original_docstring: Return new instance of the Larimichthys crocea graph.
  
      The graph is automatically retrieved from the STRING repository. 
  
  	
  
      Parameters
      -------------------
      directed: bool = False,
          Wether to load the graph as directed or undirected.
          By default false.
      verbose: int = 2,
          Wether to show loading bars during the retrieval and building
          of the graph.
      cache_path: str = "graphs",
          Where to store the downloaded graphs.
      additional_graph_kwargs: Dict,
          Additional graph kwargs.
  
      Returns
      -----------------------
      Instace of Larimichthys crocea graph.
  
  	Report
  	---------------------
  	At the time of rendering these methods (please see datetime below), the graph
  	had the following characteristics:
  	
  	Datetime: 2021-02-02 20:02:13.854735
  	
  	The undirected graph Larimichthys crocea has 21247 nodes and 6182402 weighted
  	edges, of which none are self-loops. The graph is dense as it has a density
  	of 0.02739 and has 6 connected components, where the component with most
  	nodes has 21237 nodes and the component with the least nodes has 2 nodes.
  	The graph median node degree is 408, the mean node degree is 581.96, and
  	the node degree mode is 593. The top 5 most central nodes are 215358.XP_010745182.1
  	(degree 5381), 215358.XP_010733783.1 (degree 4817), 215358.XP_010749683.1
  	(degree 4803), 215358.XP_010742609.1 (degree 4607) and 215358.XP_010741722.1
  	(degree 4555).
  	
  
  	References
  	---------------------
  	Please cite the following if you use the data:
  	
  	@article{szklarczyk2019string,
  	    title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets},
  	    author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others},
  	    journal={Nucleic acids research},
  	    volume={47},
  	    number={D1},
  	    pages={D607--D613},
  	    year={2019},
  	    publisher={Oxford University Press}
  	}
  	
  
  	Usage example
  	----------------------
  	The usage of this graph is relatively straightforward:
  	
  	.. code:: python
  	
  	    # First import the function to retrieve the graph from the datasets
  	    from ensmallen_graph.datasets.string import LarimichthysCrocea
  	
  	    # Then load the graph
  	    graph = LarimichthysCrocea()
  	
  	    # Finally, you can do anything with it, for instance, compute its report:
  	    print(graph)
  	
  	    # If you need to run a link prediction task with validation,
  	    # you can split the graph using a connected holdout as follows:
  	    train_graph, validation_graph = graph.connected_holdout(
  	        # You can use an 80/20 split the holdout, for example.
  	        train_size=0.8,
  	        # The random state is used to reproduce the holdout.
  	        random_state=42,
  	        # Wether to show a loading bar.
  	        verbose=True
  	    )
  	
  	    # Remember that, if you need, you can enable the memory-time trade-offs:
  	    train_graph.enable(
  	        vector_sources=True,
  	        vector_destinations=True,
  	        vector_outbounds=True
  	    )
  	
  	    # Consider using the methods made available in the Embiggen package
  	    # to run graph embedding or link prediction tasks.
      
docstring: Return new instance of the Larimichthys crocea graph.
  The graph is automatically retrieved from the STRING repository.
  
  
  
  Parameters
  
  bool = False,
  Wether to load the graph as directed or undirected.
  By default false.
  verbose: int = 2,
  Wether to show loading bars during the retrieval and building
  of the graph.
  cache_path: str = "graphs",
  Where to store the downloaded graphs.
  additional_graph_kwargs: Dict,
  Additional graph kwargs.
  
  Returns
  
  Instace of Larimichthys crocea graph.
  
  Report
  
  At the time of rendering these methods (please see datetime below), the graph
  had the following characteristics.
  
  
  
  The undirected graph Larimichthys crocea has 21247 nodes and 6182402 weighted
  edges, of which none are self-loops. The graph is dense as it has a density
  of 0.02739 and has 6 connected components, where the component with most
  nodes has 21237 nodes and the component with the least nodes has 2 nodes.
  The graph median node degree is 408, the mean node degree is 581.96, and
  the node degree mode is 593.
  
  References
  
  Please cite the following if you use the data.
  
  
  
  Usage example
  
  The usage of this graph is relatively straightforward.
  
