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Merged
gloriachin merged 4 commits into from
Apr 24, 2026
Merged

add new path_finder_parser in notebook, resolve empty result display issue #30

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8883bb3
resolve empty results issue
gloriachin Apr 24, 2026
81e22a0
add new application examples - endpoint overview, name resolver, etc
gloriachin Apr 24, 2026
83d30eb
Merge branch 'main' into guangrong/dev
gloriachin Apr 24, 2026
e7a3083
add parse_results_for_pathfinder in example notebook
gloriachin Apr 24, 2026
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3 changes: 3 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -17,3 +17,6 @@ __pycache__
htmlcov/
coverage.xml
.coverage

/notebooks/*.html
temp
204 changes: 106 additions & 98 deletions TCT/TCT.py
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Original file line number Diff line number Diff line change
Expand Up @@ -631,7 +631,8 @@ def visulization_one_hop_ranking_input_as_list(result_ranked_by_primary_infores,
output_png1="NE_heatmap1.png",
output_png2="NE_heatmap2.png"
):
# edited Dec 5, 2023

# if result_parsed is empty, print a message and return an empty dataframe
predicates_list = []
primary_infore_list = []
aggregator_infore_list = []
Expand Down Expand Up @@ -745,106 +746,109 @@ def visulization_one_hop_ranking(result_ranked_by_primary_infores,result_parsed
output_png2="NE_heatmap2.png"
):
# edited Dec 5, 2023
predicates_list = []
primary_infore_list = []
aggregator_infore_list = []

for i in range(0, result_ranked_by_primary_infores.shape[0]):
oupput_node = result_ranked_by_primary_infores['output_node'][i]
type_of_node = result_ranked_by_primary_infores['type_of_nodes'][i]
if type_of_node == 'object':
subject = input_query
object = oupput_node
else:
subject = oupput_node
object = input_query

predicates_list = predicates_list + result_parsed[subject + "_" + object]['predicate']
primary_infore_list = primary_infore_list + result_parsed[subject + "_" + object]['primary_knowledge_source']

if 'aggregator_knowledge_source' in result_parsed[subject + "_" + object]:
aggregator_infore_list = aggregator_infore_list + result_parsed[subject + "_" + object]['aggregator_knowledge_source']
aggregator_infore_list = list(set(aggregator_infore_list))

predicates_list = list(set(predicates_list))
primary_infore_list = list(set(primary_infore_list))
if result_parsed == {}:
print("No results found in result_parsed. Please check your input data.")
return pd.DataFrame() # Return an empty DataFrame if there are no results

else:
predicates_list = []
primary_infore_list = []
aggregator_infore_list = []

for i in range(0, result_ranked_by_primary_infores.shape[0]):
oupput_node = result_ranked_by_primary_infores['output_node'][i]
type_of_node = result_ranked_by_primary_infores['type_of_nodes'][i]
if type_of_node == 'object':
subject = input_query
object = oupput_node
else:
subject = oupput_node
object = input_query

predicates_list = predicates_list + result_parsed[subject + "_" + object]['predicate']
primary_infore_list = primary_infore_list + result_parsed[subject + "_" + object]['primary_knowledge_source']

predicates_by_nodes = {}
for predict in predicates_list:
predicates_by_nodes[predict] = []
if 'aggregator_knowledge_source' in result_parsed[subject + "_" + object]:
aggregator_infore_list = aggregator_infore_list + result_parsed[subject + "_" + object]['aggregator_knowledge_source']
aggregator_infore_list = list(set(aggregator_infore_list))

primary_infore_by_nodes = {}
for predict in primary_infore_list:
primary_infore_by_nodes[predict] = []
predicates_list = list(set(predicates_list))
primary_infore_list = list(set(primary_infore_list))

aggregator_infore_by_nodes = {}
for predict in aggregator_infore_list:
aggregator_infore_by_nodes[predict] = []

names = []
for i in range(0, result_ranked_by_primary_infores.shape[0]):
#for i in range(0, 10):
oupput_node = result_ranked_by_primary_infores['output_node'].values[i]
names.append(oupput_node)
type_of_node = result_ranked_by_primary_infores['type_of_nodes'].values[i]
if type_of_node == 'object':
subject = input_query
object = oupput_node
else:
subject = oupput_node
object = input_query
new_id = subject + "_" + object
predicates_by_nodes = {}
for predict in predicates_list:
predicates_by_nodes[predict] = []

cur_primary_infore = result_parsed[new_id]['primary_knowledge_source']
primary_infore_by_nodes = {}
for predict in primary_infore_list:
if predict in cur_primary_infore:
primary_infore_by_nodes[predict].append(1)
primary_infore_by_nodes[predict] = []

aggregator_infore_by_nodes = {}
for predict in aggregator_infore_list:
aggregator_infore_by_nodes[predict] = []

names = []
for i in range(0, result_ranked_by_primary_infores.shape[0]):
#for i in range(0, 10):
oupput_node = result_ranked_by_primary_infores['output_node'].values[i]
names.append(oupput_node)
type_of_node = result_ranked_by_primary_infores['type_of_nodes'].values[i]
if type_of_node == 'object':
subject = input_query
object = oupput_node
else:
primary_infore_by_nodes[predict].append(0)
subject = oupput_node
object = input_query
new_id = subject + "_" + object

cur_primary_infore = result_parsed[new_id]['primary_knowledge_source']
for predict in primary_infore_list:
if predict in cur_primary_infore:
primary_infore_by_nodes[predict].append(1)
else:
primary_infore_by_nodes[predict].append(0)



cur_predicates = result_parsed[new_id]['predicate']
for predict in predicates_list:
if predict in cur_predicates:
predicates_by_nodes[predict].append(1)
else:
predicates_by_nodes[predict].append(0)

