Repository: InfectionMedicineProteomics/BINN (language: python)

Issue:
Minor changes to plotting
1. Add image dimensions as arguments.
2. Add numeric_only=True to pandas groupby. This fixes compatability with pandas 2.0
3. If multiclass == False, we sum SHAP values. This makes it so that there is only 1 flow if the source and target is the same.
4. Merged if several edges are coming and going to the same nodes. This is relevant for edges incoming to "Other connections" since these are lumped to a single edge.


Discussion / hints:

Fix patch:
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..891c617
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,27 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..bbcbbe7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/binn/importance_network.py b/binn/importance_network.py
index 0b29ce3..6062d80 100644
--- a/binn/importance_network.py
+++ b/binn/importance_network.py
@@ -51,6 +51,9 @@ def plot_subgraph_sankey(
         savename: str = "sankey.png",
         val_col: str = "value",
         cmap: str = "coolwarm",
+        width: int = 1200,
+        scale: float = 2.5,
+        height: int = 500,
     ):
         """
         Generate a Sankey diagram using the provided query node.
@@ -87,7 +90,7 @@ def plot_subgraph_sankey(
             df, final_node=final_node, val_col=val_col, cmap_name=cmap
         )
 
-        fig.write_image(f"{savename}", width=1200, scale=2.5, height=500)
+        fig.write_image(f"{savename}", width=width, scale=scale, height=height)
         return fig
 
     def plot_complete_sankey(
@@ -97,6 +100,9 @@ def plot_complete_sankey(
         node_cmap: str = "Reds",
         edge_cmap: Union[str, list] = "Reds",
         savename="sankey.png",
+        width: int = 1900,
+        scale: float = 2,
+        height: int = 800,
     ):
         """
         Plot a complete Sankey diagram for the importance network.
@@ -126,7 +132,7 @@ def plot_complete_sankey(
             node_cmap=node_cmap,
         )
 
-        fig.write_image(f"{savename}", width=1900, scale=2, height=800)
+        fig.write_image(f"{savename}", width=width, scale=scale, height=height)
         return fig
 
     def create_graph(self):
diff --git a/binn/network.py b/binn/network.py
index 1f4ef05..942848f 100644
--- a/binn/network.py
+++ b/binn/network.py
@@ -43,8 +43,14 @@ def __init__(
         mapping: Union[pd.DataFrame, None] = None,
         input_data_column: str = "Protein",
         subset_pathways: bool = True,
+        source_column: str = "source",
+        target_column: str = "target",
     ):
         self.input_data_column = input_data_column
+        pathways = pathways.rename(
+            columns={source_column: "source", target_column: "target"}
+        )
+
         if isinstance(mapping, pd.DataFrame):
             self.mapping = mapping
             self.unaltered_mapping = mapping
@@ -85,7 +91,7 @@ def build_network(self):
             return self.netx
 
         net = nx.from_pandas_edgelist(
-            self.pathways, "parent", "child", create_using=nx.DiGraph()
+            self.pathways, source="target", target="source", create_using=nx.DiGraph()
         )
         roots = [n for n, d in net.in_degree() if d == 0]
         root_node = "root"
@@ -158,7 +164,7 @@ def get_connectivity_matrices(self, n_levels, direction="root_to_leaf") -> list:
 
 def _get_mapping_to_all_layers(pathways, mapping):
     graph = nx.from_pandas_edgelist(
-        pathways, source="child", target="parent", create_using=nx.DiGraph()
+        pathways, source="source", target="target", create_using=nx.DiGraph()
     )
     components = {"input": [], "connections": []}
     for translation in mapping["input"]:
@@ -244,17 +250,17 @@ def _subset_input(
 
 
 def _subset_pathways_on_idx(pathways, translation):
-    def add_pathways(idx_list, parent):
-        if len(parent) == 0:
+    def add_pathways(idx_list, target):
+        if len(target) == 0:
             return idx_list
         else:
-            idx_list = idx_list + parent
-            subsetted_pathway = pathways[pathways["child"].isin(parent)]
-            new_parent = list(subsetted_pathway["parent"].unique())
-            return add_pathways(idx_list, new_parent)
+            idx_list = idx_list + target
+            subsetted_pathway = pathways[pathways["source"].isin(target)]
+            new_target = list(subsetted_pathway["target"].unique())
+            return add_pathways(idx_list, new_target)
 