  : python
  
  First import the function to retrieve the graph from the datasets
  from ensmallen_graph.datasets.string import LarimichthysCrocea
  
  Then load the graph
  graph = LarimichthysCrocea()
  
  Finally, you can do anything with it, for instance, compute its report:
  print(graph)
  
  If you need to run a link prediction task with validation,
  you can split the graph using a connected holdout as follows:
  train_graph, validation_graph = graph.connected_holdout(
  You can use an 80/20 split the holdout, for example.
  train_size=0.8,
  The random state is used to reproduce the holdout.
  random_state=42,
  Wether to show a loading bar.
  verbose=True
  )
  
  Remember that, if you need, you can enable the memory-time trade-offs:
  train_graph.enable(
  vector_sources=True,
  vector_destinations=True,
  vector_outbounds=True
  )
  
  Consider using the methods made available in the Embiggen package
  to run graph embedding or link prediction tasks.
docstring_tokens: Return new instance of the Larimichthys crocea graph . The graph is automatically retrieved from the STRING repository . Parameters bool = False Wether to load the graph as directed or undirected . By default false . verbose : int = 2 Wether to show loading bars during the retrieval and building of the graph . cache_path : str = " graphs " Where to store the downloaded graphs . additional_graph_kwargs : Dict Additional graph kwargs . Returns Instace of Larimichthys crocea graph . Report At the time of rendering these methods ( please see datetime below ) the graph had the following characteristics . The undirected graph Larimichthys crocea has 21247 nodes and 6182402 weighted edges of which none are self - loops . The graph is dense as it has a density of 0 . 02739 and has 6 connected components where the component with most nodes has 21237 nodes and the component with the least nodes has 2 nodes . The graph median node degree is 408 the mean node degree is 581 . 96 and the node degree mode is 593 . References Please cite the following if you use the data . Usage example The usage of this graph is relatively straightforward . : python First import the function to retrieve the graph from the datasets from ensmallen_graph . datasets . string import LarimichthysCrocea Then load the graph graph = LarimichthysCrocea () Finally you can do anything with it for instance compute its report : print ( graph ) If you need to run a link prediction task with validation you can split the graph using a connected holdout as follows : train_graph validation_graph = graph . connected_holdout ( You can use an 80 / 20 split the holdout for example . train_size = 0 . 8 The random state is used to reproduce the holdout . random_state = 42 Wether to show a loading bar . verbose = True ) Remember that if you need you can enable the memory - time trade - offs : train_graph . enable ( vector_sources = True vector_destinations = True vector_outbounds = True ) Consider using the methods made available in the Embiggen package to run graph embedding or link prediction tasks .
code: def LarimichthysCrocea(
      directed: bool = False,
      verbose: int = 2,
      cache_path: str = "graphs/string",
      **additional_graph_kwargs: Dict
  ) -> EnsmallenGraph:
      """Return new instance of the Larimichthys crocea graph.
  
      The graph is automatically retrieved from the STRING repository. 
  
  	
  
      Parameters
      -------------------
      directed: bool = False,
          Wether to load the graph as directed or undirected.
          By default false.
      verbose: int = 2,
          Wether to show loading bars during the retrieval and building
          of the graph.
      cache_path: str = "graphs",
          Where to store the downloaded graphs.
      additional_graph_kwargs: Dict,
          Additional graph kwargs.
  
      Returns
      -----------------------
      Instace of Larimichthys crocea graph.
  
  	Report
  	---------------------
  	At the time of rendering these methods (please see datetime below), the graph
  	had the following characteristics:
  	
  	Datetime: 2021-02-02 20:02:13.854735
  	
  	The undirected graph Larimichthys crocea has 21247 nodes and 6182402 weighted
  	edges, of which none are self-loops. The graph is dense as it has a density
  	of 0.02739 and has 6 connected components, where the component with most
  	nodes has 21237 nodes and the component with the least nodes has 2 nodes.
  	The graph median node degree is 408, the mean node degree is 581.96, and
  	the node degree mode is 593. The top 5 most central nodes are 215358.XP_010745182.1
  	(degree 5381), 215358.XP_010733783.1 (degree 4817), 215358.XP_010749683.1
  	(degree 4803), 215358.XP_010742609.1 (degree 4607) and 215358.XP_010741722.1
  	(degree 4555).
  	
  
  	References
  	---------------------
  	Please cite the following if you use the data:
  	
  	@article{szklarczyk2019string,
  	    title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets},
  	    author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others},
  	    journal={Nucleic acids research},
  	    volume={47},
  	    number={D1},
  	    pages={D607--D613},
  	    year={2019},
  	    publisher={Oxford University Press}
  	}
  	
  
  	Usage example
  	----------------------
  	The usage of this graph is relatively straightforward:
  	
  	.. code:: python
  	
  	    # First import the function to retrieve the graph from the datasets
  	    from ensmallen_graph.datasets.string import LarimichthysCrocea
  	
