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apply pre-commit linting & blacken
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@dhimmel
dhimmel committed Apr 5, 2022
1 parent b06765f commit 2e74c65
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Showing 19 changed files with 1,919 additions and 1,124 deletions.
100 changes: 62 additions & 38 deletions hetmatpy/degree_group.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@
import pandas
import scipy.sparse

from hetmatpy.matrix import metaedge_to_adjacency_matrix
import hetmatpy.degree_weight
from hetmatpy.matrix import metaedge_to_adjacency_matrix


def degrees_to_degree_to_ind(degrees):
Expand All @@ -18,16 +18,22 @@ def degrees_to_degree_to_ind(degrees):

def metapath_to_degree_dicts(graph, metapath):
metapath = graph.metagraph.get_metapath(metapath)
_, _, source_adj_mat = metaedge_to_adjacency_matrix(graph, metapath[0], dense_threshold=0.7)
_, _, target_adj_mat = metaedge_to_adjacency_matrix(graph, metapath[-1], dense_threshold=0.7)
_, _, source_adj_mat = metaedge_to_adjacency_matrix(
graph, metapath[0], dense_threshold=0.7
)
_, _, target_adj_mat = metaedge_to_adjacency_matrix(
graph, metapath[-1], dense_threshold=0.7
)
source_degrees = source_adj_mat.sum(axis=1).flat
target_degrees = target_adj_mat.sum(axis=0).flat
source_degree_to_ind = degrees_to_degree_to_ind(source_degrees)
target_degree_to_ind = degrees_to_degree_to_ind(target_degrees)
return source_degree_to_ind, target_degree_to_ind


def generate_degree_group_stats(source_degree_to_ind, target_degree_to_ind, matrix, scale=False, scaler=1):
def generate_degree_group_stats(
source_degree_to_ind, target_degree_to_ind, matrix, scale=False, scaler=1
):
"""
Yield dictionaries with degree grouped stats
"""
Expand All @@ -41,31 +47,37 @@ def generate_degree_group_stats(source_degree_to_ind, target_degree_to_ind, matr
# row_matrix = scipy.sparse.csc_matrix(row_matrix)
for target_degree, col_inds in target_degree_to_ind.items():
row = {
'source_degree': source_degree,
'target_degree': target_degree,
"source_degree": source_degree,
"target_degree": target_degree,
}
row['n'] = len(row_inds) * len(col_inds)
row["n"] = len(row_inds) * len(col_inds)
if source_degree == 0 or target_degree == 0:
row['sum'] = 0
row['nnz'] = 0
row['sum_of_squares'] = 0
row["sum"] = 0
row["nnz"] = 0
row["sum_of_squares"] = 0
yield row
continue

slice_matrix = row_matrix[:, col_inds]
values = slice_matrix.data if scipy.sparse.issparse(slice_matrix) else slice_matrix
values = (
slice_matrix.data
if scipy.sparse.issparse(slice_matrix)
else slice_matrix
)
if scale:
values = numpy.arcsinh(values / scaler)
row['sum'] = values.sum()
row['sum_of_squares'] = (values ** 2).sum()
row["sum"] = values.sum()
row["sum_of_squares"] = (values**2).sum()
if scipy.sparse.issparse(slice_matrix):
row['nnz'] = slice_matrix.nnz
row["nnz"] = slice_matrix.nnz
else:
row['nnz'] = numpy.count_nonzero(slice_matrix)
row["nnz"] = numpy.count_nonzero(slice_matrix)
yield row


def dwpc_to_degrees(graph, metapath, damping=0.5, ignore_zeros=False, ignore_redundant=True):
def dwpc_to_degrees(
graph, metapath, damping=0.5, ignore_zeros=False, ignore_redundant=True
):
"""
Yield a description of each cell in a DWPC matrix adding source and target
node degree info as well as the corresponding path count.
Expand All @@ -78,26 +90,32 @@ def dwpc_to_degrees(graph, metapath, damping=0.5, ignore_zeros=False, ignore_red
the same DWPC.
"""
metapath = graph.metagraph.get_metapath(metapath)
_, _, source_adj_mat = metaedge_to_adjacency_matrix(graph, metapath[0], dense_threshold=0.7)
_, _, target_adj_mat = metaedge_to_adjacency_matrix(graph, metapath[-1], dense_threshold=0.7)
_, _, source_adj_mat = metaedge_to_adjacency_matrix(
graph, metapath[0], dense_threshold=0.7
)
_, _, target_adj_mat = metaedge_to_adjacency_matrix(
graph, metapath[-1], dense_threshold=0.7
)
source_degrees = source_adj_mat.sum(axis=1).flat
target_degrees = target_adj_mat.sum(axis=0).flat
del source_adj_mat, target_adj_mat

