Update data_processor.py

data_processor.py

@@ -1,235 +1,3 @@
-# import re
-# import pandas as pd
-# import os
-# from huggingface_hub import InferenceClient
-
-# class DataProcessor:
-#     INTERVENTION_COLUMN_OPTIONS = [
-#         'Did the intervention happen today?',
-#         'Did the intervention take place today?'
-#     ]
-#     YES_RESPONSES = ['yes', 'assessment day']  # Added this line
-#     ENGAGED_STR = 'Engaged'
-#     PARTIALLY_ENGAGED_STR = 'Partially Engaged'
-#     NOT_ENGAGED_STR = 'Not Engaged'
-
-#     def __init__(self, student_metrics_df=None):
-#         self.hf_api_key = os.getenv('HF_API_KEY')
-#         if not self.hf_api_key:
-#             raise ValueError("HF_API_KEY not set in environment variables")
-#         self.client = InferenceClient(api_key=self.hf_api_key)
-#         self.student_metrics_df = student_metrics_df
-#         self.intervention_column = None  # Will be set when processing data
-
-#     def read_excel(self, uploaded_file):
-#         return pd.read_excel(uploaded_file)
-
-#     def format_session_data(self, df):
-#         date_column = next((col for col in df.columns if col in ["Date of Session", "Date"]), None)
-#         if date_column:
-#             df[date_column] = pd.to_datetime(df[date_column], errors='coerce').dt.date
-#         else:
-#             print("Warning: Neither 'Date of Session' nor 'Date' column found in the dataframe.")
-        
-#         df['Timestamp'] = self.safe_convert_to_datetime(df['Timestamp'], '%I:%M %p')
-#         df['Session Start Time'] = self.safe_convert_to_time(df['Session Start Time'], '%I:%M %p')
-#         df['Session End Time'] = self.safe_convert_to_time(df['Session End Time'], '%I:%M %p')
-#         return df
-
-#     def safe_convert_to_time(self, series, format_str='%I:%M %p'):
-#         try:
-#             converted = pd.to_datetime(series, format='%H:%M:%S', errors='coerce')
-#             if format_str:
-#                 return converted.dt.strftime(format_str)
-#             return converted
-#         except Exception as e:
-#             print(f"Error converting series to time: {e}")
-#             return series
-
-#     def safe_convert_to_datetime(self, series, format_str=None):
-#         try:
-#             converted = pd.to_datetime(series, errors='coerce')
-#             if format_str:
-#                 return converted.dt.strftime(format_str)
-#             return converted
-#         except Exception as e:
-#             print(f"Error converting series to datetime: {e}")
-#             return series
-
-#     def replace_student_names_with_initials(self, df):
-#         updated_columns = []
-#         for col in df.columns:
-#             if 'Student Attendance' in col:
-#                 # Search for the last occurrence of text within square brackets at the end of the string
-#                 match = re.search(r'\[(.+?)\]$', col)
-#                 if not match:
-#                     # Handle cases where the closing bracket might be missing
-#                     match = re.search(r'\[(.+)$', col)
-#                 if match:
-#                     name = match.group(1).strip()
-#                     # Remove any trailing closing bracket if it wasn't matched earlier
-#                     name = name.rstrip(']')
-#                     # Get initials
-#                     initials = ''.join([part[0] for part in name.strip().split()])
-#                     updated_col = f'Student Attendance [{initials}]'
-#                     updated_columns.append(updated_col)
-#                 else:
-#                     # If no match is found, keep the column name as is
-#                     updated_columns.append(col)
-#             else:
-#                 updated_columns.append(col)
-#         df.columns = updated_columns
-#         return df
-
-
-#     def find_intervention_column(self, df):
-#         for column in self.INTERVENTION_COLUMN_OPTIONS:
-#             if column in df.columns:
-#                 self.intervention_column = column
-#                 return column
-#         raise ValueError("No intervention column found in the dataframe.")
-
-#     def get_intervention_column(self, df):
-#         if self.intervention_column is None:
-#             self.intervention_column = self.find_intervention_column(df)
-#         return self.intervention_column
-
-#     def compute_intervention_statistics(self, df):
-#         intervention_column = self.get_intervention_column(df)
-#         total_days = len(df)
-#         sessions_held = df[intervention_column].str.strip().str.lower().isin(self.YES_RESPONSES).sum()  # Modified line
-#         intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
-#         return pd.DataFrame({
-#             'Intervention Dosage (%)': [round(intervention_frequency, 0)],
-#             'Intervention Sessions Held': [sessions_held],
-#             'Intervention Sessions Not Held': [total_days - sessions_held],
-#             'Total Number of Days Available': [total_days]
-#         })
-
-#     def classify_engagement(self, engagement_str):
-#         engagement_str = str(engagement_str).lower()
-#         if engagement_str.startswith(self.ENGAGED_STR.lower()):
-#             return self.ENGAGED_STR
-#         elif engagement_str.startswith(self.PARTIALLY_ENGAGED_STR.lower()):
-#             return self.PARTIALLY_ENGAGED_STR
-#         elif engagement_str.startswith(self.NOT_ENGAGED_STR.lower()):
-#             return self.NOT_ENGAGED_STR
-#         else:
-#             return 'Unknown'
-    
-#     def compute_student_metrics(self, df):
-#         intervention_column = self.get_intervention_column(df)
-#         intervention_df = df[df[intervention_column].str.strip().str.lower().isin(self.YES_RESPONSES)]
-#         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')
-    
-#             # Classify each entry
-#             student_data['Engagement'] = student_data[col].apply(self.classify_engagement)
-    
-#             # Calculate attendance
-#             attendance_values = student_data['Engagement'].apply(
-#                 lambda x: 1 if 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 (excluding 'Absent')
-#             engagement_counts = {
-#                 self.ENGAGED_STR: 0,
-#                 self.PARTIALLY_ENGAGED_STR: 0,
-#                 self.NOT_ENGAGED_STR: 0
-#             }
-    
-#             # Count the engagement types, excluding 'Absent'
-#             for x in student_data['Engagement']:
-#                 if x in engagement_counts:
-#                     engagement_counts[x] += 1
-#                 # 'Absent' is not counted in engagement_counts
-    
-#             total_present_sessions = sum(engagement_counts.values())
-    
-#             engaged_pct = (
-#                 (engagement_counts[self.ENGAGED_STR] / total_present_sessions * 100)
-#                 if total_present_sessions > 0 else 0
-#             )
-#             engaged_pct = round(engaged_pct)
-    
-#             partially_engaged_pct = (
-#                 (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_present_sessions * 100)
-#                 if total_present_sessions > 0 else 0
-#             )
-#             partially_engaged_pct = round(partially_engaged_pct)
-    
-#             not_engaged_pct = (
-#                 (engagement_counts[self.NOT_ENGAGED_STR] / total_present_sessions * 100)
-#                 if total_present_sessions > 0 else 0
-#             )
-#             not_engaged_pct = round(not_engaged_pct)
-    
-#             # Engagement percentage is based on Engaged and Partially Engaged sessions
-#             engagement_pct = (
-#                 ((engagement_counts[self.ENGAGED_STR] + engagement_counts[self.PARTIALLY_ENGAGED_STR]) / total_present_sessions * 100)
-#                 if total_present_sessions > 0 else 0
-#             )
-#             engagement_pct = round(engagement_pct)
-    
-#             # Absent percentage (for reference, not used in engagement calculation)
-#             absent_sessions = student_data['Engagement'].value_counts().get('Absent', 0)
-#             absent_pct = (absent_sessions / intervention_sessions_held * 100) if intervention_sessions_held > 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 engagement_pct >= 80 else "No"
-    
-#             # Store metrics
-#             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()  # Average attendance percentage
-#         engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()  # Average engagement percentage
-        
-#         # Round the averages to whole numbers
-#         attendance_avg_stats = round(attendance_avg_stats)
-#         engagement_avg_stats = round(engagement_avg_stats)
-        
-#         return attendance_avg_stats, engagement_avg_stats
-    
-#     def evaluate_student(self, row, attendance_threshold=90, engagement_threshold=80):
-#         if row["Attended ≥ 90%"] == "No":
-#             return "Address Attendance"
-#         elif row["Engagement ≥ 80%"] == "No":
-#             return "Address Engagement"
-#         else:
-#             return "Consider barriers, fidelity, and progress monitoring"
-
-
-
 import re
 import pandas as pd
 import os
@@ -457,14 +225,32 @@ class DataProcessor:
         student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
         return student_metrics_df
     
