Metadata-Version: 2.1
Name: meshed
Version: 0.1.52
Summary: Link functions up into callable objects
Home-page: https://github.com/i2mint/meshed
License: mit
Description: # meshed
        
        Link functions up into callable objects (DAGs)
        
        To install: `pip install meshed`
        
        [Documentation](https://i2mint.github.io/meshed/)
        
        
        # Quick Start
        
        ```python
        from meshed import DAG
        
        def this(a, b=1):
            return a + b
        def that(x, b=1):
            return x * b
        def combine(this, that):
            return (this, that)
        
        dag = DAG((this, that, combine))
        print(dag.synopsis_string())
        ```
        
            x,b -> that_ -> that
            a,b -> this_ -> this
            this,that -> combine_ -> combine
        
        
        But what does it do?
        
        It's a callable, with a signature:
        
        ```python
        from inspect import signature
        signature(dag)
        ```
        
            <Signature (x, a, b=1)>
        
        And when you call it, it executes the dag from the root values you give it and
        returns the leaf output values.
        
        ```python
        dag(1, 2, 3)  # (a+b,x*b) == (2+3,1*3) == (5, 3)
        ```
            (5, 3)
        
        ```python
        dag(1, 2)  # (a+b,x*b) == (2+1,1*1) == (3, 1)
        ```
            (3, 1)
        
        
        You can see (and save image, or ascii art) the dag:
        
        ```python
        dag.dot_digraph()
        ```
        
        <img src="https://user-images.githubusercontent.com/1906276/127779463-ae75604b-0d69-4ac4-b206-80c2c5ae582b.png" width=200>
        
        
        You can extend a dag
        
        ```python
        dag2 = DAG([*dag, lambda this, a: this + a])
        dag2.dot_digraph()
        ```
        
        <img src="https://user-images.githubusercontent.com/1906276/127779748-70b47907-e51f-4e64-bc18-9545ee07e632.png" width=200>
        
        You can get a sub-dag by specifying desired input(s) and outputs.
        
        ```python
        dag2[['that', 'this'], 'combine'].dot_digraph()
        ```
        
        <img src="https://user-images.githubusercontent.com/1906276/127779781-8aac40eb-ed52-4694-b50e-4af896cc30a2.png" width=150>
        
        
        
        ## Note on flexibility
        
        The above DAG was created straight from the functions, using only the names of the
        functions and their parameters to define how to hook the network up.
        
        But if you didn't write those functions specifically for that purpose, or you want
        to use someone else's functions, one would need to specify the relation between parameters, inputs and outputs.
        
        For that purpose, functions can be adapted using the class FuncNode. The class allows you to essentially rename each of the parameters and also specify which output should be used as an argument for any other functions.
        
        Let us consider the example below.
        
        ```python
        def f(a, b):
            return a + b
        
        def g(a_plus_b, d):
            return a_plus_b * d
        ```
        
        Say we want the output of f to become the value of the parameter a_plus_b. We can do that by assigning the string 'a_plus_b' to the out parameter of a FuncNode representing the function f:
        
        ```python
        f_node = FuncNode(func=f, out="a_plus_b")
        ```
        
        We can now create a dag using our f_node instead of f:
        
        ```python
        dag = DAG((f_node, g))
        ```
        
        Our dag behaves as wanted:
        
        ```python
        dag(a=1, b=2, d=3)
        9
        ```
        
        Now say we would also like for the value given to b to be also given to d. We can achieve that by binding d to b in the bind parameter of a FuncNode representing g:
        
        ```python
        g_node = FuncNode(func=g, bind={"d": "b"})
        ```
        
        The dag created with f_node and g_node has only two parameters, namely a and b:
        
        ```python
        dag = DAG((f_node, g_node))
        dag(a=1, b=2)
        6
        ```
        
        
        
        
        # Sub-DAGs
        
        
        ``dag[input_nodes:output_nodes]`` is the sub-dag made of intersection of all
        descendants of ``input_nodes``
        (inclusive) and ancestors of ``output_nodes`` (inclusive), where additionally,
        when a func node is contained, it takes with it the input and output nodes
        it needs.
        
