def generate_graph(input_data,
dis=None,
_eu_dm=None,
eps_threshold=95,
overlap=0.75,
min_samples=3,
r=40,
filter='PCOA',
verbose=1):
if filter not in _filter_dict:
logger("Wrong filter you provide, available fitler are",
','.join(_filter_dict.keys()),
verbose=1)
return
else:
filter = _filter_dict[filter]
tm = mapper.Mapper(verbose=verbose)
t1 = time.time()
metric = Metric(metric="precomputed")
lens = [filter(components=[0, 1], metric=metric, random_state=100)]
projected_X = tm.filter(dis, lens=lens)
logger("projection takes: ", time.time() - t1, verbose=verbose)
###
t1 = time.time()
eps = optimize_dbscan_eps(input_data, threshold=eps_threshold, dm=_eu_dm)
clusterer = DBSCAN(eps=eps, min_samples=min_samples)
cover = Cover(projected_data=MinMaxScaler().fit_transform(projected_X),
resolution=r,
overlap=overlap)
graph = tm.map(data=input_data, cover=cover, clusterer=clusterer)
logger(graph.info(), verbose=verbose)
logger("graph generator take: ", time.time() - t1, verbose=verbose)
return graph
def generate_graph(input_data,
dis,
_eu_dm=None,
eps_threshold=95,
overlap_params=0.75,
min_samples=3,
resolution_params="auto",
filter_=Filter.PCOA):
tm = mapper.Mapper(verbose=1)
# TDA Step2. Projection
t1 = time.time()
metric = Metric(metric="precomputed")
lens = [filter_(components=[0, 1], metric=metric, random_state=100)]
projected_X = tm.filter(dis, lens=lens)
if global_verbose:
print("projection takes: ", time.time() - t1)
###
t1 = time.time()
eps = optimize_dbscan_eps(input_data, threshold=eps_threshold, dm=_eu_dm)
clusterer = DBSCAN(eps=eps, min_samples=min_samples)
r = resolution_params
cover = Cover(projected_data=MinMaxScaler().fit_transform(projected_X),
resolution=r,
overlap=overlap_params)
graph = tm.map(data=input_data, cover=cover, clusterer=clusterer)
if global_verbose:
print(graph.info())
print("graph time: ", time.time() - t1)
graph_name = "{eps}_{overlap}_{r}_{filter}.graph".format(
eps=eps_threshold,
overlap=overlap_params,
r=r,
filter=lens[0].__class__.__name__)
return graph, graph_name, projected_X
from scipy.spatial.distance import squareform, pdist from sklearn.cluster import DBSCAN from sklearn.preprocessing import MinMaxScaler from tmap.tda import mapper, Filter from tmap.tda.cover import Cover from tmap.tda.metric import Metric from tmap.tda.utils import optimize_dbscan_eps from tmap.test import load_data # load taxa abundance data, sample metadata and precomputed distance matrix X = load_data.FGFP_genus_profile() metadata = load_data.FGFP_metadata_ready() dm = squareform(pdist(X, metric='braycurtis')) ############################################################ tm = mapper.Mapper(verbose=1) metric = Metric(metric="precomputed") lens = [Filter.PCOA(components=[0, 1], metric=metric)] # for quick projected_X = tm.filter(dm, lens=lens) eps = optimize_dbscan_eps(X, threshold=95) clusterer = DBSCAN(eps=eps, min_samples=3) cover = Cover(projected_data=MinMaxScaler().fit_transform(projected_X), resolution=50, overlap=0.75) graph = tm.map(data=X, cover=cover, clusterer=clusterer) node_data = graph.transform_sn(X) from tmap.netx.SAFE import SAFE_batch, get_SAFE_summary n_iter = 5000 safe_scores = SAFE_batch(graph, metadata=metadata, n_iter=n_iter, nr_threshold=0.5, _mode='both', random_seed=100) enriched_SAFE_metadata, declined_SAFE_metadata = safe_scores['enrich'], safe_scores['decline'] safe_summary_metadata = get_SAFE_summary(graph=graph, metadata=metadata, safe_scores=enriched_SAFE_metadata,