Initial leaderboard setup

.ipynb_checkpoints/app-checkpoint.py

@@ -74,41 +74,32 @@ tab1, tab2, tab3, tab4 = st.tabs([
 
 def style_dataframe(df, highlight_first_column=True, show_progress_bars=True):
     numeric_cols = df.select_dtypes(include=['float64']).columns
+    first_numeric_col = numeric_cols[0] if len(numeric_cols) > 0 else None
     
-    def color_background(val):
-        """Return background color style based on value"""
-        return f'background-color: rgba({int(255 * (1 - val))}, {int(255 * val)}, 0, 0.2)'
+    # Start with basic formatting
+    styled = df.style.format({col: '{:.2f}%' for col in numeric_cols})
     
-    def apply_colors_to_series(s):
-        """Apply color gradient to a series of values"""
-        if len(s) == 0:
+    # Function for color gradient
+    def color_gradient(s):
+        if s.empty:
             return []
         normalized = (s - s.min()) / (s.max() - s.min()) if s.max() != s.min() else [0.5] * len(s)
-        return [color_background(val) for val in normalized]
-    
-    styled = df.style.format({col: '{:.2f}%' for col in numeric_cols})
-    
-    def highlight_and_color(x):
-        """Combine highlighting and coloring in a single function"""
-        result = [''] * len(df.columns)
-        if highlight_first_column and len(numeric_cols) > 0:
-            first_numeric_col = numeric_cols[0]
-            first_col_idx = df.columns.get_loc(first_numeric_col)
-            result[first_col_idx] = 'background-color: rgba(74, 144, 226, 0.2); font-weight: bold;'
-        
-        if show_progress_bars:
-            for col in numeric_cols:
-                col_idx = df.columns.get_loc(col)
-                val = x[col]
-                normalized = (val - df[col].min()) / (df[col].max() - df[col].min()) if df[col].max() != df[col].min() else 0.5
-                result[col_idx] = f'background-color: rgba({int(255 * (1 - normalized))}, {int(255 * normalized)}, 0, 0.2)'
-                if col == numeric_cols[0] and highlight_first_column:
-                    result[col_idx] = result[col_idx] + '; font-weight: bold;'
-        
-        return result
-    
-    styled = styled.apply(highlight_and_color, axis=1)
-    
+        return ['background-color: rgba({}, {}, 0, 0.2)'.format(
+            int(255 * (1 - val)), int(255 * val)
+        ) for val in normalized]
+    
+    # Apply color gradient to all numeric columns if needed
+    if show_progress_bars:
+        for col in numeric_cols:
+            styled = styled.apply(color_gradient, subset=[col])
+    
+    # Highlight first column if needed
+    if highlight_first_column and first_numeric_col:
+        styled = styled.apply(lambda x: ['background-color: rgba(74, 144, 226, 0.2); font-weight: bold;' 
+                                       if col == first_numeric_col else '' 
+                                       for col in df.columns], axis=0)
+    
+    # Apply basic styling
     styled = styled.set_properties(**{
         'padding': '10px',
         'border': '1px solid #dee2e6',
@@ -132,29 +123,28 @@ def style_dataframe(df, highlight_first_column=True, show_progress_bars=True):
 
 def style_comparison_dataframe(df):
     """Style dataframe specifically for model comparison tables"""
-    numeric_cols = df.select_dtypes(include=['float64']).columns
+    numeric_cols = [col for col in df.columns if col not in ['Class', 'Difference']]
+    
+    # Basic formatting for numeric columns
+    formatter = {
+        **{col: '{:.2f}%' for col in numeric_cols},
+        'Difference': '{:+.2f}%'  # Add plus sign for positive values
+    }
     
-    def highlight_difference(x):
+    # Define gradient function for difference column
+    def color_difference(val):
         """Color the difference column from red to green"""
-        result = [''] * len(df.columns)
-        if 'Difference' in df.columns:
-            diff_idx = df.columns.get_loc('Difference')
-            val = x['Difference']
-            if not pd.isna(val):
-                # Normalize to ±10% scale
-                normalized = max(min(val / 10, 1), -1)
-                if normalized > 0:
-                    result[diff_idx] = f'background-color: rgba(0, 128, 0, {abs(normalized) * 0.3})'
-                else:
-                    result[diff_idx] = f'background-color: rgba(255, 0, 0, {abs(normalized) * 0.3})'
-        return result
-
+        if pd.isna(val):
+            return ''
+        normalized = max(min(val / 10, 1), -1)  # Scale of ±10%
+        if normalized > 0:
+            return f'background-color: rgba(0, 128, 0, {abs(normalized) * 0.4})'
+        return f'background-color: rgba(255, 0, 0, {abs(normalized) * 0.4})'
+    
+    # Apply styling
     styled = df.style\
-        .format({
-            'Difference': '{:+.2f}%',
-            **{col: '{:.2f}%' for col in numeric_cols if col != 'Difference'}
-        })\
-        .apply(highlight_difference, axis=1)\
+        .format(formatter)\
+        .applymap(color_difference, subset=['Difference'])\
         .set_properties(**{
             'padding': '10px',
             'border': '1px solid #dee2e6',

app.py

@@ -74,41 +74,32 @@ tab1, tab2, tab3, tab4 = st.tabs([
 
 def style_dataframe(df, highlight_first_column=True, show_progress_bars=True):
     numeric_cols = df.select_dtypes(include=['float64']).columns
+    first_numeric_col = numeric_cols[0] if len(numeric_cols) > 0 else None
     