  	    # Then load the graph
  	    graph = LarimichthysCrocea()
  	
  	    # Finally, you can do anything with it, for instance, compute its report:
  	    print(graph)
  	
  	    # If you need to run a link prediction task with validation,
  	    # you can split the graph using a connected holdout as follows:
  	    train_graph, validation_graph = graph.connected_holdout(
  	        # You can use an 80/20 split the holdout, for example.
  	        train_size=0.8,
  	        # The random state is used to reproduce the holdout.
  	        random_state=42,
  	        # Wether to show a loading bar.
  	        verbose=True
  	    )
  	
  	    # Remember that, if you need, you can enable the memory-time trade-offs:
  	    train_graph.enable(
  	        vector_sources=True,
  	        vector_destinations=True,
  	        vector_outbounds=True
  	    )
  	
  	    # Consider using the methods made available in the Embiggen package
  	    # to run graph embedding or link prediction tasks.
      """
      return AutomaticallyRetrievedGraph(
          graph_name="LarimichthysCrocea",
          dataset="string",
          directed=directed,
          verbose=verbose,
          cache_path=cache_path,
          additional_graph_kwargs=additional_graph_kwargs
      )()
code_tokens: def LarimichthysCrocea ( directed : bool = False , verbose : int = 2 , cache_path : str = "graphs/string" , ** additional_graph_kwargs : Dict ) -> EnsmallenGraph : return AutomaticallyRetrievedGraph ( graph_name = "LarimichthysCrocea" , dataset = "string" , directed = directed , verbose = verbose , cache_path = cache_path , additional_graph_kwargs = additional_graph_kwargs ) ( )
short_docstring: Return new instance of the Larimichthys crocea graph.
short_docstring_tokens: Return new instance of the Larimichthys crocea graph .
comment: """Return new instance of the Larimichthys crocea graph.
  
      The graph is automatically retrieved from the STRING repository. 
  
  	
  
      Parameters
      -------------------
      directed: bool = False,
          Wether to load the graph as directed or undirected.
          By default false.
      verbose: int = 2,
          Wether to show loading bars during the retrieval and building
          of the graph.
      cache_path: str = "graphs",
          Where to store the downloaded graphs.
      additional_graph_kwargs: Dict,
          Additional graph kwargs.
  
      Returns
      -----------------------
      Instace of Larimichthys crocea graph.
  
  	Report
  	---------------------
  	At the time of rendering these methods (please see datetime below), the graph
  	had the following characteristics:
  	
  	Datetime: 2021-02-02 20:02:13.854735
  	
  	The undirected graph Larimichthys crocea has 21247 nodes and 6182402 weighted
  	edges, of which none are self-loops. The graph is dense as it has a density
  	of 0.02739 and has 6 connected components, where the component with most
  	nodes has 21237 nodes and the component with the least nodes has 2 nodes.
  	The graph median node degree is 408, the mean node degree is 581.96, and
  	the node degree mode is 593. The top 5 most central nodes are 215358.XP_010745182.1
  	(degree 5381), 215358.XP_010733783.1 (degree 4817), 215358.XP_010749683.1
  	(degree 4803), 215358.XP_010742609.1 (degree 4607) and 215358.XP_010741722.1
  	(degree 4555).
  	
  
  	References
  	---------------------
  	Please cite the following if you use the data:
  	
  	@article{szklarczyk2019string,
  	    title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets},
  	    author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others},
  	    journal={Nucleic acids research},
  	    volume={47},
  	    number={D1},
  	    pages={D607--D613},
  	    year={2019},
  	    publisher={Oxford University Press}
  	}
  	
  
  	Usage example
  	----------------------
  	The usage of this graph is relatively straightforward:
  	
  	.. code:: python
  	
  	    # First import the function to retrieve the graph from the datasets
  	    from ensmallen_graph.datasets.string import LarimichthysCrocea
  	
  	    # Then load the graph
  	    graph = LarimichthysCrocea()
  	
  	    # Finally, you can do anything with it, for instance, compute its report:
  	    print(graph)
  	
  	    # If you need to run a link prediction task with validation,
  	    # you can split the graph using a connected holdout as follows:
  	    train_graph, validation_graph = graph.connected_holdout(
  	        # You can use an 80/20 split the holdout, for example.
  	        train_size=0.8,
  	        # The random state is used to reproduce the holdout.
  	        random_state=42,
  	        # Wether to show a loading bar.
  	        verbose=True
  	    )
  	
  	    # Remember that, if you need, you can enable the memory-time trade-offs:
  	    train_graph.enable(
  	        vector_sources=True,
  	        vector_destinations=True,
  	        vector_outbounds=True
  	    )
  	
  	    # Consider using the methods made available in the Embiggen package
  	    # to run graph embedding or link prediction tasks.
      """