IndraNet Graphs (indra.assemblers.indranet)

The IndraNet assembler creates multiple different types of networkx graphs from INDRA Statements. It also allows exporting binary Statement information as a pandas DataFrame.

class indra.assemblers.indranet.net.IndraNet(incoming_graph_data=None, **attr)

A Networkx representation of INDRA Statements.

classmethod digraph_from_df(df, flattening_method=None, weight_mapping=None)

Create a digraph from a pandas DataFrame.

Parameters:

• df (pd.DataFrame) – The dataframe to build the graph from.
• flattening_method (str or function(networkx.DiGraph, edge)) – The method to use when updating the belief for the flattened edge.
• weight_mapping (function(networkx.DiGraph)) – A function taking at least the graph G as an argument and returning G after adding edge weights as an edge attribute to the flattened edges using the reserved keyword ‘weight’.

Returns:

An IndraNet graph flattened to a DiGraph

Return type:

IndraNet(nx.DiGraph)

classmethod from_df(df)

Create an IndraNet MultiDiGraph from a pandas DataFrame.

Returns an instance of IndraNet with graph data filled out from a dataframe containing pairwise interactions.

Parameters:df (pd.DataFrame) –

A pandas.DataFrame with each row containing node and edge data for one edge. Indices are used to distinguish multiedges between a pair of nodes. Any columns not part of the below mentioned mandatory columns are considered extra attributes. Columns starting with ‘agA_’ or ‘agB_’ (excluding the agA/B_name) will be added to its respective nodes as node attributes. Any other columns will be added as edge attributes.

Mandatory columns are : agA_name, agB_name, agA_ns, agA_id, agB_ns, agB_id, stmt_type, evidence_count, stmt_hash, belief and source_counts.

Returns:An IndraNet object Return type:IndraNet

classmethod signed_from_df(df, sign_dict=None, flattening_method=None, weight_mapping=None)

Create a signed graph from a pandas DataFrame.

Parameters:

• df (pd.DataFrame) – The dataframe to build the signed graph from.
• sign_dict (dict) – A dictionary mapping a Statement type to a sign to be used for the edge. By default only Activation and IncreaseAmount are added as positive edges and Inhibition and DecreaseAmount are added as negative edges, but a user can pass any other Statement types in a dictionary.
• flattening_method (str or function(networkx.DiGraph, edge)) – The method to use when updating the belief for the flattened edge.
• weight_mapping (function(networkx.DiGraph)) – A function taking at least the graph G as an argument and returning G after adding edge weights as an edge attribute to the flattened edges using the reserved keyword ‘weight’.

Returns:

An IndraNet graph flattened to a signed graph

Return type:

IndraNet(nx.MultiDiGraph)

to_digraph(flattening_method=None, weight_mapping=None)

Flatten the IndraNet to a DiGraph

Parameters:

• flattening_method (str|function) – The method to use when updating the belief for the flattened edge
• weight_mapping (function) – A function taking at least the graph G as an argument and returning G after adding edge weights as an edge attribute to the flattened edges using the reserved keyword ‘weight’.

Returns:

G – An IndraNet graph flattened to a DiGraph

Return type:

IndraNet(nx.DiGraph)

to_signed_graph(sign_dict=None, flattening_method=None, weight_mapping=None)

Flatten the IndraNet to a signed graph.

Parameters:

• sign_dict (dict) – A dictionary mapping a Statement type to a sign to be used for the edge. By default only Activation and IncreaseAmount are added as positive edges and Inhibition and DecreaseAmount are added as negative edges, but a user can pass any other Statement types in a dictionary.
• flattening_method (str or function(networkx.DiGraph, edge)) –

  The method to use when updating the belief for the flattened edge.

  If a string is provided, it must be one of the predefined options ‘simple_scorer’ or ‘complementary_belief’.

  If a function is provided, it must take the flattened graph ‘G’ and an edge ‘edge’ to perform the belief flattening on and return a number:

  >>> def flattening_function(G, edge):
  ...     # Return the average belief score of the constituent edges
  ...     all_beliefs = [s['belief']
  ...         for s in G.edges[edge]['statements']]
  ...     return sum(all_beliefs)/len(all_beliefs)
  

• weight_mapping (function(networkx.DiGraph)) –

  A function taking at least the graph G as an argument and returning G after adding edge weights as an edge attribute to the flattened edges using the reserved keyword ‘weight’.

