Update data_processor.py

data_processor.py

@@ -289,83 +289,84 @@ class DataProcessor:
         else:
             return 'Unknown'
 
-    def compute_student_metrics(self, df):
-        intervention_df = df[df[self.INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
-        intervention_sessions_held = len(intervention_df)
-        student_columns = [col for col in df.columns if col.startswith('Student Attendance')]
-    
-        student_metrics = {}
-        for col in student_columns:
-            student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
-            student_data = intervention_df[[col]].copy()
-            student_data[col] = student_data[col].fillna('Absent')
-    
-            attendance_values = student_data[col].apply(lambda x: 1 if self.classify_engagement(x) in [
-                self.ENGAGED_STR,
-                self.PARTIALLY_ENGAGED_STR,
-                self.NOT_ENGAGED_STR
-            ] else 0)
-    
-            sessions_attended = attendance_values.sum()
-            attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
-            attendance_pct = round(attendance_pct)
-    
-            engagement_counts = {
-                self.ENGAGED_STR: 0,
-                self.PARTIALLY_ENGAGED_STR: 0,
-                self.NOT_ENGAGED_STR: 0,
-                'Absent': 0
-            }
-    
-            for x in student_data[col]:
-                classified_engagement = self.classify_engagement(x)
-                if classified_engagement in engagement_counts:
-                    engagement_counts[classified_engagement] += 1
-                else:
-                    engagement_counts['Absent'] += 1  # Count as Absent if not engaged
-    
-            # Calculate percentages for engagement states
-            total_sessions = sum(engagement_counts.values())
-            
-            # Engagement (%)
-            engagement_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-            engagement_pct = round(engagement_pct)
-    
-            engaged_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-            engaged_pct = round(engaged_pct)
-    
-            partially_engaged_pct = (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-            partially_engaged_pct = round(partially_engaged_pct)
-    
-            not_engaged_pct = (engagement_counts[self.NOT_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-            not_engaged_pct = round(not_engaged_pct)
-    
-            absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
-            absent_pct = round(absent_pct)
-    
-            # Determine if the student attended ≥ 90% of sessions
-            attended_90 = "Yes" if attendance_pct >= 90 else "No"
-    
-            # Determine if the student was engaged ≥ 80% of the time
-            engaged_80 = "Yes" if engaged_pct >= 80 else "No"
-    
-            # Store metrics in the required order
-            student_metrics[student_name] = {
-                'Attended ≥ 90%': attended_90,
-                'Engagement ≥ 80%': engaged_80,
-                'Attendance (%)': attendance_pct,
-                'Engagement (%)': engagement_pct,
-                f'{self.ENGAGED_STR} (%)': engaged_pct,
-                f'{self.PARTIALLY_ENGAGED_STR} (%)': partially_engaged_pct,
-                f'{self.NOT_ENGAGED_STR} (%)': not_engaged_pct,
-                'Absent (%)': absent_pct
-            }
-    
-        # Create a DataFrame from student_metrics
-        student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
-        student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
-        return student_metrics_df
+def compute_student_metrics(self, df):
+    intervention_column = self.find_intervention_column(df)
+    intervention_df = df[df[intervention_column].str.strip().str.lower() == 'yes']
+    intervention_sessions_held = len(intervention_df)
+    student_columns = [col for col in df.columns if col.startswith('Student Attendance')]
 
+    student_metrics = {}
+    for col in student_columns:
+        student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
+        student_data = intervention_df[[col]].copy()
+        student_data[col] = student_data[col].fillna('Absent')
+
+        attendance_values = student_data[col].apply(lambda x: 1 if self.classify_engagement(x) in [
+            self.ENGAGED_STR,
+            self.PARTIALLY_ENGAGED_STR,
+            self.NOT_ENGAGED_STR
+        ] else 0)
+
+        sessions_attended = attendance_values.sum()
+        attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
+        attendance_pct = round(attendance_pct)
+
+        engagement_counts = {
+            self.ENGAGED_STR: 0,
+            self.PARTIALLY_ENGAGED_STR: 0,
+            self.NOT_ENGAGED_STR: 0,
+            'Absent': 0
+        }
+
+        for x in student_data[col]:
+            classified_engagement = self.classify_engagement(x)
+            if classified_engagement in engagement_counts:
+                engagement_counts[classified_engagement] += 1
+            else:
+                engagement_counts['Absent'] += 1  # Count as Absent if not engaged
+
+        # Calculate percentages for engagement states
+        total_sessions = sum(engagement_counts.values())
+        
+        # Engagement (%)
+        engagement_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
+        engagement_pct = round(engagement_pct)
+
+        engaged_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
+        engaged_pct = round(engaged_pct)
+
+        partially_engaged_pct = (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
+        partially_engaged_pct = round(partially_engaged_pct)
+
+        not_engaged_pct = (engagement_counts[self.NOT_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
+        not_engaged_pct = round(not_engaged_pct)
+
+        absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
+        absent_pct = round(absent_pct)
+
+        # Determine if the student attended ≥ 90% of sessions
+        attended_90 = "Yes" if attendance_pct >= 90 else "No"
+
+        # Determine if the student was engaged ≥ 80% of the time
+        engaged_80 = "Yes" if engaged_pct >= 80 else "No"
+
+        # Store metrics in the required order
+        student_metrics[student_name] = {
+            'Attended ≥ 90%': attended_90,
+            'Engagement ≥ 80%': engaged_80,
+            'Attendance (%)': attendance_pct,
+            'Engagement (%)': engagement_pct,
+            f'{self.ENGAGED_STR} (%)': engaged_pct,
+            f'{self.PARTIALLY_ENGAGED_STR} (%)': partially_engaged_pct,
+            f'{self.NOT_ENGAGED_STR} (%)': not_engaged_pct,
+            'Absent (%)': absent_pct
+        }
+
+    # Create a DataFrame from student_metrics
+    student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
+    student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
+    return student_metrics_df
+    
     def compute_average_metrics(self, student_metrics_df):
         # Calculate the attendance and engagement average percentages across students
         attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()  # Calculate the average attendance percentage