source_path = graph.get_nodes_path(metapath.source(), file_format='tsv')
source_node_df = pandas.read_csv(source_path, sep='\t')
source_node_names = list(source_node_df['name'])
source_path = graph.get_nodes_path(metapath.source(), file_format="tsv")
source_node_df = pandas.read_csv(source_path, sep="\t")
source_node_names = list(source_node_df["name"])

target_path = graph.get_nodes_path(metapath.target(), file_format='tsv')
target_node_df = pandas.read_csv(target_path, sep='\t')
target_node_names = list(target_node_df['name'])
target_path = graph.get_nodes_path(metapath.target(), file_format="tsv")
target_node_df = pandas.read_csv(target_path, sep="\t")
target_node_names = list(target_node_df["name"])

row_names, col_names, dwpc_matrix = graph.read_path_counts(metapath, 'dwpc', damping)
row_names, col_names, dwpc_matrix = graph.read_path_counts(
metapath, "dwpc", damping
)
dwpc_matrix = numpy.arcsinh(dwpc_matrix / dwpc_matrix.mean())
if scipy.sparse.issparse(dwpc_matrix):
dwpc_matrix = dwpc_matrix.toarray()

_, _, path_count = graph.read_path_counts(metapath, 'dwpc', 0.0)
_, _, path_count = graph.read_path_counts(metapath, "dwpc", 0.0)
if scipy.sparse.issparse(path_count):
path_count = path_count.toarray()

Expand All @@ -110,14 +128,14 @@ def dwpc_to_degrees(graph, metapath, damping=0.5, ignore_zeros=False, ignore_red
if ignore_zeros and dwpc_value == 0:
continue
row = {
'source_id': row_names[row_ind],
'target_id': col_names[col_ind],
'source_name': source_node_names[row_ind],
'target_name': target_node_names[col_ind],
'source_degree': source_degrees[row_ind],
'target_degree': target_degrees[col_ind],
'path_count': path_count[row_ind, col_ind],
'dwpc': dwpc_value,
"source_id": row_names[row_ind],
"target_id": col_names[col_ind],
"source_name": source_node_names[row_ind],
"target_name": target_node_names[col_ind],
"source_degree": source_degrees[row_ind],
"target_degree": target_degrees[col_ind],
"path_count": path_count[row_ind, col_ind],
"dwpc": dwpc_value,
}
yield collections.OrderedDict(row)

Expand All @@ -127,13 +145,19 @@ def single_permutation_degree_group(permuted_hetmat, metapath, dwpc_mean, dampin
Compute degree-grouped permutations for a single permuted_hetmat,
for one metapath.
"""
_, _, matrix = hetmatpy.degree_weight.dwpc(permuted_hetmat, metapath, damping=damping, dense_threshold=0.7)
source_deg_to_ind, target_deg_to_ind = hetmatpy.degree_group.metapath_to_degree_dicts(permuted_hetmat, metapath)
_, _, matrix = hetmatpy.degree_weight.dwpc(
permuted_hetmat, metapath, damping=damping, dense_threshold=0.7
)
(
source_deg_to_ind,
target_deg_to_ind,
) = hetmatpy.degree_group.metapath_to_degree_dicts(permuted_hetmat, metapath)
row_generator = hetmatpy.degree_group.generate_degree_group_stats(
source_deg_to_ind, target_deg_to_ind, matrix, scale=True, scaler=dwpc_mean)
source_deg_to_ind, target_deg_to_ind, matrix, scale=True, scaler=dwpc_mean
)
degree_grouped_df = (
pandas.DataFrame(row_generator)
.set_index(['source_degree', 'target_degree'])
.set_index(["source_degree", "target_degree"])
.assign(n_perms=1)
)
return degree_grouped_df
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