  Example:

  >>> def weight_mapping(G):
  ...     # Sets the flattened weight to the average of the
  ...     # inverse source count
  ...     for edge in G.edges:
  ...         w = [1/s['evidence_count']
  ...             for s in G.edges[edge]['statements']]
  ...         G.edges[edge]['weight'] = sum(w)/len(w)
  ...     return G
  

Returns:

SG – An IndraNet graph flattened to a signed graph

Return type:

IndraNet(nx.MultiDiGraph)

class indra.assemblers.indranet.assembler.IndraNetAssembler(statements=None)

Assembler to create an IndraNet object from a list of INDRA statements.

Parameters:statements (list[indra.statements.Statement]) – A list of INDRA Statements to be assembled.

model

An IndraNet graph object assembled by this class.

Type:IndraNet

add_statements(stmts)

Add INDRA Statements to the assembler’s list of statements.

Parameters:stmts (list[indra.statements.Statement]) – A list of indra.statements.Statement to be added to the statement list of the assembler.

make_df(exclude_stmts=None, complex_members=3)

Create a dataframe containing information extracted from assembler’s list of statements necessary to build an IndraNet.

Parameters:

• exclude_stmts (list[str]) – A list of statement type names to not include in the dataframe.
• complex_members (int) – Maximum allowed size of a complex to be included in the data frame. All complexes larger than complex_members will be rejected. For accepted complexes, all permutations of their members will be added as dataframe records. Default is 3.

Returns:

df – Pandas DataFrame object containing information extracted from statements. It contains the following columns:

agA_name

The first Agent’s name.

agA_ns

The first Agent’s identifier namespace as per db_refs.

agA_id

The first Agent’s identifier as per db_refs

ags_ns, agB_name, agB_id

As above for the second agent. Note that the Agent may be None (and these fields left empty) if the Statement consists only of a single Agent (e.g., SelfModification, ActiveForm, or Translocation statement).

stmt_type

Statement type, given by the name of the class in indra.statements.

evidence_count

Number of evidences for the statement.

stmt_hash

An unique long integer hash identifying the content of the statement.

belief

The belief score associated with the statement.

source_counts

The number of evidences per input source for the statement.

initial_sign

The default sign (polarity) associated with the given statement if the statement type has implied polarity. To facilitate weighted path finding, the sign is represented as 0 for positive polarity and 1 for negative polarity.

Return type:

pd.DataFrame

make_model(exclude_stmts=None, complex_members=3, graph_type='multi_graph', sign_dict=None, belief_flattening=None, weight_flattening=None)

Assemble an IndraNet graph object.

Parameters:

• exclude_stmts (list[str]) – A list of statement type names to not include in the graph.
• complex_members (int) – Maximum allowed size of a complex to be included in the graph. All complexes larger than complex_members will be rejected. For accepted complexes, all permutations of their members will be added as edges. Default is 3.
• graph_type (str) – Specify the type of graph to assemble. Chose from ‘multi_graph’ (default), ‘digraph’, ‘signed’. Default is multi_graph.
• sign_dict (dict) – A dictionary mapping a Statement type to a sign to be used for the edge. This parameter is only used with the ‘signed’ option. See IndraNet.to_signed_graph for more info.
• belief_flattening (str or function(networkx.DiGraph, edge)) –

  The method to use when updating the belief for the flattened edge.

  If a string is provided, it must be one of the predefined options ‘simple_scorer’ or ‘complementary_belief’.

  If a function is provided, it must take the flattened graph ‘G’ and an edge ‘edge’ to perform the belief flattening on and return a number:

  >>> def belief_flattening(G, edge):
  ...     # Return the average belief score of the constituent edges
  ...     all_beliefs = [s['belief']
  ...         for s in G.edges[edge]['statements']]
  ...     return sum(all_beliefs)/len(all_beliefs)
  

• weight_flattening (function(networkx.DiGraph)) –

  A function taking at least the graph G as an argument and returning G after adding edge weights as an edge attribute to the flattened edges using the reserved keyword ‘weight’.

  Example:

  >>> def weight_flattening(G):
  ...     # Sets the flattened weight to the average of the
  ...     # inverse source count
  ...     for edge in G.edges:
  ...         w = [1/s['evidence_count']
  ...             for s in G.edges[edge]['statements']]
  ...         G.edges[edge]['weight'] = sum(w)/len(w)
  ...     return G
  

Returns:

model – IndraNet graph object.

Return type:

IndraNet

indra.assemblers.indranet.assembler.get_ag_ns_id(ag)

Return a tuple of name space, id from an Agent’s db_refs.