def test_to_signed_graph():
ia = IndraNetAssembler([ab1, ab2, ab3, ab4, bc1, bc2, bc3, bc4])
df = ia.make_df()
net = IndraNet.from_df(df)
signed_graph = net.to_signed_graph(sign_dict=default_sign_dict,
weight_mapping=_weight_mapping)
assert len(signed_graph.nodes) == 3
assert len(signed_graph.edges) == 4
assert set([
stmt['stmt_type'] for stmt in signed_graph['a']['b'][0]['statements']
]) == {'Activation', 'IncreaseAmount'}
assert set([
stmt['stmt_type'] for stmt in signed_graph['a']['b'][1]['statements']
]) == {'Inhibition'}
assert set([
stmt['stmt_type'] for stmt in signed_graph['b']['c'][0]['statements']
]) == {'Activation', 'IncreaseAmount'}
assert set([
stmt['stmt_type'] for stmt in signed_graph['b']['c'][1]['statements']
]) == {'Inhibition', 'DecreaseAmount'}
assert all(signed_graph.edges[e].get('belief', False)
for e in signed_graph.edges)
assert all(
isinstance(signed_graph.edges[e]['belief'], (float, np.longfloat))
for e in signed_graph.edges)
assert all(signed_graph.edges[e].get('weight', False)
for e in signed_graph.edges)
assert all(
isinstance(signed_graph.edges[e]['weight'], (float, np.longfloat))
for e in signed_graph.edges)
def test_make_df():
ia = IndraNetAssembler([st1, st2, st3, st4, st5, st6])
df = ia.make_df()
assert isinstance(df, pd.DataFrame)
assert len(df) == 9
assert set(df.columns) == {
'agA_name', 'agB_name', 'agA_ns', 'agA_id', 'agB_ns', 'agB_id',
'stmt_type', 'evidence_count', 'stmt_hash', 'belief', 'source_counts'
}
def export_joint_tsv(all_stmts, fname):
all_stmts_by_hash = {}
for kinase, stmts in all_stmts.items():
for stmt in stmts:
all_stmts_by_hash[stmt.get_hash()] = stmt
all_stmts_flat = list(all_stmts_by_hash.values())
ia = IndraNetAssembler(all_stmts_flat)
df = ia.make_df()
df.to_csv(fname, index=False, sep='\t')
def test_make_df():
ia = IndraNetAssembler([st1, st2, st3, st4, st5, st6, st9])
df = ia.make_df()
assert isinstance(df, pd.DataFrame)
assert len(df) == 10
assert set(df.columns) == {
'agA_name', 'agB_name', 'agA_ns', 'agA_id', 'agB_ns', 'agB_id',
'stmt_type', 'evidence_count', 'stmt_hash', 'belief', 'source_counts',
'initial_sign', 'residue', 'position'
}
assert df.residue.isna().sum() == 9 # Check that all but one row is NaN
assert df.position.isna().sum() == 9 # Check that all but one row is NaN
# Extra column
df2 = ia.make_df(
extra_columns=[('stmt_type_upper',
lambda stmt: type(stmt).__name__.upper())])
assert isinstance(df, pd.DataFrame)
assert len(df) == 10
assert set(df2.columns) - set(df.columns) == {'stmt_type_upper'}
assert df2.stmt_type_upper[0] == 'ACTIVATION'
def assemble_signed_graph(self, mode='local', bucket=EMMAA_BUCKET_NAME):
"""Assemble the model into signed graph and return the assembled graph.
"""
if not self.assembled_stmts:
self.run_assembly()
ia = IndraNetAssembler(self.assembled_stmts)
signed_graph = ia.make_model(graph_type='signed')
if mode == 's3' and 'indranet' in self.export_formats:
fname = f'indranet_{self.date_str}.tsv'
df = ia.make_df()
df.to_csv(fname, sep='\t', index=False)
logger.info(f'Uploading {fname}')
client = get_s3_client(unsigned=False)
client.upload_file(fname, bucket, f'exports/{self.name}/{fname}')
return signed_graph
def test_from_df():
ia = IndraNetAssembler([st1, st2, st3, st4, st5, st6, st7])
df = ia.make_df()
net = IndraNet.from_df(df)
assert len(net.nodes) == 6
assert len(net.edges) == 9
# Stmt with 1 agent should not be added
assert 'e' not in net.nodes
# Complex with more than 3 agents should not be added
assert ('f', 'g', 0) in net.edges
assert ('h', 'i', 0) not in net.edges
# Test node attributes
assert net.nodes['a']['ns'] == 'HGNC', net.nodes['a']['ns']
assert net.nodes['a']['id'] == '1'
# Test edge attributes
e = net['a']['c'][0]
assert e['stmt_type'] == 'Inhibition'
assert e['belief'] == 0.76
assert e['evidence_count'] == 3
assert net['b']['d'][0]['evidence_count'] == 0
def test_to_digraph():
ia = IndraNetAssembler([ab1, ab2, ab3, ab4, bc1, bc2, bc3, bc4])
df = ia.make_df()
net = IndraNet.from_df(df)
assert len(net.nodes) == 3
assert len(net.edges) == 8
digraph = net.to_digraph(weight_mapping=_weight_mapping)
assert len(digraph.nodes) == 3
assert len(digraph.edges) == 2
assert set([
stmt['stmt_type'] for stmt in digraph['a']['b']['statements']
]) == {'Activation', 'Phosphorylation', 'Inhibition', 'IncreaseAmount'}
assert all(digraph.edges[e].get('belief', False) for e in digraph.edges)
assert all(
isinstance(digraph.edges[e]['belief'], (float, np.longfloat))
for e in digraph.edges)
assert all(digraph.edges[e].get('weight', False) for e in digraph.edges)
assert all(
isinstance(digraph.edges[e]['weight'], (float, np.longfloat))
for e in digraph.edges)
digraph_from_df = IndraNet.digraph_from_df(df)
assert nx.is_isomorphic(digraph, digraph_from_df)
def export_tsv(statements, fname):
"""Export statements into TSV."""
ia = IndraNetAssembler(statements)
df = ia.make_df()
df.to_csv(fname, index=False, sep='\t')
