def build_graph(path, term_depth=1000, skim_depth=10,
d_weights=False, **kwargs):
"""
Tokenize a text, index a term matrix, and build out a graph.
Args:
path (str): The file path.
term_depth (int): Consider the N most frequent terms.
skim_depth (int): Connect each word to the N closest siblings.
d_weights (bool): If true, give "close" nodes low weights.
Returns:
Skimmer: The indexed graph.
"""
# Tokenize text.
click.echo('\nTokenizing text...')
t = Text.from_file(path)
click.echo('Extracted %d tokens' % len(t.tokens))
m = Matrix()
# Index the term matrix.
click.echo('\nIndexing terms:')
m.index(t, t.most_frequent_terms(term_depth), **kwargs)
g = Skimmer()
# Construct the network.
click.echo('\nGenerating graph:')
g.build(t, m, skim_depth, d_weights)
return g
def frequent(path, term_depth=500, skim_depth=10, d_weights=False, **kwargs):
"""
Use most frequent terms.
"""
t = Text.from_file(path)
m = Matrix(t)
print('Indexing terms:')
m.index(t.most_frequent_terms(term_depth), **kwargs)
g = Skimmer()
print('Generating graph:')
g.build(m, skim_depth, d_weights)
return g
def clumpy(path, term_depth=500, skim_depth=10, d_weights=False, **kwargs):
"""
Use "clumpiest" terms.
"""
t = Text.from_file(path)
m = Matrix(t)
print('Indexing terms:')
m.index(t.densities(**kwargs).keys()[:term_depth], **kwargs)
g = Skimmer()
print('Generating graph:')
g.build(m, skim_depth, d_weights)
return g
def clumpy(path, term_depth=500, skim_depth=10, d_weights=False, **kwargs):
"""
Use "clumpiest" terms.
"""
t = Text.from_file(path)
m = Matrix(t)
print('Indexing terms:')
m.index(t.densities(**kwargs).keys()[:term_depth], **kwargs)
g = Skimmer()
print('Generating graph:')
g.build(m, skim_depth, d_weights)
return g
def frequent(path, term_depth=500, skim_depth=10, d_weights=False, **kwargs):
"""
Use most frequent terms.
"""
t = Text.from_file(path)
m = Matrix(t)
print('Indexing terms:')
m.index(t.most_frequent_terms(term_depth), **kwargs)
g = Skimmer()
print('Generating graph:')
g.build(m, skim_depth, d_weights)
return g
def build_graph(path,
term_depth=1000,
skim_depth=10,
d_weights=False,
**kwargs):
"""
Tokenize a text, index a term matrix, and build out a graph.
Args:
path (str): The file path.
term_depth (int): Consider the N most frequent terms.
skim_depth (int): Connect each word to the N closest siblings.
d_weights (bool): If true, give "close" nodes low weights.
Returns:
Skimmer: The indexed graph.
"""
# Tokenize text.
click.echo('\nTokenizing text...')
t = Text.from_file(path)
click.echo('Extracted %d tokens' % len(t.tokens))
m = Matrix()
# Index the term matrix.
click.echo('\nIndexing terms:')
m.index(t, t.most_frequent_terms(term_depth), **kwargs)
g = Skimmer()
# Construct the network.
click.echo('\nGenerating graph:')
g.build(t, m, skim_depth, d_weights)
return g