Test SHOULD pass also github actions should build without issues

.github/workflows/python-app.yml

@@ -43,6 +43,12 @@ jobs:
       run: |
         chmod +x scripts/setup-env.sh
         ./scripts/setup-env.sh
+
+    - name: Verify installed Python packages
+      run: |
+        source .venv/bin/activate
+        pip list
+
 
     - name: Verify R installation and WGCNA
       shell: bash
@@ -52,6 +58,7 @@ jobs:
 
     - name: Run tests with pytest
       run: |
+        source .venv/bin/activate
         pytest --cov=bioneuralnet --cov-report=xml tests/
 
     - name: Upload coverage to Codecov

bioneuralnet.egg-info/PKG-INFO

@@ -19,7 +19,7 @@ Provides-Extra: cuda117
 Provides-Extra: dev
 License-File: LICENSE
 
-# BioNeuralNet: A Graph-Based Multi-Omics Integration and Embedding Framework for Advanced Analytics
+# BioNeuralNet: A Graph-Based Multi-Omics Integration and Embedding Framework for Advanced analysis
 
 ![License](https://img.shields.io/badge/license-MIT-blue.svg)
 ![PyPI](https://img.shields.io/pypi/v/bioneuralnet)

bioneuralnet/analysis/base_feature_selector.py

@@ -35,7 +35,7 @@ def __init__(
         """
         self.num_features = num_features
         self.selection_method = selection_method
-        self.output_dir = output_dir if output_dir else self._create_output_dir()
+        self.output_dir = output_dir #if output_dir else self._create_output_dir()
         self.logger = get_logger(__name__)
         self.logger.info("Initialized BaseFeatureSelector.")
 

bioneuralnet/analysis/dynamic_visualization.py

@@ -2,17 +2,12 @@
 from typing import Optional
 import pandas as pd
 import networkx as nx
+import pyvis
 from pyvis.network import Network
+from jinja2 import Environment, FileSystemLoader
 from ..utils.logger import get_logger
 
-
 class DynamicVisualizer:
-    """
-    DynamicVisualizer Class for Generating Interactive Network Visualizations.
-
-    Utilizes Pyvis to create and save interactive HTML visualizations of networks.
-    """
-
     def __init__(
         self,
         adjacency_matrix: pd.DataFrame,
@@ -25,20 +20,6 @@ def __init__(
         width: str = "100%",
         height: str = "800px"
     ):
-        """
-        Initializes the DynamicVisualizer instance.
-
-        Args:
-            adjacency_matrix (pd.DataFrame): Adjacency matrix representing the network.
-            layout (str, optional): Layout algorithm for network visualization ('spring', 'hierarchical', etc.). Defaults to 'spring'.
-            notebook (bool, optional): Whether to generate a notebook-compatible visualization. Defaults to False.
-            bgcolor (str, optional): Background color of the visualization. Defaults to '#ffffff'.
-            font_color (str, optional): Font color for node labels. Defaults to 'black'.
-            output_dir (str, optional): Directory to save the visualization HTML file. If None, saves in the current directory. Defaults to None.
-            output_filename (str, optional): Filename for the saved visualization HTML file. Defaults to "dynamic_network.html".
-            width (str, optional): Width of the visualization. Defaults to "100%".
-            height (str, optional): Height of the visualization. Defaults to "800px".
-        """
         self.adjacency_matrix = adjacency_matrix
         self.layout = layout
         self.notebook = notebook
@@ -52,47 +33,45 @@ def __init__(
         self.logger.info("Initialized DynamicVisualizer.")
 
     def generate_graph(self) -> nx.Graph:
-        """
-        Converts the adjacency matrix into a NetworkX graph.
-
-        Returns:
-            nx.Graph: NetworkX graph constructed from the adjacency matrix.
-        """
-        self.logger.info("Generating NetworkX graph from adjacency matrix.")
+        self.logger.info("Generating NetworkX graph from adjacency_matrix.")
         G = nx.from_pandas_adjacency(self.adjacency_matrix)
         self.logger.info(f"Graph generated with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges.")
+        if G.number_of_nodes() == 0:
+            self.logger.error("Generated graph is empty.")
+            raise nx.NetworkXError("Generated graph is empty.")
         return G
 
     def visualize(self, G: nx.Graph):
-        """
-        Generates and saves an interactive visualization of the network.
-
-        Args:
-            G (nx.Graph): NetworkX graph to visualize.
-        """
         self.logger.info(f"Generating interactive visualization with layout: {self.layout}")
 
-        net = Network(height=self.height, width=self.width, bgcolor=self.bgcolor, font_color=self.font_color, notebook=self.notebook)
+        # Load the default template from pyvis/templates
+        templates_dir = os.path.join(os.path.dirname(pyvis.__file__), 'templates')
+        env = Environment(loader=FileSystemLoader(templates_dir))
+        template = env.get_template('template.html') 
+
+        net = Network(height=self.height, width=self.width, bgcolor=self.bgcolor, font_color=self.font_color, notebook=False)
 
+        # Assign the loaded template to net.template
+        net.template = template
+
         if self.layout == 'hierarchical':
             net.barnes_hut()
         elif self.layout == 'spring':
             net.force_atlas_2based()
         else:
-            self.logger.warning(f"Layout '{self.layout}' not recognized. Using default layout.")
-        
+            self.logger.warning(f"Layout '{self.layout}' not recognized. Falling back to spring layout.")
+
         net.from_nx(G)
-        
+
         for node in net.nodes:
             node['title'] = node['id']
             node['label'] = node['id']
             node['color'] = 'skyblue'
-        
+
         for edge in net.edges:
             edge['color'] = 'gray'
-        
+
         os.makedirs(self.output_dir, exist_ok=True)
         output_path = os.path.join(self.output_dir, self.output_filename)
-
         net.show(output_path)
         self.logger.info(f"Interactive network visualization saved to {output_path}")

bioneuralnet/analysis/static_visualization.py

@@ -56,10 +56,18 @@ def generate_graph(self) -> nx.Graph:
 
         Returns:
             nx.Graph: NetworkX graph constructed from the adjacency matrix.
+
+        Raises:
+            nx.NetworkXError: If the generated graph is empty.
         """
         self.logger.info("Generating NetworkX graph from adjacency matrix.")
         G = nx.from_pandas_adjacency(self.adjacency_matrix)
         self.logger.info(f"Graph generated with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges.")
+
+        if G.number_of_nodes() == 0:
+            self.logger.error("Generated graph is empty.")
+            raise nx.NetworkXError("Generated graph is empty.")
+
         return G
 
     def visualize(self, G: nx.Graph):

bioneuralnet/clustering/__init__.py

@@ -1,4 +1,4 @@
 from .hierarchical import HierarchicalClustering
-from .pagerank import PageRankClustering
+from .pagerank import PageRank
 
-__all__ = ['HierarchicalClustering', 'PageRankClustering']
+__all__ = ['HierarchicalClustering', 'PageRank']

bioneuralnet/clustering/hierarchical.py

@@ -47,7 +47,7 @@ def __init__(
         self.n_clusters = n_clusters
         self.linkage = linkage
         self.affinity = affinity
-        self.output_dir = output_dir if output_dir else self._create_output_dir()
+        self.output_dir = output_dir #if output_dir else self._create_output_dir()
 
         self.logger = get_logger(__name__)
         self.logger.info("Initialized HierarchicalClustering with the following parameters:")