Update label query

CSHVienna · Sep 11, 2024 · 4c33c31 · 4c33c31
1 parent d648fba
commit 4c33c31
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Showing 4 changed files with 8 additions and 7 deletions.
diff --git a/netin/stats/distributions.py b/netin/stats/distributions.py
@@ -146,7 +146,7 @@ def get_disparity(df: pd.DataFrame, x: str, total: float = None) -> (np.ndarray,
 
     gx, gy = get_gini_coefficient(df, x, total)
     fx, fy = get_fraction_of_minority(df, x, total)
-    f_m = df.query("class_label == @const.MINORITY_LABEL").shape[0] / df.shape[0]
+    f_m = df.query("real_label == @const.MINORITY_LABEL").shape[0] / df.shape[0]
 
     inequality_y = ranking.get_ranking_inequality(gy)
     inequity_x = ranking.get_ranking_inequity(f_m, fy)

diff --git a/netin/stats/networks.py b/netin/stats/networks.py
@@ -101,9 +101,10 @@ def get_node_metadata_as_dataframe(
             .get_node_class(node_class_values)
         class_values = node_class_values.get_class_values()
 
-    obj = {'node': list(graph.nodes()),
-            'class_label': [node_class_values[n] for n in graph.nodes()],
-            'real_label': [class_values[n] for n in graph.nodes()]}
+    l_nodes = list(graph.nodes())
+    obj = {'node': l_nodes,
+            'class_label': [node_class_values[n] for n in l_nodes],
+            'real_label': [class_values[n] for n in l_nodes]}
 
     # include graph metadata
     if include_graph_metadata:

diff --git a/netin/stats/ranking.py b/netin/stats/ranking.py
@@ -84,7 +84,7 @@ def get_fraction_of_minority_in_ranking(df: pd.DataFrame, x: str) -> \
         column = f"{x}_rank"
         tmp = df.query(f"{column} <= @rank").copy()
         total = tmp.shape[0]
-        efm = np.nan if total == 0 else tmp.query("class_label == @const.MINORITY_LABEL").shape[0] / total
+        efm = np.nan if total == 0 else tmp.query("real_label == @const.MINORITY_LABEL").shape[0] / total
         ys.append(efm)
     return xs, ys
 

diff --git a/netin/viz/handlers.py b/netin/viz/handlers.py
@@ -494,7 +494,7 @@ def plot_distribution(data: Union[pd.DataFrame, List[pd.DataFrame]],
 
         class_label: str
         iter_groups = df.groupby(hue) if hue is not None else [(None, df)]
-        f_m = df.query("class_label == @const.MINORITY_LABEL").shape[0] / df.shape[0]
+        f_m = df.query("real_label == @const.MINORITY_LABEL").shape[0] / df.shape[0]
         for class_label, group in iter_groups:
             total = df[_col_name].sum()\
                 if common_norm else group[_col_name].sum()
@@ -717,7 +717,7 @@ def get_me_label(f_m, ys, beta=None) -> Tuple[str, float, str]:
 
     def show_minority(axline, data):
         axline(
-            data.query("class_label==@const.MINORITY_LABEL")\
+            data.query("real_label==@const.MINORITY_LABEL")\
                .shape[0] / data.shape[0],
             color="black", linestyle='--')