A framework and specification language for simulating data based on user-defined graphical models
Project description
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
Quickstart
To check that DagSim is installed properly, run the following command in the console/terminal:
dagsim-quickstart
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.Node(name="X", function=np.random.normal)
Y = ds.Node(name="Y", function=square, kwargs={"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.
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