-    def color_background(val):
-        """Return background color style based on value"""
-        return f'background-color: rgba({int(255 * (1 - val))}, {int(255 * val)}, 0, 0.2)'
+    # Start with basic formatting
+    styled = df.style.format({col: '{:.2f}%' for col in numeric_cols})
     
-    def apply_colors_to_series(s):
-        """Apply color gradient to a series of values"""
-        if len(s) == 0:
+    # Function for color gradient
+    def color_gradient(s):
+        if s.empty:
             return []
         normalized = (s - s.min()) / (s.max() - s.min()) if s.max() != s.min() else [0.5] * len(s)
-        return [color_background(val) for val in normalized]
-    
-    styled = df.style.format({col: '{:.2f}%' for col in numeric_cols})
-    
-    def highlight_and_color(x):
-        """Combine highlighting and coloring in a single function"""
-        result = [''] * len(df.columns)
-        if highlight_first_column and len(numeric_cols) > 0:
-            first_numeric_col = numeric_cols[0]
-            first_col_idx = df.columns.get_loc(first_numeric_col)
-            result[first_col_idx] = 'background-color: rgba(74, 144, 226, 0.2); font-weight: bold;'
-        
-        if show_progress_bars:
-            for col in numeric_cols:
-                col_idx = df.columns.get_loc(col)
-                val = x[col]
-                normalized = (val - df[col].min()) / (df[col].max() - df[col].min()) if df[col].max() != df[col].min() else 0.5
-                result[col_idx] = f'background-color: rgba({int(255 * (1 - normalized))}, {int(255 * normalized)}, 0, 0.2)'
-                if col == numeric_cols[0] and highlight_first_column:
-                    result[col_idx] = result[col_idx] + '; font-weight: bold;'
-        
-        return result
-    
-    styled = styled.apply(highlight_and_color, axis=1)
-    
+        return ['background-color: rgba({}, {}, 0, 0.2)'.format(
+            int(255 * (1 - val)), int(255 * val)
+        ) for val in normalized]
+    
+    # Apply color gradient to all numeric columns if needed
+    if show_progress_bars:
+        for col in numeric_cols:
+            styled = styled.apply(color_gradient, subset=[col])
+    
+    # Highlight first column if needed
+    if highlight_first_column and first_numeric_col:
+        styled = styled.apply(lambda x: ['background-color: rgba(74, 144, 226, 0.2); font-weight: bold;' 
+                                       if col == first_numeric_col else '' 
+                                       for col in df.columns], axis=0)
+    
+    # Apply basic styling
     styled = styled.set_properties(**{
         'padding': '10px',
         'border': '1px solid #dee2e6',
@@ -132,29 +123,28 @@ def style_dataframe(df, highlight_first_column=True, show_progress_bars=True):
 
 def style_comparison_dataframe(df):
     """Style dataframe specifically for model comparison tables"""
-    numeric_cols = df.select_dtypes(include=['float64']).columns
+    numeric_cols = [col for col in df.columns if col not in ['Class', 'Difference']]
+    
+    # Basic formatting for numeric columns
+    formatter = {
+        **{col: '{:.2f}%' for col in numeric_cols},
+        'Difference': '{:+.2f}%'  # Add plus sign for positive values
+    }
     
-    def highlight_difference(x):
+    # Define gradient function for difference column
+    def color_difference(val):
         """Color the difference column from red to green"""
-        result = [''] * len(df.columns)
-        if 'Difference' in df.columns:
-            diff_idx = df.columns.get_loc('Difference')
-            val = x['Difference']
-            if not pd.isna(val):
-                # Normalize to ±10% scale
-                normalized = max(min(val / 10, 1), -1)
-                if normalized > 0:
-                    result[diff_idx] = f'background-color: rgba(0, 128, 0, {abs(normalized) * 0.3})'
-                else:
-                    result[diff_idx] = f'background-color: rgba(255, 0, 0, {abs(normalized) * 0.3})'
-        return result
-
+        if pd.isna(val):
+            return ''
+        normalized = max(min(val / 10, 1), -1)  # Scale of ±10%
+        if normalized > 0:
+            return f'background-color: rgba(0, 128, 0, {abs(normalized) * 0.4})'
+        return f'background-color: rgba(255, 0, 0, {abs(normalized) * 0.4})'
+    
+    # Apply styling
     styled = df.style\
-        .format({
-            'Difference': '{:+.2f}%',
-            **{col: '{:.2f}%' for col in numeric_cols if col != 'Difference'}
-        })\
-        .apply(highlight_difference, axis=1)\
+        .format(formatter)\
+        .applymap(color_difference, subset=['Difference'])\
         .set_properties(**{
             'padding': '10px',
             'border': '1px solid #dee2e6',