jChYki. This is a fictional data on hamsters and rabbits, of which some have the disease and some don’t. Use the Data Table to observe the loaded data in a spreadsheet.

EnKlik Anketa

Import data from EnKlikAnketa (1ka.si) public URL.

Outputs

• Data: survey results

The EnKlik Anketa widget retrieves survey results obtained from the EnKlikAnketa service. You need to create a public link to to retrieve the results. Go to the survey you wish to retrieve, then select Data (Podatki) tab and create a public link (javna povezava) at the top right corner.

Then insert the link into the Public link URL field. The link should look something like this: https://www.1ka.si/podatki/123456/78A9B1CD/.

1. A public link to the survey results. To observe the results live, set the reload rate (5s - 5 min).
2. Attribute list. You can change the attribute type and role, just like in the File widget.
3. Survey meta information.
4. Tick the box on the left to commit the changes automatically. Alternatively, click Commit.
5. Access widget help.

Example

EnKlik Anketa widget is great for observing results from online surveys. We have created a sample survey and imported it into the widget. We have 41 responses and we have asked 8 questions, 7 of which were recognized as features and 1 as a meta attribute.

The widget sets questions from the survey as feature names. This, however, might be slightly impractical for analytical purposes, as we can see in the Data Table. We will shorten the names with Edit Domain widget.

Edit Domain enables us to change attribute names and even rename attribute values for discrete attributes. Now our attribute names are much easier to work with, as we can see in Data Table (1).

Interactive k-Means

Educational widget that shows the working of a k-means clustering.

Inputs

• Data: input data set

Outputs

• Data: data set with cluster annotation
• Centroids: centroids position

Description

The aim of this widget is to show the working of a k-means clustering algorithm on two attributes from a data set. The widget applies k-means clustering to the selected two attributes step by step. Users can step through the algorithm and see how it works.

1. Select attributes for x and y axis.
2. Number of centroids: set the number of centroids. Randomize: randomly assigns position of centroids. If you want to add centroid on a particular position in the graph, click on this position. If you want to move the centroid, drag and drop it on the desired position. Show membership lines: if ticked, connection between data points and closest centroids are shown.
3. Recompute centroids or Reassign membership: step through different stages of the algorithm. Recompute centroids moves centroids to new positions, based on the most central position of the data assigned to the centroid. Reassign membership reassigns data points to the centroid they are the closest to. Step back: make a step back in the algorithm. Run: step through the algorithm automatically. Speed: set the speed of automatic stepping.
4. Save Image saves the image to the computer in a .svg or .png format.

Example

Here are two possible schemas that show how the Interactive k-Means widget can be used. You can load the data from File or use any other data source, such as Paint Data. Interactive k-Means widget also produces a data table with results of clustering and a table with centroids positions. These data can be inspected with the Data Table widget.

Let us demonstrate the working of the widget on Iris data set. We provide the data using File. Then we open Interactive k-Means. Say, we will demonstrate k-Means on petal length and petal width attributes, so we set them as X and Y parameters. We also decided to perform clustering for 3 clusters. This is set as the Number of centroids.

If we are not satisfied with positions of centroids we can change them with a click on the Randomize button. Then we perform the first recomputing of centroids with a click on the Recompute centroids. We get the following image.

The next step is to reassign membership of all points to the closest centroid. This is performed with a click on the Reassign membership button.

Then we repeat these two steps until the algorithm converges. This is the final result.

Perhaps we are not satisfied with the result because we noticed that maybe classification into 4 clusters would be better. So we decided to add a new centroid. We can do this by increasing the number of centroids in the control menu or with a click on the position in the graph where we want to place the centroid. We decided to add it with a click. The new centroid is the orange one.

Now we can repeat running the algorithm until it converges again, but before that we will move the new centroid to change the behavior of the algorithm. We grabbed the orange centroid and moved it to the desired position.

Then we press Run and observe the centroids while the algorithm converges again.

Gradient Descent

Educational widget that shows the gradient descent algorithm on a logistic or linear regression.

Inputs

• Data: input data set

Outputs

• Data: data with columns selected in the widget
• Classifier: model produced at the current step of the algorithm.
• Coefficients: coefficients at the current step of the algorithm.

Description

This widget incrementally shows steps of gradient descent for a logistic or linear regression. Gradient descent is demonstrated on two attributes that are selected by the user.