-    original_parent = list(translation["translation"].unique())
+    original_target = list(translation["translation"].unique())
     idx_list = []
-    idx_list = add_pathways(idx_list, original_parent)
-    pathways = pathways[pathways["child"].isin(idx_list)]
+    idx_list = add_pathways(idx_list, original_target)
+    pathways = pathways[pathways["source"].isin(idx_list)]
     return pathways
diff --git a/binn/plot.py b/binn/plot.py
index baa9b55..a75950c 100644
--- a/binn/plot.py
+++ b/binn/plot.py
@@ -42,7 +42,7 @@ def subgraph_sankey(
         else:
             new_labels.append(label)
 
-    def normalize_layer_values(df):
+    def normalize_layer_values(df: pd.DataFrame):
         new_df = pd.DataFrame()
         total_value_sum = df[val_col].sum()
         for layer in df["source layer"].unique():
@@ -57,7 +57,7 @@ def normalize_layer_values(df):
     df = _remove_loops(df)
     df = normalize_layer_values(df)
 
-    def get_connections(sources, source_target_df):
+    def get_connections(sources: list, source_target_df: pd.DataFrame):
         conn = source_target_df[source_target_df["source"].isin(sources)]
         source_code = [_get_code(s, code_map) for s in conn["source"]]
         target_code = [_get_code(s, code_map) for s in conn["target"]]
@@ -65,7 +65,7 @@ def get_connections(sources, source_target_df):
         link_colors = conn["node_color"].values.tolist()
         return source_code, target_code, values, link_colors
 
-    def get_node_colors(sources, df):
+    def get_node_colors(sources: list, df: pd.DataFrame):
         cmap = plt.cm.ScalarMappable(
             norm=matplotlib.colors.Normalize(vmin=0, vmax=1), cmap=cmap_name
         )
@@ -152,6 +152,18 @@ def complete_sankey(
     root_id: int = 0,
     other_id: int = -1,
 ):
+    if not multiclass:
+        df = df.groupby(
+            by=["source", "target", "source name", "target name"], as_index=False
+        ).agg(
+            {
+                "value": "sum",
+                "source layer": "mean",
+                "target layer": "mean",
+                "type": "mean",
+            }
+        )
+
     df["source layer"] = df["source layer"].astype(int)
     df["target layer"] = df["target layer"].astype(int)
     df = _remove_loops(df)
@@ -165,7 +177,7 @@ def complete_sankey(
         top_n[layer] = (
             df.loc[df["source layer"] == layer]
             .groupby("source")
-            .mean()
+            .mean(numeric_only=True)
             .sort_values("value", ascending=False)
             .iloc[:show_top_n]
             .index.tolist()
@@ -201,9 +213,8 @@ def normalize_layer_values(df: pd.DataFrame):
             .apply(lambda x: True if x <= other_id else False)
             .copy()
         )
-
-        other_df = df[df["Other"] is True]
-        df = df[df["Other"] is False]
+        other_df = df[df["Other"] == True]
+        df = df[df["Other"] == False]
         for layer in df["source layer"].unique():
             layer_df = df[df["source layer"] == layer].copy()
             layer_total = layer_df["value"].sum()
@@ -244,10 +255,12 @@ def get_node_colors(sources: list, df: pd.DataFrame, curr_cmap=node_cmap):
             c_df = c_df[~c_df["source_w_other"] <= other_id]
             cmap = plt.cm.ScalarMappable(
                 norm=matplotlib.colors.Normalize(
-                    vmin=c_df.groupby("source_w_other").mean()["normalized value"].min()
+                    vmin=c_df.groupby("source_w_other")
+                    .mean(numeric_only=True)["normalized value"]
+                    .min()
                     * 0.8,
                     vmax=c_df.groupby("source_w_other")
-                    .mean()["normalized value"]
+                    .mean(numeric_only=True)["normalized value"]
                     .max(),
                 ),
                 cmap=curr_cmap,
@@ -267,7 +280,7 @@ def get_node_colors(sources: list, df: pd.DataFrame, curr_cmap=node_cmap):
             else:
                 intensity = (
                     source_df.groupby("source_w_other")
-                    .mean()["normalized value"]
+                    .mean(numeric_only=True)["normalized value"]
                     .values[0]
                 )
                 cmap = cmaps[source_df["source layer"].unique()[0]]
@@ -291,7 +304,6 @@ def get_node_positions(feature_labels: list, df: pd.DataFrame):
                 .sort_values(["value"], ascending=True)
                 .copy()
             )
-
             other_df = layer_df[layer_df["source_w_other"] <= other_id].copy()
             layer_df = layer_df[layer_df["source_w_other"] > other_id].copy()
 
