mmodel | Modular modeling framework for nonlinear scientific models | Topic Modeling library

To create a nonlinear model that has the result of (x + y)log(x + y, base):. The graph is defined using grouped edges (the ``networkx`` syntax of edge the definition also works.). The functions are then added to node attributes. The order of definition is node_name, node_func, output, input (if different from original function), and modifiers. To define the model, the name, graph, and handler need to be specified. Additional parameters include modifiers, descriptions, and returns lists. The input parameters of the model are determined based on the node information. The model behaves like a Python function, with additional metadata. The graph can be plotted using the ``draw`` method. The resulting graph contains the model metadata and detailed node information. One key feature of ``mmodel`` that differs from other workflow is modifiers, which modify callables post definition. Modifiers work on both the node level and model level. Example: Use ``loop_input`` modifier on the graph to loop the nodes that require the "log_base" parameter. We can inspect the loop node as well as the new model. Use the ``draw`` method to draw the graph. There are three styles "plain", "short", and "verbose", which differ by the level of detail of the node information. A graph output is displayed in Jupyter Notebook or can be saved using the export option.
code-block:: python import math import numpy as np def func(sum_xy, log_xy): """Function that adds a value to the multiplied inputs.""" return sum_xy * log_xy + 6
code-block:: python from mmodel import ModelGraph, Model, MemHandler # create graph edges grouped_edges = [ ("add", ["log", "function node"]), ("log", "function node"), ]
code-block:: python # define note objects node_objects = [ ("add", np.add, "sum_xy", ["x", "y"]), ("log", math.log, "log_xy", ["sum_xy", "log_base"]), ("function node", func, "result"), ] G = ModelGraph(name="example_graph") G.add_grouped_edges_from(grouped_edges) G.set_node_objects_from(node_objects)
code-block:: python example_model = Model("example_model", G, handler=MemHandler, description="Test model.")
code-block:: python >>> print(example_model) example_model(log_base, x, y) returns: z graph: example_graph handler: MemHandler Test model. >>> example_model(2, 5, 3) # (5 + 3)log(5 + 3, 2) + 6 30.0 >>> example_model.draw()
.. |br| raw:: html
<br/>
.. image:: example.png
:width: 300
:alt: example model graph
code-block:: python from mmodel import loop_input H = G.subgraph(inputs=["log_base"]) H.name = "example_subgraph" loop_node = Model("submodel", H, handler=MemHandler) looped_G = G.replace_subgraph( H, "loop_node", loop_node, output="looped_z", modifiers=[loop_input("log_base")], ) looped_G.name = "looped_graph" looped_model = Model("looped_model", looped_G, loop_node.handler)
code-block:: python >>> print(looped_model) looped_model(log_base, x, y) returns: looped_z graph: looped_graph handler: MemHandler() >>> print(looped_model.node_metadata("loop_node")) submodel(log_base, sum_xy) return: looped_z functype: mmodel.Model modifiers: - loop_input('log_base') >>> looped_model([2, 4], 5, 3) # (5 + 3)log(5 + 3, 2) + 6 [30.0, 18.0]
code-block:: python G.draw(style="short") example_model.draw(style="plain", export="example.pdf") # default to draw_graph
To view the graph, Graphviz needs to be installed: `Graphviz Installation <https://graphviz.org/download/>`_ For windows installation, please choose "add Graphviz to the system PATH for all users/current users" during the setup.
code-block:: pip install mmodel