        
        ```python
        from meshed import DAG
        
        def f(a): ...
        def g(f): ...
        def h(g): ...
        def i(h): ...
        dag = DAG([f, g, h, i])
        
        dag.dot_digraph()
        ```
        
        <img width="110" alt="image" src="https://user-images.githubusercontent.com/1906276/154749811-f9892ee6-617c-4fa6-9de9-1ebc509c04ae.png">
        
        
        Get a subdag from ``g_`` (indicates the function here) to the end of ``dag``
        
        ```python
        subdag = dag['g_',:]
        subdag.dot_digraph()
        ```
        
        <img width="100" alt="image" src="https://user-images.githubusercontent.com/1906276/154749842-c2320d1c-368d-4be8-ac57-9a77f1bb081d.png">
        
        From the beginning to ``h_``
        
        ```python
        dag[:, 'h_'].dot_digraph()
        ```
        
        <img width="110" alt="image" src="https://user-images.githubusercontent.com/1906276/154750524-ece7f4b6-a3f3-46c6-a66d-7dc9b8ef254a.png">
        
        
        
        From ``g_`` to ``h_`` (both inclusive)
        
        ```python
        dag['g_', 'h_'].dot_digraph()
        ```
        
        <img width="109" alt="image" src="https://user-images.githubusercontent.com/1906276/154749864-5a33aa13-0949-4aa7-945c-4d3fe7f07e7d.png">
        
        
        Above we used function (node names) to specify what we wanted, but we can also
        use names of input/output var-nodes. Do note the difference though.
        The nodes you specify to get a sub-dag are INCLUSIVE, but when you
        specify function nodes, you also get the input and output nodes of these
        functions.
        
        The ``dag['g_', 'h_']`` give us a sub-dag starting at ``f`` (the input node),
        but when we ask ``dag['g', 'h_']`` instead, ``g`` being the output node of
        function node ``g_``, we only get ``g -> h_ -> h``:
        
        ```python
        dag['g', 'h'].dot_digraph()
        ```
        
        <img width="88" alt="image" src="https://user-images.githubusercontent.com/1906276/154750753-737e2705-0ea3-4595-a93a-1567862a6edd.png">
        
        
        If we wanted to include ``f`` we'd have to specify it:
        
        
        ```python
        dag['f', 'h'].dot_digraph()
        ```
        
        <img width="109" alt="image" src="https://user-images.githubusercontent.com/1906276/154749864-5a33aa13-0949-4aa7-945c-4d3fe7f07e7d.png">
        
        
        Those were for simple pipelines, but let's now look at a more complex dag.
        
        Note the definition: ``dag[input_nodes:output_nodes]`` is the sub-dag made of intersection of all 
        descendants of ``input_nodes``
        (inclusive) and ancestors of ``output_nodes`` (inclusive), where additionally,
        when a func node is contained, it takes with it the input and output nodes
        it needs.
        
        We'll let the following examples self-comment:
        
        ```python
        from meshed import DAG
        
        
        def f(u, v): ...
        
        def g(f): ...
        
        def h(f, w): ...
        
        def i(g, h): ...
        
        def j(h, x): ...
        
        def k(i): ...
        
        def l(i, j): ...
        
        dag = DAG([f, g, h, i, j, k, l])
        
        dag.dot_digraph()
        ```
        
        <img width="248" alt="image" src="https://user-images.githubusercontent.com/1906276/154748574-a7026125-659f-465b-9bc3-14a1864d14b2.png">
        
        ```python
        dag[['u', 'f'], 'h'].dot_digraph()
        ```
        
        <img width="190" alt="image" src="https://user-images.githubusercontent.com/1906276/154748685-24e706ce-b68f-429a-b7b8-7bda62ccdf36.png">
        
        
        ```python
        dag['u', 'h'].dot_digraph()
        ```
        
        <img width="183" alt="image" src="https://user-images.githubusercontent.com/1906276/154748865-6e729094-976a-4af3-87f0-b6dd3900fb8c.png">
        
        
        ```python
        dag[['u', 'f'], ['h', 'g']].dot_digraph()
        ```
        
        <img width="199" alt="image" src="https://user-images.githubusercontent.com/1906276/154748905-4eaeccbe-6cca-4492-a7a2-48f7c9937b95.png">
        
        
        ```python
        dag[['x', 'g'], 'k'].dot_digraph()
        ```
        
        <img width="133" alt="image" src="https://user-images.githubusercontent.com/1906276/154748937-7a278b25-6f0f-467c-a977-89a175e15abb.png">
        
        ```python
        dag[['x', 'g'], ['l', 'k']].dot_digraph()
        ```
        
        <img width="216" alt="image" src="https://user-images.githubusercontent.com/1906276/154748958-135792a6-ce16-4561-9cbe-4662113a1022.png">
        
        
        
        # Examples
        
        ## A train/test ML pipeline
        
        Consider a simple train/test ML pipeline that looks like this.
        
