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single connected component and list of threshold enhancements #1

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single connected component and list of threshold enhancements #1

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134 changes: 67 additions & 67 deletions Topyfic/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
import scanpy.external as sce
import networkx as nx
import math
from matplotlib import gridspec

from Topyfic.train import *
from Topyfic.topic import *
Expand Down Expand Up @@ -527,7 +528,7 @@ def read_analysis(file):

def compare_topModels(topModels,
output_type='graph',
threshold=0.8,
threshold=[0.8],
topModels_color=None,
topModels_label=None,
save=False,
Expand All @@ -537,13 +538,12 @@ def compare_topModels(topModels,
file_name="compare_topics"):
"""
compare several topModels

:param topModels: list of topModel class you want to compare to each other
:type topModels: list of TopModel class
:param output_type: indicate the type of output you want. graph: plot as a graph, heatmap: plot as a heatmap, table: table contains correlation
:type output_type: str
:param threshold: only apply when you choose circular which only show correlation above that
:type threshold: float
:type threshold: list of float
:param topModels_color: dictionary of colors mapping each topics to each color (default: blue)
:type topModels_color: dict
:param topModels_label: dictionary of label mapping each topics to each label
Expand All @@ -558,7 +558,6 @@ def compare_topModels(topModels,
:type plot_format: str
:param file_name: name and path of the plot use for save (default: piechart_topicAvgCell)
:type file_name: str

:return: table contains correlation between topics only when table is choose and save is False
:rtype: pandas dataframe
"""
Expand Down Expand Up @@ -609,70 +608,72 @@ def compare_topModels(topModels,

if output_type == 'graph':
np.fill_diagonal(corrs.values, 0)
corrs[corrs < threshold] = np.nan
res = corrs.stack()
res = pd.DataFrame(res)
res.reset_index(inplace=True)
res.columns = ['source', 'dest', 'weight']
res['weight'] = res['weight'].astype(float).round(decimals=2)
res['source_label'] = res['source']
res['dest_label'] = res['dest']
res['source_color'] = res['source']
res['dest_color'] = res['dest']

if topModels_label is not None:
res['source_label'].replace(topModels_label, inplace=True)
res['dest_label'].replace(topModels_label, inplace=True)
if topModels_color is None:
res['source_color'] = "blue"
res['dest_color'] = "blue"
else:
res['source_color'].replace(topModels_color, inplace=True)
res['dest_color'].replace(topModels_color, inplace=True)

G = nx.Graph()
for i in range(res.shape[0]):
G.add_node(res.source_label[i], color=res.source_color[i])
G.add_node(res.dest_label[i], color=res.dest_color[i])
G.add_edge(res.source_label[i], res.dest_label[i], weight=res.weight[i])

connected_components = sorted(nx.connected_components(G), key=len, reverse=True)

nrows = math.ceil(math.sqrt(len(threshold)))
ncols = math.ceil(len(threshold) / nrows)
outer_grid = gridspec.GridSpec(nrows, ncols, wspace=0.05, hspace=0.12)
if figsize is None:
figsize = (len(connected_components) * 2, len(connected_components) * 2)
figsize=(int(4 * nrows),int(4 * nrows))

nrows = math.ceil(math.sqrt(len(connected_components)))
ncols = math.ceil(len(connected_components) / nrows)
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize, facecolor='white')

i = 0
for connected_component in connected_components:
g_connected_component = G.subgraph(connected_component)
nodePos = nx.spring_layout(g_connected_component)

edge_labels = nx.get_edge_attributes(g_connected_component, "weight")

node_color = nx.get_node_attributes(g_connected_component, "color").values()
weights = nx.get_edge_attributes(g_connected_component, 'weight').values()

nx.draw_networkx(g_connected_component,
pos=nodePos,
width=list(weights),
with_labels=True,
node_color=list(node_color),
font_size=8,
node_size=500,
ax=axs[int(i / ncols), i % ncols])

nx.draw_networkx_edge_labels(g_connected_component,
nodePos,
edge_labels=edge_labels,
font_size=7,
ax=axs[int(i / ncols), i % ncols])

i += 1

[axi.axis('off') for axi in axs.ravel()]
plt.axis('off')
for i, thresholds in enumerate(threshold):
corrs.edit = corrs.copy()
corrs.edit[corrs.edit < thresholds] = np.nan
res = corrs.edit.stack()
res = pd.DataFrame(res)
res.reset_index(inplace=True)
res.columns = ['source', 'dest', 'weight']
res['weight'] = res['weight'].astype(float).round(decimals=2)
res['source_label'] = res['source']
res['dest_label'] = res['dest']
res['source_color'] = res['source']
res['dest_color'] = res['dest']

if topModels_label is not None:
res['source_label'].replace(topModels_label, inplace=True)
res['dest_label'].replace(topModels_label, inplace=True)
if topModels_color is None:
res['source_color'] = "blue"
res['dest_color'] = "blue"
else:
res['source_color'].replace(topModels_color, inplace=True)
res['dest_color'].replace(topModels_color, inplace=True)

G = nx.Graph()
for j in range(res.shape[0]):
G.add_node(res.source_label[j], color=res.source_color[j])
G.add_node(res.dest_label[j], color=res.dest_color[j])
G.add_edge(res.source_label[j], res.dest_label[j], weight=res.weight[j])

connected_components = sorted(nx.connected_components(G), key=len, reverse=True)
if len(connected_components)==1:
vis_ax =fig.add_subplot(outer_grid[int(i / ncols), i % ncols])
vis_ax.set_title('Threshold = ' + str(round(thresholds,2)),fontsize=14)


g_connected_component = G.subgraph(connected_components[0])
nodePos = nx.spring_layout(g_connected_component,k=1.2)
edge_labels = nx.get_edge_attributes(g_connected_component, "weight")
node_color = nx.get_node_attributes(g_connected_component, "color").values()
weights = nx.get_edge_attributes(g_connected_component, 'weight').values()

nx.draw_networkx(g_connected_component,pos=nodePos, width=list(weights),with_labels=True,node_color=list(node_color),font_size=12,node_size=500,ax = vis_ax)

nx.draw_networkx_edge_labels(g_connected_component,nodePos,edge_labels=edge_labels,font_size=8,ax=vis_ax)
else:
inner_grid = gridspec.GridSpecFromSubplotSpec(len(connected_components), 1,subplot_spec=outer_grid[i], wspace=0.0, hspace=0.0)
for j, connected_component in enumerate(connected_components):
vis_split = fig.add_subplot(inner_grid[j])
if j==0:
vis_split.set_title('Threshold = ' + str(round(thresholds,2)),fontsize=14)
g_connected_component = G.subgraph(connected_component)
nodePos = nx.spring_layout(g_connected_component)
edge_labels = nx.get_edge_attributes(g_connected_component, "weight")
node_color = nx.get_node_attributes(g_connected_component, "color").values()
weights = nx.get_edge_attributes(g_connected_component, 'weight').values()
nx.draw_networkx(g_connected_component,pos=nodePos,width=list(weights),with_labels=True,node_color=list(node_color),font_size=8,node_size=500,ax=vis_split)
nx.draw_networkx_edge_labels(g_connected_component,nodePos,edge_labels=edge_labels,font_size=7,ax=vis_split)
[axi.axis('off') for axi in axs.ravel()]
plt.tight_layout()
if save:
plt.savefig(f"{file_name}.{plot_format}")
Expand All @@ -681,5 +682,4 @@ def compare_topModels(topModels,
else:
plt.close()

return axs

return