dagsim 1.0.0

A framework and specification language for simulating data based on user-defined graphical models

DagSim

A framework and specification language for simulating data based on graphical models.

Installation

DagSim can be installed directly using pip.

Installing DagSim using pip

To install the DagSim package using pip, run:

pip install dagsim

Installing graphviz

If you use pip, you need to install graphviz on the system level in order to use the drawing functionality in DagSim. Please follow the instrcutions here on how to install graphviz depending on the operating system.

Simple example

Suppose we are interested in simulating two variables, X and Y, where X follows a standard Gaussian distribution, and Y is the square of X.

For each node we need a function to simulate the node's values:

• For X, we can use the numpy.random.normal function
• For Y, we can use either numpy.power or define our own function. We will use the second to show how you can use user-define functions.

# needed imports
import dagsim.base as ds
import numpy as np

Here, we define our own square function:

def square(arg):
    return arg * arg

Then, we define the nodes in our graph/model by giving each one a name, the function to use in order to evaluate its value, and the arguments of the function, if any:

X = ds.Generic(name="X", function=np.random.normal)
Y = ds.Generic(name="Y", function=square, arguments={"arg": X})

After that, we define the graph by giving it a name and a list containing all the nodes to be included:

graph = ds.Graph(name="demo_graph", list_nodes=[X, Y])

If you wish, you can draw the graph by calling the following function:

graph.draw()

Finally, you can simulate data from the graph by calling the simulate method, and giving it the number of samples you want to simulate, and a name for the csv_file (optional) where the data should be saved

data = graph.simulate(num_samples=10, csv_name="demo_data")

Here, data would be a dictionary with the keys being equal to the names of the nodes, and the corresponding values being the simulated values for each node.

For other examples, please refer to the tutorials folder.