#convert = False

#for item in colnames:
# if 'NCBIGene' in item:
# convert = True
#if convert:
#Gene_id_map = Gene_id_converter(colnames, "http://127.0.0.1:8000/query_name_by_id") # option 1
#Gene_id_map = Generate_Gene_id_map() # option 2
cur_predicates = result_parsed[new_id]['predicate']
for predict in predicates_list:
if predict in cur_predicates:
predicates_by_nodes[predict].append(1)
else:
predicates_by_nodes[predict].append(0)

dic_id_map = ID_convert_to_preferred_name_nodeNormalizer(names)
new_colnames = []
for item in names:
if item in dic_id_map:
new_colnames.append(dic_id_map[item])
else:
new_colnames.append(item)
dic_id_map = ID_convert_to_preferred_name_nodeNormalizer(names)
new_colnames = []
for item in names:
if item in dic_id_map:
new_colnames.append(dic_id_map[item])
else:
new_colnames.append(item)

#else:
# new_colnames = colnames

primary_infore_by_nodes_df = pd.DataFrame(primary_infore_by_nodes)
primary_infore_by_nodes_df.index = new_colnames
primary_infore_by_nodes_df = primary_infore_by_nodes_df.T
primary_infore_by_nodes_df = pd.DataFrame(primary_infore_by_nodes)
primary_infore_by_nodes_df.index = new_colnames
primary_infore_by_nodes_df = primary_infore_by_nodes_df.T


predicates_by_nodes_df = pd.DataFrame(predicates_by_nodes)
predicates_by_nodes_df.index = new_colnames
predicates_by_nodes_df = predicates_by_nodes_df.T
predicates_by_nodes_df = pd.DataFrame(predicates_by_nodes)
predicates_by_nodes_df.index = new_colnames
predicates_by_nodes_df = predicates_by_nodes_df.T

plot_heatmap(primary_infore_by_nodes_df, num_of_nodes, fontsize, title_fontsize,output_png1)
plot_heatmap(predicates_by_nodes_df, num_of_nodes, fontsize, title_fontsize,output_png2)
if not primary_infore_by_nodes_df.empty:
plot_heatmap(primary_infore_by_nodes_df, num_of_nodes, fontsize, title_fontsize, output_png1)
else:
print("No primary infores found in primary_infore_by_nodes_df.")

return(predicates_by_nodes_df)
if not predicates_by_nodes_df.empty:
plot_heatmap(predicates_by_nodes_df, num_of_nodes, fontsize, title_fontsize, output_png2)
else:
print("No predicates found in predicates_by_nodes_df.")
return pd.DataFrame() # Return empty if there's no predicate data to plot

return(predicates_by_nodes_df)

def plot_heatmap(predicates_by_nodes_df,num_of_nodes = 20,
fontsize = 6,
Expand All @@ -862,22 +866,26 @@ def plot_heatmap(predicates_by_nodes_df,num_of_nodes = 20,

# create the heatmap
# heatmap with border
p1 = sns.heatmap(df, cmap="Blues", cbar=False, ax=ax, linecolor='grey', linewidth=0.2)
# Adjust font size for x and y tick labels
p1.set_xticklabels(p1.get_xticklabels(), rotation=90, fontsize=fontsize)
p1.set_yticklabels(p1.get_yticklabels(), fontsize=fontsize)

#p1.set_title(title)
#p1.set_ylabel(ylab)
print(p1.get_xticklabels())
# set xticklabels with colnames

#p1.set_xticklabels(colnames, rotation=90, fontsize = fontsize)
plt.xticks(ticks=range(len(df.columns)), labels=df.columns)

# set title font size
p1.title.set_size(title_fontsize)
plt.show()
if df.empty:
print("No data to plot in the heatmap. Please check your input data.")
return()
else:
p1 = sns.heatmap(df, cmap="Blues", cbar=False, ax=ax, linecolor='grey', linewidth=0.2)
# Adjust font size for x and y tick labels
p1.set_xticklabels(p1.get_xticklabels(), rotation=90, fontsize=fontsize)
p1.set_yticklabels(p1.get_yticklabels(), fontsize=fontsize)

#p1.set_title(title)
#p1.set_ylabel(ylab)
print(p1.get_xticklabels())
# set xticklabels with colnames

#p1.set_xticklabels(colnames, rotation=90, fontsize = fontsize)
plt.xticks(ticks=range(len(df.columns)), labels=df.columns)

# set title font size
p1.title.set_size(title_fontsize)
plt.show()
# save the figure
#plt.savefig(output_png, bbox_inches='tight', dpi=300)

Expand Down
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