        ![image](https://user-images.githubusercontent.com/1906276/135151068-179d958e-9e96-48aa-9188-52ae22919c6e.png)
        
        With this, we might decide we want to give the user control over how to do 
        `train_test_split` and `learner`, so we offer this interface to the user:
        
        ![image](https://user-images.githubusercontent.com/1906276/135151094-661850c0-f10c-49d8-ace2-46b3d994de80.png)
        
        With that, the user can just bring its own `train_test_split` and `learner` 
        functions, and as long as it satisfied the 
        expected (and even better; declared and validatable) protocol, things will work. 
        
        In some situations we'd like to fix some of how `train_test_split` and 
        `learner` work, allowing the user to control only some aspects of them. 
        This function would look like this:
        
        ![image](https://user-images.githubusercontent.com/1906276/135151137-3d9a290f-d5e7-4f24-a418-82f1edb8a46a.png)
        
        And inside, it does:
        
        ![image](https://user-images.githubusercontent.com/1906276/135151114-926b52b8-0536-4565-bd56-95099f21e4ff.png)
        
        `meshed` allows us to easily manipulate such functional structures to 
        adapt them to our needs.
        
        
        # itools module
        Tools that enable operations on graphs where graphs are represented by an adjacency Mapping.
        
        Again. 
        
        Graphs: You know them. Networks. 
        Nodes and edges, and the ecosystem descriptive or transformative functions surrounding these.
        Few languages have builtin support for the graph data structure, but all have their libraries to compensate.
        
        The one you're looking at focuses on the representation of a graph as `Mapping` encoding 
        its [adjacency list](https://en.wikipedia.org/wiki/Adjacency_list). 
        That is, a dictionary-like interface that specifies the graph by specifying for each node
        what nodes it's adjacent to:
        
        ```python
        assert graph[source_node] == iterator_of_nodes_that_source_node_has_edges_to
        ```
        
        We emphasize that there is no specific graph instance that you need to squeeze your graph into to
        be able to use the functions of `meshed`. Suffices that your graph's structure is expressed by 
        that dict-like interface 
        -- which grown-ups call `Mapping` (see the `collections.abc` or `typing` standard libs for more information).
        
        You'll find a lot of `Mapping`s around pythons. 
        And if the object you want to work with doesn't have that interface, 
        you can easily create one using one of the many tools of `py2store` meant exactly for that purpose.
        
        
        # Examples
        
        ```pydocstring
        >>> from meshed.itools import edges, nodes, isolated_nodes
        >>> graph = dict(a='c', b='ce', c='abde', d='c', e=['c', 'b'], f={})
        >>> sorted(edges(graph))
        [('a', 'c'), ('b', 'c'), ('b', 'e'), ('c', 'a'), ('c', 'b'), ('c', 'd'), ('c', 'e'), ('d', 'c'), ('e', 'b'), ('e', 'c')]
        >>> sorted(nodes(graph))
        ['a', 'b', 'c', 'd', 'e', 'f']
        >>> set(isolated_nodes(graph))
        {'f'}
        >>>
        >>> from meshed.makers import edge_reversed_graph
        >>> g = dict(a='c', b='cd', c='abd', e='')
        >>> assert edge_reversed_graph(g) == {'c': ['a', 'b'], 'd': ['b', 'c'], 'a': ['c'], 'b': ['c'], 'e': []}
        >>> reverse_g_with_sets = edge_reversed_graph(g, set, set.add)
        >>> assert reverse_g_with_sets == {'c': {'a', 'b'}, 'd': {'b', 'c'}, 'a': {'c'}, 'b': {'c'}, 'e': set([])}
        ```
        
Keywords: dag,graph,network
Platform: any
Description-Content-Type: text/markdown