@@ -332,6 +344,16 @@ def get_node_positions(feature_labels: list, df: pd.DataFrame):
 
     df = remove_other_to_other(df)
     df = normalize_layer_values(df)
+    df = df.groupby(
+        by=["source_w_other", "target_w_other", "type"], sort=False, as_index=False
+    ).agg(
+        {
+            "normalized value": "sum",
+            "value": "sum",
+            "source layer": "mean",
+            "target layer": "mean",
+        }
+    )
     unique_features = (
         df["source_w_other"].unique().tolist() + df["target_w_other"].unique().tolist()
     )
diff --git a/docs/shap_example.ipynb b/docs/shap_example.ipynb
index 79a68ad..5c7b0f1 100644
--- a/docs/shap_example.ipynb
+++ b/docs/shap_example.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/erikh/BINN/BINN/test-venv/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from binn import Network, BINN\n",
     "import pandas as pd\n",
@@ -26,6 +17,8 @@
     "    input_data=input_data,\n",
     "    pathways=pathways,\n",
     "    mapping=translation,\n",
+    "    source_column='child',\n",
+    "    target_column='parent'\n",
     ")\n",
     "\n",
     "binn = BINN(\n",
@@ -39,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -55,14 +48,14 @@
       "\n",
       "  | Name     | Type             | Params\n",
       "----------------------------------------------\n",
-      "0 | layers   | Sequential       | 365 K \n",
+      "0 | layers   | Sequential       | 242 K \n",
       "1 | loss     | CrossEntropyLoss | 0     \n",
       "2 | loss_val | CrossEntropyLoss | 0     \n",
       "----------------------------------------------\n",
-      "365 K     Trainable params\n",
+      "242 K     Trainable params\n",
       "0         Non-trainable params\n",
-      "365 K     Total params\n",
-      "1.464     Total estimated model params size (MB)\n",
+      "242 K     Total params\n",
+      "0.972     Total estimated model params size (MB)\n",
       "The number of training batches (25) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
@@ -70,7 +63,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2: 100%|██████████| 25/25 [00:00<00:00, 47.93it/s, loss=0.648, v_num=393, train_loss=0.651, train_acc=0.741]"
+      "Epoch 2: 100%|██████████| 25/25 [00:00<00:00, 43.56it/s, loss=0.676, v_num=407, train_loss=0.679, train_acc=0.589]"
      ]
     },
     {
@@ -84,7 +77,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2: 100%|██████████| 25/25 [00:00<00:00, 42.02it/s, loss=0.648, v_num=393, train_loss=0.651, train_acc=0.741]\n"
+      "Epoch 2: 100%|██████████| 25/25 [00:00<00:00, 37.45it/s, loss=0.676, v_num=407, train_loss=0.679, train_acc=0.589]\n"
      ]
     }
    ],
@@ -110,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -166,9 +159,9 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>495</td>\n",
+       "      <td>599</td>\n",
        "      <td>A0M8Q6</td>\n",
-       "      <td>R-HSA-166663</td>\n",
+       "      <td>R-HSA-109582</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -177,9 +170,9 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>495</td>\n",
+       "      <td>599</td>\n",
        "      <td>A0M8Q6</td>\n",
-       "      <td>R-HSA-166663</td>\n",
+       "      <td>R-HSA-109582</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
@@ -188,9 +181,9 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1</td>\n",
-       "      <td>498</td>\n",
+       "      <td>513</td>\n",
        "      <td>A0M8Q6</td>\n",
-       "      <td>R-HSA-977606</td>\n",
+       "      <td>R-HSA-1643685</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -199,9 +192,9 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1</td>\n",
-       "      <td>498</td>\n",
+       "      <td>513</td>\n",
        "      <td>A0M8Q6</td>\n",
-       "      <td>R-HSA-977606</td>\n",
+       "      <td>R-HSA-1643685</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
@@ -210,9 +203,9 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1</td>\n",
-       "      <td>542</td>\n",
+       "      <td>522</td>\n",
        "      <td>A0M8Q6</td>\n",
-       "      <td>R-HSA-2029481</td>\n",
+       "      <td>R-HSA-168256</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -224,11 +217,11 @@
       ],
       "text/plain": [
        "   source  target source name    target name  value  type  source layer  \\\n",
-       "0       1     495      A0M8Q6   R-HSA-166663    0.0     0             0   \n",
-       "1       1     495      A0M8Q6   R-HSA-166663    0.0     1             0   \n",
-       "2       1     498      A0M8Q6   R-HSA-977606    0.0     0             0   \n",
-       "3       1     498      A0M8Q6   R-HSA-977606    0.0     1             0   \n",
-       "4       1     542      A0M8Q6  R-HSA-2029481    0.0     0             0   \n",
+       "0       1     599      A0M8Q6   R-HSA-109582    0.0     0             0   \n",
+       "1       1     599      A0M8Q6   R-HSA-109582    0.0     1             0   \n",
+       "2       1     513      A0M8Q6  R-HSA-1643685    0.0     0             0   \n",
+       "3       1     513      A0M8Q6  R-HSA-1643685    0.0     1             0   \n",
+       "4       1     522      A0M8Q6   R-HSA-168256    0.0     0             0   \n",
        "\n",
        "   target layer  \n",
        "0             1  \n",
@@ -238,7 +231,7 @@
        "4             1  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -307,298 +300,54 @@
          "arrangement": "snap",
          "link": {
           "color": [
-           "rgba(99.92549019607844, 169.1686274509804, 211.23921568627452, 0.3)",
-           "rgba(99.92549019607844, 169.1686274509804, 211.23921568627452, 0.3)",
-           "rgba(99.92549019607844, 169.1686274509804, 211.23921568627452, 0.3)",
-           "rgba(99.92549019607844, 169.1686274509804, 211.23921568627452, 0.3)",
-           "rgba(185.1372549019608, 213.53333333333333, 234.49803921568628, 0.3)",
-           "rgba(185.1372549019608, 213.53333333333333, 234.49803921568628, 0.3)",
-           "rgba(214.3764705882353, 229.91764705882352, 244.45882352941177, 0.3)",
-           "rgba(214.3764705882353, 229.91764705882352, 244.45882352941177, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
-           "rgba(25.35294117647059, 103.21176470588235, 173.3529411764706, 0.3)",
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           "label": [
+           "R-HSA-1640170",
+           "R-HSA-1643685",
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-           "R-HSA-204998",
-           "R-HSA-1369062",
            "P04908",
-           "R-HSA-9012999",
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+           "R-HSA-1852241",
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+           "R-HSA-202131",
+           "P01023",
+           "R-HSA-5653656",
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+           "R-HSA-2453864",
+           "R-HSA-211000",
+           "R-HSA-5362768",
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+           "P41222",
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+           "R-HSA-5678895",
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            "P67936",
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+           "R-HSA-9651496",
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-           "Other connections 3",
-           "Other connections 1"
+           "P04075",
+           "Other connections 1",
+           "R-HSA-163200"
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('f6e05185-a688-42ea-afee-580b45898a4d');\n",
+       "var gd = document.getElementById('494a0643-a70a-4030-a5c4-cad02e9e4c68');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4504,7 +2113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -4513,13 +2122,13 @@
        "246"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "IG._get_node('P15291')"
+    "IG.get_node('P15291')"
    ]
   },
   {