Gradient descent is performed on logistic regression if the class in the data set is categorical and linear regression if the class is numeric.

1. Select two attributes (x and y) on which the gradient descent algorithm is preformed. Select the target class. It is the class that is classified against all other classes.
2. Learning rate is a step size in the gradient descent With stochastic checkbox you can select whether gradient descent is stochastic or not. If stochastic is checked you can set step size that is amount of steps of stochastic gradient descent performed in one press on step button. Restart: start algorithm from the beginning
3. Step: perform one step of the algorithm Step back: make a step back in the algorithm
4. Run: automatically perform several steps until the algorithm converges Speed: set speed of the automatic stepping
5. Save Image saves the image to the computer in a .svg or .png format. Report includes widget parameters and visualization in the report.

Example

In Orange we connected File widget with Iris data set to the Gradient Descent widget. Iris data set has discrete class, so Logistic regression will be used this time. We connected outputs of the widget to Predictions widget to see how the data are classified and the Data Table widget where we inspect coefficients of logistic regression.

We open the Gradient Descent widget and set X to sepal width and Y to sepal length. Target class is set to Iris-virginica. We set learning rate to 0.02. With a click in the graph we set the initial coefficients (red dot).

We perform step of the algorithm by pressing the Step button. When we get bored with clicking we can finish stepping by pressing on the Run button.

If we want to go back in the algorithm we can do it by pressing Step back button. This will also change the model. Current model uses positions of last coefficients (red-yellow dot).

In the end we want to see the predictions for input data so we can open the Predictions widget. Predictions are listed in the left column. We can compare this predictions to the real classes.

If we want to demonstrate the linear regression we can change the data set to Housing. This data set has a continuous class variable. When using linear regression we can select only one feature which means that our function is linear. Another parameter that is plotted in the graph is intercept of a linear function.

This time we selected INDUS as an independent variable. In the widget we can make the same actions as before. In the end we can also check the predictions for each point with the Predictions widget. And check coefficients of linear regression in a Data Table.

Polynomial Regression

Educational widget that interactively shows regression line for different regressors.

Inputs

• Data: input data set. It needs at least two continuous attributes.
• Preprocessor: data preprocessors
• Learner: regression algorithm used in the widget. Default set to Linear Regression.

Outputs

• Learner: regression algorithm used in the widget
• Predictor: trained regressor
• Coefficients: regressor coefficients if any

Description

This widget interactively shows the regression line using any of the regressors from the Model module. In the widget, polynomial expansion can be set. Polynomial expansion is a regulation of the degree of the polynom that is used to transform the input data and has an effect on the shape of a curve. If polynomial expansion is set to 1 it means that untransformed data are used in the regression.

1. Regressor name.
2. Input: independent variable on axis x. Polynomial expansion: degree of polynomial expansion. Target: dependent variable on axis y.
3. Save Image saves the image to the computer in a .svg or .png format. Report includes widget parameters and visualization in the report.

Example

We loaded iris data set with the File widget. Then we connected Linear Regression learner to the Polynomial Regression widget. In the widget we selected petal length as our Input variable and petal width as our Target variable. We set Polynomial expansion to 1 which gives us a linear regression line. The result is shown in the figure below.

The line can fit better if we increase the Polynomial expansion parameter. Say, we set it to 3.

To observe different results, change Linear Regression to any other regression learner from Orange. Example below is done with the Tree learner.

Polynomial Classification

Educational widget that visually demonstrates classification in two-dimensional space.

Inputs

• Data: input data set
• Preprocessor (optional): data preprocessors
• Learner (optional): classification algorithm used in the widget (default:Logistic Regression)

Outputs

• Learner: classification algorithm used in the widget
• Classifier: trained classifier
• Coefficients: classifier coefficients if it has them

Description

This widget interactively shows classification probabilities using contours and color gradient for any classifier. The widget is particularly useful for showing effects of polynomial expansion (by adding terms like xⁱy^j where i + j is at most the selected degree) and of regularization*.

By default, the widget uses non-regularized logistic regreesion. Manually attaching learner, for instance the Logistic Regression widget, allows us to control the regularization strength.

The outline of the shown data points indicates the actual class, and the inside shows the prediction by the model. In non-binary classification, points predicted to non-target classes are painted gray.