+    # def compute_average_metrics(self, student_metrics_df):
+    #     # Filter out rows with NaN values (inactive students)
+    #     active_students_df = student_metrics_df.dropna()
+        
+    #     # Calculate the attendance and engagement average percentages across active students
+    #     attendance_avg_stats = active_students_df['Attendance (%)'].mean()
+    #     engagement_avg_stats = active_students_df[f'{self.ENGAGED_STR} (%)'].mean()
+        
+    #     # Round the averages to whole numbers
+    #     attendance_avg_stats = round(attendance_avg_stats)
+    #     engagement_avg_stats = round(engagement_avg_stats)
+        
+    #     return attendance_avg_stats, engagement_avg_stats
+
     def compute_average_metrics(self, student_metrics_df):
         # Filter out rows with NaN values (inactive students)
         active_students_df = student_metrics_df.dropna()
         
-        # Calculate the attendance and engagement average percentages across active students
+        # Calculate the attendance average percentage across active students
         attendance_avg_stats = active_students_df['Attendance (%)'].mean()
-        engagement_avg_stats = active_students_df[f'{self.ENGAGED_STR} (%)'].mean()
         
+        # Calculate the engagement average percentage across active students
+        # Only consider 'Engaged' and 'Partially Engaged' percentages, exclude 'Absent'
+        total_engagement = active_students_df[f'{self.ENGAGED_STR} (%)'] + active_students_df[f'{self.PARTIALLY_ENGAGED_STR} (%)']
+        engagement_avg_stats = total_engagement.mean()
+    
         # Round the averages to whole numbers
         attendance_avg_stats = round(attendance_avg_stats)
         engagement_avg_stats = round(engagement_avg_stats)