Test patch:
diff --git a/docs/img/test.png b/docs/img/test.png
index 15a627c..5e3001f 100644
Binary files a/docs/img/test.png and b/docs/img/test.png differ
diff --git a/tests/test_binn.py b/tests/test_binn.py
index cca3ada..7e1f8ec 100644
--- a/tests/test_binn.py
+++ b/tests/test_binn.py
@@ -8,7 +8,7 @@
 def binn_model():
     input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
     pathways = pd.DataFrame(
-        {"child": ["A", "B", "C"], "parent": ["path1", "path1", "path2"]}
+        {"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
     )
     translation = pd.DataFrame(
         {"input": ["a", "b", "c"], "translation": ["A", "B", "C"]}
diff --git a/tests/test_explainer.py b/tests/test_explainer.py
index da98a1c..2b4716b 100644
--- a/tests/test_explainer.py
+++ b/tests/test_explainer.py
@@ -8,7 +8,7 @@
 def binn_explainer():
     input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
     pathways = pd.DataFrame(
-        {"child": ["A", "B", "C"], "parent": ["path1", "path1", "path2"]}
+        {"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
     )
     translation = pd.DataFrame(
         {"input": ["a", "b", "c"], "translation": ["A", "B", "C"]}
@@ -25,7 +25,7 @@ def test_binn_explainer_init(binn_explainer):
 def test_binn_explainer_update_model(binn_explainer):
     input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
     pathways = pd.DataFrame(
-        {"child": ["A", "B", "C"], "parent": ["path1", "path1", "path2"]}
+        {"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
     )
     translation = pd.DataFrame(
         {"input": ["a", "b", "c"], "translation": ["A", "B", "C"]}
diff --git a/tests/test_network.py b/tests/test_network.py
index 0f1c24b..73f3989 100644
--- a/tests/test_network.py
+++ b/tests/test_network.py
@@ -7,7 +7,7 @@
 def network():
     input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
     pathways = pd.DataFrame(
-        {"child": ["A", "B", "C"], "parent": ["path1", "path1", "path2"]}
+        {"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
     )
     translation = pd.DataFrame(
         {"input": ["a", "b", "c"], "translation": ["A", "B", "C"]}