1. Classifier name.
2. Variables: variables used for classification; options shown only if data contains more than two independent variables. Polynomial expansion: Degree of polynomial expansion. Target class: the target to which the shown probabilities apply. In non-binary classification, other classes are merged.
3. Show legend: Show color legend. Show contours: Show contour lines for probabilities.

Example

We painted some data using the Paint widget and fed it to Polynomial Classification. We also added Logistic Regression to control the regularization.

Setting the polynomial expansion to 2 allows the classifier to construct boundaries as 2 degree polynomials. Hovering over a contour line shows the predicted probability of the target class (in this case C1) for points on that line. Moving the mouse elsewhere shows a probability at some particular point.

Red points with blue outline are “blue” data instances that are misclassified as red, and vice versa.

Changing the regularization (in Logistic regression widget) allows us to observe how the contour lines spread and shrink.

We added another class, chose C2 as the target, increased the polynomial expansion to 4 and weaken the regularization (in Logistic regression widget).

Outlines still represent the original classes. Instances of the target classes are colored red, the other two classes are gray.

Hovering at any point shows us the probability for red (e.g. 0.264).

Pie Chart

The widget for visualizing discrete attributes in the pie chart.

Inputs

• Data: input data set

The aim of this widget is to demonstrate that pie charts are a terrible visualization. Please don’t use it for any other purpose.

1. Select the attribute you want to visualize.
2. Select the attribute which is used to split data in more charts.
3. Check if you want pies to be exploded (parts of the pie will have space in between).
4. You will see your data visualized here.
5. With those buttons, you can either get help, save the plot, or include plots in the report.

Example

We load the Titanic dataset in File widget and connected the data to Pie Chart. Here we show the distribution of gender data and split pies by survived attributes. We notice that in the group of passengers that did not survive there are mainly male while there is a higher proportion of women in the group of people that survived. While the pie chart can shed some light of data we still suggest using more informative visualizations, e.g. Box Plot.

Random Data

Generate random data sample.

Inputs

• None

Outputs

• Data: randomly generated data

Random Data allows creating random data sets, where variables correspond to the selected distributions. The user can specify the number of rows (samples) and the number of variables for each distribution. Distributions from the Scipy’s stats module are used.

1. Normal: A normal continuous random variable. Set the number of variables, the mean and the variance.
2. Bernoulli: A Bernoulli discrete random variable. Set the number of variables and the probability mass function.
3. Binomial: A binomial discrete random variable. Set the number of variables, the number of trials and probability of success.
4. Uniform: A uniform continuous random variable. Set the number of variables and the lower and upper bound of the distribution.
5. Discrete uniform: A uniform discrete random variable. Set the number of variables and the number of values per variable.
6. Multinomial: A multinomial random variable. Set the probabilities and the number of trials. The probabilities should sum to one. The number of probabilities corresponds to the final number of variables generated.
7. Add more variables… enables selecting new distributions from the list and with that adding additional variables. Distributions can be removed by pressing an X in the top left corner of each distribution.
8. Define the sample size (i.e. number of rows, default 1000) and press Generate to output the data set.

1. Hypergeometric: A hypergeometric discrete random variable. Set the number of variables, number of objects, positives and trials.
2. Negative binomial: A negative binomial discrete random variable. Set the number of variables, number of successes and the probability of a success.
3. Poisson: A Poisson discrete random variable. Set the number of variables and the event rate (expected number of occurrences).
4. Exponential: An exponential continuous random variable. Set the number of variables.
5. Gamma: A gamma continuous random variable. Set the number of variables, the shape and scale. The larger the scale parameter, the more spread out the distribution.
6. Student’s t: A Student’s t continuous random variable. Set the number of variables and the degrees of freedom.
7. Bivariate normal: A multivariate normal random variable where the number of variables is fixed to 2. The number of variables is set to two and cannot be changed. Set the mean and variance of each variable and the covariance matrix of the distribution.

Example

We normaly wouldn’t create a data set with so many different distributions but rather, for instance, a set of normally distributed variables and perhaps a binary variable, which we will use as the target variable. In this example, we use the default settings, which generate 10 normally distributed variables and a single binomial variable.

We observe the generated data in a Data Table and in Distributions.

Indices and tables

• Index
• Module Index
• Search Page